Evaluation of Artificial Intelligence-Assisted Video Monitoring for Inpatient Fall Prevention: A Retrospective Matched Cohort Study
Dong-suk Lee, Young-Ju Kim, Hee-won Park, Seung-ok Choi, Gyeong-Nam Lee, JiHoon Park, MoonKi Choi · 2026
Read Evaluation of Artificial Intelligence-Assisted Video Monitoring for Inpatient Fall Prevention: A Retrospective Matched Cohort Study by Dong-suk Lee, Young-Ju Kim, Hee-won Park, Seung-ok Choi, Gyeong-Nam Lee, JiHoon Park, MoonKi Choi (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13606168.txt.
Description
Dong-suk Lee and co-authors test whether an AI camera system cut falls among hospital inpatients, in a 2026 study from South Korea. Falls in hospital cause injuries, longer stays, and higher costs. Camera systems that watch patients and flag risky movement are spreading, but proof from real wards is thin. The team compared fall rates before and after one hospital installed such a system.
The system went live in January 2022 at a large hospital in the city named C. Patients admitted in 2020 and 2021 formed the group without the system, and those from 2023 and 2024 formed the group with it. The authors matched patients by age, sex, illness count, and ward days so the groups compared fairly. They then counted falls and fall injuries per thousand patient-days in each era.
Catalog record
| CREATOR | Dong-suk Lee, Young-Ju Kim, Hee-won Park, Seung-ok Choi, Gyeong-Nam Lee, JiHoon Park, MoonKi Choi |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 34 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |