SlideShare.net/Nawanan H lth I f ti Health Informatics: The Next “Stethoscope” in Healthcare Nawanan Theera-Ampornpunt, MD, MS
May 07, 2015
SlideShare.net/Nawanan
H lth I f ti Health Informatics: The Next “Stethoscope” in Healthcarep
Nawanan Theera-Ampornpunt, MD, MS
Healthcare &
H lth ITHealth IT
Manufacturingg
Source: Guardian.co.uk
Bankingg
Source: Cablephet.com
Healthcare
Source: nj.com
Why Healthcare Isn’t Like Any Others?
• Life-or-Death
• Many & varied stakeholders
• Strong professional values
• Evolving standards of care
• Fragmented poorly-coordinated systemsFragmented, poorly coordinated systems
• Large, ever-growing & changing body of knowledge
• High volume, low resources, little time
Source: nj.com
Why Healthcare Isn’t Like Any Others?
• Large variations & contextual dependenceg p
Input Process OutputInput Process Output
Patient Decision BiologicalPatient Presentation
Decision‐Making
Biological Responses
Source: nj.com
But...Are We That Different?
Banking
Input Process Output
Transfer
Location A Location BValue‐Add
‐ SecurityC i
Location A Location B
‐ Convenience‐ Customer Service
But...Are We That Different?
Manufacturing
Input Process Output
AssemblingRaw FinishedAssemblingRaw Materials
Finished Goods
Value‐Add‐ Innovation‐ Skills‐ QA
But...Are We That Different?
Healthcare
Input Process Output
Patient CareSick Patient Well Patient
Value‐Add‐Medical technology & medications‐ Clinical knowledge & skillsg‐ Quality of care; process improvement‐ Information
Information is Everywherey
Various Forms of Health IT
Hospital Information System (HIS) Computerized Provider Order Entry (CPOE)
Electronic Health
Records Picture Archiving and Records (EHRs)
gCommunication System
(PACS)
Still Many Other Forms of Health IT
Health Information Exchange (HIE)
m Health
g ( )
m-Health
Biosurveillance
Personal Health Records (PHRs)
Telemedicine & Information Retrieval Telehealth
Images from Apple Inc., Geekzone.co.nz, Google, PubMed.gov, and American Telecare, Inc.
Why Adopting Health IT?
“Computerize”“Go paperless” ComputerizeGo paperless
“Digital Hospital”“Get a HIS”
Digital Hospital
“H EMR ”“Modernize”
“Have EMRs”
“Share data”Share data
Some Quotes
• “Don’t implement technology just for technology’s sake ”• Don t implement technology just for technology s sake.
• “Don’t make use of excellent technology.
Make excellent use of technology.”(Tangwongsan, Supachai. Personal communication, 2005.)
• “Health care IT is not a panacea for all that ails medicine.” (Hersh, 2004)
• “We worry, however, that [electronic records] are being
touted as a panacea for nearly all the ills of modern touted as a panacea for nearly all the ills of modern
medicine.”(Hartzband & Groopman, 2008)
Health IT: What’s In A Word?
Health GoalHealthf
Goal
Information Value-Add
Technology ToolsTechnology Tools
Dimensions of Quality Healthcare
• Safety• Safety
• Timeliness
• Effectiveness
• Efficiency
• E it• Equity
• Patient-centerednessat e t ce te ed ess
(IOM, 2001)
Value of Health IT
• G ideline adherence• Guideline adherence
• Better documentationBetter documentation
• Practitioner decision making or process of care
• Medication safety
• Patient surveillance & monitoring
• Patient ed cation/reminder• Patient education/reminder
Fundamental Theorem of Informatics
(Friedman, 2009)
Is There A Role for Health IT?
(IOM, 2000)
Landmark IOM Reports
(IOM, 2001)(IOM, 2000)
Landmark IOM Reports: Summary
• Humans are not perfect and are bound to make errorsp
• High-light problems in the U.S. health care system that
systematically contributes to medical errors and poor
qualityquality
• Recommends reform that would change how health
care works and how technology innovations can help
improve quality/safetyimprove quality/safety
Why We Need Health IT
• Health care is very complex (and inefficient)• Health care is very complex (and inefficient)
• Health care is information-rich
• Quality of care depends on timely availability &
quality of information
• Clinical knowledge body is too largeClinical knowledge body is too large
• Short time during a visit
• Practice guidelines are put “on-the-shelf”
• “To err is human”
To Err Is Human
• Perception errorsPerception errors
Source: interaction-dynamics.com
To Err Is Human
• Lack of AttentionLack of Attention
Source: aafp.org
To Err Is Human
• Decoy PricingDecoy Pricing
The Economist Purchase Options# of
People
• Economist.com subscription $59• Print subscription $125• Print & web subscription $125
16084• Print & web subscription $125 84
# ofThe Economist Purchase Options
• Economist com subscription $59 68
# of People
(Ariely, 2008)
• Economist.com subscription $59• Print & web subscription $125
6832
What If This Happens in Healthcare?
• It l d h• It already happens....(Mamede et al., 2010; Croskerry, 2003; Klein, 2005)
• What if health IT can help?
U.S.’s Efforts on Health IT Adoption
??
“ We will make wider use of electronic records and ...We will make wider use of electronic records and
other health information technology, to help control
costs and reduce dangerous medical errors.”President George W. Bush
Source: Wikisource.org Image Source: Wikipedia.org
Sixth State of the Union Address, January 31, 2006
Public Policy in Informatics: A US’s Case
1991: IOM’s CPR Report published1991: IOM s CPR Report published
1996: HIPAA enacted
2000‐2001: IOM’s To Err Is Human & Crossing the Quality Chasm published
2004: George W. Bush’s Executive Order establishing ONCHIT (ONC)
2009‐2010: ARRA/HITECH Act & “Meaningful use” regulationsMeaningful use regulations
U.S. Adoption of Health IT
Ambulatory (Hsiao et al, 2009) Hospitals (Jha et al, 2009)
Basic EHRs w/ notes 7.6%Comprehensive EHRs 1.5%pCPOE 17%
• U.S. lags behind other Western countries (Schoen et al, 2006;Jha et al, 2008)
• Money and misalignment of benefits is the biggest
reason
We Need “Change”
“...we need to upgrade our medical records by switching from a paper to y g p pan electronic system of record keeping...”
P id t B k ObPresident Barack ObamaJune 15, 2009
The Birth of “Meaningful Use”
“...Our recovery plan will invest in y pelectronic health records and new technology that will reduce errors, bring down costs,
ensure privacy and save lives ”ensure privacy, and save lives.
President Barack ObamaAddress to Joint Session of CongressAddress to Joint Session of Congress
February 24, 2009
Source: WhiteHouse.gov
American Recovery & Reinvestment Act
• Contains HITECH Act
(Health Information Technology for Economic and ( gy
Clinical Health Act)
• ~ 20 billion dollars for Health IT investments 20 billion dollars for Health IT investments
• Incentives & penalties for providers
National Leadership
Offi f th N ti l C di t f H lth I f ti Office of the National Coordinator for Health Information
Technology (ONC -- formerly ONCHIT)
David Blumenthal, MD, MPPNational Coordinator for Health Information TechnologyHealth Information Technology (2009 - Present)
Photo courtesy of U.S. Department of Health & Human Services
What is in the HITECH Act?
(Blumenthal, 2010)
“Meaningful Use”g
“Meaningful Use”
of a PumpkinPumpkin
of a Pumpkin
Image Source & Idea Courtesy of Pat Wise at HIMSS, Oct. 2009
“Meaningful Use” of Health ITg
Stage 1Stage 1‐ Electronic capture of health information‐ Information sharing
St 3
Better Health
‐ Data reportingStage 2
Stage 3
Use of EHRs to
Use of EHRsto improve processes of
EHRs to improve outcomes
care
(Blumenthal, 2010)
Health IT
A li tiApplications
Enterprise-wide Hospital IT
• Master Patient Index (MPI)
• Admit-Discharge-Transfer (ADT)
• Electronic Health Records (EHRs)
• C t i d Ph i i O d E t (CPOE)• Computerized Physician Order Entry (CPOE)
• Clinical Decision Support Systems (CDSSs)pp y
• Picture Archiving and Communication System (PACS)
• Nursing applications
l ( )• Enterprise Resource Planning (ERP)
Departmental IT
• Pharmacy applicationsy pp
• Laboratory Information System (LIS)
• Specialized applications (ER, OR, LR, Anesthesia,
Critical Care, Dietary Services, Blood Bank)Critical Care, Dietary Services, Blood Bank)
• Incident management & reporting system
EHRs & HISThe Challenge ‐ Knowing What It Means
Electronic Health Records (EHRs)
Hospital Information
Electronic Medical Records (EMRs)
Hospital Information System (HIS)
Records (EMRs)
Electronic Patient
C B d
Records (EPRs)
Personal Health
Clinical Information System (CIS)
Computer‐Based Patient Records
(CPRs)
Records (PHRs)
EHR Systems
Just electronic documentation?
Diag‐nosis
History & PE
Treat‐ments ...
O d th h th l ?Or do they have other values?
Functions that Should Be Part of EHR Systems
• Computerized Medication Order Entry
• Computerized Laboratory Order Entry
• Computerized Laboratory Resultsp y
• Physician Notes
• P ti t D hi• Patient Demographics
• Problem Lists
• Medication Lists
• Discharge SummariesDischarge Summaries
• Diagnostic Test Results
• Radiologic Reports
(IOM, 2003; Blumenthal et al, 2006)
Computerized Physician Order Entry
ValuesValues
• No handwriting!!!• No handwriting!!!• Structured data entry: Completeness clarity Structured data entry: Completeness, clarity,
fewer mistakes (?)
• No transcription errors!
E i f CDSS• Entry point for CDSSs
• Streamlines workflow, increases efficiencyStreamlines workflow, increases efficiency
Clinical Decision Support Systems (CDSSs)
• The real place where most of the values of health IT can be achieved
– Expert systems
• Based on artificial intelligence, machine learning, rules, or statistics
• Examples: differential diagnoses, treatment options
– Alerts & reminders– Alerts & reminders
• Based on specified logical conditions
• Examples: drug-allergy checks, drug-drug interaction checks,
reminders for preventive services or certain actions (e.g. smoking
cessation), clinical practice guideline integration
– Evidence-based knowledge sources e.g. drug database, literature
– Simple UI designed to help clinical decision making
Clinical Decision Support Systems (CDSSs)
PATIENT
PerceptionCLINICIAN
Attention
External MemoryLong Term Memory WorkingMemory
Knowledge DataKnowledge DataMemory
Inference
DECISIONFrom a teaching slide by Don Connelly, 2006
Clinical Decision Support Systems (CDSSs)
PATIENT
PerceptionCLINICIAN
AttentionAbnormal lab
highlights
External MemoryLong Term Memory WorkingMemory
Knowledge DataKnowledge DataMemory
Inference
DECISION
Clinical Decision Support Systems (CDSSs)
PATIENT
PerceptionCLINICIAN
AttentionDrug-Allergy
Checks
External MemoryLong Term Memory WorkingMemory
Knowledge DataKnowledge DataMemory
Inference
DECISION
Clinical Decision Support Systems (CDSSs)
PATIENT
PerceptionCLINICIAN
Drug-Drug Interaction
ChecksAttention
Checks
External MemoryLong Term Memory WorkingMemory
Knowledge DataKnowledge DataMemory
Inference
DECISION
Clinical Decision Support Systems (CDSSs)
PATIENT
PerceptionCLINICIAN
Clinical Practice
Attention Guideline Reminders
External MemoryLong Term Memory WorkingMemory
Knowledge DataKnowledge DataMemory
Inference
DECISION
Clinical Decision Support Systems (CDSSs)
PATIENT
PerceptionCLINICIAN
Attention
External MemoryLong Term Memory WorkingMemory
Knowledge DataKnowledge DataMemory
Inference Diagnostic/Treatment Expert Systems
DECISION
Clinical Decision Support Systems (CDSSs)
• CDSS as a supplement or replacement of clinicians?
– The demise of the “Greek Oracle” model (Miller & Masarie, 1990)
The “Greek Oracle” Model
The “Fundamental Theorem”
(Friedman, 2009)
Clinical Decision Support Systems (CDSSs)
Some risks• Alert fatigue
Workarounds
Health IT for Medication Safety
Ordering Transcription Dispensing Administrationg p p g
C OAutomatic Electronic
CPOEAutomatic Medication Dispensing
Electronic Medication
Administration Records (e-MAR)
BarcodedBarcodedMedication Di i Medication
AdministrationDispensing
Health Information Exchange (HIE)
Government
Hospital A Hospital B
Government
Clinic CL b P ti t t HLab Patient at Home
4 Quadrants of Health IT
Strategic
HIEBusiness
Intelligence
CPOE
CDSS
g
ClinicalAdministrative
CPOE
EHRsVMILIS
EHRsERP
VMI
ADT
Operational (Theera-Ampornpunt [unpublished], 2010)
Health Informatics
As A FieldAs A Field
Biomedical/Health Informatics
• “[T]he field that is concerned with the optimal use of
information, often aided by the use of technology, to
improve individual health health care public health improve individual health, health care, public health,
and biomedical research” (Hersh, 2009)
• “[T]he application of the science of information as
d t l i t bl f bi di l data plus meaning to problems of biomedical
interest” (Bernstam et al, 2010)
Data-Information-Knowledge-Wisdom Pyramid
WisdomWisdom
KnowledgeKnowledge
Information
Data
Task-Oriented View
Collection Processing UtilizationCollection Processing Utilization
StorageCommunication/Dissemination/Presentation
M/B/H Informatics As A Field
(Shortliffe, 2002)
M/B/H Informatics and Other Fields
Social Sciences (Psychology Statistics &
Cognitive & Decision
(Psychology, Sociology,
Linguistics, Law & Ethics)
Statistics & Research Methods
Medical Sciences &Decision
ScienceSciences & Public Health
Engineering Management
Biomedical/Computer & Library Science,Biomedical/Health
Informatics
Computer & Information Science
Library Science, Information Retrieval, KM
And More!
Balanced Focus of Informatics
People
Techno‐logyProcess
Informatics & Engineering
Process focusProcess-focus
• Industrial Engineering / Operations Research g g p
& Management / Business Process Reengineering
Technology-focus
• Computer & Software EngineeringComputer & Software Engineering
• Biomedical Engineering
• Electrical Engineering
Summary
• Healthcare will benefit from health IT through• Healthcare will benefit from health IT through
– Information deliveryy
– Process improvement
• The world is moving toward health IT
• H lth i f ti d ti f i i & • Health informatics needs expertise from engineering &
other fields
• Health informatics will be crucial to future’s healthcare
Let’s Build The Let s Build The
Next Generation’s
H lth !Healthcare!
References
• Bernstam EV, Smith JW, Johnson TR. What is biomedical informatics? J Biomed Inform. 2010 Feb;43(1):104‐10.
• Blumenthal D. Launching HITECH. N Engl J Med. 2010 Feb 4;362(5):382‐5.• Blumenthal D, DesRoches C, Donelan K, Ferris T, Jha A, Kaushal R, Rao S, Rosenbaum S.
Health information technology in the United States: the information base for progress [Internet]. Princeton (NJ): Robert Wood Johnson Foundation; 2006
• Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them. A d M d 2003 A 78(8) 775 80 81 A il bl fAcad Med. 2003 Aug;78(8):775‐80. 81 p. Available from: http://www.rwjf.org/files/publications/other/EHRReport0609.pdf
• Friedman CP. A "fundamental theorem" of biomedical informatics. J Am Med Inform Assoc. 2009 Apr;16(2):169‐702009 Apr;16(2):169 70.
• Hersh W. A stimulus to define informatics and health information technology. BMC Med Inform Decis Mak. 2009;9:24.
• Hsiao C, Beatty PC, Hing ES, Woodwell DA. Electronic medical record/electronic health recordHsiao C, Beatty PC, Hing ES, Woodwell DA. Electronic medical record/electronic health record use by office‐based physicians: United States, 2008 and preliminary 2009 [Internet]. 2009 [cited 2010 Apr 12]; Available from: http://www.cdc.gov/nchs/data/hestat/emr_ehr/emr_ehr.pdf
References
• Institute of Medicine, Board on Health Care Services, Committee on Data Standards for f b l f l h l h d l [ ]Patient Safety. Key Capabilities of an electronic health record system: letter report [Internet].
Washington, DC: National Academy of Sciences; 2003. 31 p. Available from: http://www.nap.edu/catalog/10781.html
• Jha AK DesRoches CM Campbell EG Donelan K Rao SR Ferris TG Shields A Rosenbaum S• Jha AK, DesRoches CM, Campbell EG, Donelan K, Rao SR, Ferris TG, Shields A, Rosenbaum S, Blumenthal D. Use of electronic health records in U.S. hospitals. N Engl J Med. 2009;360(16):1628‐38.
• Jha AK, Doolan D, Grandt D, Scott T, Bates DW. The use of health information technology in , , , , gyseven nations. Int J Med Inform. 2008;77(12):848‐54.
• Klein JG. Five pitfalls in decisions about diagnosis and prescribing. BMJ. 2005 Apr 2;330(7494):781‐3.
• Mamede S, van Gog T, van den Berge K, Rikers RM, van Saase JL, van Guldener C, Schmidt HG. Effect of availability bias and reflective reasoning on diagnostic accuracy among internal medicine residents. JAMA. 2010 Sep 15:304(11):1198‐203.Mill RA M i FE Th d i f h "G k O l " d l f di l di i• Miller RA, Masarie FE. The demise of the "Greek Oracle" model for medical diagnostic systems. Methods Inf Med. 1990 Jan;29(1):1‐2.
• Schoen C, Osborn R, Huynh PT, Doty M, Puegh J, Zapert K. On the front lines of care: primary care doctors’ office systems experiences and views in seven countries Health Affcare doctors office systems, experiences, and views in seven countries. Health Aff(Millwood). 2006;25(6):w555‐71.
• Shortliffe EH. JBI status report. Journal of Biomedical Informatics. 2002 Oct;35(5‐6):279‐80.