Plagiarism check
References and citations should be given in APA 7th edition.
Here is the link to World Health Organization – Global Health Expenditure Database –
https://apps.who.int/nha/database/country_profile/Index/en
Your task is as follows:
- Access WHO Global Health Expenditure Database (link above)
- Select two (2) countries from the ‘selected country’ field. Use the drop-down arrow to scroll through the various countries.
- Scroll down through the infographics given for your selected countries and draw interpretation
- Once you have reviewed your two (2) selected countries, you are to assimilate your interpretation as compare and contrast
Next, review the infographics for information that makes sense, or you can use for analysis and interpretation
This process will provide you with analytics based on your choices above.
From the above link select countries CANADA and INDIA.
Please follow the assignment instructions in the given document.
Please use Unit 4, unit 5, unit 6 PPT information which I have uploaded and also for preparing the presentation as it is mentioned in the instructions.
Using the analysis, you are to create a presentation of MINIMUM 10 slides and MAXIMUM 15 slides to communicate your analysis. The presentation MUST have the following within:
- description of your countries – location, population, healthcare system, economy
- compare and contrast from the infographics – similarities or differences
- with clinical decision support, how can healthcare professionals learn from outcome of the health expenditure shown for each country,
- importance of data science in providing analytics for healthcare practitioners and legislations/regulators to implement change moving forward,
- provide two (2) health and innovation strategy or process that can aid in the sustainability of healthcare practitioners, based on your retrieved analytics for each country,
- evidence to support health and innovation, and
- references of minimum three (3) required.
- Include cover page and reference page.
INFO-6061 – Health Systems Environments II
FINAL COMPREHENSIVE ASSIGNMENT – Individual
Total Marks – 100
Weight – 20%
Due Date: Week 14 – Date & Time To Be Noted in ‘Semester at a Glance’
General Instructions:
This is an INDIVIDUAL comprehensive assignment covering Unit 4 Clinical Decision Support, Unit 5 Data
Science in Healthcare, and Unit 6 Health and Innovation.
Use correct APA citation formatting and referencing. Use Microsoft Office application with default
margin, line spacing, font color and size. Remember to include a cover page and a reference page.
Late submissions will NOT be accepted beyond due date/time.
Report with a Turnitin score of greater than 20% will either be returned for adjustment and/or be
awarded a grade of zero (0); if Turnitin score is not adjusted.
Please review the marking rubric to ensure that you have completed the assignment accordingly.
Assignment Instructions:
Health Systems Management II (INFO6061) covered three key areas from Weeks 8 to 13 – clinical
decision support, data science, and health and innovation. This final individual assignment is
intended to enable you to apply the content discussed within the course material in your analysis of
the scenario below.
Scenario:
The World Health Organization (WHO) is a reputable global organization that provides guidance to
healthcare leaders around the world with the aim of maintaining a health global population. WHO
also gathers significant data from its partners around the world. In this FINAL INDIVIDUAL
ASSIGNMENT, you will be using WHO Global Health Expenditure Database. The themes will focus on
clinical decision support, data science, and health and innovation.
Here is the link to World Health Organization – Global Health Expenditure Database –
https://apps.who.int/nha/database/country_profile/Index/en
Your task is as follows:
a) Access WHO Global Health Expenditure Database (link above)
b) Select two (2) countries from the ‘selected country’ field. Use the drop-down arrow to scroll
through the various countries.
c) Scroll down through the infographics given for your selected countries and draw interpretation
d) Once you have reviewed your two (2) selected countries, you are to assimilate your
interpretation as compare and contrast
e) Next, review the infographics for information that makes sense, or you can use for analysis and
interpretation
This process will provide you with analytics based on your choices above.
Your task for your final submission is as follows:
Research and describe your two (2) countries.
Analysis the analytics you retrieve from your countries.
Using the analysis, you are to create a presentation of MINIMUM 10 slides and MAXIMUM 15 slides
to communicate your analysis. The presentation MUST have the following within:
➢ description of your countries – location, population, healthcare system,
economy
➢ compare and contrast from the infographics – similarities or differences
➢ with clinical decision support, how can healthcare professionals learn from
outcome of the health expenditure shown for each country,
➢ importance of data science in providing analytics for healthcare
practitioners and legislations/regulators to implement change moving
forward,
➢ provide two (2) health and innovation strategy or process that can aid in
the sustainability of healthcare practitioners, based on your retrieved
analytics for each country,
➢ evidence to support health and innovation, and
➢ references of minimum three (3) required.
HINT: This task is not just to look at the infographic and re-state what is shown. This task is to apply
the content from Units 4, 5 and 6 of INFO6061 and interpret the analysis to align it to clinical decision
support, data science, and health and innovation. The task is for you to showcase your understanding
of what the data and analytics mean to the health care system. In essence, think as a future ‘decision
support analyst’. Subsequently, you will need to research your interpretation and support it with
evidence (reference/resource).
RUBRIC FOR FINAL ASSIGNMENT
Criteria
Unacceptable (2) Marginal (5) Partially Proficient (7) Meets
Does not provide covers the content
the required content
appropriate
needed without
needed with minimal
content needed cohesion and analysis. details and support.
Exemplary (10)
Exceeds the
required content
needed with critical
thinking and
analysis. Extends
beyond theory and
incorporate
relevant support.
Description of countries
Compare and contrast
Clinical decision support
Data science
Recommendation
for health and
innovation #1
Recommendation
for health and
innovation #2
Evidence to support
health and
innovation
Grammar & Spelling
APA Compliant referencing/citing
Tone and Communication –
written in simulated role
with appropriate
professionalism of HSY
TOTAL MARKS – 100
Addendum to A136 Academic Integrity Policy – Pursuant to Fanshawe College’s A136 Academic
Integrity policy, the Health Systems Management program does not permit the use of any
unauthorized technology tools. Technology tools include, but are not limited to, calculators,
textbooks, translation tools, course notes and resources, search engines (e.g. Google), and
artificial intelligence applications (e.g. ChatGPT or any other similar/equivalent platform).
The unauthorized use of these technology tools in any academic deliverable will result in
the applicable penalties as per A136 Academic Integrity policy. This can be applied
individually or group capacity, dependent on the offence identified and resulting
investigation and verification.
Clinical Decision Support
INFO 6061 – Unit 4
Content Developed by Dr. Radica Bissoondial
Unit 4 Topics
•Clinical decision support
•Advantages
•Disadvantages
•Application of CDS and HIM
•Future
Dr. Radica Bissoondial
Unit 4 Learning Objectives
• Describe what is clinical decision support
• Explain advantages and disadvantages of clinical
decision support
• Analyze applications of clinical decision support
• Demonstrate the connection of clinical decision
support and health information management
• Predict the future of clinical decision support
Dr. Radica Bissoondial
Clinical Decision Support
A brief history …
https://www.youtub
e.com/watch?v=QR
3UySBxrks
Dr. Radica Bissoondial
Definition
Clinical decision support is a process for
enhancing health-related decisions and actions
with pertinent, organized clinical knowledge
and patient information, to improve health and
healthcare delivery.
Dr. Radica Bissoondial
Components
• Information recipients
• Include patients, clinicians and others involved in
patient care delivery
• Information delivered
• Include general clinical knowledge and guidance,
intelligently processed patient data, or a mixture of
both
• Information delivery formats
• Drawn from a rich palette of options that includes
data and order entry facilitators, filtered data
displays, reference information, alerts, and others.
Dr. Radica Bissoondial
Like a GPS, CDS supplies information tailored to the current
situation, and organized for maximum value.
A simple and classic CDS example:
Drug warnings
A More Elaborate CDS Example:
Order Sets
Types of CDS
• Drug-Drug Interactions
• Drug-Allergy interactions
• Dose Range Checking
• Pick lists
• Standardized evidence based
order sets
• Links to knowledge
references
• Links to local policies
• Alerts
• Rules to meet strategic
objectives (core measures,
antibiotic usage, blood
management)
• Documentation templates
• Relevant data displays
• Point of care reference
information (i.e. InfoButtons)
• Web based reference
information
• Diagnostic decision support
tools
Dr. Radica Bissoondial
Interventions of CDS
During data entry
During data review
During assessment
& understanding
Smart Documentation Forms
Relevant Data Summaries
(Single-patient)
Filtered Reference Information
and Knowledge Resources
Multi-patient Monitors
Expert Workup and
Management Advisors
Order Sets, Care Plans and
Protocols
Parameter Guidance
Critiques and Warnings
Predictive and Retrospective
Analytics
Dr. Radica Bissoondial
Advantages of CDS
• Quality
• By guiding users to best practices
• Safety
• By verifying an action was the intended one
• Cost
• Catching duplicate or unnecessary orders
• Documentation
• By bringing forth documentation tools based on a diagnosis or
problem
• Communication
• Of system priorities or initiatives
• Among providers of patient status
Dr. Radica Bissoondial
Disadvantages of CDS
• Speed
• Usability and workflow
• Avoiding over-alerting
• Cultural change
• Management backing
• Clinical leadership backing
• Improve the human-computer interface
• Disseminate best practices in CDS design,
development, and implementation
Dr. Radica Bissoondial
• Summarize patient-level information
• Prioritize and filter recommendations to the user
• Create an architecture for sharing executable CDS
modules and services
• Combine recommendations for patients with comorbidities
• Prioritize CDS content development and implementation
• Create internet-accessible clinical decision support
repositories
• Use free text information to drive clinical decision support
• Mine large clinical databases to create new CDS
Dr. Radica Bissoondial
CDS and Success Factors
The “ten commandments” are:
A. Speed is everything
B. Anticipate needs and deliver in real time
C. Fit into the user’s workflow
D. Little things can make a big difference
E. Recognize that physicians will strongly resist stopping
F. Changing directions is easier than stopping
G. Simple interventions work best
H. Ask for additional information only when you really need it
I. Monitor impact, get feedback and respond
J. Manage and maintain your knowledge-based systems
Dr. Radica Bissoondial
CDS Five Rights
Right person
Right information
Right channel
Right point in workflow
Right CDS intervention format
Dr. Radica Bissoondial
CDS is a Strategic Tool
CDS is a strategic tool for achieving an
organizations priority care delivery objectives.
– Some objectives are driven by external forces
such as payment models and regulations related to
improving care quality and safety
– Other objectives are driven by internal needs for
improving quality and patient safety, reducing
medical errors, increasing efficiency and other
performance enhancements.
Dr. Radica Bissoondial
CDS and Need for Leadership
• CDS interventions impact workflow throughout
an organization; therefore leadership at all
levels must understand and support the efforts
• CDS programs require ongoing investment of
capital and personnel
• A champion is required to be a change agent
and lead the charge
Dr. Radica Bissoondial
CDS Does Not Make Policy
• CDS should not be thought of as the only
tool available to solve the organization’s
problems or communicate change. There are
clear limitations to CDS interventions.
• When new policies or procedures are
established, they should be thoroughly
discussed by all relevant stakeholders before
they appear in a CDS intervention.
Dr. Radica Bissoondial
Application
Canada Health
Infoway
https://www.infoway
inforoute.ca/en/reso
urcecentre/advancedsearch?q=clinical+d
ecision+support
Dr. Radica Bissoondial
Application
CHIMA
https://www.echima
.ca/uploaded/pdf/e
mails/0041.16_Big
%20Data%20and%
20Data%20Analytic
s.pdf
Dr. Radica Bissoondial
CDS and Future
Personalized medicine
Clinical analytics
Natural language processing
Dr. Radica Bissoondial
Summary
HIMSS
https://www.youtub
e.com/watch?v=ka
eOBCq4NnU
Dr. Radica Bissoondial
SCENARIOS via HIMSS
Dr. Radica Bissoondial
Scenario A
You are a nurse on a busy med-surg unit in an
acute care hospital. Every time you administer a
medication, you are required to scan the bar
codes of your patient’s wristband and the
medication. Occasionally, this process prompts
an alert (CDS Type: Critiques and Warnings) that
the medication you are about to administer is
contraindicated for your patient at this time. You
contact the attending physician to communicate
this and obtain an order for an alternate
medication.
Dr. Radica Bissoondial
Scenario B
You are a primary care physician in a large
group practice that uses an electronic health
record (EHR). At the beginning of each visit,
you view a dashboard (CDS Type: Relevant
Data Summary) of preventive care
measures – such as flu vaccine, colon
cancer screening, cholesterol tests – that are
due for your patient, based on age, medical
history (problem list), and medication list
stored in the EHR.
Dr. Radica Bissoondial
Scenario C
You are a care manager in an emergency
department. Thirty patients are in the unit right now,
and new lab results, imaging studies, and orders are
constantly being posted on all of them. You have a
visual “air traffic” tracking display (CDS Type: MultiPatient Monitor) which indicates which patients have
not been seen yet, which have new orders to
process, which have abnormal lab results, which
ones have been in the unit more than four hours,
which have inpatient beds ready, and more. Your
status display filters and reformats all the information
to help you know where to direct your attention next.
Dr. Radica Bissoondial
Scenario D
You are an attending physician in an urban emergency
department. A 60 year old male presents complaining of pain and
swelling in the right leg. Upon examination, you observe red
discoloration on the surface of the right calf with the appearance of
visible surface veins. Suspecting a possible deep vein thrombosis
(DVT), you prepare to admit the patient and go to order
heparin. When you choose the DVT order set In the CPOE
system (CDS Type: Order Sets/Care Plans/Protocols), it highlights
a link to an updated American College of Chest Physicians
guideline (CDS Type: Filtered Reference Information and
Knowledge Resources), which suggests that low-molecular-weight
heparin may be a better medication for certain patients. After
reading the new information, you decide to change from heparin to
enoxaparin, and another option in the order set facilitates the
proper dosing of the new medication.
Dr. Radica Bissoondial
Scenario E
You are an ICU physician. You are in your office at the hospital
and you receive vital signs trends on your patients using your
smart phone. You set a “smart alert” on a particular elderly patient
on mechanical ventilation whom you suspect may be prone to
sepsis (CDS Type: Event-Driven Alert). You are notified of the vital
signs trends on your patient which show, among other parameters,
a noticeable increase in temperature and heart rate over the past
hour and decreasing urine output, as indicated from the Intake and
Output accumulations. You call the unit to verify these data, and
decide to access the CPOE system to order stat blood tests to
confirm the onset of sepsis, and you also start antibiotics. The
EMR notifies you that your patient is allergic to one of the
particular antibiotics you were originally ordering (CDS Type:
Critiques and Warnings), so you select one that has no
contraindications.
Dr. Radica Bissoondial
References
https://www.himss.org/library/clinical-decisionsupport/what-is
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC26
4429/
https://www.informationweek.com/healthcare/clini
cal-information-systems/clinical-decision-supportwhats-in-your-future/d/d-id/1101254
Dr. Radica Bissoondial
Data Science
INFO6061 – Unit 5
Content Developed by Dr. Radica Bissoondial
Unit 5 Topics
•Data science
•Advantages
•Disadvantages
•Application of DS
•Future
Dr. Radica Bissoondial
Unit 5 Learning Objectives
• Describe what is data science
• Explain advantages and disadvantages of
data science in healthcare
• Analyze applications of data science
• Predict the future of data science in
healthcare
Dr. Radica Bissoondial
Data science
Data science is a multidisciplinary blend
of data inference, algorithm development,
and technology in order to solve
analytically complex problems.
Data X Information
Data Science
What Is Data Science?
(Explained in 5 Minutes)
Data science life cycle
Healthcare data science
The purpose of the healthcare data
science is to make sense of all the
incoming data and make the insights
usable by those in the healthcare circle of
care
Data science in healthcare
priorities are:
• Working with key business leaders to understand
the needs and what kind of analytical data is
necessary
• Gathering incoming data
• Structuring and synchronizing datasets
• Providing contributions to Public Health Datasets
• Performing database audits
• Providing data analytics for various applications
• Collaborating to build solutions
Top 5 data science
applications in healthcare
Data Management & Data
Governance
Workflow Optimization and
Process Improvements
Medical Image Analysis
Genetics / Genomics
Predictive Analytics
Healthcare data science tends to be called:
BIG DATA
Advantages of healthcare data
science
• Clinical operations
• Research & development
• Public health
• Evidence-based medicine
• Genomic analytics
• Pre-adjudication fraud analysis
• Device/remote monitoring
• Patient profile analytics
Advantages of healthcare data
science
Advantage
Description
Clinical operations
Comparative effectiveness research to determine more clinically relevant
and cost-effective ways to diagnose and treat patients.
Research &
Development
Analyzing clinical trials and patient records to identify follow-on indications
and discover adverse effects before products reach the market
Public health
Analyzing disease patterns and tracking disease outbreaks and transmission
to improve public health surveillance and speed response
Evidence-based
medicine
Combine and analyze a variety of structured and unstructured data-EMRs,
financial and operational data, clinical data, and genomic data to match
treatments with outcomes, predict patients at risk for disease or readmission
and provide more efficient care
Advantages of healthcare data
science
Advantage
Description
Genomics
analytics
Execute gene sequencing more efficiently and cost effectively and make
genomic analysis a part of the regular medical care decision process and the
growing patient medical record
Pre-adjudication
fraud analysis
Rapidly analyze large numbers of claim requests to reduce fraud, waste
and abuse
Device/remote
monitoring
Capture and analyze in real-time large volumes of fast-moving data from inhospital and in-home devices, for safety monitoring and adverse event
prediction
Patient profile
analytics
Apply advanced analytics to patient profiles to identify individuals who would
benefit from proactive care or lifestyle changes
“Vs” of big data analytics in
healthcare
Volume
(amount of data)
Veracity
(all data are correct / quality
of data)
Variety
( different types of data )
Disadvantages of healthcare
data science
• Capture
• Cleaning
• Storage
• Security
• Stewardship
• Querying
• Reporting
• Visualization
• Updating
• Sharing
Disadvantages of healthcare data science
Disadvantage
Description
Capture
Capturing data that is clean, complete, accurate, and formatted correctly for
use in multiple systems is an ongoing battle for organizations, many of
which aren’t on the winning side of the conflict.
Cleaning
Data cleaning – also known as cleansing or scrubbing – ensures that
datasets are accurate, correct, consistent, relevant, and not corrupted in
any way
Storage
As the volume of healthcare data grows exponentially, some providers are
no longer able to manage the costs and impacts of on premise data
centers.
Security
Data security is the number one priority for healthcare organizations,
especially in the wake of a rapid-fire series of high profile breaches,
hackings, and ransomware episodes. From phishing attacks to malware to
laptops accidentally left in a cab, healthcare data is subject to a nearly
infinite array of vulnerabilities.
Disadvantages of healthcare data science
Disadvantage
Description
Stewardship
Understanding when the data was created, by whom, and for what purpose
– as well as who has previously used the data, why, how, and when – is
important for researchers and data analysts.
Querying
Robust metadata and strong stewardship protocols also make it easier for
organizations to query their data and get the answers that they are
expecting. The ability to query data is foundational for reporting and
analytics, but healthcare organizations must typically overcome a number
of challenges before they can engage in meaningful analysis of their big
data assets.
Reporting
Once again, the accuracy and integrity of the data has a
critical downstream impact on the accuracy and reliability of the
report. Poor data at the outset will produce suspect reports at the end of
the process, which can be detrimental for clinicians who are trying to use
the information to treat patients..
Disadvantages of healthcare data science
Disadvantage
Description
Visualization
Organizations must also consider good data presentation practices, such
as charts that use proper proportions to illustrate contrasting figures, and
correct labeling of information to reduce potential confusion. Convoluted
flowcharts, cramped or overlapping text, and low-quality graphics can
frustrate and annoy recipients, leading them to ignore or misinterpret data.
Updating
Understanding the volatility of big data, or how often and to what degree it
changes, can be a challenge for organizations that do not consistently
monitor their data assets
Sharing
Fundamental differences in the way electronic health records are
designed and implemented can severely curtail the ability to move data
between disparate organizations, often leaving clinicians without
information they need to make key decisions, follow up with patients, and
develop strategies to improve overall outcomes.
Applications of healthcare data science
• IBM’s Watson Diagnosing Health Care Patients
• How big data could transform the health care industry
Applications of healthcare data
science
Web and social media data: Clickstream and interaction data from Facebook, Twitter,
LinkedIn, blogs, and the like. It can also include health plan websites, smartphone apps
Machine to machine data: readings from remote sensors, meters, and other vital sign
devices
Big transaction data: health care claims and other billing records increasingly available
in semi-structured and unstructured formats
Biometric data: finger prints, genetics, handwriting, retinal scans, x-ray and other
medical images, blood pressure, pulse and pulse-oximetry readings, and other similar
types of data [6]. 5. Human-generated data: unstructured and semi-structured data such
as EMRs, physicians notes, email, and paper documents
Future of healthcare data
science
•Artificial
intelligence
•Deep Learning
and Computer
Vision Use Cases
•Improving
Diagnosis
•Lowering costs
•Detecting
Healthcare Fraud
•New Drug
Discovery
•Targeting Care
AI in healthcare
• Can AI improve our healthcare?
• The Economist: Is this the future of health?
• The future of Healthcare
References
Big data analytics in healthcare: promise and potential. Retrieved from
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4341817/pdf/13755_2013_Article_14.pdf
Data Science vs. Data Analytics vs. Machine Learning: Expert Talk. Retrieved from
https://www.simplilearn.com/data-science-vs-data-analytics-vs-machine-learning-article
How data science is changing healthcare. Retrieved from
https://theappsolutions.com/blog/development/data-science-healthcare/
The Role of Big Data Analytics and AI in the Future of Healthcare. Retrieved from
The Role of Big Data Analytics and AI in the Future of Healthcare
Top 10 Challenges of Big Data Analytics in Healthcare. Retrieved from
https://healthitanalytics.com/news/top-10-challenges-of-big-data-analytics-in-healthcare
What is data science. Retrieved from https://datascience.berkeley.edu/about/what-is-data-science/
Health and Innovation
INFO 6061 – Unit 6
Content Developed by Dr. Radica Bissoondial
Unit 6 Topics
•Innovation
•Trends and Role of Government
•Partnership and Patient Engagement
•Data Influence
•Economics
•Health System Change
Innovation
Innovation is the practical implementation
of ideas that result in the introduction of
new goods or services or improvement in
offering goods or services.
Innovation
Unleashing Innovation commissioned by Honorable Rona
Ambrose with 2 goals:
Identify the five most promising areas of innovation in
Canada and internationally that have the potential to
sustainably reduce growth in health spending while leading
to improvements in the quality and accessibility of care
Recommend the five ways the federal government could
support innovation in the areas identified above
Trends and Role of
Government
Perspectives Performance of Canada’s
Healthcare Systems
healthcare spending
health outcomes
access to healthcare in Canada
quality of care
Trends and Role of
Government
Barriers to the Scaling-Up of Innovative Ideas:
• Lack of meaningful patient engagement
• Outmoded human resources model
• System fragmentation
• Inadequate health data and information management capacity
• Lack of effective deployment of digital technology
• Barriers for entrepreneurs
• A risk-averse culture
• Inadequate focus on understanding and optimizing innovation
Trends and Role of
Government
Themes emerging from reviewing healthcare
system:
• the lack of an integrated and patient-centred
healthcare system
• the importance of efficiency and value-formoney in ensuring system sustainability
• the need to build a shared knowledge-base and
learn from it to improve services for patients and
overall system management.
Trends and Role of
Government
Pan-Canadian health organizations
(PCHOs) have shown themselves able to
function across jurisdictions, bridge
federal-provincial-territorial sensitivities in
healthcare, and, albeit with uneven
success, provide leadership and
coordination in important areas
Partnership and Patient
Engagement
Challenges which can become opportunities
with innovation:
• patient want ‘in’
•
•
•
•
Canada’s population is changing rapidly
digital revolution is disrupting healthcare
rapid growth in healthcare spending is over
healthcare social program and economic
asset
Partnership and Patient
Engagement
“The federal government should establish a National Health
System Innovation Fund targeted to provinces and
territories to support the adoption of health system
innovations. Funding criteria should be designed to not only
support the development of these innovations but to incent
their adoption on a scaled-up basis.”
“The federal government must play a leadership role in
collaborating with jurisdictional counterparts in the formation
of a pan-Canadian health mechanism to identify, promote
and advance needed healthcare innovation.”
Recommendations to Federal Government –
Patient engagement
Starting in 2015-16, create a ten
year Healthcare Innovation Fund
with a gradual ramp-up, ideally
reaching steady-state by 2020
Create the Healthcare Innovation
Agency of Canada to work with a
range of stakeholders as well as
governments to set the long-term
vision for the healthcare system
and healthcare innovation goals
across the Panel’s proposed five
areas of focus
Shift funding and staff for both the
Canadian Foundation for
Healthcare Improvement and the
Canadian Patient Safety Institute to
the new Healthcare Innovation
Agency of Canada
Continue Canada Health Infoway
pro tem as a separate organization
with staffing to complete projects
currently underway. Once the new
Agency is established, fold relevant
functions from Infoway into the
Agency, and flow future federal
funding for digital health through
the Innovation Fund
Data Influence
Turning data into knowledge
Use of electronic health record
Data influence
Meaningful use
Big data in public interest
Patient-centered data
Precision medicine: data-rich knowledge
frontier
Preventing genetic discrimination
Open data
Economics
Health System Change
Health-specific tax expenditure
Other tax measures linked to health
Refundable health tax credit
Designing new tax credit
Administering new tax credit
Costing new tax credit
Main Themes from Panel Summary
• Patient Engagement and Empowerment
• Health Systems Integration with Workforce
Modernization
• Technological Transformation via Digital
Health and Precision Medicine
• Better Value from Procurement,
Reimbursement and Regulation
• Industry as an Economic Driver and
Innovation Catalyst
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