Beyond Hospital-Level Aggregated Data: A Methodology to Adapt Clinical Data From the Electronic Health Record for Nursing Unit-Level Research.

BACKGROUND: Studies of nurse staffing frequently use data aggregated at the hospital level that do not provide the appropriate context to inform unit-level decisions, such as nurse staffing. OBJECTIVES: Describe a method to link patient data collected during the provision of routine care and recorded in the electronic health record (EHR) to the nursing units where care occurred in a national dataset. RESEARCH DESIGN: We identified all Veterans Health Administration acute care hospitalizations in the calendar year 2019 nationwide. We linked patient-level EHR and bar code medication administration data to nursing units using a crosswalk. We divided hospitalizations into segments based on the patient's time-stamped location (ward stays). We calculated the number of ward stays and medication administrations linked to a nursing unit and the unit-level and facility-level mean patient risk scores. RESULTS: We extracted data on 1117 nursing units, 3782 EHR patient locations associated with 1,137,391 ward stays, and 67,772 bar code medication administration locations associated with 147,686,996 medication administrations across 125 Veterans Health Administration facilities. We linked 89.46% of ward stays and 93.10% of medication administrations to a nursing unit. The average (standard deviation) unit-level patient severity across all facilities is 4.71 (1.52), versus 4.53 (0.88) at the facility level. CONCLUSIONS: Identification of units is indispensable for using EHR data to understand unit-level phenomena in nursing research and can provide the context-specific information needed by managers making frontline decisions about staffing.

Nurse Leader Perceptions of Data in the Veterans Health Administration: A Qualitative Evaluation.

Healthcare systems and nursing leaders aim to make evidence-based nurse staffing decisions. Understanding how nurses use and perceive available data to support safe staffing can strengthen learning healthcare systems and support evidence-based practice, particularly given emerging data availability and specific nursing challenges in data usability. However, current literature offers sparse insight into the nature of data use and challenges in the inpatient nurse staffing management context. We aimed to investigate how nurse leaders experience using data to guide their inpatient staffing management decisions in the Veterans Health Administration, the largest integrated healthcare system in the United States. We conducted semistructured interviews with 27 Veterans Health Administration nurse leaders across five management levels, using a constant comparative approach for analysis. Participants primarily reported using data for quality improvement, organizational learning, and organizational monitoring and support. Challenges included data fragmentation, unavailability and unsuitability to user need, lack of knowledge about available data, and untimely reporting. Our findings suggest that prioritizing end-user experience and needs is necessary to better govern evidence-based data tools for improving nursing care. Continuous nurse leader involvement in data governance is integral to ensuring high-quality data for end-user nurses to guide their decisions impacting patient care.

A Novel Use of Bar Code Medication Administration Data to Assess Nurse Staffing and Workload.

OBJECTIVE: The aim of the study is to introduce an innovative use of bar code medication administration (BCMA) data, medication pass analysis, that allows for the examination of nurse staffing and workload using data generated during regular nursing workflow. METHODS: Using 1 year (October 1, 2014-September 30, 2015) of BCMA data for 11 acute care units in one Veterans Affairs Medical Center, we determined the peak time for scheduled medications and included medications scheduled for and administered within 2 hours of that time in analyses. We established for each staff member their daily peak-time medication pass characteristics (number of patients, number of peak-time scheduled medications, duration, start time), generated unit-level descriptive statistics, examined staffing trends, and estimated linear mixed-effects models of duration and start time. RESULTS: As the most frequent (39.7%) scheduled medication time, 9:00 was the peak-time medication pass; 98.3% of patients (87.3% of patient-days) had a 9:00 medication. Use of nursing roles and number of patients per staff varied across units and over time. Number of patients, number of medications, and unit-level factors explained significant variability in registered nurse (RN) medication pass duration (conditional R2 = 0.237; marginal R2 = 0.199; intraclass correlation = 0.05). On average, an RN and a licensed practical nurse (LPN) with four patients, each with six medications, would be expected to take 70 and 74 minutes, respectively, to complete the medication pass. On a unit with median 10 patients per LPN, the median duration (127 minutes) represents untimely medication administration on more than half of staff days. With each additional patient assigned to a nurse, average start time was earlier by 4.2 minutes for RNs and 1.4 minutes for LPNs. CONCLUSION: Medication pass analysis of BCMA data can provide health systems a means for assessing variations in staffing, workload, and nursing practice using data generated during routine patient care activities.