PROJECT SUMMARY
High nursing workload is a threat to care quality, patient safety, and nurses’ 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
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 nursing 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 nurses and
are not designed to identify mutable microsystem factors that contribute most to nursing workload.
A promising development in nursing workload research is the increasing emphasis on measuring
situational workload which best explains the workload experienced by nurses 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 models of NICU nursing workload derived from readily accessible electronic health
record (EHR) data. The validated models will be the foundation for a future EHR-based clinical
decision support (CDS) tool that will track the real-time workload of registered nurses, 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 nursing
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).
