Using Machine Learning to Support Transfer of Best Practices in Healthcare

Sebastian Caldas, Jieshi Chen, Artur Dubrawski

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The adoption of best practices has been shown to increase performance in healthcare institutions and is consistently demanded by both patients, payers, and external overseers. Nevertheless, transferring practices between healthcare organizations is a challenging and underexplored task. In this paper, we take a step towards enabling the transfer of best practices by identifying the likely beneficial opportunities for such transfer. Specifically, we analyze the output of machine learning models trained at different organizations with the aims of (i) detecting the opportunity for the transfer of best practices, and (ii) providing a stop-gap solution while the actual transfer process takes place. We show the benefits ofthis methodology on a dataset ofmedical inpatient claims, demonstrating our abilityto identify practice gaps and to support the transfer processes that address these gaps.

Original languageEnglish (US)
Pages (from-to)265-274
Number of pages10
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Volume2021
StatePublished - 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Medicine

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