Exploratory analyses of algorithm to detect the potential adverse drug events

2023 Fiscal Year Final Research Report

• Project/Area Number: 21H03176
• Research Category: Grant-in-Aid for Scientific Research (B)
• Allocation Type: Single-year Grants
• Section: 一般
• Review Section: Basic Section 58010:Medical management and medical sociology-related
• Research Institution: Hyogo Medical University
• Principal Investigator: Morimoto Takaeshi 兵庫医科大学, 医学部, 教授 (30378640)
• Co-Investigator(Kenkyū-buntansha): 作間 未織 兵庫医科大学, 医学部, 准教授 (60349587) ; 武内 治郎 兵庫医科大学, 医学部, 助教 (60791324) ; 太田 好紀 兵庫医科大学, 医学部, 特任准教授 (10516404)
• Project Period (FY): 2021-04-01 – 2024-03-31
• Keywords: 医原性有害事象 / 医療安全 / 臨床疫学 / アルゴリズム

Outline of Final Research Achievements

We conducted a prospective cohort study to demonstrate that in-hospital mortality can be predicted using patient background and practice environment, that the effectiveness of interventions to prevent potential adverse drug events varies by drug type, and that department characteristics are associated with potential adverse drug events and medication errors. The comparison of the risk of occurrence of potential adverse drug events, which were not tested as per the package insert, as well as the validation of an algorithm that automatically suggests modifications from the electronic health record system, showed that some drugs were effective in reducing potential adverse drug events, while some drugs were not effective. Furthermore, it became clear that the algorithm should reflect the influence of the specialty as well as patient background on the risk factors for potential adverse drug events.

Free Research Field

総合内科、臨床疫学、医療の質

Academic Significance and Societal Importance of the Research Achievements

医療を含め、全ての科学技術には安全性の評価及び危険性の最小化が必要である。医療技術の発展で多くの人命が救われてきたが、一方で医療技術の高度化・複雑化により、医療そのものの危険性が高まっている。本研究課題では、潜在的な薬剤性有害事象の発生を患者背景や診療経過から予測し、電子カルテシステム上で自動的に検出できる、汎用性の高いアルゴリズムを開発することで、より効率的に薬剤性有害事象の危険性や影響を最小化できることを目指した。