Multi-Site Best Practice Discovery: From Free Text to Standardized Concepts to Clinical Decisions

This article discusses the establishment of interoperability among electronic medical records from 800 clinical sites and the use of machine learning for best practice discovery. A novel extraction-mapping algorithm is designed that accurately extracts, summarizes, and maps free text and content to concise structured medical concepts. Clinical decision processes and disease progression are also generated. The machine learning model (DAMIP) uncovers discriminatory feature sets that can predict the quality of treatment outcomes (blind prediction accuracies of 89% – 97%) for multiple diseases including heart, hypertension, and chronic kidney disease (CKD). For each disease, the best practice was used at fewer than 5% of the clinical sites, opening up excellent opportunities for knowledge sharing and rapid learning. This work led to the implementation of a new treatment policy for CKD pre-dialysis care management. The new policy offers better outcomes, saves lives, improves the quality of life, and reduces 35% of treatment costs. The system is scalable and generalizable.

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Multi-Site Best Practice Discovery: From Free Text to Standardized Concepts to Clinical Decisions

Cited by 3 publications

References 16 publications

A General-Purpose Multi-stage Multi-group Machine Learning Framework for Knowledge Discovery and Decision Support

A General-Purpose Multi-stage Multi-group Machine Learning Framework for Knowledge Discovery and Decision Support

Handling Imbalanced and Poorly Separated Data: a Multi-Stage Multi-Group Machine Learning Approach

Free Text to Standardized Concepts to Clinical Decisions

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