Effectiveness of an Automated Falls-Risk Screening and Referral Tool in the Emergency Department (ED)

NCT ID: NCT05810064

Last Updated: 2025-03-11

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 30000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-02-22
• Study Completion Date: 2026-08-31

Brief Summary

The purpose of this retrospective cohort study is to evaluate the effectiveness of an EHR-based clinical decision support system (CDS) for automatically screening older adult ED patients for risk of future falls and providing ED clinicians opportunity to place referrals orders to the UW Health Mobility and Falls Clinic for those at highest risk prior to discharge.

This CDS tool has already been implemented at the UW Hospital ED, and as a QI initiative will be implemented in a staged process at two other UW Health-affiliated emergency departments (The American Center and Swedish American Hospital).

Detailed Description

The specific aim of this retrospective cohort study is to test the effectiveness of the automated screening and referral intervention on completed referrals to the UW Health fall prevention clinic and rates of injurious falls, using a limited dataset created from EHR and Medicare claims data. The investigators hypothesize that ED patients referred using the falls risk CDS tool will have decreased healthcare use due to fall-related injuries, and that the intervention will have similar levels of effectiveness across different types of patient characteristics. The investigators will also systematically examine barriers to patients completing their clinic referrals, as well as clinic scheduling and pre-visit planning protocols that may have excluded patients from receiving Falls Clinic services.

Effectiveness will be assessed based on examination of a limited dataset consisting of EHR and Medicare claims data measuring rates of (1) patient referrals at each ED, (2) completed referrals to the Mobility and Falls Clinic (i.e., a completed clinic appointment), and (3) healthcare visits for fall-related causes occurring within the six months following the initial ED visit.

The primary analysis to evaluate effectiveness of the fall-risk CDS tool will include data from older adult patients (age ≥65) who visit study EDs during the intervention period at each site, have a UW Health System-affiliated primary care provider, and are discharged from the ED or ED observation unit (not admitted). Members of the UWH Applied Data Science team (not part of the study team) will extract data from patient EHR to create a limited dataset including: past falls and fall-related injuries (in the 12 months pre-visit, including the index ED visit), post-visit falls and fall-related injuries (in the 6 months following the index ED visit), patient demographics (age, gender, race/ethnicity, insurance), comorbidities, active medications, and utilization (e.g., primary and specialty care visits). Education and income levels will be approximated using census track data. Area Deprivation Index will also be employed based on patient address. These variables will be extracted retrospectively and stored on secure servers.

Conditions

Falls-Risk

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: RETROSPECTIVE

Study Groups

Discharged ED Patients prior to Intervention

Patients aged 65 and older who are in the emergency department and subsequently discharged (not admitted)

No interventions assigned to this group

Discharged ED Patients after Intervention

Patients aged 65 and older who are in the emergency department and subsequently discharged (not admitted)

Falls-Risk Clinical Decision Support (CDS) Tool

Intervention Type: OTHER

CDS in the Electronic Health Record (EHR) to screen patients at high-risk for future falls enabling referrals to the UW Health Mobility and Falls clinic.

Interventions

Falls-Risk Clinical Decision Support (CDS) Tool

CDS in the Electronic Health Record (EHR) to screen patients at high-risk for future falls enabling referrals to the UW Health Mobility and Falls clinic.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

Retrospective analysis will include data from:

• ED patients 65 years or older
• discharged from the ED (not admitted)

• Minimum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Agency for Healthcare Research and Quality (AHRQ)

FED

Sponsor Role: collaborator

University of Wisconsin, Madison

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Brian W Patterson, MD, MPH

Role: PRINCIPAL_INVESTIGATOR

University of Wisconsin, Madison

Locations

Swedish American Emergency Department

Rockford, Illinois, United States

Site Status: NOT_YET_RECRUITING

East Madison Hospital

Madison, Wisconsin, United States

Site Status: RECRUITING

UWHC Emergency Department

Madison, Wisconsin, United States

Site Status: RECRUITING

Countries

United States

Central Contacts

Dann Hekman, MS

Role: CONTACT

djhekman@medicine.wisc.edu

(608) 265-3178

References

Hekman DJ, Cochran AL, Maru AP, Barton HJ, Shah MN, Wiegmann D, Smith MA, Liao F, Patterson BW. Effectiveness of an Emergency Department-Based Machine Learning Clinical Decision Support Tool to Prevent Outpatient Falls Among Older Adults: Protocol for a Quasi-Experimental Study. JMIR Res Protoc. 2023 Aug 3;12:e48128. doi: 10.2196/48128.

Reference Type: DERIVED

PMID: 37535416 (View on PubMed)

Other Identifiers

1K08HS024558

Identifier Type: AHRQ

Identifier Source: secondary_id

View Link

1R18HS027735

Identifier Type: AHRQ

Identifier Source: secondary_id

View Link

SMPH/EMERG MED

Identifier Type: OTHER

Identifier Source: secondary_id

2021-0776

Identifier Type: -

Identifier Source: org_study_id