IT in Hospitals
102
IT in Hospitals Nawanan Theera‐Ampornpunt, MD, PhD SlideShare.net/Nawanan Mar 14, 2012 Ramathibodi Hospital Administration School
-
Upload
nawanan-theera-ampornpunt -
Category
Health & Medicine
-
view
1.987 -
download
0
description
A presentation in March 2012 presented at the Ramathibodi Hospital Administration School, Faculty of Medicine Ramathibodi Hospital, Mahidol University in Bangkok, Thailand. Presentation partly in English and partly in Thai.
Transcript of IT in Hospitals
IT in Hospitals
Nawanan Theera‐Ampornpunt, MD, PhD
SlideShare.net/Nawanan
Mar 14, 2012Ramathibodi Hospital Administration School
2
A Few Words About Me...
2003 M.D. (1st-Class Honors) Ramathibodi
2009 M.S. (Health Informatics) University of Minnesota
2011 Ph.D. (Health Informatics) University of Minnesota
Currently• Medical Systems Analyst, Health Informatics Division, Ramathibodi
Contacts
SlideShare.net/Nawanan
www.tc.umn.edu/~theer002
groups.google.com/group/ThaiHealthIT
3
Healthcare & Health ITHealth IT Applications in HospitalsHealth Informatics As A Field IT Management
Outline
4
Health care & Health IT
5
Manufacturing
Image Source: Guardian.co.uk
6
Banking
Image Source: Cablephet.com
7
Health care
ER ‐ Image Source: nj.com
8
Life‐or‐DeathMany & varied stakeholders Strong professional values Evolving standards of care Fragmented, poorly‐coordinated systems Large, ever‐growing & changing body of knowledge
High volume, low resources, little time
Why Health care Isn’t Like Any Others?
9
Large variations & contextual dependence
Why Health care Isn’t Like Any Others?
Input Process Output
Patient Presentation
Decision‐Making
Biological Responses
10
But...Are We That Different?
Input Process Output
Transfer
Banking
Value‐Add‐ Security‐ Convenience‐ Customer Service
Location A Location B
11
Input Process Output
Assembling
Manufacturing
Raw Materials
Finished Goods
Value‐Add‐ Innovation‐ Design‐ QC
But...Are We That Different?
12
But...Are We That Different?
Input Process Output
Patient Care
Health care
Sick Patient Well Patient
Value‐Add‐ Technology & medications‐ Clinical knowledge & skills‐ Quality of care; process improvement‐ Information
13
Information is Everywhere in Health Care
14
Various Forms of Health IT
Hospital Information System (HIS) Computerized Provider Order Entry (CPOE)
Electronic Health Records (EHRs)
Picture Archiving and Communication System
(PACS)
15
Still Many Other Forms of Health IT
m‐Health
Health Information Exchange (HIE)
Biosurveillance
Information RetrievalTelemedicine &
Telehealth
Images from Apple Inc., Geekzone.co.nz, Google, PubMed.gov, and American Telecare, Inc.
Personal Health Records (PHRs)
16
Why Adopting Health IT?
“To Computerize”“To Go paperless”
“Digital Hospital”
“To Modernize”
“To Get a HIS”
“To Have EMRs”
“To Share data”
17
“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)
Some Quotes
18
Health IT: What’s In A Word?
HealthInformationTechnology
Goal
Value‐Add
Tools
19
SafetyTimelinessEffectivenessEfficiencyEquityPatient‐centeredness
Dimensions of Quality Healthcare
(IOM, 2001)
20
Guideline adherenceBetter documentationPractitioner decision making or process of care
Medication safetyPatient surveillance & monitoring
Patient education/reminder
Value of Health IT
21
Fundamental Theorem of Informatics
(Friedman, 2009)(Friedman, 2009)
22
Is There A Role for Health IT?
(IOM, 2000)
23
Landmark IOM Reports
(IOM, 2001)(IOM, 2000)
24
Humans are not perfect and are bound to make errors
Highlight problems in the U.S. health care system that systematically contributes to medical errors and poor quality
Recommends reform that would change how health care works and how technology innovations can help improve quality/safety
Landmark IOM Reports: Summary
25
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 large Short time during a visit Practice guidelines are put “on‐the‐shelf” “To err is human”
Why We Need Health IT
26
Perception errors
To Err Is Human
Image Source: interaction‐dynamics.com
27 Image Source: aafp.org
Lack of Attention
To Err Is Human
28
Cognitive Errors - Example: Decoy Pricing
The Economist Purchase Options
• Economist.com subscription $59• Print subscription $125• Print & web subscription $125
Ariely (2008)
16084
The Economist Purchase Options
• Economist.com subscription $59• Print & web subscription $125
6832
# of People
# of People
To Err Is Human
29
It already happens....(Mamede et al., 2010; Croskerry, 2003; Klein, 2005)
What if health IT can help?
What If This Happens in Healthcare?
30
Adoption of Health IT: Assumptions
Adoption Use Outcomes
31
“...We will make wider use of electronic records and other health information technology, to help control
costs and reduce dangerous medical errors.”
U.S.’s Efforts on Health IT Adoption
Source: Wikisource.org Image Source: Wikipedia.org
President George W. BushSixth State of the Union Address, January 31, 2006
?
32
1991: 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” regulations
Public Policy in Informatics: A US’s Case
33
U.S. Adoption of Health IT
• U.S. lags behind other Western countries (Schoen et al, 2006;Jha et al, 2008)
• Money and misalignment of benefits is the biggest reason
Ambulatory (Hsiao et al, 2009) Hospitals (Jha et al, 2010)
Basic EHRs w/ notes 9.2%Comprehensive EHRs 2.7%CPOE for medications 34%
34
We Need “Change”
“...we need to upgrade our medical records by switching from a paper to an electronic system of record keeping...”
President Barack ObamaJune 15, 2009
35
“...Our recovery plan will invest in electronic health records and new technology
that will reduce errors, bring down costs, ensure privacy, and save lives.”
President Barack ObamaAddress to Joint Session of Congress
February 24, 2009
The Birth of “Meaningful Use”
Source: WhiteHouse.gov
36
Contains HITECH Act(Health Information Technology for Economic and Clinical Health Act)
~ 20 billion dollars for Health IT investments
Incentives & penalties for providers
American Recovery & Reinvestment Act
37
U.S. National Leadership
David Blumenthal, MD, MPPNational Coordinator for Health Information Technology (2009 - 2011)
Farzad Mostashari, MD, ScMNational Coordinator for Health Information Technology (2011 - Present)
Robert Kolodner, MDNational Coordinator for Health Information Technology (2006 - 2009)
David Brailer, MD, PhDNational Coordinator for Health Information Technology (2004 - 2007)
38
What is in the HITECH Act?
(Blumenthal, 2010)
39
“Meaningful Use”
“Meaningful Use” of a PumpkinPumpkin
Image Source & Idea Courtesy of Pat Wise at HIMSS, Oct. 2009
40
“Meaningful Use” of Health IT
Stage 1‐ Electronic capture of health information‐ Information sharing‐ Data reporting
Stage 2
Use of EHRsto improve processes of care
Stage 3
Use of EHRs to improve outcomes
Better Health
(Blumenthal, 2010)
41
Adoption Studies: Descriptive AspectPongpirul et al. (2004)
2011
Theera‐Ampornpunt(unpublished)
2004
42
Health IT Applications in Hospitals
43
Master Patient Index (MPI)
Admit‐Discharge‐Transfer (ADT) Electronic Health Records (EHRs) Computerized Physician Order Entry (CPOE)
Clinical Decision Support Systems (CDSSs) Picture Archiving and Communication System (PACS) Nursing applications
Enterprise Resource Planning (ERP)
Enterprise‐wide Hospital IT
44
Pharmacy applications Laboratory Information System (LIS) Radiology Information System (RIS) Specialized applications (ER, OR, LR, Anesthesia, Critical Care, Dietary Services, Blood Bank)
Incident management & reporting system
Departmental IT
45
Workflow
Hospital Information System
Master Patient
Index (MPI)
ADT
Scheduling
Order
Pharmacy IS
Operation Theatre
Billing
Clinical Notes
LIS
RIS
PACS
CCIS
Medical Records
Portals
Modified from Dr. Artit Ungkanont’s slide
46
HIT Systems (Inpatient)Clinical Decision Support:“Any system designed to improve clinical decision making related to diagnostic or therapeutic processes of care.”
From Dr. Artit Ungkanont’s slide
47
EHRs & HIS
The Challenge ‐ Knowing What It Means
Electronic Medical Records (EMRs)
Computer‐Based Patient Records
(CPRs)
Electronic Patient Records (EPRs)
Electronic Health Records (EHRs)
Personal Health Records (PHRs)
Hospital Information System
(HIS)
Clinical Information System (CIS)
48
Just electronic documentation?
Or do they have other values?
EHR Systems
Diag‐nosis
History & PE
Treat‐ments ...
49
Computerized Medication Order Entry Computerized Laboratory Order Entry Computerized Laboratory Results Physician Notes Patient Demographics Problem Lists Medication Lists Discharge Summaries Diagnostic Test Results Radiologic Reports
Functions that Should Be Part of EHR Systems
(IOM, 2003; Blumenthal et al, 2006)
50
Computerized Physician Order Entry (CPOE)
51
Values
No handwriting!!! Structured data entry: Completeness, clarity, fewer mistakes (?)
No transcription errors! Entry point for CDSSs Streamlines workflow, increases efficiency
Computerized Physician Order Entry (CPOE)
52
The real place where most of the values of health IT can be achieved
Expert systemsBased on artificial intelligence, machine learning, rules, or statistics
Examples: differential diagnoses, treatment options
Clinical Decision Support Systems (CDSSs)
(Shortliffe, 1976)
53
Alerts & reminders Based on specified logical conditions Examples:Drug‐allergy checksDrug‐drug interaction checksDrug‐disease checksDrug‐lab checksDrug‐formulary checks Reminders for preventive services or certain actions (e.g. smoking cessation)
Clinical practice guideline integration
Clinical Decision Support Systems (CDSSs)
54
Evidence‐based knowledge sources e.g. drug database, literature
Simple UI designed to help clinical decision making E.g., Abnormal Lab Highlights
Clinical Decision Support Systems (CDSSs)
55
A Basic Architecture of A CDSS
User User Interface
Patient Data
Inference Engine
Knowledge BaseOther Data
• Rules• Statistical data• Literature• Etc.
• System states• Epidemiological/surveillance data• Etc.
56
Clinical Decision Support Systems (CDSSs)
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
WorkingMemory
CLINICIAN
From a teaching slide by Don Connelly, 2006
57
Clinical Decision Support Systems (CDSSs)
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
WorkingMemory
CLINICIANAbnormal lab highlights
58
Clinical Decision Support Systems (CDSSs)
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
WorkingMemory
CLINICIANAbnormal lab highlights
59
Clinical Decision Support Systems (CDSSs)
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
WorkingMemory
CLINICIANDrug‐Allergy
Checks
60
Clinical Decision Support Systems (CDSSs)
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
WorkingMemory
CLINICIANDrug‐Drug Interaction Checks
61
Clinical Decision Support Systems (CDSSs)
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
WorkingMemory
CLINICIANClinical Practice
Guideline Reminders
62
Clinical Decision Support Systems (CDSSs)
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
WorkingMemory
CLINICIAN
Diagnostic/Treatment Expert Systems
63
CDSS as a replacement or supplement of clinicians? The demise of the “Greek Oracle” model (Miller & Masarie, 1990)
Clinical Decision Support Systems (CDSSs)
The “Greek Oracle” Model
The “Fundamental Theorem”
(Friedman, 2009)
64
Some risks Alert fatigue
Clinical Decision Support Systems (CDSSs)
65
Workarounds
66
Ordering Transcription Dispensing Administration
Health IT for Medication Safety
CPOEAutomatic Medication Dispensing
Electronic Medication
Administration Records (e‐MAR)
BarcodedMedication
Administration
BarcodedMedication Dispensing
67
Health Information Exchange (HIE)
Hospital A Hospital B
Clinic C
Government
Lab Patient at Home
68
Strategic
Operational
ClinicalAdministrative
4 Quadrants of Hospital IT
CPOE
ADT
LIS
EHRs
CDSS
HIE
ERP
Business Intelligence
VMI
PHRs
MPIWord
Processor
Social Media
PACS
69
Health Informatics As A Field
70
“[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, and biomedical research” (Hersh, 2009)
“[T]he application of the science of information as data plus meaning to problems of biomedical interest” (Bernstam et al, 2010)
Biomedical/Health Informatics
71
Wisdom
Knowledge
Information
Data
DIKW Pyramid
72
Task‐Oriented View
Collection Processing
Storage
Utilization
Communication/Dissemination/Presentation
73
M/B/H Informatics As A Field
(Shortliffe, 2002)
74
M/B/H Informatics and Other Fields
Biomedical/Health
Informatics
Computer & Information
Science
Engineering
Cognitive & Decision Science
Social Sciences (Psychology, Sociology, Linguistics,
Law & Ethics)
Statistics & Research Methods
Medical Sciences &
Public Health
Management
Library Science,
Information Retrieval, KM
And More!
75
Balanced Focus of Informatics
People
Techno‐logyProcess
76
IT Management
77
Health IT: ของดี (อาจจะ) มีประโยชน์(แต่ก็อาจมีโทษ)
บริบท (local contexts) มีความสําคญัต้องมีการบริหารจัดการที่เหมาะสม
ประเด็นพิจารณา
อะไรคือบริบทที่เกี่ยวข้อง?จะจัดการมันอย่างไร?
ความเดิมตอนที่แล้ว...
78 The sailboat image source: Uwe Kils via http://en.wikipedia.org/wiki/Sailing
The destination
The boatThe sailor(s) & people on board
The tailwind The headwind
The direction
The speed
The past journey
The sea
The sail
The current location
Context
79
Direction & Destination
รพ.มหาวิทยาลัย 900 เตียงVision เป็นโรงพยาบาลชั้นนําของ
ภูมิภาคเอเชียทีม่ีความเป็นเลศิใน
ด้านบริการ การศึกษา และวิจัย
รพ.เอกชน 200 เตียงVision เป็นโรงพยาบาล High Tech
High Touch ชั้นนําของประเทศ
80
“The Sail”
Carr (2004) Carr (2003)
81
Strategic
Operational
ClinicalAdministrative
4 Quadrants of Hospital IT
CPOE
ADT
LIS
EHRs
CDSS
HIE
ERP
Business Intelligence
VMI
PHRs
MPIWord
Processor
Social Media
PACS
82
Resources/capabilities
Valuable ?
Non-Substitutable?
Rare ?
Inimitable ?
NoCompetitive
Disadvantage
Yes
No Competitivenecessity
NoCompetitive
parity
Yes
Yes
NoPreemptiveadvantage
Yes
Sustainablecompetitiveadvantage
From a teaching slide by Nelson F. Granados, 2006 at University of Minnesota Carlson School of Management
IT As A Strategic Advantage
83
“The Sail”
รพ.มหาวิทยาลัย 900 เตียง
Vision เป็นโรงพยาบาลชั้นนําของภูมิภาคเอเชียทีม่ีความเป็นเลศิในด้านบริการ การศึกษา และวิจัย
Current IT Environment เป็น รพ.แรกๆ ที่มี HIS ซึ่งพัฒนาเอง และ
ต่อยอดจาก MPI, ADT ไปสู่ CPOE (แต่ยังขาด advanced CDSS) ระบบ HIS เข้ากับ workflow ของ รพ. เป็นอย่างดี
ปัจจุบัน ระบบ HIS ยังใช้เทคโนโลยีเดียวกับช่วงที่พัฒนาใหม่ๆ (20 ปีก่อน) เป็นหลัก มีการนําเทคโนโลยีใหม่ๆ มาใช้อย่างช้าๆ
รพ.เอกชน 200 เตียงVision เป็นโรงพยาบาล High Tech
High Touch ชั้นนําของประเทศ
Current IT Environment มี MPI, ADT, EHRs, CPOE แต่ยังมี
CDSS จํากัด
ยังไม่มี Customer Relationship Management
(CRM)
ยังไม่มี Personal Health Records (PHRs)
84
Resources/capabilities
Valuable ?
Non-Substitutable?
Rare ?
Inimitable ?
NoCompetitive
Disadvantage
Yes
No Competitivenecessity
NoCompetitive
parity
Yes
Yes
NoPreemptiveadvantage
Yes
Sustainablecompetitiveadvantage
From a teaching slide by Nelson F. Granados, 2006 at University of Minnesota Carlson School of Management
IT As A Strategic Advantage
85
“The Sailors”
People
Techno‐logyProcess
86
“The Sailors”
รพ.มหาวิทยาลัย 900 เตียง
บุคลากรมีอายุเฉลีย่ 42 ปี (range 20-65)
แผนก IT มีทั้งบุคลากรใหม่และทีเ่คยพัฒนาระบบ HIS ตั้งแต่แรกเริ่ม
แพทย์มีความเป็นตัวของตัวเองสูง, มักทํางานเอกชนด้วย, มี turn-over rate สูง
พยาบาลและวิชาชีพอื่นมักมองว่าแพทย์คืออภิสทิธิ์ชน และมีเรื่องถกเถียงกันบ่อยๆ
รพ.เอกชน 200 เตียง บุคลากรมีอายุเฉลีย่ 32 ปี
(range 20-57)
แผนก IT เข้มแข็ง
แพทย์ไม่ค่อยมี interaction กับ
บุคลากรอื่น, รายได้เป็นแรงดึงดูดหลัก
ผู้บริหารได้รับการยอมรับจากบุคลากร
ทุกวิชาชีพว่ามีวิสัยทัศน์และบริหารงาน
ได้ดี
87 The sailboat image source: Uwe Kils via http://en.wikipedia.org/wiki/Sailing
The destination
The boatThe sailor(s) & people on board
The tailwind The headwind
The direction
The speed
The past journey
The sea
The sail
The current location
Context
88
SizeResources StructuresWork Processes Facilities/Geography Etc.
“The Boat”
89
Target customers
Local competitiveness
Relationship of hospital to local players
Inter‐organizational collaboration
IT market environment
National/international trend
Regulations
Standard of care
Etc.
“The Sea”
90
Strengths Opportunities
Weaknesses Threats
“The Boat” “The Sea”
“The Tailwind” “The Tailwind”
“The Headwind” “The Headwind”
SWOT Analysis
91
IT Outsourcing Decision Tree
Does service offer competitive advantage?
Is external deliveryreliable and lower cost?
Keep Internal
Keep Internal
OUTSOURCE!
Yes
No
Yes
No
From a teaching slide by Nelson F. Granados, 2006
92
IT Outsourcing Decision Tree: Ramathibodi’s Case
Does service offer competitive advantage?
Is external deliveryreliable and lower cost?
Keep Internal
Keep Internal
OUTSOURCE!
Yes
No
Yes
No
Core HIS, CPOEStrategic advantages• Agility due to local workflow accommodations• Secondary data utilization (research, QI)• Roadmap to national leader in informatics
External delivery unreliable• Non‐Core HISExternal delivery higher cost• ERP maintenance/ongoing customization
ERP initial implementation,
PACS, RIS, Departmental
systems, IT Training
93Image source: Jeremy Kemp via http://en.wikipedia.org/wiki/Hype_cycle
http://www.gartner.com/technology/research/methodologies/hype‐cycle.jsp
Gartner Hype Cycle
94Rogers (2003)
Rogers’ Diffusion of Innovations: Adoption Curve
95
Communications of project plans & progresses
Workflow considerations
Management support of IT projects
Common visions
Shared commitment
Multidisciplinary user involvement
Project management
Training
Innovativeness
Organizational learning
Theera‐Ampornpunt (2009, 2011) [Unpublished]
Hospital IT Adoption Success Factors
96
Lorenzi & Riley (2004) Leviss (Editor) (2010)
Resources on Change Management
97
Healthcare is complex Health IT can benefit healthcare through
Information delivery
Process improvement
Empowering providers & patients
The world is moving toward health IT Health informatics is related to & relies on the field of IT, but they
are not the same Management of hospital IT is crucial to success
Know your organization (“context”)
Strategic mindset
Project & change management
Summary
98
Adoption Use Outcomes
Don’t forget our real aim...
Final Words...
99
Q & A...
Download Slides
SlideShare.net/Nawanan
Contacts
www.tc.umn.edu/~theer002
groups.google.com/group/ThaiHealthIT
100
Ariely D. Predictably irrational: the hidden forces that shape our decisions. New York City (NY):HarperCollins; 2008. 304 p.
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.
Carr NG. Does IT matter? Information technology and the corrosion of competitive advantage. Boston (MA):Harvard Business Press;2004. 208 p.
Carr NG. IT doesn’t matter. Harvard Bus Rev. 2003 May 1;81(5):41‐9. Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them. Acad
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):169‐70.
Gladwell M. The Tipping Point: how little things can make a big difference. New York City (NY):Little Brown;2000. 304 p.
Hersh W. A stimulus to define informatics and health information technology. BMC Med Inform Decis Mak. 2009;9:24.
Hersh W. Health care information technology: progress and barriers. JAMA. 2004 Nov 10:292(18):2273‐4.
References
101
Hsiao 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
Institute of Medicine, Board on Health Care Services, Committee on Data Standards for 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
Institute of Medicine, Committee on Quality of Health Care in America. To err is human: building a safer health system. Kohn LT, Corrigan JM, Donaldson MS, editors. Washington, DC: National Academy Press;2000. 287 p.
Institute of Medicine, Committee on Quality of Health Care in America. Crossing the quality chasm: a new health system for the 21st century. Washington, DC: National Academy Press; 2001. 337 p.
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 seven nations. Int J Med Inform. 2008;77(12):848‐54.
References
102
Klein JG. Five pitfalls in decisions about diagnosis and prescribing. BMJ. 2005 Apr 2;330(7494):781‐3. Leviss J (editor). H.I.T. or Miss: lessons learned from health information technology implementations.
Chicago (IL):AHIMA Press;2010. Lorenzi NM, Riley RT. Managing technological change: organizational aspects of health informatics.
New York City (NY): Springer;2004. 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.
Miller RA, Masarie FE. The demise of the "Greek Oracle" model for medical diagnostic systems. Methods Inf Med. 1990 Jan;29(1):1‐2.
Pongpirul K, Sriratana S. Computerized information system in hospitals in Thailand: a national survey. J Health Sci. 2005 Sep‐Oct;14(5):830‐9. Thai.
Rogers EM. Diffusion of innovations. 5th ed. New York City (NY): Free Press;2003. 551 p. 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 Aff (Millwood). 2006;25(6):w555‐71.
Shortliffe EH. JBI status report. Journal of Biomedical Informatics. 2002 Oct;35(5‐6):279‐80. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: toward a
unified view. MIS Quart. 2003 Sep;27(3):425‐78. Background designs are the property of Geetesh Bajaj. Used with permission. © Copyright, Geetesh
Bajaj. All Rights Reserved.
References
![[Webinar] Teleneurology: Why it Works for Rural Hospitals](https://static.fdocuments.us/doc/165x107/58a51a951a28ab143f8b4bf5/webinar-teleneurology-why-it-works-for-rural-hospitals.jpg)
