Auton Lab, Carnegie Mellon University, Pittsburgh, PA, USA.
AMIA Annu Symp Proc. 2022 Feb 21;2021:265-274. eCollection 2021.
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.
最佳实践的采用已被证明可以提高医疗机构的绩效，并且一直受到患者、支付者和外部监督者的一致要求。然而，在医疗机构之间转移实践是一项具有挑战性且尚未得到充分探索的任务。在本文中，我们通过确定这种转移可能带来的有益机会，朝着实现最佳实践转移迈出了一步。具体来说，我们分析了在不同组织中训练的机器学习模型的输出，目的是（i）检测最佳实践转移的机会，以及（ii）在实际转移过程进行的同时提供临时解决方案。我们在医疗住院索赔数据集上展示了这种方法的好处，证明了我们识别实践差距和支持解决这些差距的转移过程的能力。
