Trigger factors and outcomes of falls among korean hospitalized patients: analysis of electronic medical records

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

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.

Patient-Level Fall Risk Prediction Using the Observational Medical Outcomes Partnership's Common Data Model: Pilot Feasibility Study

BACKGROUND

Falls in acute care settings threaten patients' safety. Researchers have been developing fall risk prediction models and exploring risk factors to provide evidence-based fall prevention practices; however, such efforts are hindered by insufficient samples, limited covariates, and a lack of standardized methodologies that aid study replication.

OBJECTIVE

The objectives of this study were to (1) convert fall-related electronic health record data into the standardized Observational Medical Outcome Partnership's (OMOP) common data model format and (2) develop models that predict fall risk during 2 time periods.

METHODS

As a pilot feasibility test, we converted fall-related electronic health record data (nursing notes, fall risk assessment sheet, patient acuity assessment sheet, and clinical observation sheet) into standardized OMOP common data model format using an extraction, transformation, and load process. We developed fall risk prediction models for 2 time periods (within 7 days of admission and during the entire hospital stay) using 2 algorithms (least absolute shrinkage and selection operator logistic regression and random forest).

RESULTS

In total, 6277 nursing statements, 747,049,486 clinical observation sheet records, 1,554,775 fall risk scores, and 5,685,011 patient acuity scores were converted into OMOP common data model format. All our models (area under the receiver operating characteristic curve 0.692-0.726) performed better than the Hendrich II Fall Risk Model. Patient acuity score, fall history, age ≥60 years, movement disorder, and central nervous system agents were the most important predictors in the logistic regression models.

CONCLUSIONS

To enhance model performance further, we are currently converting all nursing records into the OMOP common data model data format, which will then be included in the models. Thus, in the near future, the performance of fall risk prediction models could be improved through the application of abundant nursing records and external validation.

Automated Fall and Pressure Injury Risk Assessment Systems: Nurses' Experiences, Perspectives, and Lessons Learned

This study examined the clinical usability of two automated risk assessment systems-the Automated Fall Risk Assessment System and Automated Pressure Injury Risk Assessment System. The clinical usability of automated assessment systems was tested in three ways: agreement between the scales that nurses generally use and the automated assessment systems, focus group interviews, and the predicted amount of time saved for risk assessment and documentation. For the analysis of agreement, 1160 patients and 1000 patients were selected for falls and pressure injuries, respectively. A total of 60 nurses participated in focus group interviews. The nurses personally checked the time taken to assess and document the risks of falls and pressure injury for 271 and 251 patient cases, respectively. The results for the agreement showed a κ index of 0.43 and a percentage of agreement of 71.55% between the Automated Fall Risk Assessment System and the Johns Hopkins Fall Risk Assessment Tool. For the agreement between the Automated Pressure Injury Risk Assessment System and the Braden scale, the κ index was 0.52 and the percentage of agreement was 80.60%. The focus group interviews showed that participants largely perceived the automated risk assessment systems positively. The time it took for assessment and documentation were about 5 minutes to administer the Johns Hopkins Fall Risk Assessment Tool and 2 to 3 minutes to administer the Braden scale per day to all patients. Overall, the automated risk assessment systems may help in obtaining time devoted to directly preventing falls and pressure injuries and thereby contribute to better quality care.

Comparison of the predictive validity of three fall risk assessment tools and analysis of fall-risk factors at a tertiary teaching hospital

AIMS AND OBJECTIVES

The main purpose of this study was to identify the best fall-risk assessment tool, among the Morse Fall Scale, the Johns Hopkins fall-risk Assessment Tool and the Hendrich II fall-risk Model, for a tertiary teaching hospital. The study also analysed fall-risk factors in the hospital, focusing on the items of each fall assessment tool.

METHODS

Data on falls were obtained from the patient safety reports and electronic nursing records of a tertiary teaching hospital. A retrospective study was conducted to compare the sensitivity, specificity, area under the curve, positive predictive value, negative predictive value, Youden index and accuracy of the Morse Fall Scale, the Johns Hopkins fall-risk Assessment Tool and the Hendrich II fall-risk Model. This study was conducted according to the Strengthening the Reporting of Observational Studies in Epidemiology guideline for reporting case-control studies.

RESULTS

By analysing the association between falls and the items included in the three tools, we identified significant fall-risk factors such as gait, dizziness or vertigo, changes in mental status, impulsivity, history of falling, elimination disorder, drugs affecting falls, and depression.

CONCLUSIONS

The Hendrich II fall-risk Model had the best predictive performance for falls of the three tools, considering the highest in the area under the curve and the Youden index that comprehensively analysed sensitivity and specificity, while the Johns Hopkins fall-risk Assessment Tool had the highest accuracy. The most significant fall-risk predictors are gait, dizziness or vertigo, change in mental state, and history of falling.

RELEVANCE TO CLINICAL PRACTICE

To improve the fall assessment performance of the Morse Fall Scale at the study hospital, we propose that it be supplemented with four most significant fall-risk predictors identified in this study.

Improving Prediction of Fall Risk Using Electronic Health Record Data With Various Types and Sources at Multiple Times

Inpatient falls are among the most common adverse events threatening patient safety. Although many studies have developed predictive models for fall risk, there are some drawbacks. First, most previous studies have relied on an incident-reporting system alone to identify fall events. Thus, it has been found that falls are more likely to be underreported. Second, there has been a controversy on how to select accurate representative values for patient status data across multiple times and various data sources in electronic health records. Given this background, this study used nurses' progress notes as a complementary data source to detect fall events. In addition, we developed criteria including coverage, currency, and granularity in order to integrate electronic health records data documented at multiple times in various data types and sources. Based on this methodology, we developed three models, logistic regression, Cox proportional hazard regression, and decision tree, to predict risk of patient falls and evaluate the predictive performance of these models by comparing the results to results from the Hendrich II Fall Risk Model. The findings of this study will be used in a clinical decision support system to predict risk of falling and provide evidence-based tailored recommendations in the future.

Testing the Predictive Validity of the Hendrich II Fall Risk Model

Cumulative data on patient fall risk have been compiled in electronic medical records systems, and it is possible to test the validity of fall-risk assessment tools using these data between the times of admission and occurrence of a fall. The Hendrich II Fall Risk Model scores assessed during three time points of hospital stays were extracted and used for testing the predictive validity: (a) upon admission, (b) when the maximum fall-risk score from admission to falling or discharge, and (c) immediately before falling or discharge. Predictive validity was examined using seven predictive indicators. In addition, logistic regression analysis was used to identify factors that significantly affect the occurrence of a fall. Among the different time points, the maximum fall-risk score assessed between admission and falling or discharge showed the best predictive performance. Confusion or disorientation and having a poor ability to rise from a sitting position were significant risk factors for a fall.