Scalable and High-Throughput Execution of Clinical Quality Measures from Electronic Health Records using MapReduce and the JBoss® Drools Engine

Kevin J. Peterson, Jyotishman Pathak

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Automated execution of electronic Clinical Quality Measures (eCQMs) from electronic health records (EHRs) on large patient populations remains a significant challenge, and the testability, interoperability, and scalability of measure execution are critical. The High Throughput Phenotyping (HTP; http://phenotypeportal.org) project aligns with these goals by using the standards-based HL7 Health Quality Measures Format (HQMF) and Quality Data Model (QDM) for measure specification, as well as Common Terminology Services 2 (CTS2) for semantic interpretation. The HQMF/QDM representation is automatically transformed into a JBoss(®) Drools workflow, enabling horizontal scalability via clustering and MapReduce algorithms. Using Project Cypress, automated verification metrics can then be produced. Our results show linear scalability for nine executed 2014 Center for Medicare and Medicaid Services (CMS) eCQMs for eligible professionals and hospitals for >1,000,000 patients, and verified execution correctness of 96.4% based on Project Cypress test data of 58 eCQMs.

Original languageEnglish (US)
Pages (from-to)1864-1873
Number of pages10
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Volume2014
StatePublished - 2014

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Cupressus
Electronic Health Records
Centers for Medicare and Medicaid Services (U.S.)
Workflow
Health
Semantics
Terminology
Cluster Analysis
Population
Data Accuracy

ASJC Scopus subject areas

  • Medicine(all)

Cite this

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