In this final installment of a three-part discussion on research, I’ll share how pre-occupancy research tools were used to establish design criteria for an inpatient floor build-out at a major Midwestern hospital.

My colleague Kara Freihoefer and I conducted a three-day data gathering event on the existing inpatient unit on a different floor, observing and interviewing day-shift nurses to record their workflow patterns and operational needs.

We then categorized the quantitative and qualitative data into common themes to formulate a comprehensive set of design criteria, which we refer to as “critical-to-quality” (CtQ) measures that guide design solutions. For example, we determined that top CtQ measures were patient safety, patient comfort, and supply storage.

The CtQ measures formed the basis for three proposed configurations for a 24-bed, 28-bed, and 32-bed inpatient unit.

Through further analyses, we utilized the data to validate design decisions. For instance, we transposed current-state workflow patterns of nursing staff into each unit prototype. Through animation, we calculated and compared differences between time spent traveling, time at the bedside, frequency of visits to medication and supply rooms, and more.

In addition, we built full-scale patient room mock-ups to test patient care scenarios. When differing design solutions arose, we had users rank solutions based on the CtQ measures. We concluded a 24-bed decentralized unit would best meet programming needs, operational efficiencies, and patient and staff safety.

The new unit has been in operation for nearly a year, and we are in the midst of conducting a three-phase post-occupancy evaluation using the same research tools to compare differences, test hypothesized outcomes, and identify further improvements to either design or process.

Pre-occupancy evaluations are a great way to gather valid and reliable data about user behavior and experience. The information can create design criteria that evaluate design decisions, improve operational processes, and positively impact patient outcomes.

Yet research is dynamic. As healthcare delivery models, medical technologies, and building methods evolve, data collection tools must be adaptable and flexible for continuous improvement.