Measuring NICU Nurse Practitioner Workload in Real-time to Improve Care Quality and Patient Safety

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PROJECT SUMMARY High provider workload is a threat to care quality, patient safety, and providers’ well-being and job satisfaction. Workload – which lacks a universally accepted definition - is a complex multi-dimensional construct that is affected by external task demands and environmental, organizational, and psychological factors. The importance of managing high workload is nowhere more evident than in neonatal intensive care units (NICUs). Critically ill neonates are highly vulnerable to iatrogenic events due to their immaturity and fragility, and high clinician workload has been directly associated with increased incidence of adverse neonatal safety outcomes. Despite the evidence and need, patient safety researchers have been slow to develop multi-level models, scalable workload measurement systems, or other health information technology interventions to improve workload management and patient safety. Conventional workload management tools predominantly measure and predict workload using unit-level (e.g., staffing ratios) or patient-level (e.g., acuity) data rather than data collected across the four levels of workload recommended by human factors engineers (HFEs) - unit, job, patient, and situation. As a result, current tools under-measure the workload experienced by providers and are not designed to identify mutable microsystem factors that contribute most to provider workload. A promising development in workload research is the increasing emphasis on measuring situational workload which best explains the workload experienced by clinicians due to healthcare microsystem design. Situational workload is most affected by performance obstacles (i.e., delays, interruptions, etc.) in the local work environment and can be applied at the unit, job, or patient-levels. Most importantly, it is diagnostic of underlying contributory factors and therefore actionable for improvement. To date, situational workload has been measured using subjective surveys which are work-interrupting, thus difficult to integrate into practice. Vanderbilt University Medical Center (VUMC), in collaboration Johns Hopkins University (JHU), will employ a systems engineering human-centered design process to design, develop, and validate new multi-level model of NICU nurse practitioner workload derived from readily accessible electronic health record (EHR) data. The validated model will be the foundation for a future EHR-based clinical decision support (CDS) tool that will track the real-time workload of NICU providers, predict near- term future unit workload, and guide workload reduction and balancing interventions. The project’s three Specific Aims are: Aim 1. To conduct a comprehensive HFE-based analysis of NICU provider (i.e., neonatal nurse practitioner) workload; Aim 2. To design and develop real-time multivariable workload models and Aim 3. To validate the real-time workload models at VUMC (A) and to determine the generalizability of the models at an external hospital (B).