Allocation optimization of medical resources using quantum annealing

2022 Fiscal Year Final Research Report

• Project/Area Number: 20K14979
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 25010:Social systems engineering-related
• Research Institution: Keio University (2021-2022); The University of Tokyo (2020)
• Principal Investigator: Kawaguchi Hideaki 慶應義塾大学, 理工学研究科(矢上), 特任講師 (30813969)
• Project Period (FY): 2020-04-01 – 2023-03-31
• Keywords: 量子アニーリング / ヘルスサービスリサーチ / 空間統計学

Outline of Final Research Achievements

Quantum annealing was performed using a Hamiltonian designed with medical resource data to create a color-coded map showing the optimal allocation of medical resources. We also verified the extent to which changing the medical resources according to the obtained map would affect the health status of each region. By performing quantum annealing with adjacency conditions, we were able to obtain a map in which different colors were assigned to neighboring areas while assigning a color with higher priority to areas with fewer medical resources. Furthermore, by increasing or decreasing the number of psychiatrists and psychosomatic physicians according to the map obtained by quantum annealing, it was confirmed that regional differences in suicide mortality rates could be reduced, and the overall average suicide mortality rate could be lowered.

Free Research Field

医療情報学

Academic Significance and Societal Importance of the Research Achievements

本研究の成果は、医療政策のシミュレーション研究における量子アニーリングの応用事例として、国内外で先進的な知見をもたらす成果である。本研究成果から、量子アニーリングの結果得られた塗分け図に従って、医療資源を増減させることで、各疾患の健康状況の地域差を減少させる可能性が得られた一方、十分に地域差を解消するためには、相当数の医療資源数の増減が必要であることも確認された。医療資源数の増減のみでは十分な健康格差の解消につながらない可能性が想定されたため、各地域の特性に合わせた運用も同時に重要である可能性が考えられた。