Older Americans rely on the emergency department (ED) for acute, unscheduled care. Unfortunately, many older adults experience poor outcomes after ED visits, suggesting that these encounters represent missed opportunities to identify high risk patients and intervene to improve the transition to outpatient care. In particular, significant falls among older adults are a serious and preventable problem. Unscheduled ED visits offer an opportunity to identify older adults at higher risk of falls than the general primary care population at a time when fall risk factors can be modified, and thus offer an ideal additional setting for fall screening beyond primary care. Such screening is advocated in the ED, but screening interventions often fail due to time constraints of providers in the emergency setting. As electronic health record (EHR) systems evolve, computerized decision support offers the potential to support fall screening with less provider burden. The objective of this proposal is to identify adults at high risk for future falls and improve their care both during their ED visit and after discharge. As a physician scientist, my goal is to lead an independent research program to improve transitions to outpatient care following ED visits for older adults. This 5-year proposal will advance these goals by providing the necessary support and training in implementation science as well as informatics-based interventions. I have a unique background in engineering, emergency medicine, and health services research and am well prepared to successfully complete the proposal. I will be aided by a team of expert mentors at an institution with substantial resources and an outstanding environment to conduct the research proposed and transition to an independent investigator with R01 support. The proposed aims are to: 1) Compare EHR-based data extraction to in-person screening of future outpatient fall risk, 2) Using data available in the EHR at the time of presentation, develop a predictive algorithm to risk- stratify ED patients for risk of significant falls in the next 6 months, and 3) Design and pilot a clinical decision support intervention to identify older adults at high risk of falls and improve their care both in the ED and after discharge.
These aims will be accomplished by creating and analyzing a database linking the EHR and claims data, incorporating novel elements derived by natural language processing, by utilizing machine learning in addition to traditional statistical techniques, and by developing and piloting an intervention in one health system.
This proposal directly addresses the public health burden of the rising rates of significant falls in older adults which are the leading cause of traumatic mortality in the elderly. There is an unmet need to improve screening practices for a variety of conditions in the emergency department, where individuals are often at high risk for poor outcomes after discharge. The proposed work will utilize a novel approach based on the electronic health record to screen for future fall risk that can be expanded and adapted to other high-risk patients in future work.
| Patterson, Brian W; Repplinger, Michael D; Pulia, Michael S et al. (2018) Using the Hendrich II Inpatient Fall Risk Screen to Predict Outpatient Falls After Emergency Department Visits. J Am Geriatr Soc 66:760-765 |
| Pulia, Michael S; Schwei, Rebecca J; Patterson, Brian W et al. (2018) Effectiveness of Outpatient Antibiotics After Surgical Drainage of Abscesses in Reducing Treatment Failure. J Emerg Med 55:512-521 |
| Repplinger, Michael D; Bracken, Rebecca L; Patterson, Brian W et al. (2018) Downstream Imaging Utilization After MR Angiography Versus CT Angiography for the Initial Evaluation of Pulmonary Embolism. J Am Coll Radiol 15:1692-1697 |
| Patterson, Brian W; Smith, Maureen A; Repplinger, Michael D et al. (2017) Using Chief Complaint in Addition to Diagnosis Codes to Identify Falls in the Emergency Department. J Am Geriatr Soc 65:E135-E140 |
