ArticleJournal of the American Geriatrics Society2017
Using Chief Complaint in Addition to Diagnosis Codes to Identify Falls in the Emergency Department.
Article in Journal of the American Geriatrics Society, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A work systems approach to characterizing emergency department disposition decision-making under low and high demand.Applied ergonomics · 2026Article
- Extracting geriatric syndromes from electronic health records: a scoping review.European geriatric medicine · 2026Article
- Article
- Evaluation of falls detected by natural language processing algorithm and not coded external cause of morbidity.JAMIA open · 2025Article
- 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 research protocols · 2023Article
- Collaborative design and implementation of a clinical decision support system for automated fall-risk identification and referrals in emergency departments.Healthcare (Amsterdam, Netherlands) · 2022Article
- Operationalizing a real-time scoring model to predict fall risk among older adults in the emergency department.Frontiers in digital health · 2022Article
- Reducing Fall-related Revisits for Elderly Diabetes Patients in Emergency Departments: A Transition Flow Model.IEEE robotics and automation letters · 2021Article
- Trends and Characteristics of Emergency Medical Services in Italy: A 5-Years Population-Based Registry Analysis.Healthcare (Basel, Switzerland) · 2020Article
- RESEARCHComparing Strategies for Identifying Falls in Older Adult Emergency Department Visits Using EHR Data.Journal of the American Geriatrics Society · 2020Observational
- Development and validation of a pragmatic natural language processing approach to identifying falls in older adults in the emergency department.BMC medical informatics and decision making · 2019Article
- Using the Hendrich II Inpatient Fall Risk Screen to Predict Outpatient Falls After Emergency Department Visits.Journal of the American Geriatrics Society · 2018Observational
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
objectivesTo compare incidence of falls in an emergency department (ED) cohort using a traditional International Classification of Diseases, Ninth Revision (ICD-9) code-based scheme and an expanded definition that included chief complaint information and to examine the clinical characteristics of visits "missed" in the ICD-9-based scheme.
designRetrospective electronic record review.
settingAcademic medical center ED.
participantsIndividuals aged 65 and older seen in the ED between January 1, 2013, and September 30, 2015. MEASUREMENTS: Two fall definitions were applied (individually and together) to the cohort: an ICD-9-based definition and a chief complaint definition. Admission rates and 30-day mortality (per encounter) were measured for each definition.
resultsTwenty-three thousand eight hundred eighty older adult visits occurred during the study period. Using the most-inclusive definition (ICD-9 code or chief complaint indicating a fall), 4,363 visits (18%) were fall related. Of these visits, 3,506 (80%) met the ICD-9 definition for a fall-related visit, and 2,664 (61%) met the chief complaint definition. Of visits meeting the chief complaint definition, 857 (19.6%) were missed when applying the ICD-9 definition alone. Encounters missed using the ICD-9 definition were less likely to lead to an admission (42.9%, 95% confidence interval (CI) = 39.7-46.3%) than those identified (54.4%, 95% CI = 52.7-56.0%).
conclusionIdentifying individuals in the ED who have fallen based on diagnosis codes underestimates the true burden of falls. Individuals missed according to the code-based definition were less likely to have been admitted than those who were captured. These findings call attention to the value of using chief complaint information to identify individuals who have fallen in the ED-for research, clinical care, or policy reasons.
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