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Experience in improving healthcare decision-making with health IT: integrating theory, research, and practice. Matthew Samore, MD VA Salt Lake City Health Care System Professor of Internal Medicine Adjunct Professor of Biomedical Informatics University of Utah. Acknowledgments.

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slide1

Experience in improving healthcare decision-making with health IT: integrating theory, research, and practice

Matthew Samore, MD

VA Salt Lake City Health Care System

Professor of Internal Medicine

Adjunct Professor of Biomedical Informatics

University of Utah

acknowledgments
Acknowledgments
  • Michael Rubin, MD, PhD
  • Kim Bateman, MD
  • Brian Sauer, PhD
  • Lucy Savitz, PhD
  • Tom Greene, PhD
  • R. Scott Evans, PhD
  • Randall Rupper, MD, MPH
  • Salt Lake VA Informatics, Decision Enhancement, ,and Surveillance (IDEAS) Center Selected Investigators and Collaborators
    • Jonathan Nebeker, MD
    • Charlene Weir, PhD
    • Frank Drews, PhD
  • Partners: University of Utah, VA Salt Lake City, Healthinsight, CaduRx, Intermountain Healthcare
  • Funding:
    • VA HSR&D REA 08-264
    • AHRQ R01 HS15413
    • AHRQ 1R18HS017308-01
thesis of this talk
Thesis of this talk
  • Theory and models provide scientific underpinnings for generalization
    • Which supports comparative effectiveness research
  • For health services research and epidemiology:
    • Use of models understood
  • For clinical decision support:
    • Not so much
  • Health information technology ≠ informatics
    • Sub-disciplines such as cognitive informatics crucial
more succinctly expressed
More succinctly expressed:
  • “The difference between theory and practice is that in theory there is no difference but in practice there is”
statement of the problem
Statement of the problem:
  • “A disproportionate amount of literature on the benefits [of health information technology] that have been realized comes from a small set of early-adopter institutions that implemented internally developed health information technology systems..”

Chaudhry et. al. Ann Intern Med. 2006;144:742-752

addressing generalizability
Addressing generalizability
  • In what contexts will effects generalize?
  • What accounts for variability in results?
  • Why are impacts lower in magnitude or narrower in scope in larger trials compared to single institution studies?
  • How to incorporate information about implementation, adoption, formative evaluation?
relevance to comparative effectiveness research cer
Relevance to comparative effectiveness research (CER)
  • CER priorities
    • Directly focused on health information technology
      • Compare the effectiveness of alternative redesign strategies—using decision support capabilities, electronic health records, personal health records
    • Indirectly tied to health information technology
      • Compare the effectiveness of various strategies
        • To control MRSA
        • To control healthcare associated infection
        • To enhance patients’ adherence to medication regimens
addressing cer challenges
Addressing CER challenges
  • Need to explicitly formulate causal question
    • Determining identifiability
    • Defining level of inference
    • Validating methods to reduce bias
conceptual frameworks theory
Conceptual frameworks (THEORY)
  • Natural & engineered systems
    • Co-evolution
  • Cognitive processing
    • Information overload ↔ fit-to-workflow
  • Cyclical models of control
    • Feedback and feed-forward
system co evolution
System co-evolution
  • Fundamental theorem in informatics
    • C. Friedman J Am Med Inform Assoc. 2009;16:169-170
  • Proposed modification:
    • Computers plus humans create a distinct socio-technical system
      • Characteristics are not equivalent to other industries
relevance
Relevance
  • Level of inference needed to assess causal effect of health information technology:
    • Socio-technical system
  • Potential benefits of simulation
cognitive processing
Cognitive processing
  • Motivation, mental models, tasks, goals
    • Influenced by social context
  • Lack of fit-to-workflow experienced as:
    • Information overload
    • Interruptions
relevance1
Relevance
  • Cognitive informatics methods
    • Task analysis
    • Direct observation
    • Match implementation strategy to task complexity
second law of thermodynamics as applied to cognition
Second law of thermodynamics as applied to cognition:
  • Humans seek states of reduced cognitive effort
    • Workarounds
  • As cognitive load increases, automatic processing systems kick-in
for those who believe that there is a simpson s quote for every situation
For those who believe that there is a Simpson’s quote for every situation
  • “In this house, we obey the laws of thermodynamics!
  • Homer Simpson’s response when his daughter builds a perpetual motion machine in which energy increases with time
contextual control model
Contextual Control Model
  • Feed-back systems not sufficient
    • Need to anticipate and predict
      • Pure feedback systems subject to loss of stability
    • Time horizon is long in strategic control modes
  • Relevance
    • Link between decision support and surveillance
      • Surveillance contributes feedback and feed-forward capabilities
illustrative experience with decision support for antimicrobial prescribing
Illustrative experience with decision support for antimicrobial prescribing
  • Two different technologies studied
  • Clinical task:
    • Management of patient with acute respiratory infection in outpatient setting
      • Whether or not to prescribe an antibiotic
      • Choosing the antibiotic
      • Diagnostic label
      • Impact of perceived or actual patient demand
application of theory to practice implementation of electronic health records in rural settings
Application of theory to practiceimplementation of electronic health records in rural settings
  • Socio-technical system
    • Hook was electronic prescribing
  • Stepwise approach to adoption
  • Accommodating variation
    • Readiness to change
  • Social context and clinic culture
  • Encouraging play
  • Avoiding information overload
community intervention plus clinical decision support system
Community intervention plus clinical decision support system
  • Standalone algorithms on handheld computers
  • Community randomized trial

Effect on prescribing any antibiotic

Samore MH et. al. JAMA. 2005 Nov 9;294(18):2305-14.

clinical decision support system integrated with computerized clinic order entry
Clinical decision support system integrated with computerized clinic order entry
  • Algorithm usually triggered by ordering antibiotic
  • Clinic randomized trial

Effect on macrolide prescriptions

interpretation
Interpretation
  • Deciding whether to prescribe an antibiotic and choosing the drug involve different cognitive processes
  • Given that decision to prescribe an antibiotic is made
    • Possible to embed correct choice in workflow
  • Feed-forward decision support needed to impact the “is this a situation that warrants an antibiotic” decision
  • Relevant to drug-drug interaction alerting
recommendations and conclusions
Recommendations and conclusions
  • Models fundamental to translation of research into practice
  • Incorporation of theory and models into comparative effectiveness research
  • Role of simulation
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