She is an associate professor of faculty of informatics, Shizuoka University. She earned Doctor of Management Engineering in 2013 from Waseda University. Her research has focused on Quality Management System (QMS), Business Continuity Management System (BCMS) and Kansei Quality. Additionally, since 2023, she has been serving as a Deming Prize Examiner and will assume the position of Executive Committee Member of the QC Circle Headquarters, Union of Japanese Scientists and Engineers (JUSE) starting in 2025.

Title of the Presentation:

A method for preventing overturn and fall accidents considering patients’ characteristics

Key words:

Patient Falls Prevention, Risk Assessment, Countermeasure Selection, Decision-Support Tool, Hospital Safety Management

Key message: This study proposes a systematic approach to fall prevention by analyzing incident patterns and deriving countermeasures based on patient characteristics. A decision-support tool was developed, enabling nurses to select effective countermeasures consistently. This approach reduces variability in fall prevention strategies, improving patient safety and hospital risk management.

Background and Purpose:

One of the problems that hospitals face is the prevention of patient overturn and fall accidents. To prevent these accidents, nurses first use a fall assessment sheet to identify the patients’ characteristics and risk of falls. This sheet shows the characteristics of a patient that can lead to accidents such as age and cognitive impairment. The nurse checks for the relevant characteristics of each patient. Based on the results, the nurse selects safety equipment to be used as an accident countermeasure and determines the nursing care to be provided. However, decisions on equipment and nursing care often depend on nurses’ personal experience and knowledge.

This study discusses effective countermeasures to reduce accidents using Hospital A and Hospital B as examples. Then, a tool is created that can automatically identify countermeasures based on the characteristics of the patient.

Conclusion and Future Challenges:

This study extracted key fall risk factors while addressing gaps in previous research. By visualizing fall incident patterns, countermeasures were systematically derived based on incident progression. Additionally, a decision-support tool was developed to enable all nurses to select appropriate countermeasures effectively.

Future research should focus on validating the tool’s effectiveness in actual hospital environments. Expanding its application to various hospitals and refining its usability will be crucial for improving fall prevention strategies.