Using Monte Carlo/Gaussian Based Small Area Estimates to Predict Where Medicaid Patients Reside

Jess J. Behrens, Xuejin Wen, Satyender Goel, Jing Zhou, Lina Fu, Abel N. Kho

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Electronic Health Records (EHR) are rapidly becoming accepted as tools for planning and population health1,2. With the national dialogue around Medicaid expansion12, the role of EHR data has become even more important. For their potential to be fully realized and contribute to these discussions, techniques for creating accurate small area estimates is vital. As such, we examined the efficacy of developing small area estimates for Medicaid patients in two locations, Albuquerque and Chicago, by using a Monte Carlo/Gaussian technique that has worked in accurately locating registered voters in North Carolina11. The Albuquerque data, which includes patient address, will first be used to assess the accuracy of the methodology. Subsequently, it will be combined with the EHR data from Chicago to develop a regression that predicts Medicaid patients by US Block Group. We seek to create a tool that is effective in translating EHR data's potential for population health studies.

Original languageEnglish (US)
Pages (from-to)305-309
Number of pages5
JournalAMIA ... Annual Symposium proceedings. AMIA Symposium
Volume2016
StatePublished - Jan 1 2016

ASJC Scopus subject areas

  • Medicine(all)

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