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P.J Haug et al.

Clinical Decision Support at Intermountain Healthcare Chapter 8 of Clinical Decision Support Systems, Theory and Practice. P.J Haug et al. Clinical decision support systems. Variety of programs to assist: Drug dosing Health maintenance Diagnosis Etc In 1999

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P.J Haug et al.

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  1. Clinical Decision Support at Intermountain HealthcareChapter 8 of Clinical Decision Support Systems, Theory and Practice P.J Haug et al.

  2. Clinical decision support systems • Variety of programs to assist: • Drug dosing • Health maintenance • Diagnosis • Etc • In 1999 between 44,000 – 98,000 Americans die each year because of medical errors by humans

  3. HELP System • More than 25 years of development and testing • Installed in 20 hospitals • Operated by Intermountain Healthcare (IHC) • Contains decision support subsystems to: • Define the data that is used for making decisions • Encode the logic that converts the raw data into decision

  4. HELP System • Includes 2 types of CDSS systems: • Focuses on narrowly limited medical conditions. • Simple logic • Not much data required • Discriminate among diagnostic entities using raw data

  5. Decision Support Technologies • Alerting system • A rule-based system • Functions continuously • A flashing yellow light or each time a nurse accesses the program • Critiquing system • Functions when an order is entered in system • e.g. transfusion of blood

  6. Decision Support Technologies • Suggestion system • Reacts to requests for assistance • Suggests which action should be taken next • The protocols are created by: • Physicians • Nurses • Respiratory therapists • Specialists

  7. Diagnostic Decision Support in HELP • 2 types of applications • Proven diagnostic application • Research into complex diagnostic applications

  8. Proven Diagnostic Application • Adverse drug events • In US, drug-related morbidity and morality costs more than $136 billion per year • A rule-base system

  9. Proven Diagnostic Application • Nosocomial infections • Based on different decision algorithms • A group of statistical programs to identify risk factors • Logistic regression was used with these risk factors to estimate the risk of Nosocomial infections.

  10. Proven Diagnostic Application • Antibiotic assistant • Assembles relevant data for physician • Suggests a course of therapy appropriate to patients condition • Allows the clinician to review the experience of the hospital in past 6 month and 5 years • Goal of analysis: define the probability of each potential pathogen as a causative agent for a certain class of patient • This probabilistic knowledge is then filtered through a set of rules created by experts

  11. Research into complex diagnostic applications • Assisting data collection • Hierarchical questionnaire • Decision-driven data acquisition • Two-stage questionnaire • Assessing the quality of medical reports

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