3075 — Predicting Serious Injurious Falls in Community Living Centers
Powell-Cope GM (HSR&D/RR&D Center of Excellence, Maximizing Rehabilitation Outcomes), Campbell RR
(HSR&D/RR&D Center of Excellence, Maximizing Rehabilitation Outcomes), Hahm B
(HSR&D/RR&D Center of Excellence, Maximizing Rehabilitation Outcomes), Joseph I
(HSR&D/RR&D Center of Excellence, Maximizing Rehabilitation Outcomes), Bulat T
(HSR&D/RR&D Center of Excellence, Maximizing Rehabilitation Outcomes), Bass E
(Congressional Budget Office), Westphal J
(Outcome Engineering, LLC), Thatcher E
(Bay Pines VAMC), Shorr R
(Gainesville HSR&D REAP)
Falls in older adults are a high volume, high cost problem for the VHA, resulting in injury, disability, and death. The goal of this 18-month study was to apply socio-technical probabilistic risk assessment (ST-PRA) to proactively prioritize risks and identify prevention strategies for serious injurious falls in community living centers (CLCs).
ST-PRA methodology was used to build risk models. Three types of data (outcomes, human error, and behavior) and three data sources (literature, risk management, and risk modeling teams) were used. The Risk Modeling Team consisted of 26 clinical and non-clinical staff from three VA CLCs and one State Veteran’s nursing home. During the data collection procedures eight team meetings were held and participants were separated into groups by discipline. A core risk modeling team was selected to refine models. Participants were asked to identify at-risk provider and resident behaviors and equipment failures, assign probabilities to each failure, and identify prevention strategies for the highest risk failures. RelexTM software was used to enter risk data and create probabilistic models using Bayesian methods.
Six risk models mapped unassisted movements and wheelchair transfers by residents. Models predicted 28 serious, injurious falls during one-year, compared to 21 actual serious injurious falls reported by study participants. The greatest paths of risk were for residents with impaired mobility and high fragility, who engaged in unassisted wheelchair transfers to and from the bed and toilet, when the locks on the wheelchair were not engaged and when protective measures were not in place. A 26% reduction in injurious falls could be achieved by (a) reducing the number of unassisted transfers through a modest improvement in response time to alarms; (b) installing automatic break locks on 90% of wheelchairs; (c) making the wheelchair maintenance process highly reliable; and (d) decreasing improper transfer techniques by 10%.
Quantifying multiple factors that contributed to the risk of serious injurious falls in CLC residents provided the evidence base for identifying interventions to decrease risk.
Based on risk models, decision support tools are being developed to help staff assess injury risk for individual residents and to implement prevention strategies as indicated.