It is known that health-related behaviors (adherence) such as disease prevention behavior, medication compliance, and multiple consultations have a great influence not only on treatment results but also on medical finance.For example, it has been reported that improving medication adherence not only reduces the burden on patients' illnesses but also on the financial burden, and there are concerns about an increase in medical expenses due to the normalization of multiple consultations.
Medical / long-term care insurance As finances become tight, it is desirable to utilize long-term risk assessment and prediction models that contribute to new disease prevention and financial management.However, assessing the impact of adherence on the clinical economy has been difficult to assess in routine clinical trials due to the intertwining of diverse and complex factors.
Therefore, Tomoyuki Takura, a specially appointed professor of medical economic policy at the Graduate School of Medicine, the University of Tokyo, applied medical big data and machine learning (artificial intelligence: AI) for the purpose of reducing the clinical economic burden, and adherence. Has newly developed a model (Adherence Score for Healthcare Resource Outcome: ASHRO) that predicts the effects on medical / nursing care costs, life prognosis, and other clinical indicators in the long term (48 months).Of the medical big data managed by the University of Tokyo, a cohort of about 5 people in the cardiovascular field was verified, and the calculated correspondence between ASHRO and medical / long-term care costs was calculated from the displacement ratio to the average value in the cohort. Created a standard ASHRO score.
Since this score can predict future clinical economic risks of the target person (insured person or patient), the administrator can manage the insurance finance (medical care / nursing care), and the medical staff can promote disease prevention. It is said that it can be used for improving the burden of illness and contributing to the development of social security.In addition, the group is currently promoting similar research in other areas such as musculoskeletal and renal failure, and is expected to contribute to the sustainable progress of the overall medical system.
