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Knowledge and the Electronic Health Record

Knowledge and the Electronic Health Record. Health Informatics. Tends to talk about knowledge in the context of ontologies and guideline formalisms Tends to focus on clinician decision support at the time of patient care (diagnostic support, guideline support) See www.openclinical.org

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Knowledge and the Electronic Health Record

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  1. Knowledge and the Electronic Health Record

  2. Health Informatics • Tends to talk about knowledge in the context of ontologies and guideline formalisms • Tends to focus on clinician decision support at the time of patient care (diagnostic support, guideline support) • See www.openclinical.org • Remarkably unsuccesful

  3. Knowledge management • Learning • Negotiation

  4. Learning • Meta-cognition • Experience • Individuality • Culture • Professional • Sociological

  5. Negotiation • Knowledge exchange • Team, inter-professional,organisational inter-dependence • Contextual • Priorities • Agendas • Choice • Decisions • Iteration, decision inter-dependence

  6. Domains of Knowledge and Patient Care • Clinical knowledge • Disease evolution • Diagnosis, treatment, outcome • Contributing knowledge • Physiology, engineering, etc • Organisational knowledge • Work flow, planning

  7. Why distinguish domains of knowledge • Natural knowledge growth • Recognised specialisation, specific journals • Knowledge closest to main decision making needs • Knowledge domains have their own characteristics and dynamic • Articulates where knowledge can be shared between domains

  8. Knowledge representation of the clinical domain • Syntax • Semantics

  9. Syntax

  10. folder EHCR composition original component complex headed section cluster record component link item data item ENV13606 a practical generic EHCR Standard contains Are types of 1 1..* 1..* Are types of Various specialised types of data item

  11. Semantic • Care strategies • According to clinical problem(s) • Diagnosis – therapy – outcome • Problem definition, risk analysis, treatment selection

  12. 2 papers comparison of guideline formalisms • Approaches for creating computer-interpretable guidelines that facilitate decision support • Paul A. de Clercq, Johannes A. Blomb, Hendrikus H.M. Korsten, Arie Hasmana • Artificial Intelligence in Medicine (2004) 31, 1—27 • Comparing Computer-interpretable Guideline Models:A Case-study Approach • MOR PELEG, PHD, SAMSON TU, MS, JONATHAN BURY, MBCHB,PAOLO CICCARESE, MSC, JOHN FOX, PHD, ROBERT A. GREENES, MD, PHD, RICHARD HALL, MSC, PETER D. JOHNSON, MBBS, NEILL JONES, MBBS,, ANAND KUMAR, MBBS, SILVIA MIKSCH, PHD, SILVANA QUAGLINI, PHD, ANDREAS SEYFANG, MSC, EDWARD H. SHORTLIFFE, MD, PHD,MARIO STEFANELLI, PHD • J Am Med Inform Assoc. 2003;10:52–68

  13. Computer based clinical guidelines • Redundancy and overlap • Little standardisation to facilitate sharing

  14. Formalisms reviewed by De Clercq et al • The Arden Syntax • GLIF • Guideline interchange format • PROforma • Asbru • EON

  15. Critique of Guideline formalisms and current application aims • Do not adequately address guideline root knowledge • Tend to relate to existing guidelines and their current inherent variation rather than guideline knowledge sources • Do not well take into account different characteristics of different domain knowledge • Clinical versus organisational • Do not take into account basic requirements of knowledge management • Contextual learning and decision making

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