Improving Hospital-Wide Early Resource Allocation through Machine Learning.
The objective of this paper is to evaluate the extent to which early determination of diagnosis-related groups (DRGs) can be used for better allocation of scarce hospital resources. When elective patients seek admission, the true DRG, currently determined only at discharge, is unknown. We approach the problem of early DRG determination in three stages: (1) test how much a Naïve Bayes classifier can improve classification accuracy as compared to a hospital's current approach; (2) develop a statistical program that makes admission and scheduling decisions based on the patients' clincial pathways and scarce hospital resources; and (3) feed the DRG as classified by the Naïve Bayes classifier and the hospitals' baseline approach into the model (which we evaluate in simulation). Our results reveal that the DRG grouper performs poorly in classifying the DRG correctly before admission while the Naïve Bayes approach substantially improves the classification task. The results from the connection of the classification method with the mathematical program also reveal that resource allocation decisions can be more effective and efficient with the hybrid approach.
The H. John Heinz III College, Carnegie Mellon University, Pittsburgh, PA, USA.
Daniel
D
Gartner
The H. John Heinz III College, Carnegie Mellon University, Pittsburgh, PA, USA.
Rema
R
Padman
D057225:Data Mining / Q000379:methods; D003953:Diagnosis-Related Groups / Q000145:classification*; D015276:Health Care Rationing / Q000458:organization & administration*; D006739:Hospital Administration / Q000379:methods*; D006751:Hospital Information Systems / Q000145:classification / Q000706:statistics & numerical data*; D000069550:Machine Learning; D009323:Natural Language Processing; D020380:Needs Assessment / Q000458:organization & administration; D058996:Quality Improvement / Q000458:organization & administration*