An introduction to health
information technology systems
Babak Mohit MPH, MBA.
DrPH candidate
Department of Health Policy and Management
Johns Hopkins Bloomberg School of Public Health
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2013
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Highlights
• Comparison of levels of observation in public
health and information technology (IT).
• Overlapping domains of public health and health
information technology (HIT).
• Functions of IT in public health with practical
examples.
• Theoretical frameworks of IT in public health.
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Level of observation
• WHO definition of health recognizes
various levels of influence on health:
“Health is a state of complete physical,
mental and social well-being and not merely
the absence of disease or infirmity.”
Preamble to the Constitution of the World Health Organization as adopted by the International Health
Conference, New York, 19-22 June, 1946; Official Records of the World Health Organization, no. 2, p. 100
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Levels of observation in
information technology (IT)
• Data: symbols
• Information: data that are processed to be useful;
provides answers to "who", "what", "where", and
"when" questions
• Knowledge: application of data and information;
answers "how" questions
• Understanding: appreciation of "why"
• Wisdom: evaluated understanding
Ackoff, R. L., (1989) "From Data to Wisdom", Journal of Applied Systems Analysis, (16) 3-9.
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Example levels of observation in
information technology
• Data represents a fact or statement of event without relation to other things.
Ex: It is raining.
•
Information embodies the understanding of a relationship of some sort,
possibly cause and effect or as we say in epidemiology an association.
Ex: The temperature dropped 15 degrees and then it started raining.
•
Knowledge represents a pattern that connects and generally provides a high
level of predictability as to what is described or what will happen next.
Ex: If the humidity is very high and the temperature drops substantially the
atmosphere is often unlikely to be able to hold the moisture so it rains.
•
Wisdom embodies more of an understanding of fundamental principles
embodied within the knowledge that are essentially the basis for the
knowledge being what it is. Wisdom is essentially systemic.
Ex: When it rains, it pours.
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Domains
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A. Biomedical/biological sciences (e.g.,
genetics, neurosciences)
B. Clinical sciences (evidence based
medicine)
C. Informatics and analytic sciences
(e.g., decision sciences, computer
sciences, statistical)
1. A and C: Bio-informatics (aka,
computational biology)
2. B and C: Clinical/medical
informatics
3. B and D: Population based medicine
and “care management” within clinical
settings (not informatics)
4. B, C and D: HIT support of care
management and population based
medicine in clinical settings
5. C and D: Public health/population
health informatics
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What HIT systems cannot do!
•
Replace physicians with computers
Doctors are trained to make diagnoses
They don't desire or use software that purports to do this for them
• Create “superdoctors” through the use of computer tools
The practice of medicine is learned through personal experiences
Doctors do not need simulations of reality
• Vastly improve upon books and traditional teaching strategies
Books are an adequate method of conveying knowledge. Computers can provide some
improvements to book learning, but there is no reason to think that a system of learning
based on printed literature, which works well, can be vastly improved.
• Solve subtle or complex problems via the use of medical ontology
Complex systems are inherently chaotic hence inferences reached through a logical ontology
modeling a complex system are likely to be misleading
• Create, within the next decade, comprehensive medical records for all U.S. citizens
that can be accessed and annotated by all authorized caregivers
This “Holy Grail” of U.S. medical informatics is a worthy long-term pursuit, but there is no
reason to expect that it can be achieved within a decade or even two decades
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Why do public health professionals
need to know health IT?
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Functions of an IT system
1.
2.
3.
4.
5.
Collect data (input)
Manage data (verify, store, upload, etc.)
Integrate data
Analyze data (SAS, SPSS, STRATA, GIS)
Generate reports (output, e.g., summary, reminders,
notification, alert, update, etc.)
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Requirements Elicitation Includes:
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Specifying goals (SMART)
Specifying actors (business and technical)
Specifying functional and nonfunctional requirements
Specifying use cases
Developing models/diagrams (Use case, workflow, and
dataflow)
Specifying high-level system architecture
Specifying hardware and software requirements
Specifying system evaluation plan
Specifying project timeline and documentation
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Example: Immunization System
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Unified Modeling Language (UML)
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Workflow & Dataflow
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The stack framework
What?
How?
Lehmann, H. P. (2011). The stack framework. Introduction to bio-medical and public health informatics (315.707.81)
Topic 5- Frameworks for health IT and informatics.
Class
lecture
notes.-Johns
Hopkins University. Baltimore, MD.
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Prism Framework
Aqil, A., Lippeveld, T., & Hozumi, D. (2009).
a paradigm
shift for designing, strengthening and
BabakPRISM
Mohitframework:
for SAPHIR
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evaluating routine health information systems. Health Policy Plan, 24(3), 217-228.
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Questions? Comments?
Babak Mohit, MPH, MBA
bmohit@jhsph.edu
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The evolution of the electronic
health record (EHR) in the US.
Babak Mohit MPH, MBA.
DrPH candidate
Department of Health Policy and Management
Johns Hopkins Bloomberg School of Public Health
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Highlights
• The necessity of an Electronic Health Record
(EHR)
• History and current efforts towards an EHR.
• Meaningful Use
• Architecture
• Steps towards a future public health grid
• Wrap-up with ways to get involved.
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The Aday Andersen model of
healthcare utilization
Aday LA, Andersen R. A framework for the
studyMohit
of access
medical -care.
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Decision making in healthcare
Lab results
Perception of Disease
Radiology
Genetics
Mostly self
Current Symptoms reported
History of comorbidities
Decision towards treatment
Family History
Socio Economic Status
Education, Current residence, etc
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Building a HIT infrastructure…
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$20 billion in context
• Medical care = $2.4 trillion per year (U.S.)
• Budget of Centers for Disease Control and
Prevention: $6 billion
• Mean per capita spending for public health in
2004-05 was $149, compared to $6,423 for overall
health care
• Most industries spend 3% on IT = $72 billion
• Hollywood worldwide revenue: $40 billion
• Practitioners spend about 30% of their time on
information
Source: FSU study draws attention to U.S. public health spending. Available at:
http://www.fsu.edu/news/2006/07/19/health.spending/
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Presumed benefits
• Ambulatory computerized provider order
entry: $44 bil per year
• Healthcare information exchange and
interoperability: $33.5 bil per year
• Interoperable personal health records: $19 bil
per year
• Provider-to-provider telehealth technologies:
$3.6 billion per year
Source: Center for Information Technology Leadership (CITL). Clinical Informatics Research and
Development (CIRD), Partners Healthcare System (http://www.partners.org/cird/).
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History
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Meaningful Use
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Meaningful Use
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Architecture
Best of Breed EHR
Integrated EHR
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EPIC
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EHR runs at the
application level
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Conceptual representation of a
Public Health Grid
Savel T, Hall K, Lee B, McMullin V, Miles M, Stinn J, White P, Washington D, Boyd T, Lenert L., A Public Health Grid (PHGrid):
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Architecture and value proposition for 21st century public health. Int J Med Inform. 2010 Jul;79(7):523-9.
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It all boils down to standards…
Lopez DM, Blobel BG., A development framework for semantically interoperable
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health information systems. Int J Med Inform. 2009 Feb;78(2):83-103
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How to get involved…
• American Medical Informatics
Association (AMIA):
http://www.amia.org
http://jamia.bmj.com/
• Healthcare Information and
Management Systems Society
(HiMSS)
http://www.himss.org
• Public Health Data Standards
Consortium (PHDSC)
http://www.phdsc.org/
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Thank you.
Questions? Comments?
Babak Mohit, MPH, MBA
bmohit@jhsph.edu
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