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 Article Swipe
Daniel J Hekman
,
Amy L. Cochran
,
Apoorva Maru
,
Hanna J. Barton
,
Manish N. Shah
,
Douglas A. Wiegmann
,
Maureen A. Smith
,
Frank Liao
,
Brian W. Patterson
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.2196/48128
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.2196/48128
Background Emergency department (ED) providers are important collaborators in preventing falls for older adults because they are often the first health care providers to see a patient after a fall and because at-home falls are often preceded by previous ED visits. Previous work has shown that ED referrals to falls interventions can reduce the risk of an at-home fall by 38%. Screening patients at risk for a fall can be time-consuming and difficult to implement in the ED setting. Machine learning (ML) and clinical decision support (CDS) offer the potential of automating the screening process. However, it remains unclear whether automation of screening and referrals can reduce the risk of future falls among older patients. Objective The goal of this paper is to describe a research protocol for evaluating the effectiveness of an automated screening and referral intervention. These findings will inform ongoing discussions about the use of ML and artificial intelligence to augment medical decision-making. Methods To assess the effectiveness of our program for patients receiving the falls risk intervention, our primary analysis will be to obtain referral completion rates at 3 different EDs. We will use a quasi-experimental design known as a sharp regression discontinuity with regard to intent-to-treat, since the intervention is administered to patients whose risk score falls above a threshold. A conditional logistic regression model will be built to describe 6-month fall risk at each site as a function of the intervention, patient demographics, and risk score. The odds ratio of a return visit for a fall and the 95% CI will be estimated by comparing those identified as high risk by the ML-based CDS (ML-CDS) and those who were not but had a similar risk profile. Results The ML-CDS tool under study has been implemented at 2 of the 3 EDs in our study. As of April 2023, a total of 1326 patient encounters have been flagged for providers, and 339 unique patients have been referred to the mobility and falls clinic. To date, 15% (45/339) of patients have scheduled an appointment with the clinic. Conclusions This study seeks to quantify the impact of an ML-CDS intervention on patient behavior and outcomes. Our end-to-end data set allows for a more meaningful analysis of patient outcomes than other studies focused on interim outcomes, and our multisite implementation plan will demonstrate applicability to a broad population and the possibility to adapt the intervention to other EDs and achieve similar results. Our statistical methodology, regression discontinuity design, allows for causal inference from observational data and a staggered implementation strategy allows for the identification of secular trends that could affect causal associations and allow mitigation as necessary. Trial Registration ClinicalTrials.gov NCT05810064; https://www.clinicaltrials.gov/study/NCT05810064 International Registered Report Identifier (IRRID) DERR1-10.2196/48128
Related Topics
Concepts
Emergency department
Medicine
Clinical decision support system
Referral
Psychological intervention
Intervention (counseling)
Fall prevention
Logistic regression
Poison control
Injury prevention
Medical emergency
Decision support system
Family medicine
Artificial intelligence
Nursing
Computer science
Internal medicine
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2196/48128
- https://jmir.org/api/download?alt_name=resprot_v12i1e48128_app1.pdf&filename=fd067b737a522c6699b7429d6544b753.pdf
- OA Status
- gold
- Cited By
- 6
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4377821039
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4377821039Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.2196/48128Digital Object Identifier
- Title
-
Effectiveness of an Emergency Department–Based Machine Learning Clinical Decision Support Tool to Prevent Outpatient Falls Among Older Adults: Protocol for a Quasi-Experimental StudyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-23Full publication date if available
- Authors
-
Daniel J Hekman, Amy L. Cochran, Apoorva Maru, Hanna J. Barton, Manish N. Shah, Douglas A. Wiegmann, Maureen A. Smith, Frank Liao, Brian W. PattersonList of authors in order
- Landing page
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https://doi.org/10.2196/48128Publisher landing page
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https://jmir.org/api/download?alt_name=resprot_v12i1e48128_app1.pdf&filename=fd067b737a522c6699b7429d6544b753.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://jmir.org/api/download?alt_name=resprot_v12i1e48128_app1.pdf&filename=fd067b737a522c6699b7429d6544b753.pdfDirect OA link when available
- Concepts
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Emergency department, Medicine, Clinical decision support system, Referral, Psychological intervention, Intervention (counseling), Fall prevention, Logistic regression, Poison control, Injury prevention, Medical emergency, Decision support system, Family medicine, Artificial intelligence, Nursing, Computer science, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 1, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
32Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.identified | 262 |
| abstract_inverted_index.meaningful | 366 |
| abstract_inverted_index.mitigation | 436 |
| abstract_inverted_index.necessary. | 438 |
| abstract_inverted_index.population | 389 |
| abstract_inverted_index.preventing | 9 |
| abstract_inverted_index.providers, | 314 |
| abstract_inverted_index.regression | 195, 218, 407 |
| abstract_inverted_index.threshold. | 214 |
| abstract_inverted_index.Conclusions | 341 |
| abstract_inverted_index.appointment | 337 |
| abstract_inverted_index.conditional | 216 |
| abstract_inverted_index.demonstrate | 384 |
| abstract_inverted_index.discussions | 143 |
| abstract_inverted_index.implemented | 290 |
| abstract_inverted_index.possibility | 392 |
| abstract_inverted_index.statistical | 405 |
| abstract_inverted_index.NCT05810064; | 442 |
| abstract_inverted_index.Registration | 440 |
| abstract_inverted_index.administered | 205 |
| abstract_inverted_index.associations | 433 |
| abstract_inverted_index.intelligence | 151 |
| abstract_inverted_index.intervention | 203, 352, 396 |
| abstract_inverted_index.methodology, | 406 |
| abstract_inverted_index.International | 444 |
| abstract_inverted_index.applicability | 385 |
| abstract_inverted_index.collaborators | 7 |
| abstract_inverted_index.demographics, | 238 |
| abstract_inverted_index.discontinuity | 196, 408 |
| abstract_inverted_index.effectiveness | 130, 160 |
| abstract_inverted_index.intervention, | 170, 236 |
| abstract_inverted_index.intervention. | 137 |
| abstract_inverted_index.interventions | 50 |
| abstract_inverted_index.observational | 415 |
| abstract_inverted_index.identification | 425 |
| abstract_inverted_index.implementation | 381, 420 |
| abstract_inverted_index.time-consuming | 70 |
| abstract_inverted_index.decision-making. | 155 |
| abstract_inverted_index.intent-to-treat, | 200 |
| abstract_inverted_index.ClinicalTrials.gov | 441 |
| abstract_inverted_index.quasi-experimental | 189 |
| abstract_inverted_index.DERR1-10.2196/48128 | 449 |
| abstract_inverted_index.https://www.clinicaltrials.gov/study/NCT05810064 | 443 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5014690496 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I135310074 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.7599999904632568 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.83198227 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |