Service Selection using Predictive Models and Monte-Carlo Tree Search
This addresses the problem of reducing re-hospitalization costs and improving care for patients in home and hospice settings, representing a domain-specific incremental improvement.
The paper tackles the problem of automated service selection to improve treatment efficacy and reduce re-hospitalization costs by developing a predictive model from the NHHCS dataset and using Monte-Carlo Tree Search to optimize service combinations, achieving an average 11.89 percentage points risk reduction compared to clinician selections.
This article proposes a method for automated service selection to improve treatment efficacy and reduce re-hospitalization costs. A predictive model is developed using the National Home and Hospice Care Survey (NHHCS) dataset to quantify the effect of care services on the risk of re-hospitalization. By taking the patient's characteristics and other selected services into account, the model is able to indicate the overall effectiveness of a combination of services for a specific NHHCS patient. The developed model is incorporated in Monte-Carlo Tree Search (MCTS) to determine optimal combinations of services that minimize the risk of emergency re-hospitalization. MCTS serves as a risk minimization algorithm in this case, using the predictive model for guidance during the search. Using this method on the NHHCS dataset, a significant reduction in risk of re-hospitalization is observed compared to the original selections made by clinicians. An 11.89 percentage points risk reduction is achieved on average. Higher reductions of roughly 40 percentage points on average are observed for NHHCS patients in the highest risk categories. These results seem to indicate that there is enormous potential for improving service selection in the near future.