Development of a predictive model for integrated medical and long-term care resource consumption based on health behaviour: application of healthcare big data of patients with circulatory diseases.

BACKGROUND: Medical costs and the burden associated with cardiovascular disease are on the rise. Therefore, to improve the overall economy and quality assessment of the healthcare system, we developed a predictive model of integrated healthcare resource consumption (Adherence Score for Healthcare Resource Outcome, ASHRO) that incorporates patient health behaviours, and examined its association with clinical outcomes. METHODS: This study used information from a large-scale database on health insurance claims, long-term care insurance, and health check-ups. Participants comprised patients who received inpatient medical care for diseases of the circulatory system (ICD-10 codes I00-I99). The predictive model used broadly defined composite adherence as the explanatory variable and medical and long-term care costs as the objective variable. Predictive models used random forest learning (AI: artificial intelligence) to adjust for predictors, and multiple regression analysis to construct ASHRO scores. The ability of discrimination and calibration of the prediction model were evaluated using the area under the curve and the Hosmer-Lemeshow test. We compared the overall mortality of the two ASHRO 50% cut-off groups adjusted for clinical risk factors by propensity score matching over a 48-month follow-up period. RESULTS: Overall, 48,456 patients were discharged from the hospital with cardiovascular disease (mean age, 68.3 ± 9.9 years; male, 61.9%). The broad adherence score classification, adjusted as an index of the predictive model by machine learning, was an index of eight: secondary prevention, rehabilitation intensity, guidance, proportion of days covered, overlapping outpatient visits/clinical laboratory and physiological tests, medical attendance, and generic drug rate. Multiple regression analysis showed an overall coefficient of determination of 0.313 (p < 0.001). Logistic regression analysis with cut-off values of 50% and 25%/75% for medical and long-term care costs showed that the overall coefficient of determination was statistically significant (p < 0.001). The score of ASHRO was associated with the incidence of all deaths between the two 50% cut-off groups (2% vs. 7%; p < 0.001). CONCLUSIONS: ASHRO accurately predicted future integrated healthcare resource consumption and was associated with clinical outcomes. It can be a valuable tool for evaluating the economic usefulness of individual adherence behaviours and optimising clinical outcomes.

[Explanation method for health economic value of neurology].

As for the one with value, the reward rises generally. How should we discuss the value of the medicine or the examination in Neurology? I am recognizing that it is a complex and difficult theme. In this lecture, I tried to think about the idea of technology value in Neurology and the evaluation of doctor fee in public health system. It was a purpose to maximize well-being in the society. Medical value is that expressing by the performance of cost-effectiveness is an ideal. On the other hand, the total work and the resource cost with a lot of reports have both merits and demerits respectively. It is necessary to explain the technology of Neurology with combinations by three indexes of the total work and the resource cost and the outcome.