An Electronic Medical Record–Based Discharge Disposition Tool Gets Bundle Busted: Decaying Relevance of Clinical Data Accuracy in Machine Learning

• Main Authors: Alexander S. Greenstein, MD, Jack Teitel, MS, David J. Mitten, MD, Benjamin F. Ricciardi, MD, Thomas G. Myers, MD, MPT
• Format: Article
• Language: English
• Published: Elsevier 2020-12-01
• Series: Arthroplasty Today
• Subjects: Machine learning; Artificial intelligence; Arthroplasty; Discharge
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352344120301722

Background: Determining discharge disposition after total joint arthroplasty (TJA) has been a challenge. Advances in machine learning (ML) have produced computer models that learn by example to generate predictions on future events. We hypothesized a trained ML algorithm’s diagnostic accuracy will be better than that of current predictive tools to predict discharge disposition after primary TJA. Methods: This study was a retrospective cohort study from a single, tertiary referral center for primary TJA. We trained and validated an artificial neural network (ANN) based on 4368 distinct surgical encounters between 1/1/2013 and 6/28/2016. The ANN’s ability to identify discharge disposition was then tested on 1452 distinct surgical encounters between 1/3/17 and 11/30/17. Results: The area under the curve and accuracy achieved during model validation were 0.973 and 91.7%, respectively, with 25% of patients being discharged to skilled nursing facilities (SNFs). Within our testing data set, 6.7% of patients went to SNFs. The performance in the testing set included an area under the curve of 0.804, accuracy of 61.3%, sensitivity of 28.9%, and specificity of 93.8%. Conclusions: This is the first prediction tool using an electronic medical record–integrated ANN to predict discharge disposition after TJA based on locally generated data. Dramatically reduced numbers of patients discharged to SNFs due to implementation of a bundled payment model lead to poor recall in the testing model. This model serves as a proof of concept for developing an ML prediction tool using a relatively small data set and subsequent integration into the electronic medical record.