Clinical and financial outcomes analysis with existing hospital patient records

Authors:
R. Bharat Rao;Sathyakama Sandilya;Radu Stefan Niculescu;Colin Germond;Harsha Rao
Affiliations:
Siemens Medical Solutions, Malvern, PA;Siemens Medical Solutions, Malvern, PA;Carnegie Mellon University, Pittsburgh, PA;Cancer Care Ontario, Sudbury, ON, Canada;Univ. of Pittsburgh Medical Center, Pittsburgh, PA
Venue:
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2003

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Abstract

Existing patient records are a valuable resource for automated outcomes analysis and knowledge discovery. However, key clinical data in these records is typically recorded in unstructured form as free text and images, and most structured clinical information is poorly organized. Time-consuming interpretation and analysis is required to convert these records into structured clinical data. Thus, only a tiny fraction of this resource is utilized. We present REMIND, a Bayesian Framework for Reliable Extraction and Meaningful Inference from Nonstructured Data. REMIND integrates and blends the structured and unstructured clinical data in patient records to automatically created high-quality structured clinical data. This structuring allows existing patient records to be mined for quality assurance, regulatory compliance, and to relate financial and clinical factors. We demonstrate REMIND on two medical applications: (a) Extract "recurrence", the key outcome for measuring treatment effectiveness, for colon cancer patients (ii) Extract key diagnoses and complications for acute myocardial infarction (heart attack) patients, and demonstrate the impact of these clinical factors on financial outcomes.