Abstract
In this article, we briefly describe our use of a computational modeling tool, OrgAhead, details of which have been reported previously, then discuss several of the challenges computational modeling presented and our solutions. We used OrgAhead to simulate 39 nursing units in 13 Arizona hospitals and then predict changes to improve overall patient quality and safety outcomes. Creating the virtual units required (1) collecting data from managers, staff, patients, and quality and information services on each of the units; (2) mapping specific data elements (eg, control over nursing practice, nursingworkload, patient complexity, turbulence, orientation/tenure, education) to OrgAhead's parameters and variables; and then (3) validating that the newly created virtual units performed functionally like the actual units (eg, actual patient medication errors and fall rates correlated with the accuracy outcome variable in OrgAhead). Validation studies demonstrated acceptable correspondence between actual and virtual units. For all but the highest performing unit, we generated strategies that improved virtual performance and could reasonably be implemented on actual units to improve outcomes. Nurse managers, to whom we reported the results, responded positively to the unit-specific recommendations, which other methods cannot provide. In the end, resolving the modeling challenges we encountered has improved OrgAhead's functionality and usability.