National Health Insurance Service Ilsan Hospital Reports Findings in Medical Informatics (Clinical Impact of an Analytic Tool for Predicting the Fall Risk in Inpatients: Controlled Interrupted Time Series): Information Technology – Medical Informatics

2021 DEC 14 (NewsRx) -- By a News Reporter-Staff News Editor at South Korea Daily Report -- New research on Information Technology - Medical Informatics is the subject of a report. According to news reporting originating in Gyeonggi Do, South Korea, by NewsRx journalists, research stated, “Patient falls are a common cause of harm in acute-care hospitals worldwide. They are a difficult, complex, and common problem requiring a great deal of nurses’ time, attention, and effort in practice.”

The news reporters obtained a quote from the research from National Health Insurance Service Ilsan Hospital, “The recent rapid expansion of health care predictive analytic applications and the growing availability of electronic health record (EHR) data have resulted in the development of machine learning models that predict adverse events. However, the clinical impact of these models in terms of patient outcomes and clinicians’ responses is undetermined. The purpose of this study was to determine the impact of an electronic analytic tool for predicting fall risk on patient outcomes and nurses’ responses. A controlled interrupted time series (ITS) experiment was conducted in 12 medical-surgical nursing units at a public hospital between May 2017 and April 2019. In six of the units, the patients’ fall risk was assessed using the St. Thomas’ Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) system (control units), while in the other six, a predictive model for inpatient fall risks was implemented using routinely obtained data from the hospital’s EHR system (intervention units). The primary outcome was the rate of patient falls; secondary outcomes included the rate of falls with injury and analysis of process metrics (nursing interventions that are designed to mitigate the risk of fall). During the study period, there were 42,476 admissions, of which 707 were for falls and 134 for fall injuries. Allowing for differences in the patients’ characteristics and baseline process metrics, the number of patients with falls differed between the control (n=382) and intervention (n=325) units. The mean fall rate increased from 1.95 to 2.11 in control units and decreased from 1.92 to 1.79 in intervention units. A separate ITS analysis revealed that the immediate reduction was 29.73% in the intervention group (z=-2.06, P=.039) and 16.58% in the control group (z=-1.28, P=.20), but there was no ongoing effect. The injury rate did not differ significantly between the two groups (0.42 vs 0.31, z=1.50, P=.134). Among the process metrics, the risk-targeted interventions increased significantly over time in the intervention group. This early-stage clinical evaluation revealed that implementation of an analytic tool for predicting fall risk may to contribute to an awareness of fall risk, leading to positive changes in nurses’ interventions over time.”

According to the news reporters, the research concluded: “Clinical Research Information Service (CRIS), Republic of Korea KCT0005286; https://cris.”

For more information on this research see: Clinical Impact of an Analytic Tool for Predicting the Fall Risk in Inpatients: Controlled Interrupted Time Series. JMIR Medical Informatics, 2021;9(11). JMIR Medical Informatics can be contacted at: Jmir Publications, Inc, 130 Queens Quay E, Ste 1102, Toronto, On M5A 0P6, Canada.

Our news correspondents report that additional information may be obtained by contacting In Sun Jin, Dept. of Nursing, National Health Insurance Service Ilsan Hospital, Gyeonggi-do, South Korea. Additional authors for this research include Insook Cho, Hyunchul Park and Patricia C. Dykes.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.2196/26456. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

The publisher of the journal JMIR Medical Informatics can be contacted at: Jmir Publications, Inc, 130 Queens Quay E, Ste 1102, Toronto, On M5A 0P6, Canada.

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