Experience in improving healthcare decision-making with health IT: integrating theory, research, and...
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Transcript of Experience in improving healthcare decision-making with health IT: integrating theory, research, and...
Experience in improving healthcare decision-making with health IT: integrating theory, research, and practice
Matthew Samore, MDVA Salt Lake City Health Care SystemProfessor of Internal MedicineAdjunct Professor of Biomedical InformaticsUniversity of Utah
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 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:
“The difference between theory and practice is that in theory there is no difference but in practice there is”
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
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) 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
Need to explicitly formulate causal question Determining identifiability Defining level of inference Validating methods to reduce bias
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
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
Level of inference needed to assess causal effect of health information technology: Socio-technical system
Potential benefits of simulation
Cognitive processing Motivation, mental models, tasks, goals
Influenced by social context Lack of fit-to-workflow experienced as:
Information overload Interruptions
Relevance Cognitive informatics methods
Task analysis Direct observation Match implementation strategy to task
complexity
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
“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
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 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 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
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
Clinical decision support system integrated with computerized clinic order entry
Algorithm usually
triggered by ordering antibiotic
Clinic randomized trial
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Effect on macrolide prescriptions
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