mod4

Mod 4 Steps:

INSTRUCTIONS:

1.

Read

 

Chapter 9 Healthcare Professionals Legal-Ethical Issues (p. 245-258) (stop at “Dietary”) Legal and Ethical Issues for Health Professionals by George D. Pozgar (5th Edition).

 

2.

Read the articles posted under this week’s topic on D2L 

· The Legal and Ethical Concerns That Arise from Using Complex Predictive Analytics in Health Care

· Electronic Health Records: Patient Care and Ethical and Legal Implications for Nurse Practitioners

· Integrity of the Healthcare Record: Best Practices for HER Documentation.) 

3.

Experience the Simulation and Complete Your Role by Answering the Questions in a Word Document: Watch the Jones & Bartlett LearnScapes for Health Care Ethics episode: “
Equipment Purchase”

 

You do not need to answer the questions asked at the end of the simulation or submit your recommendation in an email. You will need to answer the questions I give you in Step 4 instructions below.

 

4.

Complete Written Assignment #4 to upload in the Dropbox: 

This written assignment will not be separately graded but is your ticket to participate in class and receive a grade for Class Preparation and Participation so complete all steps and provide thoughtful written answers to the questions! 

For this assignment you will have both a Word document and an Excel spreadsheet to upload. Click on the link below for the instructions.
 

This module is dedicated to big data, EHR, & healthcare professionals’ legal and ethical issues. As you proceed through the module, if you have any questions please contact me at sandy.benson@mtsu.edu.

At the conclusion of this module, you should be able to:

 

· Describe challenges and solutions to legal and ethical issues with Electronic Health Records and Predictive Analytics models.

· Experience the simulation and formulate your own recommendation to resolve a whether the hospital should close its labor and delivery services synthesizing multiple stakeholder perspectives, legal concerns, guiding values and fair process principles.

· Advocacy cause/capstone project: Research and examine data related to your Advocacy Cause. Generate short-term, intermediate and long-term goals in your LOGIC MODEL Spreadsheet.

· Persuasively articulate your positions. Critique a peer’s argument and provide constructive feedback.

 

These outcomes correspond to the following course objectives as stated in your syllabus:

 

· Identify a variety of key legal responsibilities, civil and criminal liability, perspectives and rights of major stakeholders, including selected healthcare organizations, providers, and patients, in the U.S. healthcare system.

· Evaluate and debate resolutions to current healthcare legal issues and ethical dilemmas by applying ethical and legal frameworks and reasoning.

· Creatively formulate and persuasively communicate your strategic recommendations to solve pressing health care problems with ethical and legal solutions.

Book: https://books.google.com/books?hl=en&lr=&id=j7J5DwAAQBAJ&oi=fnd&pg=PP1&dq=Legal+and+Ethical+Issues+for+Health+Professionals&ots=vMqsTnjhzu&sig=v_eGtmtjpSj1LUQwkxVISxwC79U#v=onepage&q=Legal%20and%20Ethical%20Issues%20for%20Health%20Professionals&f=false

MODULE #4 ASSIGNMENT Big Data, EHR & Healthcare Professionals Legal & Ethical Issues

INSTRUCTIONS: This written assignment will not be separately graded but is your ticket to participate in class and receive a grade for Class Preparation and Participation.

Copy the questions for Parts I – II below into a Word document. Type and save the questions and your answers in the Word document, using Times New Roman 12 point font, 1 inch margins. Save and upload it to the same Dropbox in D2L by the due date and times posted in D2L.

There is no partial credit for preparing but not attending class or vice versa. 

Part I: The Articles

a.

Electronic Health Records:

(1) Identify three (3) benefits of EHR.

(2) Discuss three (3) problems and/or liability issues with EHR.

(3) Evaluate three (3) tips/strategies to design into EHR systems.

(4) Evaluate three (3) tips/strategies to ensure the integrity of the health record.

b.

Data Analytics (The Legal and Ethical Concerns that Arise From Using Complex Predictive Analytics in Health Care):

(1) Describe one (1) challenge in Phase I: Acquiring Data in the healthcare setting. What solutions do YOU suggest based on the article, your studies or your experience?

(2) Describe one (1) challenge in Phase II: Building and Validating the Model. What solutions do YOU suggest based on the article, your studies or your experience?

(3) Describe one (1) challenge in Phase III: Testing the Model. What solutions do YOU suggest based on the article, your studies or your experience?

(4) Describe one (1) challenge in Phase IV: Broader Dissemination. What solutions do YOU suggest based on the article, your studies or your experience?

Part II: Design a Recommendation to the Health Care Ethics Equipment Purchase Dilemma

FORMULATE a Recommendation by completing the four sections below.

NOTE: You do not have to answer the multiple choice questions at the end of the episode. Also, do not email your recommendation to me (the instructor) as indicated in the LearnScapes episode. Instead, type your answers to the sections below to upload as part of this assignment.

RECOMMENDATION:

a. Section One – Guiding Values: Consider the most appropriate ethical values to resolve this dilemma. These values can be from our textbook, the article “Ethical Framework for the Allocation of Personal Protective Equipment” or from other sources. List the top three (3) Guiding Values that you consider highest priority to apply to this dilemma.

b. Section Two – Recommendation: Clearly state your recommendation in a few sentences.

c. Section Three – Reasons for Your Recommendation: Fully explain your recommendation in 2-4 paragraphs. Your explanation should meet the following criteria

Indicate that you carefully considered and analyzed all facts and perspectives.

(1) Demonstrates that you paid close attention to the simulation.

(2) Considers both the legal and ethical issues.

(3) Is supported with evidence and facts stated in the simulation video.

(4) Is well-organized with a logical flow.

(5) Is clear in what why you are making your recommendation.

d. Section Four- Fair Process:

(1) Did your decision-making process meet the first principle in the article, “Ethical Decision-Making About Scarce Resources: A Guide for Managers and Governors”? Why or why not?

(2) Should Bright Roads publicize your recommendation (second principle)? Why or why not?

(3) Was there effective stakeholder participation in this decision-making process? Why or why not?

Integrity of the Healthcare Record: Best Practices for EHR Documentation (2013 update)

Electronic documentation tools offer many features that are designed to increase both the quality and the utility of clinical documentation, enhancing communication between all healthcare providers. These features address traditional well-known requirements for documentation principles while supporting expansive new technologies. Use of these features without appropriate management and guidelines, however, may create information integrity concerns such as invalid auto-population of data fields and manufactured documentation aimed to enhance expected reimbursement. Processes must be in place to ensure the documentation for the health information used in care, research, and health management is valid, accurate, complete, trustworthy, and timely.

There are a number of existing rules and regulations on documentation principles and guidelines that primarily address documentation authorship principles, auditing, and forms development in a paper health record. New guidelines are being sought by the healthcare industry that ensure and preserve documentation integrity in an age of electronic exchange and changes in the legal evidentiary requirements for electronic business and health records.

With the continued advancement of electronic health records (EHRs), there is increasing concern that a potential loss of documentation integrity could lead to compromised patient care, care coordination, and quality reporting and research as well as fraud and abuse. This practice brief provides guidance for maintaining documentation integrity while using automated EHR functions.

Ensuring Documentation Integrity

Documentation integrity involves the accuracy of the complete health record. It encompasses information governance, patient identification, authorship validation, amendments and record corrections as well as auditing the record for documentation validity when submitting reimbursement claims. EHRs have customizable documentation applications that allow the use of templates and smart phrases to assist with documentation. Unless these tools are used appropriately, however, the integrity of the data may be questioned and the information deemed inaccurate—or possibly even perceived as fraudulent activity. Established policies and procedures such as audit functions must be in place to ensure compliant billing.

Without safeguards in place, records could reflect an inaccurate picture of the patient’s condition, either at admission or as it changes over time. The provider must understand the necessity of reviewing and editing all defaulted data to ensure that only patient-specific data for that visit is recorded, while all other irrelevant data pulled in by the default template is removed. For example, the automatic generation of common negative findings within a review of systems for each body area or organ system may result in a higher level of service delivered, unless the provider documents any pertinent positive results and deletes the incorrect auto-generated entries.

Appendix B

, available in the online version of this practice brief in the AHIMA Body of Knowledge, illustrates examples of worst and best case scenarios observed in documentation practices for healthcare delivery. These scenarios show how the ability to copy previous entries and paste into a current entry can lead to a record where a provider may, upon signing the documentation, unwittingly attest to the accuracy and comprehensiveness of substantial amounts of duplicated or inapplicable information, as well as the incorporation of misleading or erroneous documentation. The scenarios further illustrate that while helping to improve apparent timeliness and legibility of documentation, additional adverse effects were created by the inability to verify actual authors or to authenticate services provided at any given time.

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 From a billing perspective, defaulting or copying and pasting clinical information with previous existing documentation from other patient encounters in a different health record facilitates billing at a higher level of service than was actually provided.

Providers must recognize each encounter as a standalone record, and ensure the documentation within that encounter reflects the level of service actually provided and meets payer requirements for appropriate reimbursement. The integrity of this information is vital. As Michelle Dougherty, MA, RHIA, CHP, noted in her testimony to the Office of the National Coordinator for Health IT’s (ONC) HIT Policy Committee, “If clinical documentation was inaccurate when used for billing or legal purposes, it was wrong when it was used by another provider, another provider at transition, a researcher, the public health authority, or quality reporting agency.”

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 The documentation may need to include any health information such as labs, changes in medications, or updates to any chronic health conditions impacting an encounter that was reviewed by the provider during the visit.

Time’s Ticking for Information Governance

Data quality and record integrity issues must be addressed now, before widespread deployment of health information exchange (HIE). Poor data quality will be amplified with HIE if erroneous, incomplete, redundant, or untrustworthy data and records are allowed to cascade across the healthcare system. Healthcare organizations must manage information as an asset and adopt proactive decision making and oversight through information asset management, information governance, and enterprise information management (EIM) to achieve data trustworthiness. AHIMA defines information governance as “the accountability framework and decision rights to achieve EIM. EIM is defined as the infrastructure and processes that ensure information is trustworthy and actionable.”

The multitude of federal and state health information exchange initiatives are making information governance and the integrity of EHRs more challenging every day. An accurate information governance program will ensure the accountability of how information is managed and the information’s integrity.

Legal Issues Surrounding EHRs

HIM professionals consistently identify the following documentation practices as problematic in EHRs. These practices contribute to data quality and information integrity issues. Risky documentation practices that create the potential for patient safety, quality of care, and compliance concerns—such as those described below—may leave an organization vulnerable to patient safety errors and medical liability.

Template Documentation Challenges

Documentation templates can play an important role in improving the efficiency of data collection, ensuring all relevant elements are collected in a structured format. However, these templates also have limitations:

· Templates may not exist for a specific problem or visit type. This issue can occur if the structure of the note is not a good clinical fit and does not accurately reflect the patient’s condition and services.

· Atypical patients may have multiple problems or extensive interventions that must be documented in detail.

· Templates designed to meet reimbursement criteria may miss relevant clinical information. Templates may also encourage over-documentation to meet reimbursement requirements even when services are not medically necessary or are never delivered.

Cloning, Copy/Paste Practice Problems

Cloned documentation continues to be a significant problem that creates unnecessary redundancy and at times inaccurate information in the EHR. Some EHR systems are designed to facilitate cloning with such popular features as “make me the author” to assume the content of another person’s entry, “demo recall” to copy forward vital signs, “copy and paste” to replicate information from a previous visit, or the use of “smart phrases”—a function that pulls in specific identical data elements. Automated insertion of previous or outdated information through EHR tools, when not modified to be patient-specific and pertinent to the visit, may raise significant quality of care and compliance concerns—creating a potential for medical liability issues.

Organizations must develop policies designed to address inappropriate use of these tools to minimize non-compliance. Common documentation risks that can result from cloning features include:

· Vital signs that never change from visit to visit

· Information “copied and pasted” from a different patient’s record

· Documentation from another provider including their attestation statement

· Identical verbiage used repeatedly for all patients seen by a provider for a specific timeframe with little or no modification regardless of the nature of the presenting problem or intensity of the service; at times, such verbiage includes contradictory indications (i.e., use of pronoun “he” instead of “she,” indication that patient has no pain when the documentation includes a record of pain)

Providers must recognize that every patient is unique and must ensure that the health service provided is documented distinctly from all others.

Dictation Errors without Validation

Organizations using voice recognition without a validation step in place are experiencing significant data quality problems and documentation errors. Organizations should have in place a process to ensure providers review, edit, and approve dictated information in a timely manner. Since these documents are often used and exchanged, the importance of accurate and quality documentation in EHR systems is critical.

EHRs have created tremendous changes in the provider’s workflow and documentation process. Best practices for documentation that ensures quality have not been well defined for EHRs and are not well understood by providers. Innovations are needed to improve documentation tools and techniques; a back-to-the-basics focus on the importance of data accuracy and quality must take priority before widespread deployment of interoperable health information exchange occurs.

Healthcare fraud has signalled sharper focus on specific avenues for improper claims or billing, including EHRs. The Office of Inspector General’s 2012 Work Plan included a focus on fraud vulnerabilities specifically presented by EHRs, making it the first work plan in which the agency explicitly named EHRs a a target for review.

Patient Identification Errors

Documentation integrity is at risk when the wrong information is documented on the wrong patient health record. Errors in patient identification can affect clinical decision making and patient safety, impact a patient’s privacy and security, and result in duplicate testing and increased costs to patients, providers, and payers. Patient identification errors can grow exponentially within the EHR, personal health record, and HIE network(s) as the information proliferates.

Failure of organizations to employ front end solutions that include measures like sophisticated matching algorithms or other methods such as use of biometrics, photography, or fingerprinting can put the organizations at risk.

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 Special alerts can be designed and implemented within an EHR to avoid potential safety issues, such as when a patient blood type or allergy does not match the patient undergoing treatment.

Organizations must have a patient identity integrity program that includes performance improvement measurements that monitor the percentage of error rates and duplicate records within its electronic master patient index. Policies and procedures must ensure that key demographic data are accurate and used to link records within and across systems. Policies must address the initial point of capture as a key front end verification.

Authorship Integrity Issues

Authorship attributes the origin or creation of a particular unit of information to a specific individual or entity acting at a particular time. When there are multiple authors or contributors to a document, all signatures should be retained so that each individual’s contribution is unambiguously identified.

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 Some EHR systems allow more than one individual to add text to the same progress note entry or flow sheet. If the EHR does not have functionality to enable both providers to document and sign, it may be impossible to verify the actual service provider or the amount of work performed by each provider.

Integrity of Amendments

As outlined in the AHIMA toolkit “Amendments in the Electronic Health Record,” addendums, corrections, deletions, and patient amendments should be included in the record as defined by HIPAA. In order to support the integrity of the health record, EHR systems need to allow providers to make amendments, have the ability to track corrections, and identify that an original entry has been changed. The functionality to do this can be a combination of EHR applications along with policies and procedures that outline when changes need to be made, what changes can be made, who can make the changes, and how these changes will be tracked and monitored.

The original entry must be viewable, along with a date and time stamp, the name of the person making the change, and the reason(s) for the change. Without this information, the date sequence may be impossible to follow—adversely affecting appropriate patient care and resulting in questionable supporting documentation for reported services. See case study 2 in 

Appendix B
 for examples of best and worst case scenarios and discussion questions related to data integrity.

The EHR functionality may also determine whether or not an original note or amendment includes the correct date and time. Some systems automatically assign the date that the entry was made, while others allow authorized users to revise the date of entry to the date of the visit or service.

All users are responsible for ensuring that documentation authorship is accurately recorded in all approved uses of the available documentation tools, and for making sure that any changes or deletions made outside of routine record use are maintained in the EHR system. 

Appendix C

, available in the AHIMA Body of Knowledge, provides guidance on steps to prevent fraud in EHR documentation.

Healthcare Fraud and Abuse

Healthcare fraud is defined as an “intentional deception or misrepresentation that the individual or entity makes knowing that the misrepresentation could result in some unauthorized benefit to the individual, to the entity or to some other party.”

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 The intentional fabrication of medical records in order to improve reimbursement may be considered fraudulent. This fabrication could result from overuse of “copy and paste” functionalities or misuse of templates originally designed for documentation efficiency.

Healthcare abuse describes incidents or practices which are not usually fraudulent but are not consistent with accepted medical or business practices that may result in unnecessary costs to payers. These unintentional practices may involve repeated billing and coding errors that over time may be considered fraudulent if patterns of continued practice are found upon external review.

When misrepresentation occurs—whether it is intentional or unintentional—the staff member that has responsibility for ensuring an accurate claim has the obligation to proactively identify and prevent fraud. All providers involved in the patient’s care must be held accountable to ensure the integrity of the documentation is compliant with existing law and that the level of service reported meets all payer billing, coding, and documentation requirements. According to the Medicare Claims Processing Manual, “Medical necessity is the overarching criterion for reimbursement… and the volume of documentation should not be the primary influence upon which a specific level of service is billed.”

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Audit Integrity

Audits are essential to ensuring that the health record documentation present supports the level of service reported, that all payer requirements for reimbursement are met, and that only authorized users are accessing or making entries to patient medical records.

Audit trails must include the name of the user, the application triggering the audit, the workstation, the specific document, a description of the event being audited (i.e., amendment, correction, or deletion), and the date and time. The audit trail must capture what is amended (including deletions) within the health record and provide auditors with a starting point for compliance audits.

EHRs that lack adequate audit trail functionality create uncertainty in the integrity of health record documentation, and may create legal liability for the organization while inadvertently making or protecting criminal activity. There may also be no way to determine if and when corrections or amendments were made to the documentation, who made the changes, or the nature of the changes. In addition to the normal unintentional errors that may occur in documentation, audit trail functionality can help to detect situations where an alteration of records is meant to prevent the discovery of damaging information.

Organizations may utilize the audit trail functionality of the EHR system to identify and trend utilization of health records. The functionality typically allows users to generate reports for a specified time frame by provider or provider type, with the results sent directly to a compliance committee or the organization’s governing body.

Compliance Education

Organizations may need to devote more strategy to ensure providers are well-informed about compliance and legal risks. This starts in the EHR training process. Organizations may need to develop initiatives in EHR education to make sure they do not risk compliance problems.

Staff education on best practices for documentation should focus on the integrity of the health record. The education program must be monitored, maintained, and offered quarterly or annually. Answering questions of who, what, why, and how will help to ensure individuals have a solid understanding of the organizational practices and measures that maintain individual best practices. Education geared toward understanding who, what, why, and how must include:

· The definition of who (entities or individuals) could commit fraud

· Both universal and organizational best practices with regards to security and log-in, validity of data, authorship/authentication, use and storage of data, and data transmittals

· The importance of continual education

· Strategies for applying fraud prevention best practices on a daily basis

Recommendations for Maintaining Integrity

Organizations should have policies and procedures in place that prevent fraud as a result of deliberate falsification of information. At minimum, organizations should consider these four primary conditions:

· Desire and commitment to conduct business and provide care in an ethical manner

· Purchasing systems that include functions and capabilities to prevent or discourage fraudulent activity

· Implementing and using policies, procedures, and system functions and capabilities to prevent fraud

· Inclusion of an HIM professional such as a record content expert on the IT design and EHR implementation team to ensure the end product is compliant with all billing, coding, documentation, regulatory, and payer guidelines

Ensuring documentation integrity in the record is a fundamental practice. Organizations should use the guidelines and checklists in Appendices C and D to assess their compliance. These appendices contain:

· Steps organizations can take to prevent falsification of EHRs

· Guidelines for selecting EHR system features to reduce the likelihood for falsification

· Guidelines for implementing EHR systems features designed to reduce the likelihood of falsification

· Fraud prevention education programs (training requirements, security and integrity requirements, violation of EHR policy and procedure consequences)

· Recommendations for establishing a process for logging all activity on EHR systems (audits and audit trails recommended)

· Sample business rules for EHR systems

…

Appendix B: Case Studies: Integrity of the Healthcare Record

Case Study 1

Issue: Electronic Tools That Enable Borrowing Data from another Source

Electronic tools make it easy to copy and paste documentation from one record to another or pull information forward from a previous visit, someone else’s records, or other sources. Failure to build in technical or policy and procedural safeguards creates an environment in which documentation manufacturing is encouraged and fraudulent entries are possible—thereby compromising data integrity. There also are instances in which borrowed documentation cannot be tracked to the original source, creating both legal and quality of care concerns.

The scenarios below illustrate how technology may be used effectively to achieve either positive results (illustrated in the best case example) or undesirable outcomes (illustrated in the worst case examples). Health record documentation elements can be repetitive because some conditions and situations are frequently encountered and similar processes are followed. Health interventions also follow a standard course. However, each patient is unique, making each health service distinct from all others. Documentation created for one patient or a specific visit is most often not suitable for others, and copying text entries from one record to another should be carefully controlled.

Worst Case Examples

Professional Services

While Patient A was a patient at Medical Center A, a number of medical tests and diagnostic evaluations were performed in an outpatient clinic over a two-week period. Concern arose about the health plan claim, so Patient A requested a copy of his medical records along with the bill for services. The statement included evaluation and management codes consistently reported at the highest level of service (level 5).

Because Patient A is a retired auditor for health plans, he examined the documentation and discovered that the medical history was pulled through within departments, between departments, and in subsequent visits with the same provider using the electronic health record (EHR) system, even when the visits did not include the clinician taking a history. The health plan was billed for a high level of service (of history) for each hospital outpatient clinic visit.

Patient A is concerned that the EHR does not have the functionality (or it is not used) to show that the history (or any documentation component) obtained during a previous encounter was copied and reused as documentation for subsequent visits to support physician intensity of service. After many attempts to have services billed at the correct level (what Patient A insists is really a level 2 or 3 evaluation and management when the pulled through data are not considered for service intensity), he contacts the fraud division of the health plan about his concerns.

Academic Medical Center and Physician Services

Patient B was admitted to Medical Center A for a workup to determine the cause of hypertensive episodes. She has undergone mitral valve replacement with a porcine graft and also requires a pacemaker to regulate and stabilize her heart rate.

The physician progress notes in a hospital-based EHR were copied and pasted multiple times by the attending physicians, consulting physicians, and residents by using a convenient macro feature available in the software. The teaching physicians regularly copy and paste the residents’ notes as their own, which saves time in a very busy environment and covers the Medicare requirement of teaching physicians personally performing services for reimbursement.

A new resident misdiagnosed adrenal insufficiency and recorded the incorrect diagnosis in Patient B’s record. Because of the normal routine for borrowing documentation from other sources, the physicians copied and pasted this documentation and relied on the erroneous assessment several times, resulting in an increased level of evaluation and management services complexity for the Medicare claim and at the same time creating a patient safety and quality of care issue. Ultimately, Patient B died from a medication error after administration of steroids to treat the adrenal insufficiency the patient didn’t have, and the case is now in litigation.

Behavioral Health Services

Across the street at Mental Health Center A, a state department of health surveyor identified Nurse A repeatedly documenting the same text on progress notes completed for several patients on her caseload. Nurse A explained that when completing notes for patients receiving medication management services, she always copied and pasted entries between patient records. She stated that medication dosage was an exception most of the time because they are more variable. Nurse A used this shortcut for documentation as one way to get her charting completed in the EHR before the end of her shift.

The state surveyor calls this pattern of documentation “cookie cutting.” This practice involves copying and pasting the same text from one record to another, neglecting to document the variations accurately from one patient to another. For example, the patient’s response to the medication may be different, regimen compliance may be different, and request for follow-up date and time may also be different. This practice by Nurse A and other nurses from Mental Health Center A resulted in a large focused review conducted by the Medicaid Fraud Division along with fines and penalties for payment for care that was not rendered at the level of service claimed.

Best Case Example

Both the hospital and clinic associated with Medical Center A use an EHR system. The EHR has specific patient safety and documentation integrity tools built into its design. Memorial provides orientation to all medical students and residents providing patient care services on how to use the tools for accurate and complete documentation. Because it is very important that only those services personally provided or supervised by teaching physicians generate a bill for services, the computer-generated templates guide all of the participants in patient care to the correct place and format for recording observations within the record. These entries always include a date/time stamp and the author of the note. Teaching physicians must sign on to the system so the appropriate authentication is attached to their chart entries, and any templates must be modified to reflect specific conditions and observations unique to the service. Teaching physicians must be physically present to report services for health plan claims. Medical necessity and intensity of service documentation are unique to each visit, so when EHR templates and macros are not modified, they are clearly identified both by a different screen color and by a watermark across the text saying “Unmodified Documentation Template.” Info buttons provide the documentation guidelines and reporting requirements for teaching physicians and are available at the click of a mouse. Alerts are generated when a copy or paste function is used warning the EHR user about plagiarism and the risk of copying documentation out of context in a legal document.

Medical Center A also created a full slate of documentation guidelines, policies, and procedures surrounding use of the EHRs and related tools for capturing information. Special emphasis was placed on the prohibition of pulling forward information from previous visits as a basis for increasing the level of evaluation and management for billing. There are now clear protocols about the completion of an entry or record—when information displays (or not) to users and when the record gets locked down for either pulling forward or copying text content to another location. Situations and examples are provided that describe the appropriate use of pulled forward and copied entries taken from other sources. Policies about the use of scribes or surrogates making entries in an EHR are created and monitored for compliance. All designated scribes or surrogates have the ability to create entries but require countersignature authorization from the supervising clinician before they display to other users of the EHR system.

Mental Health Center A also started a clinical documentation improvement program that included appropriate use of nursing documentation templates suitable for recording medication management. These templates create the framework for required documentation unique to each patient. They include built-in edits to ensure correct recording of dosages by comparing nurse entries with the issuing pharmacy instructions and the original scripts.

Discussion Questions:

1. Which of the guidelines included in appendix C of the January 2007 AHIMA e-HIM practice brief “Guidelines for EHR Documentation to Prevent Fraud” could be used to discourage evaluation and management upcoding because of the pull-forward or copy-and-paste habits of the physicians on staff at Medical Center A?

2. What other adverse effects may result from the cookie-cutter approach used at Mental Health Center A?

3. There are times when pulling forward of entries from previous visits into current records is appropriate. What are some examples of this practice in electronic environments that is a fully legitimate and desirable method for documentation?

Case Study 2

Issue: Data Integrity

A wide spectrum of data is collected in healthcare and must be collected accurately, completely, and consistently. Data integrity is of extreme importance because it is used to identify and track patients as they move from one level of care to another. Data are used to verify the identity of an individual to ensure that the correct patient is receiving the appropriate care and to support billing activity. According to Johns in 
Health Information Management Technology: An Applied Approach, (2nd edition, page 851) “Data integrity means that data should be complete, accurate, consistent and up-to-date. Ensuring the integrity of healthcare data is important because providers use them in making decisions about patient care.”

The scenarios below are examples of worst case and best case examples associated with data integrity. Because of the large amount of data collected in healthcare, data integrity can be compromised repeatedly. Information can be entered incorrectly or in incorrect formats in various healthcare settings, so procedures must be defined to ensure that data are collected consistently regardless of the medium being used.

Worst Case Examples

Clinical Notes with Difficulty in Date Association

A patient was seen by a clinician on September 1, 2013, just before lunch. Once the patient was examined, the clinician got sidetracked and was not able to enter his note on the date the patient was seen. During the visit, the patient discussed a possible reaction to a prescribed medication. On September 5, 2013, the clinician was back on duty after a long weekend; upon review of the record, he realized that he did not make an entry on September 1, 2013.

As the clinician began documenting, he decided that he wanted the date to reflect the actual date the patient was seen. He changed the date to September 1, 2013, at 11:30 a.m. He proceeded to enter the documentation as best he could. He remembered and documented the symptoms the patient described surrounding the potential medication reaction.

When another clinician reviewed the record, he saw the new note. This second clinician worked over the weekend and did not recall seeing this information but sees now that the date displayed is September 1, 2013, at 11:30 a.m. This alarmed the clinician, as he prescribed the medication that the patient had indicated a possible reaction to in the past.

Note and Event Entries—Date/Time Stamp

A facility has multiple biomedical peripherals connected to the EHR such as portable ECGs and intravenous infusion pumps. The main system has a synchronized clock for display with date and time stamping on notes, laboratory results, etc. Performance measures established by the Joint Commission on Accreditation of Healthcare Organizations, ORYX, and the Centers for Medicare and Medicaid Services (CMS) are monitored, tracked, and reported. Some payments are tied to quality of service. Indicators for chest pain include requiring that the ECG be performed within 10 minutes of arrival in the emergency room.

A patient is brought to the emergency room at 23:55 on September 1, 2013. An ECG is started and completed according to orders entered at 23:57 on September 1, 2013. The ECG is uploaded, read, and interpreted. At 00:30 on September 2, 2013, the clinician completes her documentation of the assessment and orders admission for acute myocardial infarction.

After a retrospective review of the case, the ECG is reported as being ordered at 23:57 but not completed until September 2, 2013, at 00:45. This is 15 minutes after the note entered by the clinician stating the ECG was done and showed ST-elevation myocardial infarction. Not only has this case fallen out for performance measures but it will also have difficulty standing up in court. It could possibly fail a third-party review if the outpatient was treated and released because the chest pain was thought to be gastrointestinal in nature. An audit might determine the ECG was not a covered service if done after the time of discharge.

In addition, the facility might not receive proper credit (or in the reverse if the clock times show it was done on time, but it really wasn’t) and either receive wrongful payment or no payment when reimbursement is based on quality indicators. The linkage of peripherals needs to have the clocks on each system synchronized to support the integrity of the data collected for the care provided.

Touch Pads in Long-Term Care

Nursing Care Facility A implemented an EHR to streamline documentation so that the resident assessment instrument (RAI) is integrated with the assessment process/protocol (RAP) and the Minimum Data Set (MDS). A special feature of the software ensures optimal reimbursement for skilled beds through a point-of-care system that prompts nursing personnel to enter data elements.

The nurses and nursing assistants enjoy the convenience of the touch pad technology and the time the new system saves them for charting. However, the director of nursing has discovered that the system is creating documentation inconsistent with actual patient conditions. The MDS being transmitted to CMS is overstating the type of care for therapy units and suppressing one of the reportable quality indicators (residents with pain). The documentation in the records supports the optimized payment from Medicare for the skilled-care patients, but the director of nursing is very concerned about the consequences of using it.

Best Case Examples and Solutions

Clinical Notes with Difficulty in Date Association

Text entries into the EHR have a hard-coded date/time stamp that cannot be altered by the author. However, the clinician making a late entry can associate the date of the visit/service by using a second date/time field option, which allows for dates of reference for both a late entry and the date the care was provided. The ability to make amendments to the EHR is defined by business rules and policy. Entry errors are defined and reported accordingly.

Documentation Tools in a Teaching Hospital

University Hospital A uses an EHR for both the hospital and the clinic. The EHR has specific patient safety and documentation integrity tools built into the design. University Hospital A provides an orientation to all medical students and residents on how to use the documentation tools so the information collected is always accurate and complete.

It is very important that only those services personally provided or supervised by teaching physicians generate a bill for services. The computer-generated templates guide all users to the correct place and format to record observations, including a date/time stamp and the author of the note. Teaching physicians must sign on to the system so the appropriate authentication is attached to their chart entries, and any templates must be modified to reflect specific conditions and observations unique to the service. Teaching physicians must be physically present to report services for health plan claims. Medical necessity and intensity of service documentation are unique to each visit.

The templates and macros in the EHR not modified are clearly identified both by a different screen color and by a watermark across the text that says “Unmodified Documentation Template.” Info buttons providing documentation guidelines and reporting requirements for teaching physicians are available to the physicians at the click of a mouse. Alerts are generated when a copy or paste function is used to warn the end user about plagiarism and the risk of copying documentation out of context in a legal document.

The authority for developing templates and implementing documentation content and formats is spelled out in policy (bylaws) and is done through collaboration of EHR and HIM/medical record committees at the facility.

Clinical Notes with Difficulty in Date Association

The date that a note is entered into the EHR is hard coded. However, clinicians have the ability to associate the note with a date of service to reflect a reference date of when they saw patients as well as an indication of a late entry. Both of these dates are important to best practices in HIM.

Note and Event Entries—Date/Time Stamp

The facility made a conscious effort to ensure a standard for date and time stamps. To accomplish this goal, the facility inventoried all interfaced applications and biomedical equipment. Each equipment vendor was contacted to determine the best method of synchronizing peripherals to the main system, which minimized or eliminated users having to keep track of the time themselves. However, some equipment may need to be checked at the beginning of shifts or at 00:01 as the staff do with crash carts, etc.

Touch Pads in Long-Term Care (Best Case)

Nursing Care Center A implemented an EHR to streamline documentation so that the RAI is integrated with the RAP and the MDS. A special feature of the software ensures optimal reimbursement for skilled beds through a point-of-care system that prompts all personnel to enter data elements. Each section of the MDS requires various personnel to provide coded data supported by their patient-specific documentation in the EHR.

Clinical, nonclinical, and medical staff have all found the convenience of the touch pad technology to be a time savings for both charting and completing their portion of the MDS. The software for collecting the MDS data has built-in hierarchy for the user (physician or nurse assistant) and for most data elements. For example, any activities of daily living (ADLs) or sleep patterns checked off by a nurse assistant would be accepted, but if a physician or nurse documented items relative to ADLs during the same MDS reporting period, a pop-up window would ask, “Section X has information already entered for ADLs; do you want to proceed?” This prompt allows the nurse or physician to proceed or to double-check what the nurse assistant previously recorded.

Orders in EHR

The orders section in an EHR can be a large database. Prescriptions must have specific fields associated with them to identify the details of the individual order—which physician placed the order; the date, time, reason, or diagnosis associated with the medication; status, etc.

Diagnosis on Note Different Than Final Diagnosis Coded and Billed

The provider may document a diagnosis that attaches itself to a template note. The coder may decide from the physician’s documentation that the diagnosis should be coded more specifically. Thus, the diagnosis in the EHR template note might be different than what was coded and billed.

Discussion Questions:

1. What procedures can be established to ensure that medication reactions described by a patient are documented in an accurate and timely manner to prevent medication errors and negative medication reactions?

2. When dealing with disparate systems, what time-safe rules can be established to prevent staff from being able to enter data after a subsequent visit has been documented without systematic alerts to notify specific end users of a late entry or a change in documentation?

3. What steps can an agency take to develop an electronic process to perform thorough data quality audits at specified time intervals?

4. In a teaching facility, what documentation guidelines can be established to ensure that documentation completed by residents and interns is countersigned by tenured medical staff to prevent inconsistent documentation and billing discrepancies that can lead to fraudulent billing activities?

Application of Guidelines:

These case studies have been prepared along with guidelines to provide further references. See guidelines 1-3.

Case Study 3

Issue: Patient Identification and Demographic Data: Automated Patient Registration Data Elements/Patient Safety Risks

Failure of an EHR system to provide appropriate safeguards against medication errors, including the wrong patient, the wrong drug, or failure to consider all available data, can contribute to poor quality care. Examples of automated patient registration data elements and patient safety issues illustrate the need for identity management safeguards.

Worst Case Example

Dr. Rogers is ordering a prescription by using electronic order entry for a nursing home resident in the geriatric outpatient clinic at City Hospital A on October 15. The patient with dementia presents to the clinic with a nursing assistant from Nursing Care Facility A, she is registered as Ethel Mertz, and her health records are placed in queue for Dr. Rogers.

Nursing Care Facility A had been contacted the previous day to gather information for the appointment. The registration clerk from the hospital asked only for the patient’s name then used the lookup feature in the EHR system to locate existing health records and place them in Dr. Roger’s authorized access list for the upcoming appointment. The City Hospital A system automatically populates registration data and places patient records in an authorized access queue for scheduled patients in the clinics on the day of the visit.

The nurse has downloaded a printout from the EHR system for Dr. Rogers to use in the examination room while caring for the patient, but he doesn’t see that the Ethel Mertz in the record is 27 years old and has an address in another city. It’s easy to locate Ethel’s record in the system by typing in the first three numbers of her Social Security number (also stamped on the fee ticket) used to bill Medicaid for services. The clinic staff has already verified that Ethel is eligible for Medicaid.

The physician order entry software provides the capability for default self-selection upon entering the first three letters of the drug. The physician wanted to order Norfloxacin for an eye infection. As soon as “Nor” was entered, the software prompted for Norflex, which was accepted. The prescription/medication order was received in the pharmacy and was filled for Norflex, which is a muscle relaxant rather than an antibiotic. Both are oral medications, although muscle tightening or spasms could result from Norflex. The order was signed electronically, the medication was made available for the nursing assistant to pick up, and the patient was returned to the nursing facility.

The patient with an infection requiring treatment with Norfloxacin began taking Norflex and returned to the emergency room later the same week with septic shock due to a very serious bacterial infection of the left eye. When the emergency room staff accessed her health record, there was no entry for a geriatric clinic visit on October 15, so the findings from her care were not available.

City Hospital A filed a Medicaid claim for Ethel Mertz and was paid for a clinic visit on October 15 with pharmacy charges for a Norflex prescription. Unfortunately, the Nursing Care Facility A patient’s name is Ethel Merts, age 93. She has a number of chronic health problems, takes a number of medications, and has an allergy to drugs containing quinolone.

Best Case Examples

City Hospital A uses a certified EHR system with built-in safeguards in the computerized physician order entry (CPOE) software suite to prevent medication errors.

· This system does not allow software to self-select (or default) the first alphabetical choice in the order process and requires a second validation to make sure the drug indicated is the intended substance and dose.

· This system provides the user the opportunity to finish typing before any suggestions are made by the software.

· The software provides a list of options (or drop-down menus) to the user to select from and then provides alerts or reminders from a knowledge base.

· City Hospital A does not allow use of abbreviations in ordering; the full name of the drug is always displayed to avoid any errors between similar medications.

· The system also provides a warning message at the time of signature for contraindications and potential adverse effects.

During the ordering process used at City Hospital A’s outpatient clinic, Mrs. Merts’s physician is asked by the EHR system to verify selection of Norfloxacin because the current medication history indicates that the patient had an anaphylactic reaction to another antibacterial agent that includes quinolone. The physician selects another type of antibiotic that is equally effective and avoids the risk of an adverse reaction.

Patient Identity Management

A nursing home resident presents to the City Hospital A geriatric clinic with 
Staphylococcus aureus conjunctivitis. The nursing home had arranged the appointment with Dr. Rogers by using an online registration portal that requires verification of five critical demographic data elements to establish patient identity. Because there are two patients with similar names at Nursing Facility A, the home is careful to make sure that this patient, Mrs. Ethel Merts, is registered with her physician Dr. Rogers. Her current medication list, problem list, and allergies are uploaded to the system from the nursing home EHR. The EHR at City Hospital A sends a verification message of receipt, and Dr. Rogers has a printout of the nursing home records at the time of the examination. At any time when verification is required, Dr. Rogers is able to access the full EHR including the uploaded information provided by the nursing home.

Discussion Questions:

1. What safeguards should be built into procedures to verify patient identity?

2. What process would be used to correct the entries made incorrectly on the record of Ethel Mertz (age 27)?

3. What steps are needed to resolve the Medicaid claims issue generated on the basis of false information for Ethel Mertz?

4. What business process steps should be taken to prevent erroneous entries in a CPOE system?

Appendix C: Steps to Prevent Fraud in EHR Documentation

The following guidelines provide recommendations for organizations to reduce the likelihood of fraud when EHRs are being used.

Establish Organizational Policies

An organization communicates its ethics and commitment to complying with laws and regulations through its policies. Organization-wide policies that should be established to reduce the likelihood of fraud include the following:

· Stating the organization’s commitment to complying with all laws and regulatory requirements and to operating in an ethical manner

· Prohibiting the entry of false information into any of the organization’s records

· Defining individual responsibility and accountability for the accuracy and integrity of information and establishing a notification process consistent with language in the medical staff bylaws or rules and regulations when errors are discovered.

· Specifying consequences for the falsification of information.

· Requiring periodic training covering the falsification of information and information security.

· Defining management-level responsibility for the organization’s information security program.

Organizations should also establish EHR- and HIM-related policies:

· Specifying administrative documentation requirements.

· Specifying clinical documentation requirements.

· Requiring the logging of activity on EHR systems.

· Covering changes (i.e., corrections and amendments) to records.

· Establishing timeframes for correcting information once the incorrect documentation is discovered.

This is a list of highly recommended policies and is not meant to be exhaustive. Organizations implementing an EHR may need to develop additional policies according to their needs.

Fraud Prevention Education Programs

Education programs need to address the different functionality of an electronic versus a paper environment specifically for individuals who have previously worked in a paper health record environment. EHR users more than likely will continue to use paper records along with the EHR, so distinctions regarding the unique fraud risks of the EHR must be conveyed. In the paper environment, data are usually static, and alterations or changes to documents are more readily apparent. In the EHR, alterations can more easily go undetected, and errors can grow exponentially. EHR fraud prevention education programs should address:

Training Requirements

·
Annual education and training: The organization is responsible for ensuring that EHR users receive regularly scheduled education and training on the organization’s policies and procedures for maintaining EHR integrity, including security and log in, validity of data, authorship/authentication, use and storage of data, and data transmittals. This training should be updated annually and can be incorporated as a separate focus area into the organization’s compliance and HIPAA training.

·
Documentation of education activity: Education programs are to be documented and become a part of the physician, provider, or employee’s permanent medical staff or human resource record. In the event of any possible future issues regarding false or fraudulent entries, the organization will be able to demonstrate that due diligence was exercised in the training of its staff.

·
Mandatory requirement: All users must complete the education program.

·
Documentation guidelines: The education program needs to clarify and reinforce that the HIM documentation requirements and documentation guidelines accepted and established for the paper record also apply to the EHR. In addition, all regulatory and oversight agency requirements for documentation, such as for the Centers for Medicare and Medicaid Services (CMS), The Joint Commission (TJC), the Accreditation Association for Ambulatory Health Care (AAAHC), and the American Osteopathic Association (AOA) apply to the EHR as well.

Security and Integrity Requirements

·
Personal responsibility for protecting system access: All EHR users must protect their log-in or sign-in from unauthorized access. The user is prohibited from sharing individual security information with others and must report breaches of log-in or sign-in security immediately. EHR users must secure their desktops and laptops or other data access devices whenever they are away from them. Time-out screens, shut-offs and other security measures should be taken.

·
Personal responsibility for notifying management of actual or suspected problems: EHR users are not to hesitate in notifying management of problems even if a problem is only suspected and cannot be confirmed by the EHR user. These problems may be security breaches, suspicious activity, uncharacteristic data entries, unauthorized access, data entry errors that the user is unable to correct, amendments to data that are not in line with the organization’s policies and procedures for amendments, or any other activity not in accordance with the organization’s policies and procedures.

·
Personal responsibility for creating accurate records: It is particularly important to verify the patient record selected on an EHR because, unlike the paper record, once the patient is selected, the EHR screen flows may not alert the user to the patient’s identification. Furthermore, the paper record is three-dimensional and has many labels and visual prompts at the fingertips, whereas patient identification on an EHR may not be prominently displayed. Education should be directed to training EHR users to verify routinely a minimum of two or three unique patient identifiers such as name, date of birth, and account number.

The EHR users are responsible for each element of data they enter into the record and must provide electronic verification of authorship/authentication, which includes data that have been copied and pasted or pulled forward from other parts of the patient’s record or from sources outside of the patient’s record. Each entry that is not solely authored by the user must be validated by the user in a manner similar to that for bibliographic notations and include the name, date, time, and source of the data. This requirement can be satisfied by system software design that routinely provides this validation. Compliance with these elements will ensure that the requirements for regulatory agencies and payers will be met.

·
Logging, time stamping, and fraud-prevention software: The education sessions should explain that routine security programs are run on a regular basis and reviewed for unusual or invalid activity. As a deterrent to fraudulent activity, if the organization uses fraud prevention software, a general explanation of its purpose could be discussed.

Violations of EHR Policies and Procedures

Educational programs need to address clearly the organization’s disciplinary and termination policies governing falsification of records, security and access breaches, or violations. The education program should also refer to the organization’s policy for HIPAA and note should be made that EHR security is in line with protection and security of health information. Further reference should be made to various federal and state legislation and the requirements of various oversight agencies:

· Federal False Claims Act

· HIPAA

· Deficit Reduction Act of 2005

· Department of Health and Human Services Office of Inspector General (OIG) Guidance for Hospitals and Physicians

· CMS Conditions of Participation

· TJC Accreditation Requirements

· AOA Accreditation Requirements

· AAAHC Accreditation Requirements

· Medical Staff Bylaws of the Organization

· DNV Accreditation Agency

Note: the program and content outlined above are recommended as a starting point for organizations. Modifications and additions should be made as appropriate to meet organizational needs.

Establishing a Process for Logging and Auditing Activity in EHR Systems

An audit trail is a business record of all transactions and activities, including access, that are associated with the EHR. Monitoring audit trails in your system will ensure that users can be held accountable for following the organization’s policies and procedures, adhering to compliance rules and regulations, and following HIM protocols for access and maintenance.

Facilities using electronic health information systems need to ensure that individuals entering information into the EHR are aware that system audit trail functionality is in place allowing them to legally access, amend, retract, correct, or edit entries that were made during the normal course of business, at or near the time the care.

Determine Which Logging Features Should Be Used and Determine System Logging Capabilities

· The system should be able to generate an audit record when auditable events happen, including but not limited to the following (which include success, attempt, and failure):

· User login/logouts

· Chart created, viewed, updated, or deleted

· System security administration

· System start and stop

· Scheduling

· Query

· Order

· Node-authentication failure

· Signature created or validated

· Personal health information (PHI) export (e.g., print)

· PHI import (e.g., from external information source)

· System administration

· The system should record within each audit record the following information when it is available:

· Date and time of the event.

· Component of the information system (e.g., software component, hardware component) where the event occurred.

· Type of event (including data description and patient identifier when relevant).

· Subject identity (e.g., user identity).

· Outcome (success or failure) of the event.

· The system should provide authorized administrators with the capability to read all audit information from audit records in one of the following two ways:

· The system should provide the audit records in a manner suitable for the user to interpret the information. The system should provide the capability to generate reports on the basis of ranges of system date and time that audit records were collected.

· The system should be able to export logs into text format and correlate records on the basis of time (e.g., universal coordinated time [UTC] synchronization).

· The system should be able to provide time synchronization by using an industry standard format and use this synchronized time in all security records of time.

· The system should record time stamps by using UTC on the basis of ISO 8601-2000 (i.e., “1994-11-05T08:15:30-05:00” corresponds to November 5, 1994, 8:15 a.m., US Eastern Standard Time).

· The system should prohibit all users read access to the audit records, except users who have been granted explicit read-access. The system should protect the stored audit records from unauthorized deletion. The system should be able to prevent modifications to the audit records.

· The system should continue normal operation even when the security audit functionality is nonfunctional. For example, if the audit log reaches capacity, the system should continue to operate; issue a warning to system administrators; and suspend logging, start a new log, or begin overwriting the existing log.

Note: This section is adapted from the Certification Commission for Healthcare Information Technology Test Scripts for 2006 Certification of Ambulatory EHRs, Version 1.0, May 2006 at 

http://www.cchit.org/work/criteria.htm

.

Assign Responsibility for Auditing of Log Entries and Reported Exceptions

· Leadership and management are ultimately responsible for developing policies and procedures that spell out and assign responsibility to professional and ancillary leadership staff to determine system functionality, system security, and system usability as well as report any system inefficiencies or discrepancies potentially resulting in fraudulent entries into the EHR.

· Leadership and management should always adhere to legal and regulatory standards and follow ethical business principles when auditing the system for integrity and trustworthiness of the data.

· The system should allow an authorized administrator to set the inclusion or exclusion of audited events on the basis of organizational policy and operating requirements and limits.

Define Retention Periods and Procedures for Log Records

· The system should generate a backup copy of the application data, security credentials, and log and audit files.

· The system restore functionality should result in a fully operational and secure state, which should include the restoration of the application data, security credentials, and log and audit files to their previous state.

· If the system claims to be available 24/7, it should have the ability to run a backup concurrently with the operation of the application.

· The audit report must include a copy of the output of the audit as well as the steps taken to produce the report.

· Retention of audit logs is based on state and/or federal laws, whichever applies to the organization. Please see AHIMA practice briefs “Update: Maintaining a Legally Sound Health Record—Paper and Electronic” from November-December 2005 and “New Electronic Discovery Civil Rule” from September 2006.

Areas Recommended for Monitoring or Auditing for Detecting Alleged Fraud and Abuse Related to EHR Documentation

There are reasons other than documentation and fraud and abuse concerns that would encourage monitoring and auditing. Each organization must determine which monitors and audits are appropriate to address the requirements of applicable laws, regulations, needs, and available resources. Each organization is also responsible for specifying the method for determining whether the activity is legitimate or suspect and any necessary consequences such as.

· Abnormal patterns of activity:

· Spike in the number of people accessing a particular record or document.

· Sudden variation in the magnitude or types of changes made in a record.

· Unusual repetition of particular entries in a record.

· Entries or other transactions occurring at unusual times of the day or days of the week.

· Routine documentation monitoring:

· Review of problem lists and medication lists against prior lists for consistency.

· Audit providers’ orders for medications and ancillary services to determine if a provider properly documented the reason(s)/diagnosis(es) for the test(s)/medication(s) ordered.

· Audit to determine whether transcription/dictation reports are downloaded to and appear in the correct patient and correct visit date fields.

· Routine coding monitoring and auditing:

· Monitoring the computerized assignment of codes according to applicable coding system guidelines.

· Ensuring the documentation supports the code assigned.

· Noting unusual changes in the frequency of use of certain types of codes, etc.

· Auditing of EHR access and documentation to ensure users are authorized according to privileges and business rules.

· System-generated warning messages related to attempted unauthorized access.

· Monitor software upgrades and system changes to verify that security settings, user privilege settings, and logging parameters were not disabled or modified as a result of the upgrade or change.

Possible Techniques for Auditing or Monitoring:

· Standard sampling techniques backed by rigorous claims audits involving external validation procedures.

· Use test vignettes to evaluate the legal record for amendments, attestation, authorship, integrity, and nonrepudiation.

System Audit Trails:

The HIPAA Title II security rule CFR Part 136.316(b)(1) (taken from the March 26, 2013, Federal Register) is the source for this paragraph. It mandates audit trails be maintained within the EHR. Internal audit processes must be in place, and regular system activity reviews must be completed for logins and accessing files. Security incidents must be monitored and resolved. Keep in mind that logging and auditing processes may affect the performance of a system, and an organization may need to purchase additional hardware, memory upgrades, and/or bandwidth to support these audit requirements. Audit data must continuously be reviewed and analyzed, processes that may also require additional resources.

Sample Business Rules for EHR Systems

Establishing business rules is very similar to the process historically occurring in the medical record committee, and in medical staff bylaws, rules and regulations. Business rules implement these processes and designate who can document what in the record and how the documents are to be handled.

This business rule presented here should not be considered a complete business rule, nor does it represent all of the business rules needed for an EHR system. Business rules are specific to an organization and its EHR system configuration.

Business rules authorize specific users or groups of users to perform specified actions on documents in particular statuses.

· A completed clinical document can be viewed by a user.

· An unsigned clinical document can be edited by a provider who is also the expected signer of the note.

· An unsigned clinical document can be deleted by the appropriately authorized personnel.

Business rules apply to document definition, user class, or user role. You can then add, edit, or delete rules, as appropriate.

…

List Business Rules by Document for Clinical Documents:

· An untranscribed clinical document may be entered by a user.

· An unreleased clinical document may be released by a transcriber.

· An unsigned clinical document may be edited by an author/dictator.

· An unsigned clinical document may be edited by an expected signer.

· An unsigned clinical document may be signed by an expected signer.

· An unsigned clinical document may be signed by a provider who is also an expected cosigner.

· An unreleased clinical document may be edited by a transcriber.

· An uncosigned clinical document may be cosigned by an expected cosigner.

· An unsigned clinical document may be signed by a student who is also an expected signer.

· An unsigned clinical document may be edited by an expected cosigner.

· An untranscribed clinical document may be entered by a nurse.

· An uncosigned clinical document may be sent back by a provider who is also an expected cosigner.

· An amended nurse’s note may be edited by a nurse or an author/dictator.

· An amended nurse’s note may be edited by a nursing supervisor or an author/dictator.

Status of Business Rules: Actions Permitted for a Given Document Definition and Status:

Amended

The document has been completed, and a HIPAA issue has required its amendment.

Completed

The document has acquired all necessary signatures and is legally authenticated.

Deleted

The document has been deleted but the audit trail is retained.

Incomplete

This status applies to document definitions only.

Purged

The grace period for purge has expired and the report text has been removed from the online record to recover disk space. Note: only completed documents can be purged. The chart copy of the document should be retained for archival purposes.

Uncosigned

The document is complete, with the exception of co-signature by the attending physician.

Undictated

The document is required and a record has been created in anticipation of dictation and transcription, but the system hasn’t been informed of its dictation.

Unreleased

The document is in the process of being entered into the system but hasn’t been released by the originator (i.e., the person who entered the text online).

Unsigned

The document is online in a draft state, but the author’s signature hasn’t yet been obtained.

Untranscribed

The document is required, and the system has been informed of its dictation, but the transcription hasn’t yet been entered or uploaded.

Unverified

The document has been released or uploaded, but an intervening verification step must be completed before the document is displayed.

Business rules are complex, and there are additional rules for inheritance of business rules, inheritance along the document definition line, overriding business rule inheritance, inheritance along the user class line, and inheritance and addenda, etc.

Selecting EHR System Features to Prevent Fraud

Organizations should consider selecting EHR systems with the following capabilities:

Access Control:

Verifying authorship hinges on two concepts: authentication and access management. In the simplest terms, identity and access management can be defined as an integrated system of business processes, policies, and technologies that enable organizations to facilitate and control their users’ access to critical electronic applications and resources while protecting confidential personal and business information from unauthorized users. Authentication and access management can be executed either through the EHR software, or it can be controlled through a separate, or layered, software application.

·
User authentication:
Authentication is the process of determining whether someone or something is, in fact, who or what it is declared to be. The purpose of authentication is to show authorship and assign responsibility for an act, event, condition, opinion, or diagnosis. Entries in the healthcare record should be authenticated by the author.1 The method of authentication should be considered in selecting an EHR system.

· Three basic elements can be used for authentication:

· Something the user is such as some form of biometric identifier (e.g., fingerprint or retinal pattern, DNA sequence voice pattern, signature recognition).

· Something the user has such as an identification card, security token, or software token.

· Something the user knows such as a password or a personal identification number (PIN).

· If biometric authentication is not available, then a dual-element authentication should be considered as a reasonable control policy.

Extensive privilege assignment and control features:

Access management, also known as authorization, is the process of verifying that a known person has the authority to perform a certain operation. Verification of the identity of a user or other entity is a prerequisite to allowing access to information systems.2 When an organization selects an EHR system, the organization should be able to control access through role-based descriptors and individual identification and the ability to configure multiple access management levels.

Logging of All Activity:

The ability of the organization to maintain a legal medical record in the electronic environment is a paramount consideration in the selection and use of an EHR system. The EHR must have the ability to record all activity that occurs within the system. (See Section B.) A good EHR system must include a robust and complete logging and auditing function.

Data Entry Editing:

·
Verify validity of information on entry when possible:
The ability of the system to either warn or not allow impossible information, such as a hysterectomy CPT code for a male patient or a prostate examination for a female. These systems can be more sophisticated to not allow or at least prompt or warn the user of less likely events or occurrences, including using billing codes that do not meet the medical necessity criteria for payers.

·
Check for duplication and conflicts:
The system that will not allow duplication of patient identification numbers or codes and one that will warn of conflicting medical management options, such as life-threatening drug interactions may be useful. The ability of a system’s prompt capability should be thoroughly explored by the clinical users because there is emerging evidence of a phenomenon known as “prompt fatigue.” A system without the ability to control the prompt occurrence can lead to lack of use or even misuse by the providers entering information.

·
Control and limit automatic creation of information:
The ability to create documentation automatically, whether through a copy-and-paste or pull-forward function, selection of generic documentation, or use of “auto-neg” or other documentation by exception functions should be avoided. In most circumstances, such features should be disabled. These features, although they are time-savers, are dangerous to the organization and the individual practitioner because they foster the ability to commit fraud, intentionally or unintentionally. They are also a source of “dirty data” that will compromise good patient care and data-mining capabilities.

·
Monitor corrections and additions to the medical record:
Corrections, amendments, clarifications, and additions to a medical record are a normal part of clinical documentation. These changes to the EHR should always be made available to the user of the record unless such changes are detrimental (e.g., incorrect information was originally recorded about the patient). The EHR should have the ability to handle these events easily and thoroughly, and of most importance, properly, as outlined in CMS guidelines; federal, state, and local laws; and hospital bylaws and accreditation standards.

These features are not intended to be a complete list of necessary or desired EHR system capabilities. Their inclusion in an EHR system will assist in preventing the potential for falsifying documentation in the patient record.

…Prepared By

Kim Baldwin-Stried Reich, MBA, MJ, RHIA, FAHIMA, PBCI, CPHQ
Ann Botros, PhD, RHIA
Kristen Denney, MA, RHIA
Julie Dooling, RHIA
Lisa Fink, MBA, RHIA, CPHQ
Deshawna Hill, RHIA, HIT Pro-CP
Sandra Huyck, RHIT, CCS-P, CPC, CPC-H
Renii Modisette, MHA, RHIT, CCS
Mona Nabers, MBA, RHIA
Diane Premeau, MBA, RHIA, RHIT, CHP, CHC
Laura Rizzo, MHA, RHIA
Diana Warner, MS, RHIA, CHPS, FAHIMA
Lou Ann Wiedemann, MS, RHIA, CDIP, FAHIMA, CPEHR

Acknowledgements

Cecilia Backman, MBA, RHIA, CPHQ
Karen Fabrizio, RHIA
Barry S. Herrin, JD, CHPS, FACHE
Sandra L. Joe, MJ, RHIA
Theresa Jones, MSEd, RHIA
Patti Kritzberger, RHIT, CHPA
Katherine Lusk, MHSM, RHIA
Angela Dinh Rose, MHA, RHIA, CHPS, FAHIMA
Amy Richardson, RHIA
Gail Woytek, RHIA

Original Authors

AHIMA e-HIM Work Group Members

Danita Arrowood, RHIT, CCS
Emily Choate, CPC
Elizabeth Curtis, RHIA
Susan DeCathelineau, MS, RHIA
Barbara Drury, BA, SHIMSS
Susan Fenton, MBA, RHIA
Reed Gelzer, MD, MPH, CHCC
Alan Goldberg, JD, LLM
Pawan Goyal, MD, MHA, MS, PMP, CPHIMS
Teresa Hall, RHIT
Melissa Harper, RHIT
Patrice Jackson
Neisa Jenkins, MA, RHIA
Elaine King, MHS, RHIA, CHP
Jaclyn Kirkey, MBA, RHIA
Dorothy Knuth, RHIT, CCS-P
Susan Lee, RN, CHCQM, CCS-P, AHFI
Dale Miller
Deborah Neville, RHIA, CCS-P
Laurie Peters, RHIT, CCS
Erik Pupo
Ulkar Qazen, RHIA
Sandra Saunders, MPH, RHIA, CHP
Rita Scichilone, MHSA, RHIA, CCS, CCS-P
Patricia Trites, MPA, CHP, CPC, EMS, CHCC, CHCO, CHBC, CMP
JoAnn Von Plinsky, MS, RHIA
Linda Whaley, RN, CPC, CPC-H
Margaret Williams, AM

The information contained in this practice brief reflects the consensus opinion of the professionals who developed it. It has not been validated through scientific research.

Article citation:
AHIMA Work Group. “Integrity of the Healthcare Record: Best Practices for EHR Documentation (2013 update)” 
Journal of AHIMA 84, no.8 (August 2013): 58-62 [extended web version].

CONTINUING EDUCATION

Electronic Health Records: Patient
Care and Ethical and Legal
Implications for Nurse
Practitioners

Melanie L. Balestra, JD, NP

ABSTRACT

Electronic health records (EHRs), with their adoptio

n incentivized as part of the American Recovery and
Reinvestment Act of 2009, are now a ubiquitous part of the health care landscape. Although these systems
promised to improve the quality of patient care, increase efficiency, and reduce costs, health care providers
are finding that current EHRs instead require time-consuming data entry, can interfere with patient
interactions, and cause medical errors. Nurse practitioners should implement practical tips and best practices
for navigating and successfully using EHRs, as well as risk management strategies to ensure better patient care
and avoid malpractice litigation or licensing issues.

Keywords: best practices, electronic health record, liability, maintaining ethical standards, medical errors,
malpractice/disciplinary action, risk management
� 2016 Elsevier Inc. All rights reserved.

Melanie L. Balestra, JD, NP works at the Law Offices of Melanie Balestra in Irvine, CA. She can be reached at balestralaw@cox
.net. The author has no conflict of interest. This article was submitted on behalf of The American Association of Nurse Attorneys
(TAANA).

INTRODUCTION

ealth care innovations have had a signifi-
cant impact on patient care, helping people

Hlive longer and with an increased quality

of life. New treatments, therapies, drugs, and di-
agnostics are saving lives daily. Take, for example
vaccines, which are among the most important
medical advances of the 20th century. Since 1900,
considerable declines in morbidity have been seen

This CE learning activity is designed to augment the knowledge, skills, and attitudes of

electronic health record use.

At the conclusion of this activity, the participant will be able to:
A. Identify 3 benefits of EHR.

B. Discuss 3 problems and/or liability issues with EHR.

C. Evaluate 3 tips/strategies to use in EHR documentation.

The authors, reviewers, editors, and nurse planners all report no financial relationships

The authors do not present any off-label or non-FDA-approved recommendations for t

This activity has been awarded 1.0 Contact Hours of which 0 credit are in the area of P

www.npjournal.org

in 9 vaccine-preventable diseases, including smallpox,
polio, and measles.1 The discovery of antimicrobial
drugs was another watershed moment, providing
treatment options for bacterial infections.2 Other
important advances include surgical anesthetic and
antisepsis, as well as improvements in heart surgery,
cardiac care, and radiologic imaging.3

More recently, advances in health information
technology have avowed to save lives and reduce

nurse practitioners and assist in their understanding potential legal liabilities with

that would pose a conflict of interest.

reatment.

harmacology. The activity is valid for CE credit until March 1, 2019.

The Journal for Nurse Practitioners – JNP

105

mailto:balestralaw@cox.net

mailto:balestralaw@cox.net

http://crossmark.crossref.org/dialog/?doi=10.1016/j.nurpra.2016.09.010&domain=pdf

http://www.npjournal.org

costs. Among these advances is the use of computers
to track patient records and manage care, thereby
improving health quality by reducing errors.
Practice-specific electronic medical records (EMRs)
were the first sources used to digitize patient infor-
mation, followed by electronic health records
(EHRs), to go beyond standard clinical data collected
in a provider’s office and include a broader view of a
patient’s care.4

An early study reviewing automation of infor-
mation showed that patients treated in hospitals that
ranked highest in use of health information tech-
nology to manage patient records and physician notes
were 15% less likely to die compared with patients in
lower ranking hospitals.5 EHRs were found to offer
the potential to provide medical practice efficiencies
and cost savings. Thus, there also was evidence of
early success. According to a national survey of
doctors who had complied with all phases of the
Centers of Medicare & Medicaid Services (CMS)
Electronic Health Record Incentive Program, 79% of
providers reported that, with an EHR, their practice
functioned more efficiently, and 82% reported that
sending prescriptions electronically (e-prescribing)
saved time.6,7

But now, more than 7 years since the push to
include EHRs as part of the American Recovery and
Reinvestment Act of 2009, it seems unlikely that
these goals will be reached. This was highlighted by
the RAND Corporation, which in 2005 predicted
that widespread use of EMRs could save $81 billion
per year.8 This report was promoted by the
technology industry and used by the federal
government to advance the stimulus plan to pay for
the installation of electronic systems, only to be
followed 7 years later by a new analysis from RAND
showing that reduced costs with EMRs had not been
achieved. According to the follow-up analysis, “the
technology’s impact on healthcare efficiency and
safety are mixed.”9(p65) The analysis cited that annual
health care expenditures in the United States had
actually grown by $800 billion.9

In addition to unmet cost savings, EHRs are
negatively impacting patient care. RAND researchers
interviewed physicians who reported that EHR
technology “significantly worsened professional
satisfaction in multiple ways.”9(p68) According to the

The Journal for Nurse Practitioners – JNP106

report, aspects of current EHRs that were
“particularly common sources of dissatisfaction
included poor usability, time-consuming data entry,
interference with face-to-face patient care, inefficient
and less fulfilling work content, inability to exchange
health information, and degradation of clinical
documentation.”10(p98)

Another study showed that physicians are now
devoting more time to data entry than patient con-
tact. A study of physicians using EMRs in emergency
departments showed that doctors spent an average of
43% of their time on data entry and only 28% of their
time on direct patient contact.11

Nurse practitioners (NPs) who routinely use
EMRs and EHRs may agree. Rather than supporting
patient care, computerized health information sys-
tems could create barriers, requiring NPs to act as
data entry clerks, thus hindering patient interaction.
These systems also could make it difficult to docu-
ment a note in the patient record, write a prescrip-
tion, or generate a referral.

With that in mind, this article provides a brief
history of EMRs and EHRs, as well as a discussion
about concerns NPs may have when using these
systems, including patient care, privacy, ethics, and
liability issues. In addition, it is clear that EMRs and
EHRs are here to stay. So, until the promises of
current systems are realized, this article will provide
NPs with advice for navigating these systems and
maintaining ethical standards, as well as risk man-
agement strategies and suggestions that NPs should
implement to avoid and/or reduce litigation or
disciplinary proceedings.

EHRs
The roots of current EHR systems go back to the
1960s and 1970s, when academic medical centers
developed systems with the idea of compiling patient
health information so that it could be centrally
managed and shared. Development work also was
underway by industry and the federal government,
which instituted an EHR in the US Department of
Veterans Affairs in the 1970s.12

This was followed by the Institute of Medicine’s
analysis of paper health records in 1991 (and with
revisions in 1997) advocating for computer-based
patient records.13 Then, in 1999, the Institute of

Volume 13, Issue 2, February 2017

Medicine published its landmark study of medical
errors, To Err is Human: Building a Safer Health System,
which stated that health information technology
would help reduce medical errors by facilitating
transfer of important patient information.14

The most recent development occurred with the
federal American Recovery and Reinvestment Act of
2009. This stimulus package allocated $19.2 billion to
increase the use of the EHRs by physicians and
hospitals under the Health Information Technology
for Economic and Clinical Health Act, also known as
the HITECH Act. Beginning in 2011, incentive
payments were paid to eligible professionals, hospi-
tals, and critical access hospitals participating in
Medicare and Medicaid programs that adopted and
demonstrated “meaningful use” of certified EHR
technology (ie, using the technology to improve
quality, safety, efficiency, and reduce health dispar-
ities; engage patients and family; improve care
coordination and population and public health; and
maintain privacy and security of patient health
information).15

As ofOctober 2015, more than 479,000 health care
providers received payment for participating in the
Medicare and Medicaid Electronic Health Records
Incentive Programs. The CMS published a final rule
specifying the criteria that eligible professionals,
eligible hospitals, and critical access hospitalsmustmeet
to participate in theMedicare andMedicaid Electronic
Health Record Incentive Programs in 2015-2017
(Modified Stage 2) and in Stage 3 in 2017 and
beyond.16 A key part of this program is the reporting of
clinical quality measures that measure and track the
quality of health care services provided by eligible
professionals, hospitals, and critical access hospitals. To
participate in the incentive programs and receive an
incentive payment, providers are required to submit
clinical quality measures data from certified EHR
technology.17

For eligible professionals participating in the
incentive program, there are financial penalties for
those professionals who do not demonstrate mean-
ingful use. Beginning in 2015, eligible professionals
who did not successfully demonstrate meaningful use
were subject to a payment adjustment starting at 1%
and increasing each year the eligible professional does

www.npjournal.org

not demonstrate meaningful use, to a maximum
of 5%.16

In April 2016, CMS proposed a rule that would
replace meaningful use for Medicare physicians and
establish key parameters for the new Quality Pay-
ment Program, a framework that includes the
Merit-based Incentive Payment System and Alter-
native Payment Models. These policies were estab-
lished by the Medicare Access and CHIP
Reauthorization Act of 2015. The proposal would
consolidate 3 currently disparate Medicare quality
programs into the Merit-based Incentive Payment
System, including meaningful use of EHRs.18 MIPS
will go into effect over a timeline from 2015
through 2021 and beyond.19

ISSUES WITH EHRs AND RISKS FOR NPs
Computerized health information systems (both
cloud- and server-based) are now used routinely by
NPs to document patient information. The systems
also often serve as clinical data repositories to be
shared by other health care professionals and support
billing processes. Unfortunately, these systems can be
cumbersome to use, and there are potential patient
care and ethical issues, as well as medical liability risks
associated with their use, as described in
what follows.

Patient Care
Eye contact is important to patients when commu-
nicating with health care providers. When NPs turn
away from a patient to use the EMR or EHR,
patients can feel ignored, creating a barrier to
communication,20 and potentially interfering with
discussions about a patient’s health status, test results,
or prescribed medications. This is especially true
when dealing with wall-mounted systems that
require NPs to turn their back to the patient when
entering data.

In addition, the rigorous data entry requirements,
often done via difficult-to-navigate user interfaces,
can create additional problems. Medication safety is a
primary concern, as the categories of prescribing,
transcribing, dispensing, and administering can be
disjointed, leading to EHR-associated medication
administration errors. Difficult-to-use screen and font

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sizes, auto-correct or auto-fill functions, inadequate
LED lighting, and a lack of hand-held devices used to
bar code scan medications, as well as inefficient and
delayed access to laboratory results or a lack of
interoperability with other systems, could also lead to
incorrect medication names and/or dosing and safety
problems.21

Templates, designed for common procedures and
consultations, also offer the potential for inaccurate
data entry and documentation of testing and/or
procedures that may not have been performed.
Templates also can distance the recording of data
from the visit with the patient.22

Finally, clinical decision-making is based on real-
time information, so other patient care issues can arise
when NPs have access to only partial or incomplete
medical data when dealing with a patient, such as
when a system is offline and no back-up is available
or patient medical data are contained in a mix of both
electronic and paper charts.

Patient Privacy and Security Risks
A breach of confidentiality of medical information
can occur with paper medical records, but the risk of
a breach occurring is greatly elevated with an EMR
or EHR system. As a result, NPs must be vigilant in
preventing unauthorized access to patient informa-
tion, including internal threats from poor password
management, disgruntled or disloyal coworkers,
transparent physical security measures, and external
threats, such as theft of electronic devices containing
health information.23,24

In addition, the Health Insurance Portability and
Accountability Act of 1996 (HIPAA) provides data
privacy and security provisions for safeguarding med-
ical information. The security management process
standard is a requirement in theHIPAA Security Rule,
and HIPAA privacy and security requirements are
embedded in the Medicare and Medicaid Electronic
Health Record Incentive Programs through the
meaningful use requirements.25 Violations and/or lack
of privacy when using an EHR system could result in
HIPAA violations.

Liability and Ethical Problems
It is a fact that widespread use of EMRs or EHRs
may contribute to more errors and malpractice

The Journal for Nurse Practitioners – JNP108

liability.26 Some of the risk concerns associated with
these systems are similar to those that exist with paper
documentation, and some are unique to online
patient charting. Legal liability exposure can come
from a variety of areas, including:

� Copying and pasting notes. NPs may want to
cut and paste (or “clone”) details from a pre-
vious exam, patient history, or event between
patients, but data could be outdated or inac-
curate, diminishing the integrity of the medical
record. Vital signs that never change or repeated
information in the EHR could be used by a
plaintiff attorney in a malpractice case.27

� Use of templates. EMR and EHR systems do
not create a template for every disease process or
condition. Templates that are narrow in scope
or do not allow entry of data or impressions
suggesting alternative diagnoses can expose the
user to legal liability.28

� Ignoring clinical decision support. The NP may
be tempted to ignore the continuous alerts and
recommendations that EHRs provide, but sys-
tems record time spent reviewing alerts. If that
time is limited and something happens to a
patient, the NP could be at risk.27

� Late entries and changes. The importance of
maintaining the integrity of the EMR or EHR
cannot be overstated. It is the legal and medical
record, and must meet federal and state regu-
lations.29 It can be a challenge for NPs to
capture all pertinent information in the
electronic record in real time. As a result,
amendments and changes to the EMR or
EHR can occur. This can serve as double-
edged sword for NPs who face the ethical
obligation to ensure that the record is com-
plete by adding information after the patient
visit, knowing full well that post-visit adden-
dums, corrections, retractions, deletions, or
other late entries to the electronic record can
expose them to liability and/or Board of
Nursing issues.

� Failure to document or incomplete/inaccurate
documentation. Regardless of a paper or elec-
tronic system, failure to document or incom-
plete/inaccurate documentation in the patient
record could lead to patient injury and

Volume 13, Issue 2, February 2017

malpractice litigation and/or licensing issues
from the Board of Nursing. This could range
from failure to transfer all information from the
paper chart to EMR or EHR, failure of the NP
to sign his/her notes, or checking boxes indi-
cating that services were performed without
providing supporting documentation. Auto-fill
functions also can create problems by inaccu-
rately completing fields. The same applies to
dictation errors. Failure to check could cast
doubt on the quality of care provided and the
accuracy of the entire EHR.27 Drop-down
boxes used for documentation also can be
incomplete or limit the ability to chart
information.

TIPS FOR NAVIGATING AN EHR
Regardless of liabilities associated with EMR or
EHR use, the technology is not going away. NPs
should consider the following recommendations to
prevent adverse events when using these systems and
to help protect themselves from malpractice and
Board of Nursing/license issues. Recommenda-
tions include:

� Complete basic training in the system in use at
your practice or hospital and participate in all
training updates. This is important if you work
as an independent contractor practitioner at
several practices or hospital locations and each
use different platforms.

� Request written basics for use of the system,
including how to order a lab test, submit
e-prescriptions, and order referrals. Consider
preparing an outline to assist with daily use.

� Advocate for regular staff meetings to discuss
problems with the systems and potential solu-
tions, and access information technology staff
for help when needed.

� Based on patient load, work ahead if possible
and enter available information before patient
visits. To facilitate this task, ask for administra-
tive time to complete data entry.

� Explain the electronic documentation proced-
ure to patients and let them know they can
interrupt with questions or relay additional
information.

www.npjournal.org

� Maintain as much eye contact with patients as
possible. Talk with patients before entering
information into the electronic record, and
alternate between documentation and
conversation.20

� Read back information as you type it into the
system. When providers follow this practice for
verification, patients feel more secure that their
medical records are correct.20

� If possible, work with practice management to
design or arrange examination rooms to
facilitate communication with the patient,20

such as sitting at a small desk and facing the
patient or utilizing a tablet when documenting
data.

� Do not rush when entering data, avoid jargon,
and review information before finalizing the
entry, including patient name, age, and sex;
diagnosis; current medications and new pre-
scriptions ordered; lab and/or radiology tests
ordered; follow-up appointments; when to see
his or her primary care physician; and referrals
to specialists. This is critical if using voice
dictation or working with a “medical scribe” or
unlicensed staff member who handles docu-
mentation and data entry.

� Many systems either warn or will not allow
“impossible” information, such as a hysterec-
tomy code for a male patient, but NPs should
not rely on the system for detecting docu-
mentation errors.

� Remember that each patient encounter should
be recorded as a stand-alone record and that the
integrity of the EHR is vital.30 Try to avoid
cut-and-paste and cloning notes to save time.

� Templates may not exist for specific problems
or visit type. This can occur if the structure of a
note does not fit clinically or reflect the patient’s
condition and services. In addition, atypical
patients may have multiple problems or exten-
sive interventions that must be documented in
detail.30

� Have ICD-10 codebooks available as a refer-
ence in case templates or drop-down boxes are
narrow in focus and do not provide appropriate
diagnostic information. Create a “cheat sheet”

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110

or coding cards for common codes used based
on specialty or care areas.

� To avoid medication errors, request that check
boxes associated with dose amounts be placed
further apart on the drug ordering screen or
have a pop-up box that confirms the ordered
medications before closing out of the record.
Consider working with practice administration
to institute procedures that routinely update
and reconcile patient medication lists.31

� With practice administration, establish processes
for logging all activity for the EMR or EHR,
including processes for addendums, corrections,
retractions, and deletions, as well as a definition
for the time period for “locking” and
“unlocking” the EHR.32 Corrections,
amendments, clarifications, and additions to a
medical record are a normal part of clinical
documentation. The system should handle
these events easily, thoroughly, and properly, as
outlined by the facility; CMS guidelines; and
federal, state, and local laws (personal
communication, CNA and Nurses Service
Organization, May 9, 2016). If you make a
mistake in a patient record and depend on
someone else to correct it, confirm that the
record has been appropriately corrected.31

� Check for duplication and conflicts. Despite
systems that will not allow duplication of pa-
tient identification numbers or ones that warn
of conflicting medical management options,
there is emerging evidence of a phenomenon
known as “prompt fatigue.” This can lead to
lack of use or even misuse by providers entering
information (personal communication, CNA
and Nurses Service Organization, May 9, 2016).

� In conjunction with practice administration,
define a policy if your workplace is using both a
computerized health information system and
paper charts.

� To ensure confidentiality, do not share your
password and change it frequently. Review
your office or hospital’s medical information
confidentiality policy.

� Ensure that you are fully trained on policies,
procedures, and system functions and capabil-
ities to prevent system fraud, as well as security

The Journal for Nurse Practitioners – JNP

and integrity requirements, what constitutes
violations of EHR policy, and procedure
consequences.30

� Carry your own malpractice/disciplinary in-
surance (versus insurance for your hospital and/
or private practice). This is important with
increased adoption of EMRs and EHRs and the
anticipated increase in medical professional lia-
bility claims associated with their use.

Additional resources for best practices to prevent
adverse events related to health information tech-
nology include the Office of the National Coordi-
nator of Health Information Technology Safety
Assurance Factors for EHR Resilience Guides for
EHRs.33 The American Health Information
Management Association also has compiled best
practices for EHR documentation.30

CONCLUSION
Computerized health information systems have
become a fixture in health care. However, in their
current state, these systems can be inefficient and hard
to use, and their promises of improved quality,
increased efficiency, and reduced costs remain un-
fulfilled. NPs are balancing heavy patient loads with
data entry and reporting requirements, often
bemoaning the negative impact these systems have
made on all encounters. At the same time, NPs
increasingly recognize that information technology
generally is critical for improving the quality of care,
and therefore are committed to working with these
systems rather than against them.

With that said, NPs must work to protect them-
selves and their patients. They can do this by taking
advantage of all training that supports the EMR or
EHR in their practice or hospital. They need to
ensure accuracy in the record and learn how to work
with the systems as they were intended. NPs also
should be aware of hidden liabilities associated with
these systems and follow best practices when entering
information, especially with annotations, addenda,
and corrections after patient visits. By incorporating
these recommendations into their practices, NPs can
help ensure quality patient care and increased effi-
ciency, as well as help protect themselves against a
malpractice claim or Board of Nursing complaint that
could affect their ability to practice medicine.

Volume 13, Issue 2, February 2017

With luck, next generation systems will have
improved ease of use and better efficiencies, allowing
us to return our focus on the patients we are there
to serve.

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• Electronic Health Records: Patient Care and Ethical and Legal Implications for Nurse Practitioners

Introduction

EHRs

Issues With EHRs And Risks For NPs

Patient Care

Patient Privacy and Security Risks

Liability and Ethical Problems

Tips For Navigating An EHR

Conclusion

References

By I. Glenn Cohen, Ruben Amarasingham, Anand Shah, Bin Xie, and Bernard Lo

The Legal And Ethical Concerns
That Arise From Using Complex
Predictive Analytics In Health Care

ABSTRACT Predictive analytics, or the use of electronic algorithms to
forecast future events in real time, makes it possible to harness the power
of big data to improve the health of patients and lower the cost of health
care. However, this opportunity raises policy, ethical, and legal
challenges. In this article we analyze the major challenges to
implementing predictive analytics in health care settings and make broad
recommendations for overcoming challenges raised in the four phases of
the life cycle of a predictive analytics model: acquiring data to build the
model, building and validating it, testing it in real-world settings, and
disseminating and using it more broadly. For instance, we recommend
that model developers implement governance structures that include
patients and other stakeholders starting in the earliest phases of
development. In addition, developers should be allowed to use already
collected patient data without explicit consent, provided that they comply
with federal regulations regarding research on human subjects and the
privacy of health information.

T
he health care system stands on the
edge of a breakthrough: New tech-
nologies will soon be available that
can harness the power of large data
sets to help identify which medical

interventions will benefit which patients. Imag-
ine aphysicianwho is trying todecidewhether to
send a patient with moderate organ dysfunction
to the intensive care unit (ICU). The patient
might benefit from a stay in the ICU, but other
patients might benefit more, and ICU beds are
limited. An evaluation of the first patient’s risk
for cardiopulmonary arrest or other preventable
serious adverse events might take hours and
have limited prognostic accuracy, discrimina-
tion, and interrater reliability (or agreement
among evaluators). Now imagine that there is
a technology that could ascertain the risk accu-
rately for a thousand separate patients and con-
tinuously update that evaluation every second to
help a physician decidewhom to send to the ICU.

Predictive analytics could be that technology.
We define predictive analytics as the use of elec-
tronic algorithms that forecast future events in
real time. The technology promises health care
systems the ability to use “big data”—vast, real-
time data sets, such as those collected by elec-
tronic health record (EHR) systems—to improve
patients’ outcomes and lower health care costs,
in conjunction with targeted care. However, this
opportunity raises a series of policy, ethical, and
legal challenges.
For example, predictive analyticsmodelsmake

treatment recommendations that are designed
to improve overall health outcomes in a popula-
tion, and these recommendations may conflict
with physicians’ ethical obligations to act in the
best interests of individual patients. A model
may recommend withholding a potentially ben-
eficial intervention from some patients with a
given condition because there is a significantly
lower probability that they will benefit, while

doi: 10.1377/hlthaff.2014.0048
HEALTH AFFAIRS 33,
NO. 7 (2014): 1139–1147
©2014 Project HOPE—
The People-to-People Health
Foundation, Inc.

I. Glenn Cohen (igcohen@law
.harvard.edu) is a professor of
law and director of the Petrie-
Flom Center for Health Law
Policy, Biotechnology, and
Bioethics, both at Harvard
Law School, in Cambridge,
Massachusetts.

Ruben Amarasingham is
president and CEO of PCCI, a
nonprofit research and
development corporation, and
an associate professor in the
Departments of Internal
Medicine and of Clinical
Sciences at the University of
Texas Southwestern Medical
Center, both in Dallas.

Anand Shah is vice president
of clinical services at PCCI.

Bin Xie is a health services
manager at PCCI.

Bernard Lo is president of the
Greenwall Foundation and
professor emeritus of
medicine and director
emeritus of the Program in
Medical Ethics, both at the
University of California, San
Francisco.

July 2014 33:7 Health Affairs 1139

Predictive Analytics

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offering the intervention to others who aremore
likely to benefit. The use of predictive analytics
may also heighten concerns that across a popu-
lation of patients, those who are already disad-
vantaged—for example, because of illness, lack
of access to health care, or poverty—may become
worse off.
This article analyzes the major legal, policy,

and ethical issues raised by predictive analytics.
It alsomakes broad recommendations regarding
the four phases of the life cycle of a predictive
analyticsmodel: acquiring data to build themod-
el, building and validating it, testing it in real-
world settings, and disseminating and using it
more broadly.

Predictive Analytics And The
Practice of Medicine
The use of predictive analytics has accelerated in
numerous industries in the past decade, with the
emergence of real-time electronic data sets so
large and complex that traditional data-process-
ing tools have proved inadequate. With the ad-
vent of the EHR, it has become possible to apply
predictive analytics to health care. The use of
predictive analytics in health care leverages dec-
ades of work in statistics, computer science, and
clinical decision support. In this emerging era of
big data, predictive analytics models can use a
variety of current or historical information such
as claims, clinical, social, and genomic data to
make predictions about the future.
The early use of predictive analytics models in

medicine has focused on identifying patients at
high or low risk for serious complications or
adverse clinical events, preventing those adverse
events, and optimally allocating scarce clinical
resources. The most common example is identi-
fying patients at high risk of hospital read-
mission.
In the hypothetical example above, a potential

predictive analytics model might ascertain the
instantaneous risk for cardiopulmonary arrest
of every one of a thousand patients in a given
hospital at every second and determine which
patients would most benefit from ICU admis-
sion. Use of the model might reduce the aggre-
gate rate of cardiopulmonary arrest if thosehigh-
risk patientswere admitted to the ICU—provided
they or their surrogates agreed to that admis-
sion. Conversely, a predictive analytics model
might also suggest which low-risk patients could
be discharged safely from the ICUor could not be
admitted to the ICU in the first place, thereby
allocating scarce resources more equitably.
Developing suchamodel forpredicting cardio-

pulmonary arrest would require four develop-
mental phases that we describe as the life cycle

of a predictive model. First, data on a sufficient
number of patients in a sufficient number of
hospitals who are at risk for cardiopulmonary
arrest would be acquired; “cleaned”—that is,
with corrupt or incorrect records detected and
removed from the data; and harmonized, or pre-
pared according to a common set of specifica-
tions so that they can be combined with data
from other sources. This would be challenging
because of the heterogeneity of EHR systems,
clinical practices, and patient populations,
among other factors.
Second, the predictive analytics model would

be developed under exacting programming
standards that were transparent and replicable,
even if they were proprietary. Once developed,
the model would be properly validated, prefera-
bly through use on a different data set from a
different population.
Third, the model would be tested under real-

world conditions with adequate protections and
precautions for the patients, in proportion to the
seriousness of their condition. And fourth, a
broadly disseminated version of the model
would be monitored, refined, and reconfigured
to local contexts.
Predictive analytics models are already being

deployed to help identify in real time high-risk
patients like those in the cardiopulmonary arrest
example. In the near future, models based on
machine learning (that is, onesusinga computer
that can learn from data instead of requiring
additional programming) will be able to instan-
taneously consider the risk of all patients in a
hospital, their individual therapeutic goals and
preferences, hospital staffing (including staff
members’ experience and performance), re-
source constraints, and external conditions such
as whether other hospitals are diverting patients
in the emergency department in the case of a
disaster. The model could then advise hospital
administrators onwhom to admit to the ICU and
how to staff it.
Approximately 87 percent of US hospitals cur-

rently have some formofEHR,1whichgives them
the ability to use predictive analytics. That per-
centage is increasing rapidly. However, there are
no widely accepted policies and standards re-
garding the use of predictive analytics in health
care.
Webelieve that recommendations frompredic-

tive analytics models should be discretionary in-
stead of binding. Physicians should be able to
override or appeal recommendations when they
have sound reasons for doing so—for example,
because of considerations that themodel did not
capture. Such exceptions would allow treating
physicians toplay their traditional role aspatient
advocates within the constraints set by society

Predictive Analytics

1140 Health Affairs July 2014 33:7
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and insurers. Importantly, analyzing these ex-
ceptions can inform improvements to themodel,
which highlights the fact that the work of model
design does not end with the model’s initial im-
plementation.

Phase 1: Acquiring Data
Developers of predictive analytics models need
access to big data in health care—for example,
millions of EHRs containing both narrative and
structured data such as physicians’ and nurses’
notes, laboratory and physiological data, and
doctors’ orders. Acquiring those data raises in-
terrelated questions about consent, privacy, and
fairness.

Consent And Privacy We recommend that
model developers be allowed to use patient data
that have already been collected without explicit
consent, provided the developers comply with
federal regulations regarding research on hu-
man subjects and privacy of health information.
Under the Common Rule that governs such re-
search, the informed consent of human subjects
is not required if researchers cannot identify the
individuals whose data are being analyzed.2

The privacy rule in the Health Insurance Por-
tability and Accountability Act (HIPAA) of 1996
also allows the use of deidentified patient-level
datawithout consent andaccepts twomethodsof
deidentification. First, eighteen defined identi-
fiers, including the patient’s city name and e-
mail address, could be removed. However, doing
so reduces the predictive power of the data set.
Second, an “individual with appropriate exper-
tise” candeclare that “the risk that data canbe re-
identified is ‘very small.’”3 The objective stand-
ards formaking such a declaration are still being
established.4

Tobe sure, even a robust deidentificationproc-
ess doesnotmake reidentification impossible, as
shown by a recent study on reidentifying people
from genomic sequence data.5With enough time
and money, someone could reidentify some of
the formally deidentified patient-level data used
for predictive analytics.However, fewpeoplewill
have the resources or motivation to engage in

deliberate reidentification.
Still, data breaches remain possible. There-

fore, we recommend that collectors of predictive
analyticsdata alsonotify all patients that thedata
gathered on them in the course of regular health
care may be used in deidentified form in predic-
tive analytics models.
The notification that we have in mind would

not be the pro formamethod of notification used
to comply with HIPAA requirements, which is
typically a highly legalistic form requiring the
patient’s signature. Instead, we propose a notifi-
cation that would enable most people to under-
stand how and why their data were being used
and inform them of the risk of data breach. This
would be similar to telling patients when they
enter a hospital or a physician’s practice that
their records may be used for quality im-
provement.
Additional privacy safeguardsmaybe justified,

such as providing free credit violation monitor-
ing or tort remedies to patients who experience
harms from privacy breaches. Another option
would be to establish a third-party standard for
declaring that the risk of reidentification is “very
small” and to require such an assessment before
allowing the use of patients’ information in big
data. If the risk were deemed not to be “very
small,” another option might be to require pa-
tients’ consent. Other safeguards might include
certification processes for people or institutions
seeking access to big data and “naming and
shaming” strategies that publicize institutions
that have serious data breaches.
Equitable Representation Predictive ana-

lytics models require vast amounts of data that
are representative of the whole population. The
history of abuses during research involving Afri-
can Americans, people with disabilities and a
loss of decision-making capacity, and other vul-
nerable groups, as in the experiments at the
Tuskegee Institute and the Willowbrook State
School, raises fears of abuse of big data. For
instance, the data could be used to identify vul-
nerablehigh-risk,high-cost patients andexclude
them from care.6–8

Such concerns could bemitigated by the use of
community engagement boards, whose mem-
berswould advisemodelers as they acquireddata
and designed models. In addition, governance
structures that supervise the construction and
deployment of predictive models could include
representatives ofminority groups.Wediscuss in
greater detail below how this might be accom-
plished, and how equitable representation
should be paired with equitable access to the
benefits of the model.

The work of model
design does not end
with the model’s
initial implementation.

July 2014 33:7 Health Affairs 1141
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Phase 2: Building And Validating
The Model
The Importance Of Patient-Centered Per-
spectives The development of a predictive ana-
lytics model inevitably involves choices about
which problems to make high priorities, how
the model will be used, what clinical interven-
tions will be provided to patients at risk for ad-
verse outcomes, and what outcomes will be mea-
sured in the model’s evaluation. Different
stakeholders might have different perspectives
on these issues. For example, a health care insti-
tution might give the highest priority to out-
comes linked to reimbursement or reputation,
physicians might be concerned with work flow
and income, and patients might be concerned
about functional abilities or quality of life. These
different perspectives need to be acknowledged
and reconciled.
One way to do this is to include patient repre-

sentation in the governance of organizations
that develop and implement predictive analytics
models inmedicine. In the context of biobanks—
large repositories of tissue samples collected
from patients in the course of research—a trust
model has been proposed. The trustee, whomay
be an individual or a group of people, is respon-
sible for overseeing the use of the specimens and
their commercial offshoots (such as commercial
cell lines) on behalf of the sample donors. That
trustee owes donors fiduciary duties such as loy-
alty and good faith—that is, unwaveringly acting
in their interests.9

This approach can be contrasted with one in
whichpatients and theownersor creators of data
have purely market relationships or no relation-
ships at all. For an example of a trustmodel, as of
2011 the Michigan Department of Community
Health Dried Blood Spot Specimen Bank had
collected almost four million dried blood spot
specimens from newborns for potential use in
scientific research. The trust governing the bank
established a Community Values Advisory Board
to represent the citizens of Michigan and pro-
vide guidance on bank policies, including the
types of research permitted; a scientific advisory
board to review researchers’ proposals for scien-
tific merit; and an Institutional Review Board
(IRB) to consider ethical issues and the interests
of individual tissue donors as well as commu-
nities.10

Similar bodies could be created to govern the
use of big data in predictive analytics. Key stake-
holders (such as patients, physicians, hospitals,
and predictive analyticsmodelers) and oversight
bodies (such as the Centers for Medicare and
Medicaid Services and the Joint Commission)
would need to address questions about how far
the governance function could gowithout damp-

ening the development and commercial viability
of predictive analytics models; concerns about
control of trade secrets; and other issues.
Itwouldbeprematureat this point toprescribe

governance andoversightmethods for a technol-
ogy that is changing and in the process of being
implemented. Instead, itwouldbeuseful to allow
different approaches and learn from their
outcomes.
Standards For Validation And Transpar-

ency Before a predictive analytics model is used
in clinical care, it should be carefully evaluated
for effectiveness and any adverse consequences.
One key question is,What standard of validation
is appropriate? Rigorous standards (such as pre-
specified outcomes and analysis plans, separate
derivation and validation populations, and peer
review) would be appropriate in cases where the
risks to patients are substantial—for example, if
themodel’s predictions could direct clinicians to
withhold interventions recommended by cur-
rent evidence-based practice guidelines.
In contrast, if the risks of misclassification—

when a model puts patients in the wrong risk
group—are low, then the model will primarily
lead to additional services’ being offered to pa-
tients, and the risk to them will be low. In these
cases, less rigorous validation standards, such as
a comparison of patient outcomes before and
after the model’s implementation, would be ap-
propriate.
Distributed models for data sharing may facil-

itate the validation of predictive analytics mod-
els. In distributedmodels, data are shared across
organizations, but identifiable data never leave
the organization where the patient receives care.
TheMini-Sentinel programof theFoodandDrug
Administration (FDA) uses distributed models
to monitor the safety of FDA-regulated medical
products after they reach the market.11 There are
also private-sector distributed models that use
deidentified patient-level clinical trial data—an
approach that might be adopted for predictive
analytics.12

The transparency of predictive analytics mod-
els is also crucial. Thekey independent variables,
such as age, sex, primary payer, inpatient utili-
zation, and blood pressure, should be described,
even if the relative contributions of each variable
to the outcome coefficients in the model are
trade secrets. Transparency is key to fostering
trust. Physicians andother clinical decisionmak-
ers cannot evaluate a “black-box model” that
simply provides instructions for its implementa-
tion and use. Instead, they need to know how a
model is making its decisions.
Some predictive analytics companies will be

unwilling or unable to be transparent, however.
For instance, models dependent on artificial in-

Predictive Analytics

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telligence or machine learning that does not in-
volve human judgment may be difficult to make
transparent because of their ever-changing na-
ture. With such models, rigorous validation of
performance characteristics such as sensitivity,
positive and negative predictive values, and c-
statistics (that is, the model’s ability to distin-
guish between positive and negative outcomes)
will be particularly important.
Many groups that develop models have a con-

flict of interest between promoting transparency
and protecting their intellectual property and
profits when they sell theirmodels to health care
institutions. Thus, scientific peer review and in-
dependent validationaredesirable. For instance,
predictive models might be accredited by third
parties such as the Joint Commission, or valida-
tion might need to meet standards set by the
National Quality Forum.

Outcomes Assessed In Model Validation
The primary outcomes for any predictive analyt-
ics model should be “hard” clinical end points—
for example, the length of inpatient hospitaliza-
tions and the numbers of hospital readmissions
and serious adverse clinical events, such as hos-
pital-acquired infections. It also would be desir-
able to assess secondary patient-centered out-
comes, such as satisfaction with care, trust,
anxiety, and patient activation to improve
health. Provider-centered outcomes such as the
impact of the model on providers’ (physicians’
and nurses’) work flow and satisfaction should
also be assessed.
Collecting multiple outcomes is important be-

cause each onemay tell a different story: Amodel
may have beneficial impacts on some outcomes
but negative impacts on others. Of note, collect-
ing “soft” outcomes such as patient satisfaction
may be more costly than collecting hard ones,
which can be derived from administrative or
EHR data.
Validation must be setting-dependent and

process-oriented: A model may be useful in
one setting at one time but not in other situa-
tions, because of differences in patient popula-

tion, severity of illness, and delivery of care. For
example, amodel to identify patients with sepsis
that was derived from data at ten community
hospitals may need to be changed for use in a
tertiary care center that serves a large transplant
population or in hospitals that do not have
an ICU.
Patients’ and physicians’ representatives

should have a strong voice in decisions to adopt,
modify, or discontinue the use of models. This
would help ensure that a broad and meaningful
set of outcomes are considered.
Empowering patients to participate in the gov-

ernance process of model validation is challeng-
ing. Patients cannot be expected to master or
critique a model’s software algorithms, but if
suitably trained and empowered, they can help
ensure that patients’ concerns are adequately
taken into account. The most promising ap-
proach would be to educate them about the
trade-offs for patients in various model designs
and enable them toweigh in onwhat the patients
they represent would prefer. A similar approach
has enabled nonscientific community represen-
tatives to participate successfully on IRBs.

Phase 3: Testing The Model In Real-
World Settings
Once a predictive analytics model has been de-
veloped and validated internally, themodelmust
be tested in the field. This raises significant ques-
tions regarding consent, liability, and choice ar-
chitecture.
Consent It is unclearwhetherexplicit consent

to the use of personal data in predictive analytics
is legally or ethically required in patient-doctor
encounters involving outpatients or during hos-
pitalizations. As an analogy, patients are gener-
ally unaware if their physicians are using com-
puterized decision aids to guide treatment.
Indeed, patients receive little information about
what sources their physicians consult. Nor do
patients explicitly consent to the current policies
by which hospitals allocate ICU beds or other
scarce resources.
Requiring consent in such situations would be

unworkable: If people could opt out of existing
allocation systems, their decisions might unfair-
ly give them priority over other patients. It could
be argued that using predictive analytics adds
sophistication to these existing techniques and
requires no additional consent from patients.
However, it could also be argued that the im-

personal nature of predictive analytics means
that the treating physician is not in full control
of decisions or is prioritizing resource allocation
over the best interests of the individual patient.
Moreover, because the technology is so new and

Empowering patients
to participate in the
governance process of
model validation is
challenging.

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because building patient trust and demonstrat-
ing transparency are key, the question of wheth-
er consent is required deserves serious consid-
eration.
Predictive analytics falls between two well-

accepted models regarding consent. On the
one hand, predictive analytics resembles clinical
research, in which explicit consent is usually
required. On the other hand, it also resembles
quality assurance or quality improvement activi-
ties, in which consent is not generally required.
The few quality improvement interventions

that are implemented at multiple sites instead
of a single one may show that interventions are
effective in a range of clinical settings, thus pro-
viding assurance of a favorable benefit-to-risk
balance.13 Like predictive analytics, quality im-
provement efforts lead to system-level interven-
tions that affect the careof individual patients. In
the case of quality improvement measures de-
signed to increase adherence to evidence-based
guidelines, physicians usually have the option to
override the default ordering of treatments, just
as they would in the form of predictive analytics
we champion.
When prospective patients are choosing a

health care institution, the institutions under
consideration should be required to explain
whatever predictive analytics development and
evaluation they are undergoing and the likely
benefits and risks. At this time, patients are in
a better position to seek an alternative source of
health care if they object, compared towhen they
are seeking medical care for a specific ailment.
Thenotificationandeducationprovidedby the

institution must be meaningful, not pro forma.
An institution’s community advisory board and
the communitymembers of the institution’s pre-
dictive analytics governance structure can sug-
gest ways to provide this information effectively.
Withpropernotification andappropriate patient
safeguards, the use of predictive analytics can be
regarded ethically as a condition of care that the
patient accepts by agreeing to receive care.
Liability Clinicians who are early users of

predictive analytics models may face increased
risks of liability or at least litigation. The case law
on EHRs, for instance, establishes that “physi-
cians can be held liable for harm that could have
been averted had they more carefully studied
their patients’ medical records.”14(p1541) Use of
the models could cause clinicians to reduce the
time they spend with those medical records and
thus increase their liability.
Predictive analyticsmodels will bemost useful

when integrated into decision support systems,
but that alsomay increase clinicians’ exposure to
liability. For instance, plaintiffs might use evi-
dence that a doctor overrode an alert or recom-

mendation from the model as proof that he or
she was negligent.12 It is clinically appropriate to
override many computerized alerts in the prac-
tice of medicine. However, there is a significant
risk that “a doctor who is accustomed to overrid-
ing alerts may become desensitized to them and
occasionally ignore a critical one,” and evidence
of a doctor’s overriding alerts may prove damag-
ing in litigation.14(p1547–8)

Doctorsmay also face liability if they follow the
recommendations of a predictive analytics mod-
el that contains an error. In the case of computer
decision support software more generally, some
legal scholars suggest that courts are likely to
fault a physician for failing to question bad ad-
vice given by the software—even if the error was
in the software—because courts would assume
that physicians would ultimately rely on their
own judgment and professional knowledge.15

If a predictive analytics model malfunctions,
the health care system using the model may be
liable for any patient injury caused by defective
equipment that the system procured and imple-
mented.Moreover, themore the system imposes
workplace rules and regulations on providers,
the greater its risk of vicarious liability—accord-
ing to which the system is liable for clinicians’
negligent operation of predictive analytics mod-
els, as happens when a clinician ignores mes-
sages or recommendations from themodel with-
out a reasoned justification.14

In fact, the health care system will likely face
greater liability risk themore it adapts or tinkers
with a predictive analyticsmodel: In the comput-
er decision software context, courts have treated
such actions as a reason to shield the software
vendor from liability, which is a form of liability
we discuss next.15 As we note below, for a predic-
tive analytics model to work well in the real
world, it is crucial to adapt and customize the
model for a given practice setting.
This suggests that the liability balancebetween

the makers of predictive analytics models and
the health care systems that use the models
may not lead to the optimal amount of adapta-

The presentation of
the results of a
predictive analytics
model could influence
the course of action.

Predictive Analytics

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tion and customization, because of the systems’
fear of liability. If it turns out that the liability
risk will impede otherwise desirable innovation
in these areas, stateor federal legislationcurbing
liability may be justified.
Themakers of predictive analyticsmodelsmay

also be subject to product liability claims. It re-
mains to be seen how the FDA will regulate pre-
dictive analytics models, which could meet the
definition of a medical device and be regulated
similarly to other medical devices that use soft-
ware. The way in which the FDA chooses to reg-
ulate the models will have important effects on
whether the developer, manufacturer, or imple-
menting hospital is liable for patients’ in-
juries.14,16,17

Choice Architecture When patients, pro-
viders, and administrators receive the results
of a predictive analytics model, many questions
arise. What does a particular risk score mean,
where does it come from, how was it tested,
and does it apply to a particular patient? Ulti-
mately, the most important question is, What
should I do?
To help the consumers of the model—both

providers and patients—the model must present
them with choices. The concept of choice archi-
tecture, which was popularized by Richard Tha-
ler and Cass Sunstein,18 aims to design interven-
tions that can influence consumers’ decisions
without infringing on their freedom of choice.
Choice architecture often uses the way in which
information is organized to help with decision
making. For example, to push decision makers
toward certain choices, default rules or framing
effects—such as placing healthy food in a more
accessible location in a cafeteria than unhealthy
food—may be used.16

Similarly, the presentation of the results of a
predictive analytics model could influence the
course of action. Designers should consider

the choice architecture as well as the benefits
and risks posed by themodel itself. For instance,
if the model identifies a patient as being at high
risk of sepsis, it could provide an alert in the
patient’s EHR, accompany the alert with a spe-
cific recommendation, link the alert to a prespe-
cified order, or even automatically trigger
an order.
These choice architecture decisions are cru-

cial, but they are also ethically problematic.
The benefits of the use of choice architecture will
notnecessarily accrue to agivenpatient. Instead,
the health care system or other patients may
benefit. This raises concerns about the physi-
cian’s role as the patient’s agent and questions
about who should bear the risks of false positive
versus false negative predictions.
The use of choice architecture in predictive

analytics involves decisions based on values,
such as where to set thresholds for various alerts
and how to incorporate patients’ preferences.
Furthermore, the choice architecture of predic-
tive analytics models could specify default op-
tions: The model could trigger a patient care
intervention that would occur unless the physi-
cian or nurse overrode it (opting out) or could
suggest an option that the physician or nurse
would have to order or carry out (opting in).
The choice of default options becomes evenmore
challenging if the recommended action carries
substantial risks as well as benefits.
The role of predictive analytics in decision

making is evolving. Given the current state of
testing and validation in predictive analytics
models, decision making should ultimately re-
main at the level of the treating provider. The
ease with which a physician can override a mod-
el’s recommendation should depend onmultiple
factors, including the risks of the proposed in-
tervention, the rate and kinds of errors involved
in the model’s recommendation (misses versus
false alarms), stakeholder opinion, potential li-
ability, and risk of automation bias (which oc-
curs when a person automaticallymakes the cus-
tomary choice even if the situation calls for
another choice). Decisions about the ease of
overriding themodel should bemade at the level
of the institution that will implement the model.
Modelers and health care systems must be

transparent about choice architecture; make
tough decisions relating to the acceptability of
model errors; and respond to the views of all
stakeholders, including patients. Moreover, to
identify unanticipated adverse consequences of
the use of a model, they must perform continu-
ous, rigorous evaluations of how various ap-
proaches play out in practice.

Imperfect
implementation
threatens the trust of
patients, providers,
and the public in
predictive analytics
models.

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Phase 4: Broader Dissemination Of
The Model
The last phase of the development of predictive
analytics models is their broader dissemination.
Three critical issues in this phase are equitable
access; imperfect implementation; and effects
on the role of the physician, including the doc-
tor-patient relationship.
Equitable Access To be generalizable, pre-

dictive analytics models should be built on data
fromawide variety of patients.However, there is
a risk that all patients will not benefit equally
from these models. In particular, because the
models are being developed in the private sector,
some health care systems may not be able to
afford the licensing fees and other costs. Ideally,
the use of these models will narrow health dis-
parities instead of widening them.
As a matter of fairness, those who contribute

most to developing a model, including the pa-
tients who contribute their data, should propor-
tionally enjoy its benefits. Balancing this princi-
ple with the need for predictive analytics to be
commercially viable is challenging. If thesemod-
els prove successful enough, federal, state, or
local governments may fund their use in hospi-
tals that disproportionately treat patients from
underserved populations.
Alternatively, model developers could adopt

graduated licensing fees, charging less for insti-
tutions with fewer resources. This approach
would be similar to pharmaceutical companies’
charging lower prices for life-saving drugs in
developing countries and providing financial as-
sistance for low-income patients in developed
countries.
Imperfect Implementation Predictive ana-

lytics models can be implemented to perform
various functions, such as augmentingphysician
decision aids, providing constant real-timemon-
itoring of an ICU, and advising hospital admin-
istrators on how to allocate clinical staff and
financial resources. Because of zeal or pressures
to cut costs, some health care systems may be
overly ambitious in their use of predictive ana-
lytics. Flawed implementation may result from
poorly constructed work flows, insufficient con-
sideration of patients’ preferences, and inade-
quate checks and balances on machine-based
decision making.
Imperfect implementation threatens the trust

of patients, providers, and the public in predic-
tive analytics models. Therefore, carefully con-
trolled experiments with the models in different
contexts should be rigorously conducted. Con-
trols should be added on the software side to
detect a model’s improper application and
underperformance. Continuous quality im-
provement and the need for an institution’s clin-

ical committee to sign off on the adoption and
use of a model can promote clinical ownership
of it.
“Off-label” uses of the model pose further dif-

ficulties. It is unrealistic to expect modelers to
predict all potential uses of their model. Howev-
er, in some cases, it is clear that amodelwith one
intended use is likely to be bought by health care
systems for an “unapproved”use. Thus, it should
be incumbent upon model designers to at least
anticipate other likely uses in their design of
choice architecture.
The Role Of The Physician The most far-

reaching ethical challenge of predictive analytics
is its potential impact on the role of the physi-
cian. Predictions of adverse clinical events by the
models can promise greater accuracy than prog-
nostication by clinicians. Thus, physicians’ clin-
ical expertise and self-esteem may be called into
question. Physicians will need to master new
skills, including how to communicate effectively
with patients or their families about the trade-
offs involved in different clinical outcomes.
The role of the physician in the delivery of care

across inpatient and outpatient settings may
need to be reconfigured. The separation of hos-
pitalists from ambulatory care providers, the fre-
quent handoffs of responsibility for inpatients
from one physician to another, and the rarity of
long-term primary care relationships all mean
that when a predictive analytics model identifies
a patient as being at risk, the treating physician
might not know the patient or his or her values
and preferences.
A model’s predictions also raise novel ques-

tions about the doctor-patient relationship. Tra-
ditionally, a single physician provided care to an
individual patient based on the patient’s best
interests, as guided by his or her preferences
and values. In the era of predictive analytics
and team-based care, clinical decision making
may be heavily influenced by default rules set
by the health care organization. These rules
may be driven by financial and administrative
incentives and by a desire to maximize popula-

We remain optimistic
that predictive
analytics will help
build a stronger and
more dynamic system.

Predictive Analytics

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tion-based health. It may seem to patients that
the treating physician is no longer exercising
clinical judgment and acting in their best in-
terests.
We believe that a physician should have the

power to override default decisions recom-
mendedby thepredictive analyticsmodel if there
are good reasons to do so. For example, there
may be additional considerations that the model
did not take into account, or a recommendation
by the model could severely compromise a spe-
cific patient’s best interests. Discussions of
the overarching question—How much modifica-
tion of physicians’ clinical judgments is appro-
priate?—are only beginning.

Conclusion
Predictive analytics promises to make dramatic
changes in the way health care is practiced and
delivered. The question is not if the health care
system must be prepared to address the legal,

policy, and ethical challenges that predictive an-
alytics raises, but when it will be ready to ad-
dress them.
The legal, policy, and ethical frameworks that

will emerge in the next few years and decades to
govern theuseof predictive analyticswill have an
enormous impact on the roles that the technolo-
gy will play in reforming the health care system.
We remain optimistic that predictive analytics
will help build a stronger and more dynamic
system.
That said, this terrain is changing rapidly, and

it is not easy to predict where the technology will
lead and how physicians and patients will react
to it. For these reasons, it is essential that pre-
dictive analyticsmodels be constantly evaluated,
updated, reimplemented, and reevaluated. This
process is one that should involvenot onlymodel
designers, but also everyone concerned with the
legal and ethical issues raised by the tech-
nology. ▪

Funding for the development and
preparation of this article was provided
by the Gordon and Betty Moore
Foundation Framework and Action Plan

for Predictive Analytics (Grant
No. 3861). Glenn Cohen is supported by
a Greenwall Faculty Scholars in
Bioethics award. The authors thank Jody

Liu and Gabrielle Hodgson for research
assistance.

NOTES

1 HealthIT Dashboard. Hospitals re-
ceiving incentive payments for elec-
tronic health record adoption or
meaningful use: May 2011 to De-
cember 2013. Health IT Quick-Stat
[serial on the Internet]. 2014 Feb
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quickstats/PDFs/Health-IT-Quick-
Stat-Hospitals-Receiving-Payments-
for-MU-and-Adoption

2 45 C.F.R. Sect. 46.102(f) (2012).
3 45 C.F.R. Sect. 164.514(b) (2012).
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fication of personal health informa-
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Francis; 2013.

5 Gymrek M, McGuire AL, Golan D,
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6 Katz J, comp., with the assistance of
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8 Presidential Commission for the
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2011 Sep [cited 2014 May 1]. Avail-
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Impossible_PCSBI

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10 Chrysler D, McGee H, Bach J,
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11 McGraw D, Rosati K, Evans B. A
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12 Nisen P, Rockhold F. Access to pa-
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13 Pronovost P, Needham D,
Berenholtz S, Sinopoli D, Chu H,
Cosgrove S, et al. An intervention to
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14 Hoffman S, Podgurski A. E-health
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15 Ridgely MS, Greenberg MD. Too
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Page 1 of 6
Framework Authors: Sally Bean, Kevin Reel, Maxwell J. Smith, Blair Henry, and Maria McDonald
sally.bean@sunnybrook.ca
Version: March 9, 2020

Ethical Framework for the Allocation of Personal Protective Equipment (during COVID-19)

Background

This ethical framework is intended to guide institutional resource allocation decisions for Personal

Protective Equipment (PPE) during the COVID-19 public health emergency. In a guidance document

issued Feb. 27, 2020, the WHO recommends rational use of PPE for treating patients with confirmed or

suspected COVID-19. Relevant PPE includes gloves, medical masks, goggles or face shields, gowns, and

respirators. The WHO has indicated that the current global stockpile of masks and respirators is

insufficient and shortages in gowns and goggles is also anticipated.

The WHO has issued three overarching recommendations for use of PPE:

1) minimize the need for PPE; and

2) ensure PPE use is rationalized and appropriate; and

3) coordinate PPE supply chain mechanisms.

The WHO recommendations have been integrated into this framework. This ethical framework is a living

document and will require review and updating as the COVID-19 situation evolves and new evidence

emerges. This framework is advisory and was developed to support key decision-makers at the

institutional level regarding the distribution of available PPE supply and potential modification to health

services to conserve PPE. Although this framework is tailored for the acute care setting, ideally there

should be consistency between and among healthcare institutions across the continuum of care to foster a

consistent approach, and as a result, promote the ethical principles of justice and fairness. This framework

may be adapted to address a broader health system perspective.

This ethical framework is adapted from the Ethical Framework for Resource Allocation during the

Drug Supply Shortage, which was drafted by an Ethics Working Group convened by the University of

Toronto Joint Centre for Bioethics in 2012 and endorsed by the Ontario Ministry of Health. The

Allocation of PPE Ethical Framework is comprised of:

a. Allocation principles that are articulated in three stages;

b. Fair process principles; and

c. Guiding values.

Balancing allocation principles and making decisions about PPE allocation should occur according to fair

process principles and generally aim to promote seven guiding values. The guiding value of reciprocity

has been added to the six principles included in the 2012 Drug Supply Shortage framework. The guiding

values are beneficence, equity, reciprocity, solidarity, stewardship, trust, and utility. In addition to the

allocation principles and guiding values, fair process principles, such as the Accountability for

Reasonableness (A4R) Ethical Framework should help inform how decisions are made. The five fair

process principles comprising A4R include relevance, publicity, revision, enforcement, and

empowerment.

The following seven guiding values appear in alphabetical order and are not rank-ordered.

mailto:sally.bean@sunnybrook.ca

https://apps.who.int/iris/bitstream/handle/10665/331215/WHO-2019-nCov-IPCPPE_use-2020.1-eng

http://www.health.gov.on.ca/en/pro/programs/drugs/supply/docs/ethical_framework

http://www.health.gov.on.ca/en/pro/programs/drugs/supply/docs/ethical_framework

Page 2 of 6
Framework Authors: Sally Bean, Kevin Reel, Maxwell J. Smith, Blair Henry, and Maria McDonald
sally.bean@sunnybrook.ca
Version: March 9, 2020

Table 1.Guiding Values

Value Definition

Beneficence Promoting highest quality of safe and effective care within resource constraints by:

a. Ensuring standard of care and best Infection Prevention & Control (IP&C) practices whenever possible

b. Training healthcare providers (construed broadly to include anyone with direct contact with patients including both regulated and unregulated

providers, administrative staff, environmental services, porters, etc.) to select the proper PPE, how to safely doff, don, and dispose of PPE after

use

c. Committing to use best available data/evidence to inform PPE allocation decision-making

d. Using alternative PPE where evidence suggests similar or similarly adequate efficacy

e. Informing and educating healthcare providers about risks and benefits of alternate PPE including risk mitigation strategies

f. Enabling delivery of care in the most appropriate setting, e.g. negative pressure rooms or decontamination areas to help mitigate risk of exposure

Equity Promote just/fair access to PPE by:

a. Using allocation processes for distribution of PPE that do not arbitrarily disadvantage any healthcare provider

b. Not discriminating between healthcare providers based on factors not relevant to provision of healthcare (e.g., social status)

c. Treating similar cases similarly and treating dissimilar cases in a manner that reflects the differences.

Reciprocity To support healthcare providers that may be or are exposed to COVID-19 in the course of their employment, mitigate potential harms/burdens this may

cause to the individual by:

a. Describing the steps healthcare providers should take to reduce exposure or spread to others, including family members

b. Working with Occupational Health & Safety to clarify requirements and implications for fitness to work

c. Ensure that healthcare providers exposed to COVID-19 are aware of all known ways to reduce symptoms and complications associated with

COVID-19

d. Prioritizing healthcare providers most at risk of COVID-19 exposure in the course of their employment for future vaccines or treatments that

may be developed or become available

e. If hospital visitation is suspended, support use of technology for patients and staff that are isolated from families to safely communicate

Solidarity To build, preserve and strengthen interprofessional and intra-institutional collaboration is the responsibility of all leaders and decision-makers through:

a. Embracing a shared commitment to the well-being of patients and healthcare providers regardless of care setting (i.e. all sites and more broadly

across the continuum of care)

b. Establishing, encouraging, and enabling open lines of communication and coordination

c. Sharing and redistributing PPE within the healthcare institution

d. Supporting allocation decisions that are consistent with ethical framework

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e. Recognizing importance of collaboration with health system partners beyond the acute care setting

f. Recognizing some healthcare providers may feel a strong duty to care for patients despite inadequate PPE but this individual decision may have

overriding negative consequences, e.g. resources required if the healthcare provider becomes ill

g. Acknowledging that due to individual circumstances, some healthcare providers may have competing interests (e.g. ill family members),

underlying health issues that put them at an elevated risk if infected, etc. such that they may be unavailable to provide care or might need to be

redeployed to other low risk areas

h. Providing psychosocial support to healthcare providers delivering care to COVID-19 patients to ensure they feel supported and not marginalized

Stewardship Upholding principles use of available PPE carefully and responsibly by:

a. Ensuring PPE utilization is consistent with best available evidence

b. Avoiding stockpiling for personal use

c. Postponing elective procedures/treatments that require use of PPE that are in limited

supply

d. Prioritizing access to scarce PPE based on risk of exposure and pathogen transmission dynamics

e. Monitoring PPE utilization and distribution to facilitate course corrections as needed

f. If deemed acceptable for IP&C practices, extend life of PPE through extended PPE use (e.g. use same respirator while caring for multiple

patients with the same diagnosis without removing PPE)

Trust Foster and maintain public, patient, and health care provider confidence in PPE distribution system by:

a. Communicating in a clear and timely fashion, including expectations around accepting or refusing work assignments

b. Making decisions in an open, inclusive and transparent way with clearly defined decision-making authority and accountability

c. Being transparent and providing a rationale about what criteria are informing PPE allocation and staff assignment decisions

d. Collating short and long-term lessons learned

Utility While balancing the other principles, maximize the greatest possible good for the greatest possible number of individuals by:

a. Promote administrative control measures that minimize direct patient care to essential encounters

b. Distributing PPE in short supply to healthcare providers administering direct patient care

c. Distributing PPE in short supply to healthcare providers with the highest risk of exposure (e.g. providing direct care and aerosol-generating

procedures) and pathogen transmission dynamics

d. Sharing PPE within the healthcare institution

e. Where feasible, sourcing additional PPE supply

f. Identifying healthcare providers that may be at increased risk for the more serious (health-related) impacts of COVID-19 if they were to become

infected and potentially redeploy to lower risk areas.

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Allocation Principles:

The following allocation principles apply generally across all types of PPE. They provide a foundation to

inform discussion and decision-making at the relevant governance level during.

Stage 1. Implement strategies to preserve or approximate standard of care and best IP&C practices to

the extent possible within available PPE supply

When there is risk of PPE shortage,

1a. Conserve existing supply of PPE using strategies such as:

 Developing an inventory of available PPE and review at frequent intervals

 Reviewing PPE usage practices in light of best available evidence

 Reducing wastage of PPE (e.g., where evidence does not support use or is weak)

 Minimize need for PPE by using alternatives to face-to-face care such as telemedicine or

consultation across physical barriers for appropriate interactions

 Using alternative PPE where evidence suggests adequately similar efficacy to the PPE in short

supply

 Limit or prohibit hospital visitation (to reduce or eliminate visitors use of PPE)

 Limit access to PPE to only those providing direct patient care to COVID-19 (or other diseases

that require PPE)

 Cancelling non-urgent or elective procedures that require use of PPE

 Co-horting COVID-19 patients (i.e. create a care ecology so that healthcare providers can

optimally use PPE for treating a group of similarly situated patients)

 Utilize expired PPE for training purposes and consider if safe to use for direct care

 Delaying new enrollment in research studies using PPE in short supply

1b. Access new supply of PPE by:

 Collaborating with partners and governments to identify and procure alternative sources

And if these strategies are insufficient…

1c. Postpone or reduce procedures/treatments that require the use of PPE in short supply that are not

related to COVID-19.

Stage 2. Apply Primary Allocation Principles based on risk of exposure and risk of harm (to self and

others, e.g. if work with a patient population that might be more negatively impacted) if infected:

When Stage 1 strategies are insufficient to meet the need for PPE in short supply, give priority access in

rank order to:

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2a. Healthcare providers who are at highest risk for exposure to (or risk of harm from) COVID-19 (or

other diseases that require PPE) that are providing direct care to patients.

2b. Healthcare providers who are at moderate risk for exposure to (or risk of harm from) COVID-19

(or

other diseases that require PPE) that are providing direct care to patients.

2c. Healthcare providers who are at lowest risk for exposure to (or risk of harm from) COVID-19 (or

other diseases that require PPE) that are providing direct care to patients.

Meanwhile…

 Continue with Stage 1 strategies, and

 Reassess healthcare provider’s risk of exposure on an ongoing basis to identify any changes in

level of priority.

Stage 3. Apply Secondary Allocation Principles to Ensure Fair Access to PPE

When decisions must be made between healthcare providers within a level of priority as described in

Stage 2, prioritize healthcare providers using a fair and unbiased procedure that does discriminate

between healthcare providers based on factors not relevant to their risk of exposure (e.g., race, social

value, sex, age) or risk of harm if infected such as:

 First come, first served (where queuing is feasible with regular clinical practice), or

 Other procedure that is developed and sanctioned by affected stakeholders (e.g., random

selection). A lottery system would mean that only some healthcare providers get PPE and only

those healthcare providers would be able to provide care.

Meanwhile…

 Continue with Stage 1 strategies, and

 Reassess healthcare providers’ risk of exposure on an ongoing basis to identify any changes in

level of priority.

A4R Ethical Framework (Process Conditions) for Resource Allocation Decision-Making:

The A4R framework has been adopted by Sunnybrook as a tool to help shape ethically defensible
processes for resource allocation decision-making. It outlines 5 fair process principles that help ensure
the process fair and perceived as such:

 Relevance; Publicity; Revision; Enforcement; and Empowerment

When considering implementing this framework, every effort should be made to promote fairness in
decision-making. Fairness can be promoted by ensuring that this process aligns with Sunnybrook’s

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framework for organizational decision-making, A4R. Further details of the A4R framework and its use
are available on Sunnynet. External individuals can read more about implementing A4R here.

Appendix I:

Areas Requiring Further Consideration:

 Redistributing PPE among health system partners

 IP&C guidelines/direction on PPE minimum standards, PPE substitutions, or alternations to

standard usage such as PPE extended use or reuse

 Expectations around reporting to work or self-quarantine if a family member living in the same

residence is positive for COVID-19

 Legal context if emergency measures are invoked

 Staff assignments to care for COVID-19 patients

 Healthcare providers ability to refuse “unsafe” work or assignments

 Access to PPE in community and unique challenges of allocation in community setting

 If healthcare providers have contracted COVID-19 and since recovered, what is the risk of re-

infection?

 If PPE supply gets to zero, can healthcare providers independently decide to provide care without

PPE (i.e. assume risk)?

 End-of-life decision-making issues (withholding or withdrawing treatment)

 Allocation of potentially life sustaining treatments, e.g. ventilators, ECMO, etc.

mailto:sally.bean@sunnybrook.ca

http://sunnynet.ca/data/1/rec_docs/5866_Organizational_Ethics_Decision_Making_Framework

http://www.jcb.utoronto.ca/docs/A4R_Implementation_Guide2011_hospitals