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.

An Alternative Method of Public Reporting of Comparative Hospital Quality and Performance Data for Transparency Initiatives.

BACKGROUND: Hospital performance comparisons for transparency initiatives may be inadequate if peer comparison groups are poorly defined. OBJECTIVE: The objective of this study was to evaluate a new approach identifying hospital peers for comparison. DESIGN/SETTING: We used Mahalanobis distance as a new method of developing peer-specific groupings for hospitals to incorporate both external and internal complexity. We compared the overlap in groups with an existing method used by the Veterans' Health Administration's Office for Productivity, Efficiency, and Staffing (OPES). PARTICIPANTS: One hundred twenty-two acute-care Veterans' Health Administration's Medical Facilities as defined in the OPES fiscal year 2014 report. MEASURES: Using 15 variables in 9 categories developed from expert input, including both hospital internal measures and community-based external measures, we used principal components analysis and calculated Mahalanobis distance between each hospital pair. This method accounts for correlation between variables and allows for variables having different variances. We identified the 50 closest hospitals, then eliminated any potential peer whose score on the first component was >1 SD from the reference hospital. We compared overlap with OPES measures. RESULTS: Of 15 variables, 12 have SDs exceeding 25% of their means. The first 2 components of our analysis explain 24.8% and 18.5% of variation among hospitals. Eight of 9 variables scaling positively on the first component measure internal complexity, aligning with OPES groups. Four of 5 variables scaling positively on the second component but not the first are factors from the policy environment; this component reflects a dimension not considered in OPES groups. CONCLUSION: Individualized peers that incorporate external complexity generate more nuanced comparators to evaluate quality.

Examining clinical performance feedback in Patient-Aligned Care Teams.

BACKGROUND: The move to team-based models of health care represents a fundamental shift in healthcare delivery, including major changes in the roles and relationships among clinical personnel. Audit and feedback of clinical performance has traditionally focused on the provider; however, a team-based model of care may require different approaches. OBJECTIVE: Identify changes in audit and feedback of clinical performance to primary care clinical personnel resulting from implementing team-based care in their clinics. DESIGN: Semi-structured interviews with primary care clinicians, their department heads, and facility leadership at 16 geographically diverse VA Medical Centers, selected purposively by their clinical performance profile. PARTICIPANTS: An average of three interviewees per VA medical center, selected from physicians, nurses, and primary care and facility directors who participated in 1-hour interviews. APPROACH: Interviews focused on how clinical performance information is fed back to clinicians, with particular emphasis on external peer-review program measures and changes in feedback associated with team-based care implementation. Interview transcripts were analyzed, using techniques adapted from grounded theory and content analysis. KEY RESULTS: Ownership of clinical performance still rests largely with the provider, despite transitioning to team-based care. A panel-management information tool emerged as the most prominent change to clinical performance feedback dissemination, and existing feedback tools were seen as most effective when monitored by the nurse members of the team. Facilities reported few, if any, appreciable changes to the assessment of clinical performance since transitioning to team-based care. CONCLUSIONS: Although new tools have been created to support higher-quality clinical performance feedback to primary care teams, such tools have not necessarily delivered feedback consistent with a team-based approach to health care. Audit and feedback of clinical performance has remained largely unchanged, despite material differences in roles and responsibilities of team members. Future research should seek to unpack the nuances of team-based audit and feedback, to better align feedback with strategic clinical goals.