Acuity-Based Staffing in Labor and Delivery Using Electronic Health Record Data.

BACKGROUND: Planning safe and effective nurse staffing can be challenging for nurse leaders of labor and delivery units due to the dynamic nature of birth and unpredictable fluctuations in census and acuity. The electronic health record (EHR) has a vast source of patient data that can be used to retrospectively review patient needs and nurse staffing gaps that can serve as a basis for prospective planning for nurse staffing. PURPOSE: This quality improvement project was initiated with the goal of developing real-time and longitudinal reports to quantify hourly nurse staffing needs based on patient census, acuity, and required clinical interventions from data that are contained with the EHR. The plan was to determine trends and nurse staffing needs for each 24-hour period every day of the week and identify ongoing staffing patterns to meet the Association of Women's Health, Obstetric, and Neonatal Nurses' (AWHONN) staffing guidelines. METHODS: The obstetric nursing leadership team worked with the information technology specialists and developed an algorithm that identifies patient acuity level, indicated clinical interventions, and outlines necessary staffing requirements to provide safe high-quality care. Various reports were built in the EHR to inform the nursing leadership team about nurse staffing on a real-time and historical basis. RESULTS: The reports provided quantitative data that supported a budgetary increase in nurse staffing and a more flexible nurse staffing scheduling system to meet the needs of the patients. The project was successfully implemented in all four of the hospital system maternity units. CLINICAL IMPLICATIONS: Use of EHR in labor and delivery units is nearly universal. Working with the information technology specialists to integrate nurse staffing data into the EHR is one way to align nurse staffing with the AWHONN nurse staffing standards in real-time and for projections of nurse staffing needs based on unit historical patient data.