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Applying intention-based guidelines for critiquing Robert-Jan Sips, Loes Braun, and Nico Roos

Applying intention-based guidelines for critiquing Robert-Jan Sips, Loes Braun, and Nico Roos Department of Computer Science, Maastricht University, P.O.Box 616, 6200 MD Maastricht. Contents. Introduct ion. Intention-based matching. Experiments. Results. Conclusions.

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Applying intention-based guidelines for critiquing Robert-Jan Sips, Loes Braun, and Nico Roos

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  1. Applying intention-based guidelines for critiquing Robert-Jan Sips, Loes Braun, and Nico Roos Department of Computer Science, Maastricht University, P.O.Box 616, 6200 MD Maastricht.

  2. Contents • Introduction. • Intention-based matching. • Experiments. • Results. • Conclusions. • Further Research.

  3. Medical Guidelines IntroductionIntention-based matchingExperimentsResultsConclusionsFurther Research • Standardisation of care. • Current development: Evidence-based. • Proven improvement of care. • but • Physicians tend to reject „cookbook-medicine“. • Not flexible concerning deviations.

  4. Expert Critiquing Systems IntroductionIntention-based matchingExperimentsResultsConclusionsFurther Research • (Expert) system providing feedback on performed actions. • Guiding the physician in a subtle manner. • but • Difficult to adapt to new developments. • Current systems rely on user interaction.

  5. The best of two worlds IntroductionIntention-based matchingExperimentsResultsConclusionsFurther Research • Proposal: • Combine expert critiquing and medical guidelines. • + = • Prerequisite: • Matching a physician‘s actions (reported in an EPR) and those prescribed in a medical guideline. (No user interaction).

  6. Matching IntroductionIntention-based matchingExperimentsResultsConclusionsFurther Research • Previous research learns: • Physicians do not follow a guidelines exact actions. (Van der Lei (1991)). • Solution: Match intentions (Advani et al. (1998)). • Differences in intentions reported by a physician and modeled in a guideline (Marcos et al.(2001)).

  7. Intentions IntroductionIntention-based matchingExperimentsResultsConclusionsFurther Research • Observation: • 2 types of intentions: • High-level intentions. • Diagnosis and treatment goals. • E.g. • Low-level intentions. • Application independent intentions of clinical interventions. • E.g. • Low-level intentions. • Described in standard literature (e.g. Merck Manual, pharmacotherapeutical compass).

  8. Medical Guidelines IntroductionIntention-based matchingExperimentsResultsConclusionsFurther Research • Each high-level intention can be described by a set of high-level and low-level intentions. • Each high-level intention can be described by a set of low-level intentions. • Therefore • The most general high-level intention in a guideline can be replaced by one or more sequences of low-level intentions: the guideline execution.

  9. Distance to guideline IntroductionIntention-based matchingExperimentsResultsConclusionsFurther Research • Measure of similarity between the low-level intentions performed by a physician and a guideline execution. • Informal: • |physician actions in the execution| - |actions in the execution not performed by the physician|

  10. Experiments IntroductionIntention-based matchingExperimentsResultsConclusionsFurther Research • Asbru modeled guideline (obtained from Marcos et all). • Case interpretations from two pediatricians. • Case interpretations entered in EPR in 3 ways. • Normal (as reported by the pediatrician). • Consultation basis (3 actions in arbitrary order per consult). • Reversed order (worst-case scenario).

  11. Results IntroductionIntention-based matchingExperimentsResultsConclusionsFurther Research • Normal sequence: • Average of 69,4% correct over all actions. • Without actions outside the guidelines scope: 80,1% correct. • Without actions outside the guidelines scope and correction for error in the guideline 95,8% correct. • Better performance on long sequences than on short. • No significant difference in performance between the two physicians after discarding a case outside the guideline‘s scope (Liver infection as cause). • No significant difference in performance on sequences in different orders.

  12. Results IntroductionIntention-based matchingExperimentsResultsConclusionsFurther Research • RBE and backwards • No significiant difference in the performance on sequences in a different order.

  13. Conclusions IntroductionIntention-based matchingExperimentsResultsConclusionsFurther Research • Algorithm performs adequately. • Our results support the claim that physicians follow a guideline‘s intentions. • Our results indicate that there is no difference in performance on different treatment styles.

  14. Further Research IntroductionIntention-based matchingExperimentsResultsConclusionsFurther Research • Test our algorithm more extensively. • Prove performance. • Different measures for matching. • E.g. Use Temporal data. • Expand our algorithm to match on multiple guidelines. • Changing treatment goals. • Using real-life patient records. • Terminology. • Effect on the treatment process. • Does this way of critiquing improve care?

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