# Preventing Future Falls among Older Adults Presenting to the Emergency Department

> **NIH AHRQ K08** · UNIVERSITY OF WISCONSIN-MADISON · 2020 · $156,859

## Abstract

PROJECT SUMMARY/ABSTRACT
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.

## Key facts

- **NIH application ID:** 10007879
- **Project number:** 5K08HS024558-05
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Brian W Patterson
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2020
- **Award amount:** $156,859
- **Award type:** 5
- **Project period:** 2016-09-30 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10007879

## Citation

> US National Institutes of Health, RePORTER application 10007879, Preventing Future Falls among Older Adults Presenting to the Emergency Department (5K08HS024558-05). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10007879. Licensed CC0.

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