Improving Hand Hygiene Compliance through Collaborative Computational Design

Hand sanitization by healthcare staff remains one of the most effective ways for controlling infection in healthcare settings. However, predicting faithful adherence to Hand Hygiene Compliance (HHC) is difficult in complex environments such as inpatient hospital settings. The main challenge is understanding how different components of human and built systems interact to achieve specific goals such as HHC at the critical moments of care delivery. The aim of this explorative study was to evaluate how Human Factors derived visual salience cues and proximity-compatibility principles might be used in the design of healthcare spaces to support nurse moments of HHC through increased perceived behavioral control and intention. The investigative team used a Collaborative Computational Scenario Planning (CCSP) Model approach to determine the integrative effects of reinforcing and detracting operational and environmental factors on discrete moments of HHC behavior. Supervised Machine Learning analysis was conducted on data collected by a large academic medical center that included HHC observance in clinical staff spanning from 2017 to 2021 in two inpatient hospital units. The probabilistic outcomes of unit based HHC observance likelihood were used to compute Fuzzy Cognitive Model Edge Probabilities between Hand Hygiene (HH) cues and detected HHC at key moments. Hospital infection control experts were then engaged to identify the weight of various reinforcing and detracting operational and environmental factors contributing to HHC observance. Combining the quantitative and qualitative methods, allowed the team to then develop integrative CCSP models which facilitated predictive insight into the development of targeted environmental improvements that might contribute to HHC control and intention to support safer patient care.