it for mds (part 2)
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IT for MDs (Part 2)
Nawanan Theera‐Ampornpunt, MD, PhD
SlideShare.net/Nawanan
Feb. 20, 2013Faculty of Medicine Ramathibodi Hospital
2
Information is everywhere in medicine
Computerizing health care is difficult because of its complexity
Health IT has a role because “To Err Is Human”
Health IT is just a tool. Whether it improves patient care and outcomes depends on its design and implementation
Recap from Part 1
3
Health IT: What’s In A Word?
HealthInformationTechnology
Goal
Value‐Add
Tools
4
Fundamental Theorem of Informatics
(Friedman, 2009)(Friedman, 2009)
5
Health IT Applications in Hospitals
6
Master Patient Index (MPI)
Admission‐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
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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
8
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
9
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)
10
Just electronic documentation?
Or do they have other values?
EHR Systems
Diag‐nosis
History & PE
Treat‐ments ...
11
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)
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Computerized Physician Order Entry (CPOE)
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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)
14
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)
15
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)
16
Example of “Alerts & Reminders”
17
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)
18
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
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Clinical Decision Support Systems (CDSSs)
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
WorkingMemory
CLINICIANAbnormal lab highlights
20
Clinical Decision Support Systems (CDSSs)
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
WorkingMemory
CLINICIANDrug‐Allergy
Checks
21
Clinical Decision Support Systems (CDSSs)
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
WorkingMemory
CLINICIANDrug‐Drug Interaction Checks
22
Clinical Decision Support Systems (CDSSs)
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
WorkingMemory
CLINICIANClinical Practice
Guideline Reminders
23
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
24
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)
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Ordering Transcription Dispensing Administration
Health IT for Medication Safety
CPOEAutomatic Medication Dispensing
Electronic Medication
Administration Records (e‐MAR)
BarcodedMedication
Administration
BarcodedMedication Dispensing
26
Some risks Alert fatigue
Clinical Decision Support Systems (CDSSs)
27
Workarounds
28
Unintended Consequences of Health IT
“Unanticipated and unwanted effect of health IT implementation” (ucguide.org)
Resources www.ucguide.org Ash et al. (2004) Campbell et al. (2006) Koppel et al. (2005)
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Unintended Consequences of Health IT
Ash et al. (2004)
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Unintended Consequences of Health IT
Errors in the process of entering and retrieving information A human‐computer interface that is not suitable for a highly
interruptive use context Causing cognitive overload by overemphasizing structured and
“complete” information entry or retrieval Structure Fragmentation Overcompleteness
Ash et al. (2004)
31
Unintended Consequences of Health IT
Errors in the communication and coordination process Misrepresenting collective, interactive work as a linear, clearcut,
and predictable workflow Inflexibility Urgency Workarounds Transfers of patients
Misrepresenting communication as information transfer Loss of communication Loss of feedback Decision support overload Catching errors
Ash et al. (2004)
32
Unintended Consequences of Health IT
Errors in the communication and coordination process Misrepresenting collective, interactive work as a linear, clearcut,
and predictable workflow Inflexibility Urgency Workarounds Transfers of patients
Misrepresenting communication as information transfer Loss of communication Loss of feedback Decision support overload Catching errors
Ash et al. (2004)
33
Unintended Consequences of Health IT
Campbell et al. (2006)
34
Unintended Consequences of Health IT
Campbell et al. (2006)
35
Unintended Consequences of Health IT
Koppel et al. (2005)
36
Unintended Consequences of Health IT
Koppel et al. (2005)
37
Critical Success Factors in Health IT Projects
Theera‐Ampornpunt (2011)
Communications of plans & progressesPhysician & non‐physician user involvementAttention to workflow changesWell‐executed project managementAdequate user trainingOrganizational learningOrganizational innovativeness
38
Health IT Successes & Failures
Kaplan & Harris‐Salamone (2009)
39
Health IT Successes & Failures
What success is Different ideas and definitions of success Need more understanding of different stakeholder views & more longitudinal and qualitative studies of failure
What makes it so hard Communication, Workflow, & Quality Difficulties of communicating across different groups makes it harder to identify requirements and understand workflow
Kaplan & Harris‐Salamone (2009)
40
Health IT Successes & Failures
What We Know—Lessons from Experience Provide incentives, remove disincentives Identify and mitigate risks Allow resources and time for training, exposure, and learning to input data
Learn from the past and from others
Kaplan & Harris‐Salamone (2009)
41
Health IT Change Management
Lorenzi & Riley (2000)
42
Health IT Change Management
Lorenzi & Riley (2000)
43
Health IT Change Management
Lorenzi & Riley (2000)
44
Health IT Change Management
Lorenzi & Riley (2000)
45
Considerations for a successful implementation of CPOE
Ash et al. (2003)
ConsiderationsMotivation for implementationCPOE vision, leadership, and personnelCostsIntegration: Workflow, health care processesValue to users/Decision support systemsProject management and staging of implementationTechnologyTraining and Support 24 x 7Learning/Evaluation/Improvement
46
Minimizing MD’s Change Resistance
Involve physician champions Create a sense of ownership through communications & involvement
Understand their values Be attentive to climate in the organization Provide adequate training & support
Ash et al. (2003)
47
Reasons for User Involvement
Better understanding of needs & requirements Leveraging user expertise about their tasks & how organization functions
Assess importance of specific features for prioritization
Users better understand project, develop realistic expectations
Venues for negotiation, conflict resolution
Sense of ownership Pare & Sicotte (2006): Physician ownership
important for clinical information systems
Ives & Olson (1984)
48Image source: Jeremy Kemp via http://en.wikipedia.org/wiki/Hype_cycle
http://www.gartner.com/technology/research/methodologies/hype‐cycle.jsp
Gartner Hype Cycle
49Rogers (2003)
Rogers’ Diffusion of Innovations: Adoption Curve
50
Balanced Focus of Informatics
People
Techno‐logyProcess
51
A Physician’s Story...
52
Health IT applications vary in their “mechanisms of actions” (improvements in outcomes).
Health IT can lead to “unintended consequences” and bad outcomes if poorly designed, implemented, or managed.
Realizing benefits of health IT depends on critical success factors, mostly management aspects (including change management, project management)
Summary
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Q & A...
Download Slides
SlideShare.net/Nawanan
Contacts
nawanan.the@mahidol.ac.th
www.tc.umn.edu/~theer002
groups.google.com/group/ThaiHealthIT
54
Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: the nature of patient care information system‐related errors. J Am Med Inform Assoc. 2004 Mar‐Apr;11(2):104‐12.
Ash JS, Stavri PZ, Kuperman GJ. A consensus statement on considerations for a successful CPOE implementation. J Am Med Inform Assoc. 2003 May‐Jun;10(3):229‐34.
Campbell, EM, Sittig DF, Ash JS, et al. Types of Unintended Consequences Related to Computerized Provider Order Entry. J Am Med Inform Assoc. 2006 Sep‐Oct; 13(5): 547‐556.
Friedman CP. A "fundamental theorem" of biomedical informatics. J Am Med Inform Assoc. 2009 Apr;16(2):169‐70.
References
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Ives B, Olson MH. User involvement and MIS success: a review of research. Manage Sci. 1984 May;30(5):586‐603.
Kaplan B, Harris‐Salamone KD. Health IT success and failure: recommendations from the literature and an AMIA workshop. J Am Med Inform Assoc. 2009 May‐Jun;16(3):291‐9.
Koppel R, Metlay JP, Cohen A, Abaluck B, Localio AR, Kimmel SE, Strom BL. Role of computerized physician order entry systems in facilitating medication errors. JAMA. 2005 Mar 9;293(10):1197‐203.
Lorenzi NM, Riley RT. Managing change: an overview. J Am Med Inform Assoc. 2000 Mar‐Apr;7(2):116‐24.
References
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Miller RA, Masarie FE. The demise of the "Greek Oracle" model for medical diagnostic systems. Methods Inf Med. 1990 Jan;29(1):1‐2.
Rogers EM. Diffusion of innovations. 5th ed. New York City (NY): Free Press;2003. 551 p.
Riley RT, Lorenzi NM. Gaining physician acceptance of information technology systems. Med Interface. 1995 Nov;8(11):78‐80, 82‐3.
Theera‐Ampornpunt N. Thai hospitals' adoption of information technology: a theory development and nationwide survey [dissertation]. Minneapolis (MN): University of Minnesota; 2011 Dec. 376 p.
References
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