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Shankar KN, Li A. Older Adult Falls in Emergency Medicine, 2023 Update. Clin Geriatr Med 2023; 39:503-518. [PMID: 37798062 DOI: 10.1016/j.cger.2023.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/07/2023]
Abstract
Of 4 older adults, 1 will fall each year in the United States. Based on 2020 data from the Centers of Disease Control, about 36 million older adults fall each year, resulting in 32,000 deaths. Emergency departments see about 3 million older adults for fall-related injuries with falls having the ability to cause serious injury such as catastrophic head injuries and hip fractures. One-third of older fall patients discharged from the ED experience one of these outcomes at 3 months.
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Affiliation(s)
- Kalpana N Shankar
- Department of Emergency Medicine, Brigham and Women's Hospital, 75 Francis Street, Neville House, Boston, MA 02115, USA.
| | - Angel Li
- Department of Emergency Medicine, The Ohio State University, 376 West 10th Avenue, Columbus, OH 43210, USA
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2
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Qian XX, Chau PH, Fong DYT, Ho M, Woo J. Post-Hospital Falls Among the Older Population: The Temporal Pattern in Risk and Healthcare Burden. J Am Med Dir Assoc 2023; 24:1478-1483.e2. [PMID: 37591487 DOI: 10.1016/j.jamda.2023.07.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 07/05/2023] [Accepted: 07/10/2023] [Indexed: 08/19/2023]
Abstract
OBJECTIVES Older adults are prone to falls following hospital discharge, resulting in healthcare utilization and costs. The fall risk might change over time after discharge. To fill research gaps in this area, this study examined the temporal pattern in incidence and healthcare burden of post-hospital falls in older adults. DESIGN A territory-wide retrospective cohort study was conducted. SETTING AND PARTICIPANTS Participants were Hong Kong adults aged ≥65 years and discharged from hospitals between January 2007 and December 2017. METHODS The participants were followed for 12 months to identify fall-related inpatient episodes, accident and emergency department (AED) visits, and mortality after discharge. The post-hospital falls were further analyzed in 2 subcategories (1) only requiring AED visits and (2) requiring hospitalization. The incidence rate and faller incidence proportion for total falls and subcategories during the different periods were examined. The corresponding healthcare utilization and costs were calculated. RESULTS Among the 606,392 older adults discharged from hospitals during the study period, 28,593 individuals (4.7%) experienced at least 1 post-hospital fall within 12 months, resulting in a total of 33,158 falls (57 per 1000 person-years). Out of post-hospital falls presenting to hospitals, one-third only required AED visits, and two-thirds required hospitalization. The fall incidence rate peaked in the first 3 weeks after discharge and gradually decreased to a stable level from the fourth to ninth week. The annual healthcare costs related to post-hospital falls exceeded USD 28.9 million in older adults, with the mean cost per faller and fall being USD 11,129 and USD 9596. CONCLUSIONS AND IMPLICATIONS The fall-related healthcare utilizations after discharge impose a substantial economic burden on older adults. During the first 9 weeks, particularly the first 3 weeks, older adults were at high risk of falling. The efforts on resource allocation for fall prevention are suggested to prioritize this period.
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Affiliation(s)
- Xing Xing Qian
- School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Pui Hing Chau
- School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
| | - Daniel Y T Fong
- School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Mandy Ho
- School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Jean Woo
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
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3
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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; 12:e48128. [PMID: 37535416 PMCID: PMC10436111 DOI: 10.2196/48128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/04/2023] [Accepted: 05/23/2023] [Indexed: 08/04/2023] Open
Abstract
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.
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Affiliation(s)
- Daniel J Hekman
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States
| | - Amy L Cochran
- Department of Population Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Apoorva P Maru
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States
| | - Hanna J Barton
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Manish N Shah
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States
| | - Douglas Wiegmann
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, United States
| | - Maureen A Smith
- Health Innovation Program, University of Wisconsin-Madison, Madison, WI, United States
| | - Frank Liao
- Department of Applied Data Science, UWHealth Hospitals and Clinics, University of Wisconsin-Madison, Madison, WI, United States
| | - Brian W Patterson
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States
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4
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Engstrom CJ, Adelaine S, Liao F, Jacobsohn GC, Patterson BW. Operationalizing a real-time scoring model to predict fall risk among older adults in the emergency department. Front Digit Health 2022; 4:958663. [PMID: 36405416 PMCID: PMC9671211 DOI: 10.3389/fdgth.2022.958663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 09/27/2022] [Indexed: 11/06/2022] Open
Abstract
Predictive models are increasingly being developed and implemented to improve patient care across a variety of clinical scenarios. While a body of literature exists on the development of models using existing data, less focus has been placed on practical operationalization of these models for deployment in real-time production environments. This case-study describes challenges and barriers identified and overcome in such an operationalization for a model aimed at predicting risk of outpatient falls after Emergency Department (ED) visits among older adults. Based on our experience, we provide general principles for translating an EHR-based predictive model from research and reporting environments into real-time operation.
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Affiliation(s)
- Collin J. Engstrom
- Department of Emergency Medicine, UW-Madison, Madison, WI, United States
- Department of Computer Science, Winona State University, Rochester, MN, United States
- Correspondence: Collin J. Engstrom
| | - Sabrina Adelaine
- Department of Enterprise Analytics, UW Health, Madison, WI, United States
| | - Frank Liao
- Department of Enterprise Analytics, UW Health, Madison, WI, United States
| | | | - Brian W. Patterson
- Department of Emergency Medicine, UW-Madison, Madison, WI, United States
- Department of Biostatistics and Medical Informatics, UW-Madison, Madison, WI, United States
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Jacobsohn GC, Leaf M, Liao F, Maru AP, Engstrom CJ, Salwei ME, Pankratz GT, Eastman A, Carayon P, Wiegmann DA, Galang JS, Smith MA, Shah MN, Patterson BW. Collaborative design and implementation of a clinical decision support system for automated fall-risk identification and referrals in emergency departments. HEALTHCARE (AMSTERDAM, NETHERLANDS) 2022; 10:100598. [PMID: 34923354 PMCID: PMC8881336 DOI: 10.1016/j.hjdsi.2021.100598] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 11/15/2021] [Accepted: 11/22/2021] [Indexed: 11/04/2022]
Abstract
Of the 3 million older adults seeking fall-related emergency care each year, nearly one-third visited the Emergency Department (ED) in the previous 6 months. ED providers have a great opportunity to refer patients for fall prevention services at these initial visits, but lack feasible tools for identifying those at highest-risk. Existing fall screening tools have been poorly adopted due to ED staff/provider burden and lack of workflow integration. To address this, we developed an automated clinical decision support (CDS) system for identifying and referring older adult ED patients at risk of future falls. We engaged an interdisciplinary design team (ED providers, health services researchers, information technology/predictive analytics professionals, and outpatient Falls Clinic staff) to collaboratively develop a system that successfully met user requirements and integrated seamlessly into existing ED workflows. Our rapid-cycle development and evaluation process employed a novel combination of human-centered design, implementation science, and patient experience strategies, facilitating simultaneous design of the CDS tool and intervention implementation strategies. This included defining system requirements, systematically identifying and resolving usability problems, assessing barriers and facilitators to implementation (e.g., data accessibility, lack of time, high patient volumes, appointment availability) from multiple vantage points, and refining protocols for communicating with referred patients at discharge. ED physician, nurse, and patient stakeholders were also engaged through online surveys and user testing. Successful CDS design and implementation required integration of multiple new technologies and processes into existing workflows, necessitating interdisciplinary collaboration from the onset. By using this iterative approach, we were able to design and implement an intervention meeting all project goals. Processes used in this Clinical-IT-Research partnership can be applied to other use cases involving automated risk-stratification, CDS development, and EHR-facilitated care coordination.
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Affiliation(s)
- Gwen Costa Jacobsohn
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA.
| | - Margaret Leaf
- Applied Data Science, Enterprise Analytics, UW Health, Madison, WI, USA.
| | - Frank Liao
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; Applied Data Science, Enterprise Analytics, UW Health, Madison, WI, USA.
| | - Apoorva P. Maru
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Collin J. Engstrom
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA,Department of Computer Science, Winona State University, Rochester, MN, USA
| | - Megan E. Salwei
- Department of Industrial and Systems Engineering, University of Wisconsin, Madison, Wisconsin, USA,Center for Quality and Productivity Improvement, University of Wisconsin-Madison, Madison, Wisconsin, USA,Center for Research and Innovation in Systems Safety, Departments of Anesthesiology and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Gerald T Pankratz
- Department of Medicine, Division of Geriatrics and Gerontology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
| | - Alexis Eastman
- Department of Medicine, Division of Geriatrics and Gerontology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
| | - Pascale Carayon
- Department of Industrial and Systems Engineering, University of Wisconsin, Madison, WI, USA; Center for Quality and Productivity Improvement, University of Wisconsin-Madison, Madison, WI, USA.
| | - Douglas A. Wiegmann
- Department of Industrial and Systems Engineering, University of Wisconsin, Madison, Wisconsin, USA,Center for Quality and Productivity Improvement, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Joel S. Galang
- Applied Data Science, Enterprise Analytics, UW Health, Madison, Wisconsin, USA
| | - Maureen A. Smith
- Health Innovation Program, University of Wisconsin-Madison, Madison, Wisconsin, USA,Department of Population Health Sciences, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Manish N. Shah
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA,Department of Medicine, Division of Geriatrics and Gerontology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA,Department of Population Health Sciences, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Brian W. Patterson
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA,Health Innovation Program, University of Wisconsin-Madison, Madison, Wisconsin, USA
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Zhu W, DeLonay A, Smith M, Carayon P, Li J. Reducing Fall-related Revisits for Elderly Diabetes Patients in Emergency Departments: A Transition Flow Model. IEEE Robot Autom Lett 2021; 6:5642-5649. [PMID: 34179457 PMCID: PMC8224474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
This paper introduces a transition flow model to study fall-related emergency department (ED) revisits for elderly patients with diabetes. Five diabetes classes are used to classify patients at discharge, within 7-day revisits, and between 8 and 30-day revisits. Analytical formulas to evaluate patient revisiting risks are derived. To reduce revisits, sensitivity analysis is introduced to identify the most critical, i.e., dominant, factors whose changes can lead to the largest reduction in revisits. In addition, a case study at University of Wisconsin (UW) Hospital ED is described to illustrate the applicability of the model.
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Affiliation(s)
- Wenjun Zhu
- Department of Industrial and Systems Engineering, University of Wisconsin, Madison, WI 53706, USA
| | - Allie DeLonay
- Department of Population Health Sciences, University of Wisconsin, Madison, WI 53705 USA
| | - Maureen Smith
- Departments of Population Health Sciences and Family Medicine & Community Health, University of Wisconsin, Madison, WI 53705 USA
| | - Pascale Carayon
- Department of Industrial and Systems Engineering, University of Wisconsin, Madison, WI 53706, USA
| | - Jingshan Li
- Department of Industrial and Systems Engineering, University of Wisconsin, Madison, WI 53706, USA
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Zhu W, DeLonay A, Smith M, Carayon P, Li J. Reducing Fall-Related Revisits for Elderly Diabetes Patients in Emergency Departments: A Transition Flow Model. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3082115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Campagna S, Conti A, Dimonte V, Dalmasso M, Starnini M, Gianino MM, Borraccino A. Trends and Characteristics of Emergency Medical Services in Italy: A 5-Years Population-Based Registry Analysis. Healthcare (Basel) 2020; 8:healthcare8040551. [PMID: 33322302 PMCID: PMC7763006 DOI: 10.3390/healthcare8040551] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 12/04/2020] [Accepted: 12/09/2020] [Indexed: 12/03/2022] Open
Abstract
Background: Emergency Medical Services (EMS) plays a fundamental role in providing good quality healthcare services to citizens, as they are the first responders in distressing situations. Few studies have used available EMS data to investigate EMS call characteristics and subsequent responses. Methods: Data were extracted from the emergency registry for the period 2013–2017. This included call and rescue vehicle dispatch information. All relationships in analyses and differences in events proportion between 2013 and 2017 were tested against the Pearson’s Chi-Square with a 99% level of confidence. Results: Among the 2,120,838 emergency calls, operators dispatched at least one rescue vehicle for 1,494,855. There was an estimated overall incidence of 96 emergency calls and 75 rescue vehicles dispatched per 1000 inhabitants per year. Most calls were made by private citizens, during the daytime, and were made from home (63.8%); 31% of rescue vehicle dispatches were advanced emergency medical vehicles. The highest number of rescue vehicle dispatches ended at the emergency department (74.7%). Conclusions: Our data showed that, with some exception due to environmental differences, the highest proportion of incoming emergency calls is not acute or urgent and could be more effectively managed in other settings than in an Emergency Departments (ED). Better management of dispatch can reduce crowding and save hospital emergency departments time, personnel, and health system costs.
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Affiliation(s)
- Sara Campagna
- Department of Public Health and Paediatrics, University of Torino, 10126 Torino, Italy; (S.C.); (A.C.); (V.D.); (A.B.)
| | - Alessio Conti
- Department of Public Health and Paediatrics, University of Torino, 10126 Torino, Italy; (S.C.); (A.C.); (V.D.); (A.B.)
| | - Valerio Dimonte
- Department of Public Health and Paediatrics, University of Torino, 10126 Torino, Italy; (S.C.); (A.C.); (V.D.); (A.B.)
| | - Marco Dalmasso
- Epidemiology Unit, Local Health Unit TO3, Piedmont Region, 10195 Grugliasco, Italy;
| | - Michele Starnini
- Institute of Scientific Interchange (ISI) Foundation, 10126 Torino, Italy;
| | - Maria Michela Gianino
- Department of Public Health and Paediatrics, University of Torino, 10126 Torino, Italy; (S.C.); (A.C.); (V.D.); (A.B.)
- Correspondence:
| | - Alberto Borraccino
- Department of Public Health and Paediatrics, University of Torino, 10126 Torino, Italy; (S.C.); (A.C.); (V.D.); (A.B.)
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Trends in Pediatric Emergency Department Utilization after Institution of Coronavirus Disease-19 Mandatory Social Distancing. J Pediatr 2020; 226:274-277.e1. [PMID: 32702427 PMCID: PMC7370904 DOI: 10.1016/j.jpeds.2020.07.048] [Citation(s) in RCA: 91] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 07/13/2020] [Accepted: 07/15/2020] [Indexed: 12/26/2022]
Abstract
We conducted a descriptive time-series study of pediatric emergency healthcare use during the onset of severe acute respiratory syndrome coronavirus 2 pandemic after a state-wide stay-at-home order. Our study demonstrated decreased volume, increased acuity, and generally consistent chief complaints compared with the prior 3 years (2017 through 2019). Ingestions became a significantly more common chief complaint in 2020.
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Patterson BW, Jacobsohn GC, Maru AP, Venkatesh AK, Smith MA, Shah MN, Mendonça EA. RESEARCHComparing Strategies for Identifying Falls in Older Adult Emergency Department Visits Using EHR Data. J Am Geriatr Soc 2020; 68:2965-2967. [PMID: 32951200 DOI: 10.1111/jgs.16831] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 08/18/2020] [Accepted: 08/21/2020] [Indexed: 11/29/2022]
Affiliation(s)
- Brian W Patterson
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin.,Health Innovation Program, University of Wisconsin-Madison, Madison, Wisconsin.,Department of Industrial and Systems Engineering, Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin
| | - Gwen Costa Jacobsohn
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin
| | - Apoorva P Maru
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin
| | - Arjun K Venkatesh
- Department of Emergency Medicine and Center for Outcomes Research and Evaluation, Yale University School of Medicine, New Haven, Connecticut
| | - Maureen A Smith
- Health Innovation Program, University of Wisconsin-Madison, Madison, Wisconsin
| | - Manish N Shah
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin.,Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin.,Department of Population Health Sciences, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin
| | - Eneida A Mendonça
- Department of Pediatrics and Biostatistics, Indiana University School of Medicine, Indianapolis, Indiana.,Regenstrief Institute, Indianapolis, Indiana
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Patterson BW, Jacobsohn GC, Shah MN, Song Y, Maru A, Venkatesh AK, Zhong M, Taylor K, Hamedani AG, Mendonça EA. Development and validation of a pragmatic natural language processing approach to identifying falls in older adults in the emergency department. BMC Med Inform Decis Mak 2019; 19:138. [PMID: 31331322 PMCID: PMC6647058 DOI: 10.1186/s12911-019-0843-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Accepted: 06/20/2019] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND Falls among older adults are both a common reason for presentation to the emergency department, and a major source of morbidity and mortality. It is critical to identify fall patients quickly and reliably during, and immediately after, emergency department encounters in order to deliver appropriate care and referrals. Unfortunately, falls are difficult to identify without manual chart review, a time intensive process infeasible for many applications including surveillance and quality reporting. Here we describe a pragmatic NLP approach to automating fall identification. METHODS In this single center retrospective review, 500 emergency department provider notes from older adult patients (age 65 and older) were randomly selected for analysis. A simple, rules-based NLP algorithm for fall identification was developed and evaluated on a development set of 1084 notes, then compared with identification by consensus of trained abstractors blinded to NLP results. RESULTS The NLP pipeline demonstrated a recall (sensitivity) of 95.8%, specificity of 97.4%, precision of 92.0%, and F1 score of 0.939 for identifying fall events within emergency physician visit notes, as compared to gold standard manual abstraction by human coders. CONCLUSIONS Our pragmatic NLP algorithm was able to identify falls in ED notes with excellent precision and recall, comparable to that of more labor-intensive manual abstraction. This finding offers promise not just for improving research methods, but as a potential for identifying patients for targeted interventions, quality measure development and epidemiologic surveillance.
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Affiliation(s)
- Brian W Patterson
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA. .,Health Innovation Program, University of Wisconsin-Madison, Madison, WI, 53705, USA.
| | - Gwen C Jacobsohn
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Manish N Shah
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.,Department of Medicine, Division of Geriatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Yiqiang Song
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.,Department of Pediatrics and Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Apoorva Maru
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Arjun K Venkatesh
- Department of Emergency Medicine and Center for Outcomes Research and Evaluation, Yale University School of Medicine, New Haven, CT, USA
| | - Monica Zhong
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Katherine Taylor
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Azita G Hamedani
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Eneida A Mendonça
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.,Department of Pediatrics and Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA.,Regenstrief Institute, Indianapolis, IN, USA
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Olij BF, Panneman MJ, van Beeck EF, Haagsma JA, Hartholt KA, Polinder S. Fall-related healthcare use and mortality among older adults in the Netherlands, 1997–2016. Exp Gerontol 2019; 120:95-100. [DOI: 10.1016/j.exger.2019.03.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 01/14/2019] [Accepted: 03/08/2019] [Indexed: 12/01/2022]
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Abstract
"Standing-level falls represent the most frequent cause of trauma-related death in older adults and a common emergency department (ED) presentation. However, these patients rarely receive guideline-directed screening and interventions during or following an episode of care. Reducing injurious falls in an aging society begins with prehospital evaluations and continues through definitive risk assessments and interventions that usually occur after ED care. Although ongoing obstacles to ED-initiated, evidence-based older adult fall-reduction strategies include the absence of a compelling emergency medicine evidence basis, innovations under way include validation of pragmatic screening instruments and incorporation of contemporary technology to improve fall detection rates."
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Patterson BW, Repplinger MD, Pulia MS, Batt RJ, Svenson JE, Trinh A, Mendonça EA, Smith MA, Hamedani AG, Shah MN. Using the Hendrich II Inpatient Fall Risk Screen to Predict Outpatient Falls After Emergency Department Visits. J Am Geriatr Soc 2018; 66:760-765. [PMID: 29509312 PMCID: PMC5937931 DOI: 10.1111/jgs.15299] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
OBJECTIVES To evaluate the utility of routinely collected Hendrich II fall scores in predicting returns to the emergency department (ED) for falls within 6 months. DESIGN Retrospective electronic record review. SETTING Academic medical center ED. PARTICIPANTS Individuals aged 65 and older seen in the ED from January 1, 2013, through September 30, 2015. MEASUREMENTS We evaluated the utility of routinely collected Hendrich II fall risk scores in predicting ED visits for a fall within 6 months of an all-cause index ED visit. RESULTS For in-network patient visits resulting in discharge with a completed Hendrich II score (N = 4,366), the return rate for a fall within 6 months was 8.3%. When applying the score alone to predict revisit for falls among the study population the resultant receiver operating characteristic (ROC) plot had an area under the curve (AUC) of 0.64. In a univariate model, the odds of returning to the ED for a fall in 6 months were 1.23 times as high for every 1-point increase in Hendrich II score (odds ratio (OR)=1.23 (95% confidence interval (CI)=1.19-1.28). When included in a model with other potential confounders or predictors of falls, the Hendrich II score is a significant predictor of a return ED visit for fall (adjusted OR=1.15, 95% CI=1.10-1.20, AUC=0.75). CONCLUSION Routinely collected Hendrich II scores were correlated with outpatient falls, but it is likely that they would have little utility as a stand-alone fall risk screen. When combined with easily extractable covariates, the screen performs much better. These results highlight the potential for secondary use of electronic health record data for risk stratification of individuals in the ED. Using data already routinely collected, individuals at high risk of falls after discharge could be identified for referral without requiring additional screening resources.
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Affiliation(s)
- Brian W Patterson
- BerbeeWalsh Department of Emergency Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
- Health Innovation Program, University of Wisconsin-Madison, Madison, Wisconsin
| | - Michael D Repplinger
- BerbeeWalsh Department of Emergency Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
| | - Michael S Pulia
- BerbeeWalsh Department of Emergency Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
| | - Robert J Batt
- BerbeeWalsh Department of Emergency Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
- Wisconsin School of Business, University of Wisconsin-Madison, Madison, Wisconsin
| | - James E Svenson
- BerbeeWalsh Department of Emergency Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
| | - Alex Trinh
- BerbeeWalsh Department of Emergency Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
| | - Eneida A Mendonça
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
| | - Maureen A Smith
- Health Innovation Program, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
- Department of Family Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
| | - Azita G Hamedani
- BerbeeWalsh Department of Emergency Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
| | - Manish N Shah
- BerbeeWalsh Department of Emergency Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
- Division of Geriatrics, Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
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