Clinical Department of Psychiatry University of Michigan Medical School Ann Arbor, June 28, 2002 Why Medicine Should be an Information Science Bruce R.

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Presentation transcript:

Clinical Department of Psychiatry University of Michigan Medical School Ann Arbor, June 28, 2002 Why Medicine Should be an Information Science Bruce R. Schatz School of Library & Information Science School of Biomedical & Health Information Sciences University of Illinois at Urbana-Champaign

What is an Information Science? Correlations from Sources before Results from Experiments Informational not Computational Searching not Calculating

Biology is an Information Science Many Sources for Interpretation now available in Databases Bioinformatics determine Function or predict Experiments

Scientific Reason Multigene Pathways for Multifactorial Diseases

The Era of Acute Illness Straightforward to Diagnose Beyond lab-test thresholds Require immediate attention Straightforward to Treat Surgery in Hospitals Drugs for Follow-ups

Medical Informatics Acute Illness Hospital and Clinics EMR – Electronic Medical Record Conflicts across Locations Links to Literatures

Evidence-Based Medicine Patient Data Logical inference, clinical decisions Case Studies Similarity analysis, practical outcomes

The Era of Chronic Illness Hard to Diagnose and Treat Changes frequently and Not curable Most People have Chronic Illness Mind & Body – Depression & Heart Dominates costs as people live longer

Health Informatics Health Monitor versus Medical Record Continuous versus Discrete Measure Health not Disease Monitor all of the people all of the time Average case not Extreme Threshold

Clinical Reason Population Monitoring of Average Health

Medicine as Information Science Health Status Continuous Full-spectrum Lifestyles Treatment Outcomes Longitudinal Tracking with Specifics Diagnosis Cohorts Similarity Clustering beyond Category

Health Status Full-Spectrum Lifestyles Evans categories, Healthy People 2010 Hereditary & Environment Physiological & Psychological Fine granularity, 30K versus 30

Treatment Outcomes Everyday Status over Whole Lifetimes Fine grain tracking for Long periods Efficacy Tracking Beyond small-sample clinical trials

Diagnosis Cohorts Population Databases Look for clusters of similar persons Traditional vs. Alternative Medicine Treat the disease or the person? Beyond simple categories

Healthcare Infrastructure Provider Pyramids Scale to Volumes for Chronic Illness Risk Assessment Automatically Determine Level of Care

The Future of Health Systems Effective Prevention Infrastructure supports Routine Care Historical Nexus Telephone: Everyone is an Operator Healthcare: Everyone is a Doctor

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