A Clinical Data Warehouse Analysis of Risk Factors for Inpatient Falls in a Tertiary Hospital: A Case-Control Study

Abstract

Objectives The aims of the study are to identify fall risk factors and to establish automatic risk assessments based on clinical data from electronic medical records of hospitalized patients. Methods In this retrospective case-control study, we reviewed the electronic medical records of 1454 patients (292 and 1162 patients in the fall and nonfall groups, respectively) who were hospitalized at a 1800-bed tertiary hospital in South Korea between January 1, 2017, and December 31, 2017. Patients’ age, sex, and clinical department were matched, and all laboratory reports, clinical flow sheets, and nursing initial assessment records of case from the Clinical Data Warehouse system were analyzed. The collated patient records data were analyzed using SAS (version 9.4) and logistic regression. Results Overall, 65 risk factors, including low body mass index, low blood pressure, low albumin levels, high fasting blood sugar level, low red blood cell counts, and high potassium levels, that significantly increased the incidence of falls were identified. Falls were also associated with 21 items from the clinical flow sheet and nursing initial assessment, including frequent bowel movements, 24-hour urine tests, imaging tests, biopsy, pain, intravenous tubes, unclear consciousness, and taking medication. Conclusions Fall risk factors identified via the Clinical Data Warehouse can be used to build an automated detection system to detect fall risk in electronic medical records, enabling nurses to assess the fall risk in addition to using the fall scale.