Predicting demand for long-term care using Japanese healthcare insurance claims data

Abstract

Background: Driven by the rapid aging of the population, Japan introduced public long-term care insurance to reinforce healthcare services for the elderly in 2000. Precisely predicting future demand for long-term care services helps authorities to plan and manage their healthcare resources and citizens to prevent their health status deterioration.

Methods: This paper presents our novel study for developing an effective model to predict individual-level future long-term care demand using previous healthcare insurance claims data. We designed two discriminative models and subsequently trained and validated the models using three learning algorithms with medical and long-term care insurance claims and enrollment records, which were provided by 170 regional public insurers in Gifu, Japan.

Results: The prediction model based on multiclass classification and gradient-boosting decision tree achieved practically high accuracy (weighted average of Precision, 0.872; Recall, 0.878; and F-measure, 0.873) for up to 12 months after the previous claims. The top important feature variables were indicators of current health status (e.g., current eligibility levels and age), risk factors to worsen future healthcare status (e.g., dementia), and preventive care services for improving future healthcare status (e.g., training and rehabilitation).

Conclusions: The intensive validation tests have indicated that the developed prediction method holds high robustness, even though it yields relatively lower accuracy for specific patient groups with health conditions that are hard to distinguish.

Keywords: Demand prediction; Healthcare insurance claims database; Long-term care service.

Fig. 1

Study framework for future prediction of individuals’ long-term care eligibility levels.

Fig. 2

ROC curves and AUC values in the future eligibility level prediction. (a) 3-month prediction; (b) 6-month prediction; and (c) 12-month prediction.

Fig. 3

Top 10 feature variables of prediction models designed by multiclass classification and GBDT. (a) 3-month prediction; (b) 6-month prediction; and (c) 12-month prediction. 1*, frequency of occurrence of long-term care service code; 2*, frequency of occurrence of disease diagnosis code; and 3*, frequency of occurrence of medical treatment procedure code.