Optimizing physician visits for knee replacement patients via assignment policies - a machine learning approach

Project description

Knee replacement patients regularly see their physician after surgery to receive feedback on their recovery progress, and on potential alteration of their post-surgery treatment protocol. In a world with increasing healthcare costs and limited availability of healthcare personnel, only patients that require a visit should see their physician. Analytics can help to steer patients to visit their physician only if it benefits their recovery progress.  

With this paper we aim to develop an algorithm that optimally assigns rules for physician visits at three-, six- and twelve-months post-surgery based on patient characteristics and recovery pathways. We aim to build optimal assignment rules to maximize functional improvement and functional outcome of the patient, and to analyze the distributional effect of assignment rules depending on the chosen dependent variable. 

 

Project team

Johannes Cordier, Benedikt Langenberger, Irene Salvi, Dr. David Kuklinski, Prof. Dr. Alexander Geissler 

 

 

Cooperation partner

Technical University of Berlin

 

Funding source

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Duration

12 months (July 2023 to July 2024)

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