IT for MDs (Part 2)

IT for MDs (Part 2)

Nawanan Theera‐Ampornpunt, MD, PhD

SlideShare.net/Nawanan

Feb. 20, 2013Faculty of Medicine Ramathibodi Hospital

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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

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Health IT: What’s In A Word?

HealthInformationTechnology

Goal

Value‐Add

Tools

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Fundamental Theorem of Informatics

(Friedman, 2009)(Friedman, 2009)

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Health IT Applications in Hospitals

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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

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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

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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)

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Just electronic documentation?

Or do they have other values?

EHR Systems

Diag‐nosis

History & PE

Treat‐ments ...

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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)

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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)

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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


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Example of “Alerts & Reminders”

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Evidence‐based knowledge sources e.g. drug database, literature

Simple UI designed to help clinical decision making E.g., Abnormal Lab Highlights


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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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External Memory

Knowledge Data

Long Term Memory

Knowledge Data

Inference

DECISION

PATIENT

Perception

Attention

WorkingMemory

CLINICIANAbnormal lab highlights

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External Memory

Knowledge Data

Long Term Memory

Knowledge Data

Inference

DECISION

PATIENT

Perception

Attention

WorkingMemory

CLINICIANDrug‐Allergy

Checks

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External Memory

Knowledge Data

Long Term Memory

Knowledge Data

Inference

DECISION

PATIENT

Perception

Attention

WorkingMemory

CLINICIANDrug‐Drug Interaction Checks

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External Memory

Knowledge Data

Long Term Memory

Knowledge Data

Inference

DECISION

PATIENT

Perception

Attention

WorkingMemory

CLINICIANClinical Practice

Guideline Reminders

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External Memory

Knowledge Data

Long Term Memory

Knowledge Data

Inference

DECISION

PATIENT

Perception

Attention

WorkingMemory

CLINICIAN

Diagnostic/Treatment Expert Systems

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CDSS as a replacement or supplement of clinicians? The demise of the “Greek Oracle” model (Miller & Masarie, 1990)


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

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Some risks Alert fatigue


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Workarounds

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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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Ash et al. (2004)

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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)

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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)

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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)

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Campbell et al. (2006)

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Campbell et al. (2006)

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Koppel et al. (2005)

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Koppel et al. (2005)

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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

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Health IT Successes & Failures

Kaplan & Harris‐Salamone (2009)

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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


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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


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Health IT Change Management

Lorenzi & Riley (2000)

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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

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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)

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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

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Balanced Focus of Informatics

People

Techno‐logyProcess

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A Physician’s Story...

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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

[email protected]

www.tc.umn.edu/~theer002

groups.google.com/group/ThaiHealthIT

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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

Health & Medicine

IT for MDs (Part 2)