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

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On September 14, 2009, Matthew Samore, MD made this presentation at the 2009 Annual Conference. Select to access the PowerPoint® presentation (537 KB).

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Slide 1

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

 

Slide 2

Acknowledgments

• Salt Lake VA Informatics, Decision Enhancement, ,and Surveillance (IDEAS) Center Selected Investigators and Collaborators
• Jonathan Nebeker, MD
• Charlene Weir, PhD
• Frank Drews, PhD
• Michael Rubin, MD, PhD
• Kim Bateman, MD
• Brian Sauer, PhD
• Lucy Savitz, PhD
• Tom Greene, PhD
• R. Scott Evans, PhD
• Randall Rupper, MD, MPH

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
 

 

Slide 3

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

 

Slide 4

More succinctly expressed:

"The difference between theory and practice is that in theory there is no difference but in practice there is"

 

Slide 5

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
 

 

Slide 6

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?

 

Slide 7

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

 

Slide 8

Addressing CER challenges

Need to explicitly formulate causal question
 

• Determining identifiability
• Defining level of inference
• Validating methods to reduce bias

 

Slide 9

Conceptual frameworks (THEORY)

Natural & engineered systems
- Co-evolution
Cognitive processing
- Information overload �" fit-to-workflow
Cyclical models of control
- Feedback and feed-forward

 

Slide 10

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

 

Slide 11

Relevance

Level of inference needed to assess causal effect of health information technology:
- Socio-technical system
Potential benefits of simulation

 

Slide 12

Cognitive processing

Motivation, mental models, tasks, goals
- Influenced by social context
Lack of fit-to-workflow experienced as:
- Information overload
- Interruptions

 

Slide 13

Relevance

Cognitive informatics methods
- Task analysis
- Direct observation
- Match implementation strategy to task complexity

 

Slide 14

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

 

Slide 15

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

 

Slide 16

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

 

Slide 17

Feedback & feed-forward decision support

 

Slide 18

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

 

Slide 19

Application of theory to practice
implementation 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

 

Slide 20

Community intervention plus clinical decision support system

• Standalone algorithms on handheld computers
• Community randomized trial

Samore MH et. al. JAMA. 2005 Nov 9;294(18):2305-14.
Effect on prescribing any antibiotic

 

Slide 21

Clinical decision support system integrated with computerized clinic order entry

Effect on macrolide prescriptions

• Algorithm usually triggered by ordering antibiotic
• Clinic randomized trial

 

Slide 22

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

 

Slide 23

Recommendations and conclusions

Models fundamental to translation of research into practice
Incorporation of theory and models into comparative effectiveness research
Role of simulation