AMIA Annu Symp Proc - Scalable and High-Throughput Execution of Clinical Quality Measures from Electronic Health Records using MapReduce and the JBoss? Drools Engine.

Tópicos

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Resumo

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.

Resumo Limpo

autom execut electron clinic qualiti measur ecqm electron health record ehr larg patient popul remain signific challeng testabl interoper scalabl measur execut critic high throughput phenotyp htp httpphenotypeportalorg project align goal use standardsbas hl health qualiti measur format hqmf qualiti data model qdm measur specif well common terminolog servic cts semant interpret hqmfqdm represent automat transform jboss drool workflow enabl horizont scalabl via cluster mapreduc algorithm use project cypress autom verif metric can produc result show linear scalabl nine execut center medicar medicaid servic cms ecqm elig profession hospit patient verifi execut correct base project cypress test data ecqm

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