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



  • 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; 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.

publication date

  • November 14, 2014



  • Electronic Health Records
  • Medical Informatics Applications
  • Quality Indicators, Health Care


PubMed Central ID

  • PMC4419873

Scopus Document Identifier

  • 84964316062

PubMed ID

  • 25954459

Additional Document Info


  • 2014