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.

Predicting Long-Term Care Service Demands for Cancer Patients: A Machine Learning Approach

“…Previous research has largely overlooked the prediction of long-term care needs, specifically for cancer patients, focusing more on evaluating the likelihood of individuals qualifying for government financial subsidies. For example, Sato J.’s study leveraged historical healthcare insurance claims data to build a predictive model using multiclass classification and a gradient-boosting decision tree, achieving a high level of accuracy with a weighted average precision of 0.872, recall of 0.878, and an F-value of 0.873 [ 9 ]. Simultaneously, the study conducted by H. Fukunishi also utilized the same dataset but concentrated on predicting the needs of individuals aged 75 and older.…”

“…Prior studies have employed machine learning (ML) techniques for predicting LTC needs; however, they often limit their scope to very specific service usage scenarios. For instance, two Japanese studies used healthcare insurance claims and multiclass classification to predict eligibility for government allowances instead of assessing demand for LTC services directly, with one study focusing on people over 75 [ 9 , 10 ]. In contrast, a study from Taiwan forecasts the scores for difficulties in Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL), achieving mean absolute errors of 17.67 and 1.31, respectively [ 11 ].…”

Virtual patient identifier (vPID): Improving patient traceability using anonymized identifiers in Japanese healthcare insurance claims database

“…Third, vPID cannot merge ID1 or ID2 of a citizen for whom no healthcare insurance claims are recorded because vPID is obtained from a claim dataset. In our experience [ [13] , [14] , [15] , [16] , [17] , [18] ], many healthcare analysts are primarily interested in the reality of healthcare service provision to patients. This limitation likely has a negligible impact on such studies that do not focus on the citizens being unserviced of healthcare.…”

“…Potentially, NDB contains the entire information about healthcare services being provided to the citizens. The database (more than 14,810 million claims of 126 million citizens as of March 2018 [ 11 ]) has been actively studied [ [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] ].…”

Predicting Medical Event Occurrence Using Medical Insurance Claims Big Data

“…Medical and healthcare institutions yield vast amounts of digital data every day. One of the goals of the informatics field is to utilize these big data to optimize future; big data analytics can be used to predict medical events in the future and facilitate decisionmaking ahead of them [1][2][3][4][5]. One of the datasets for this purpose is medical insurance claims (MICs), that are bills submitted by medical service providers to patient's insurance providers.…”