Fall-Related Measurement Data Validation Methods
Quality improvement strategies require the proper measurement of indicators related to the practice problem. Patient falls are included in the National Database of Nursing Quality Indicators (NDNQI), allowing to evaluate and monitor staffing effectiveness (Yoder-Wise, 2019). The collaboration interview with the key leaders in my practice setting allowed me to determine the measures for the problem of patient falls in the Med/Surg unit. The data analysis of falls suggests that the issue is addressed in several ways. The primary measures include the overall rate of falls/falls with injury per 1,000 patient days and the number of falls by location, service line, day of the week, time of the day, and zoning. The healthcare provider calculates the total number of falls by registered nurse/patient care technician (RN/PCT) ratios. The percentages of assisted versus non-assisted falls, contributing factors, and injury types are registered for performance assessment purposes. Currently, there are no gaps in the data obtained from the report, as the results are consistent and follow the provider’s fall prevention protocol.
The interview revealed two major challenges impacting measurement for the practice problem. First, patients manage to leave the bed before the alarm alerts the staff, which can affect the accuracy of timing and zoning estimates. Second, the assessment of contributing factors is complicated when a patient’s risk of fall is not properly assessed due to the absence/loss of the patient’s medical records. Spath (2018) suggests that the accuracy of performance measures is essential for quality improvement as it determines the validity of measures. US healthcare providers use injury codes (E-codes) to determine whether an injury was caused by a fall event (Min et al., 2019). The Agency for Healthcare Research and Quality (2017) maintains NDNQI indicators can be employed to evaluate falls and fall injuries and determine their severity. Alternatively, the data from electronic health records (EHRs) might be assessed automatically to calculate fall rates and fall prevention indicators (Cho et al., 2018). Overall, there is a significant number of ways that healthcare providers can introduce to ensure the accuracy and validity of fall-related measurement data.
The Agency for Healthcare Research and Quality. (2017). Module 5: How to measure fall rates and fall prevention practices – Training guide. AHRQ. Web.
Cho, I., Boo, E. H., Lee, S. Y., & Dykes, P. C. (2018). Automatic population of eMeasurements from EHR systems for inpatient falls. Journal of the American Medical Informatics Association, 25(6), 730–738. Web.
Min, L., Tinetti, M., Langa, K. M., Ha, J., Alexander, N., & Hoffman, G. J. (2019). Measurement of fall injury with health care system data and assessment of inclusiveness and validity of measurement models. JAMA Network Open, 2(8). Web.
Spath, P. (2018). Introduction to healthcare quality management (3rd ed.). Health Administration Press.
Yoder-Wise, P. S. (2019). Leading and managing in nursing (7th ed.). Mosby.