Preventing Future Falls in Older Adult ED Patients: Evaluating the Implementation and Effectiveness of a Novel Automated Screening and Referral Intervention

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PROJECT SUMMARY/ABSTRACT Falls are the leading traumatic cause of both injury and death among older adults. American emergency departments (EDs) see over 3 million fall victims yearly, yet they play little role in primary or secondary fall prevention. The ED is an ideal site to identify patients at risk of future falls, however in this setting preventive care cannot be implemented at the expense of the primary mission of the ED: the provision of emergency care in a time-pressured environment. As the population ages, and the ED continues to expand its role as the primary site for delivery of acute unscheduled care, there is an urgent need to create a scalable intervention to assess older adults for fall risk and link them to appropriate risk reduction interventions after discharge without adding additional workload for nurses or physicians. Through an AHRQ K08, our study team has developed and validated an innovative automated screening and referral intervention for fall risk. This intervention harnesses existing data to select and connect patients to appropriate primary and secondary prevention services after ED visits without adding burden to nurse or physician workloads. This intervention features smart use of automation for screening and referral tasks maintaining physician decision autonomy, as well as the unique ability to adjust referral rates based on clinic availability. This intervention features smart use of automation for screening and referral tasks maintaining physician decision autonomy, as well as the unique ability to adjust referral rates based on clinic availability. Based on our work, UW Health is currently piloting the intervention, and has committed to implementing it at three diverse ED sites. This study will adapt the intervention for implementation at additional sites, and investigates the implementation and effectiveness of the automated screening and referral process in all three EDs through three specific aims: 1) Adapt the design of an automated screening and referral intervention for implementation in three diverse ED settings, using a human factors approach. 2) Test the effectiveness of the automated screening and referral intervention on both completed referrals to a multidisciplinary fall prevention clinic and rates of injurious falls using EHR data generated during implementation. 3) Evaluate implementation of the automated screening and referral intervention in three diverse ED sites using a mixed methods approach. This grant proposal builds upon our previous innovative work developing both CDS and risk- stratification algorithms to improve the quality and safety of care delivered to older adult ED patients. We will address the urgent and growing need for a scalable strategy for fall risk reduction from the ED by demonstrating the effectiveness of our novel approach in a study spanning diverse hospital types and patient populations. Furthermore, knowledge gained from this work will inform other use cases which could benefit from automated risk-stratification and care coordination in the ED and beyond.