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Samuels-Kalow ME, Mayes K, Cash RE, Schiavoni KH, Vogeli C, Thorndike AN, Camargo CA. Missed Screening for Adverse Social Determinants of Health and Emergency Department Utilization. Ann Emerg Med 2024; 83:416-418. [PMID: 38142374 PMCID: PMC10960706 DOI: 10.1016/j.annemergmed.2023.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/01/2023] [Accepted: 11/06/2023] [Indexed: 12/25/2023]
Affiliation(s)
- Margaret E Samuels-Kalow
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA.
| | - Katherine Mayes
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA
| | - Rebecca E Cash
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Katherine H Schiavoni
- Population Health Management, Mass General Brigham, Somerville, MA; Departments of Medicine and Pediatrics, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | | | - Anne N Thorndike
- Harvard Medical School, Boston, MA; Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Carlos A Camargo
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
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Kligler SK, Vargas-Torres C, Abbott EE, Lin M. Inhaled Corticosteroids Rarely Prescribed at Emergency Department Discharge Despite Low Rates of Follow-Up Care. J Emerg Med 2023; 64:555-563. [PMID: 37041095 PMCID: PMC10192099 DOI: 10.1016/j.jemermed.2023.02.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 01/31/2023] [Accepted: 02/17/2023] [Indexed: 04/13/2023]
Abstract
BACKGROUND Guidelines recommend an inhaled corticosteroid (ICS) prescription on emergency department (ED) discharge after acute asthma exacerbations. OBJECTIVE We sought to identify rates and predictors of ICS prescription at ED discharge. Secondary outcomes included ICS prescription rates in a high-risk subgroup, outpatient follow-up rates within 30 days, and variation in ICS prescriptions among attending emergency physicians. METHODS This was a retrospective cohort study of adult asthma ED discharges for acute asthma exacerbation across 5 urban academic hospitals. We used multivariable logistic regression to evaluate predictors of ICS prescription after adjusting for patient characteristics and hospital-level clustering. RESULTS Among 3948 adult ED visits, an ICS was prescribed in 6% (n = 238) of visits. Only 14% (n = 552) completed an outpatient visit within 30 days. Among patients with 2 or more ED visits in 12 months, the ICS prescription rate was 6.7%. ICS administration in the ED (odds ratio [OR] 9.91; 95% CI 7.99-12.28) and prescribing a β-agonist on discharge (OR 2.67; 95% CI 2.08-3.44) were associated with higher odds of ICS prescription. Decreased odds of ICS prescription were associated with Hispanic ethnicity (OR 0.71; 95% CI 0.51-0.99) relative to Black race, and private (OR 0.75; 95% CI 0.62-0.91) or no insurance (OR 0.54; 95% CI 0.35-0.84) relative to Medicaid. One-third (36%, n = 66) of ED attendings prescribed 0 ICS prescriptions during the study period. CONCLUSIONS An ICS is infrequently prescribed on ED asthma discharge, and most patients do not have an outpatient follow-up within 30 days. Future studies should examine the extent to which ED ICS prescriptions improve outcomes for patients with barriers to accessing primary care.
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Affiliation(s)
- Sophie Karwoska Kligler
- Icahn School of Medicine at Mount Sinai, Department of Emergency Medicine, 1 Gustave L. Levy Place, New York, NY 10029
| | - Carmen Vargas-Torres
- Icahn School of Medicine at Mount Sinai, Department of Emergency Medicine, 1 Gustave L. Levy Place, New York, NY 10029
| | - Ethan E Abbott
- Icahn School of Medicine at Mount Sinai, Department of Emergency Medicine, 1 Gustave L. Levy Place, New York, NY 10029
| | - Michelle Lin
- Icahn School of Medicine at Mount Sinai, Department of Emergency Medicine, 1 Gustave L. Levy Place, New York, NY 10029
- Icahn School of Medicine at Mount Sinai, Department of Population Health Science and Policy, 1 Gustave L. Levy Place, New York, NY 10029
- Icahn School of Medicine at Mount Sinai, Institute for Health Equity Research, 1 Gustave L. Levy Place, New York, NY 10029
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Tong Y, Messinger AI, Wilcox AB, Mooney SD, Davidson GH, Suri P, Luo G. Forecasting Future Asthma Hospital Encounters of Patients With Asthma in an Academic Health Care System: Predictive Model Development and Secondary Analysis Study. J Med Internet Res 2021; 23:e22796. [PMID: 33861206 PMCID: PMC8087967 DOI: 10.2196/22796] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 10/31/2020] [Accepted: 03/22/2021] [Indexed: 02/06/2023] Open
Abstract
Background Asthma affects a large proportion of the population and leads to many hospital encounters involving both hospitalizations and emergency department visits every year. To lower the number of such encounters, many health care systems and health plans deploy predictive models to prospectively identify patients at high risk and offer them care management services for preventive care. However, the previous models do not have sufficient accuracy for serving this purpose well. Embracing the modeling strategy of examining many candidate features, we built a new machine learning model to forecast future asthma hospital encounters of patients with asthma at Intermountain Healthcare, a nonacademic health care system. This model is more accurate than the previously published models. However, it is unclear how well our modeling strategy generalizes to academic health care systems, whose patient composition differs from that of Intermountain Healthcare. Objective This study aims to evaluate the generalizability of our modeling strategy to the University of Washington Medicine (UWM), an academic health care system. Methods All adult patients with asthma who visited UWM facilities between 2011 and 2018 served as the patient cohort. We considered 234 candidate features. Through a secondary analysis of 82,888 UWM data instances from 2011 to 2018, we built a machine learning model to forecast asthma hospital encounters of patients with asthma in the subsequent 12 months. Results Our UWM model yielded an area under the receiver operating characteristic curve (AUC) of 0.902. When placing the cutoff point for making binary classification at the top 10% (1464/14,644) of patients with asthma with the largest forecasted risk, our UWM model yielded an accuracy of 90.6% (13,268/14,644), a sensitivity of 70.2% (153/218), and a specificity of 90.91% (13,115/14,426). Conclusions Our modeling strategy showed excellent generalizability to the UWM, leading to a model with an AUC that is higher than all of the AUCs previously reported in the literature for forecasting asthma hospital encounters. After further optimization, our model could be used to facilitate the efficient and effective allocation of asthma care management resources to improve outcomes. International Registered Report Identifier (IRRID) RR2-10.2196/resprot.5039
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Affiliation(s)
- Yao Tong
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States
| | - Amanda I Messinger
- The Breathing Institute, Department of Pediatrics, University of Colorado School of Medicine, Children's Hospital Colorado, Aurora, CO, United States
| | - Adam B Wilcox
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States
| | - Sean D Mooney
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States
| | - Giana H Davidson
- Department of Surgery, University of Washington, Seattle, WA, United States.,Department of Health Services, University of Washington, Seattle, WA, United States
| | - Pradeep Suri
- Seattle Epidemiologic Research and Information Center & Division of Rehabilitation Care Services, VA Puget Sound Health Care System, Seattle, WA, United States.,Clinical Learning, Evidence, and Research (CLEAR) Center, University of Washington, Seattle, WA, United States.,Department of Rehabilitation Medicine, University of Washington, Seattle, WA, United States
| | - Gang Luo
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States
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Luo G, Johnson MD, Nkoy FL, He S, Stone BL. Automatically Explaining Machine Learning Prediction Results on Asthma Hospital Visits in Patients With Asthma: Secondary Analysis. JMIR Med Inform 2020; 8:e21965. [PMID: 33382379 PMCID: PMC7808890 DOI: 10.2196/21965] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 10/25/2020] [Accepted: 11/15/2020] [Indexed: 12/27/2022] Open
Abstract
Background Asthma is a major chronic disease that poses a heavy burden on health care. To facilitate the allocation of care management resources aimed at improving outcomes for high-risk patients with asthma, we recently built a machine learning model to predict asthma hospital visits in the subsequent year in patients with asthma. Our model is more accurate than previous models. However, like most machine learning models, it offers no explanation of its prediction results. This creates a barrier for use in care management, where interpretability is desired. Objective This study aims to develop a method to automatically explain the prediction results of the model and recommend tailored interventions without lowering the performance measures of the model. Methods Our data were imbalanced, with only a small portion of data instances linking to future asthma hospital visits. To handle imbalanced data, we extended our previous method of automatically offering rule-formed explanations for the prediction results of any machine learning model on tabular data without lowering the model’s performance measures. In a secondary analysis of the 334,564 data instances from Intermountain Healthcare between 2005 and 2018 used to form our model, we employed the extended method to automatically explain the prediction results of our model and recommend tailored interventions. The patient cohort consisted of all patients with asthma who received care at Intermountain Healthcare between 2005 and 2018, and resided in Utah or Idaho as recorded at the visit. Results Our method explained the prediction results for 89.7% (391/436) of the patients with asthma who, per our model’s correct prediction, were likely to incur asthma hospital visits in the subsequent year. Conclusions This study is the first to demonstrate the feasibility of automatically offering rule-formed explanations for the prediction results of any machine learning model on imbalanced tabular data without lowering the performance measures of the model. After further improvement, our asthma outcome prediction model coupled with the automatic explanation function could be used by clinicians to guide the allocation of limited asthma care management resources and the identification of appropriate interventions.
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Affiliation(s)
- Gang Luo
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States
| | - Michael D Johnson
- Department of Pediatrics, University of Utah, Salt Lake City, UT, United States
| | - Flory L Nkoy
- Department of Pediatrics, University of Utah, Salt Lake City, UT, United States
| | - Shan He
- Care Transformation and Information Systems, Intermountain Healthcare, Salt Lake City, UT, United States
| | - Bryan L Stone
- Department of Pediatrics, University of Utah, Salt Lake City, UT, United States
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Luo G, He S, Stone BL, Nkoy FL, Johnson MD. Developing a Model to Predict Hospital Encounters for Asthma in Asthmatic Patients: Secondary Analysis. JMIR Med Inform 2020; 8:e16080. [PMID: 31961332 PMCID: PMC7001050 DOI: 10.2196/16080] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 11/01/2019] [Accepted: 12/01/2019] [Indexed: 12/12/2022] Open
Abstract
Background As a major chronic disease, asthma causes many emergency department (ED) visits and hospitalizations each year. Predictive modeling is a key technology to prospectively identify high-risk asthmatic patients and enroll them in care management for preventive care to reduce future hospital encounters, including inpatient stays and ED visits. However, existing models for predicting hospital encounters in asthmatic patients are inaccurate. Usually, they miss over half of the patients who will incur future hospital encounters and incorrectly classify many others who will not. This makes it difficult to match the limited resources of care management to the patients who will incur future hospital encounters, increasing health care costs and degrading patient outcomes. Objective The goal of this study was to develop a more accurate model for predicting hospital encounters in asthmatic patients. Methods Secondary analysis of 334,564 data instances from Intermountain Healthcare from 2005 to 2018 was conducted to build a machine learning classification model to predict the hospital encounters for asthma in the following year in asthmatic patients. The patient cohort included all asthmatic patients who resided in Utah or Idaho and visited Intermountain Healthcare facilities during 2005 to 2018. A total of 235 candidate features were considered for model building. Results The model achieved an area under the receiver operating characteristic curve of 0.859 (95% CI 0.846-0.871). When the cutoff threshold for conducting binary classification was set at the top 10.00% (1926/19,256) of asthmatic patients with the highest predicted risk, the model reached an accuracy of 90.31% (17,391/19,256; 95% CI 89.86-90.70), a sensitivity of 53.7% (436/812; 95% CI 50.12-57.18), and a specificity of 91.93% (16,955/18,444; 95% CI 91.54-92.31). To steer future research on this topic, we pinpointed several potential improvements to our model. Conclusions Our model improves the state of the art for predicting hospital encounters for asthma in asthmatic patients. After further refinement, the model could be integrated into a decision support tool to guide asthma care management allocation. International Registered Report Identifier (IRRID) RR2-10.2196/resprot.5039
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Affiliation(s)
- Gang Luo
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States
| | - Shan He
- Care Transformation, Intermountain Healthcare, Salt Lake City, UT, United States
| | - Bryan L Stone
- Department of Pediatrics, University of Utah, Salt Lake City, UT, United States
| | - Flory L Nkoy
- Department of Pediatrics, University of Utah, Salt Lake City, UT, United States
| | - Michael D Johnson
- Department of Pediatrics, University of Utah, Salt Lake City, UT, United States
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Singh P, Chakravarthy B, Yoon J, Snowden L, Bruckner TA. Psychiatric-related Revisits to the Emergency Department Following Rapid Expansion of Community Mental Health Services. Acad Emerg Med 2019; 26:1336-1345. [PMID: 31162887 DOI: 10.1111/acem.13812] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Revised: 05/01/2019] [Accepted: 05/21/2019] [Indexed: 11/27/2022]
Abstract
OBJECTIVES Repeat visits (revisits) to emergency departments (EDs) for psychiatric care reflect poor continuity of care and impose a high financial cost. We test whether rapid expansion of community health centers (CHCs)-which provide regional, low-cost primary care-correspond with fewer repeat psychiatric-related ED visits (PREDVs). METHODS We obtained repeated cross-sectional time-series data for 7.8 million PREDVs from the State Emergency Department Database for four populous U.S. states (California, Florida, North Carolina, and New York) from 2006 to 2011. We specified as the outcome variable the count of repeat visits per ED visitor with a psychiatric diagnosis. We retrieved aggregate-level mental health visits at CHCs from the Uniform Data System. Negative binomial regression methods controlled for individual-level confounders, county health system and sociodemographic attributes, year fixed effects, and county fixed effects. RESULTS The risk of a repeat PREDV decreased with a county-level increase in mental health patients seen at CHCs (incidence rate ratio = 0.986, 95% confidence interval = 0.98 to 0.99). Conversion of this rate ratio to the number of revisits averted indicated 34,000 fewer repeat PREDVs in these four states statistically associated with a 1% expansion in CHC mental health visits. Exploratory analyses found that revisits decline for relatively mild/moderate illnesses (e.g., mood, anxiety disorders) but not for severe illnesses (e.g., schizophrenia/psychoses). CONCLUSION An increase in mental health services at CHCs corresponds with a modest decline in repeat PREDVs. This decline concentrates among those with less severe mental illnesses.
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Affiliation(s)
- Parvati Singh
- Program in Public HealthUniversity of California at IrvineIrvine CA
| | | | - Jangho Yoon
- College of Public Health and Human Sciences School of Social and Behavioral Health Science Oregon State University Corvallis OR
| | - Lonnie Snowden
- School of Public Health University of California at Berkeley Berkeley CA
| | - Tim A. Bruckner
- Program in Public HealthUniversity of California at IrvineIrvine CA
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Chiu Y, Racine-Hemmings F, Dufour I, Vanasse A, Chouinard MC, Bisson M, Hudon C. Statistical tools used for analyses of frequent users of emergency department: a scoping review. BMJ Open 2019; 9:e027750. [PMID: 31129592 PMCID: PMC6537981 DOI: 10.1136/bmjopen-2018-027750] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
OBJECTIVE Frequent users represent a small proportion of emergency department users, but they account for a disproportionately large number of visits. Their use of emergency departments is often considered suboptimal. It would be more efficient to identify and treat those patients earlier in their health problem trajectory. It is therefore essential to describe their characteristics and to predict their emergency department use. In order to do so, adequate statistical tools are needed. The objective of this study was to determine the statistical tools used in identifying variables associated with frequent use or predicting the risk of becoming a frequent user. METHODS We performed a scoping review following an established 5-stage methodological framework. We searched PubMed, Scopus and CINAHL databases in February 2019 using search strategies defined with the help of an information specialist. Out of 4534 potential abstracts, we selected 114 articles based on defined criteria and presented in a content analysis. RESULTS We identified four classes of statistical tools. Regression models were found to be the most common practice, followed by hypothesis testing. The logistic regression was found to be the most used statistical tool, followed by χ2 test and t-test of associations between variables. Other tools were marginally used. CONCLUSIONS This scoping review lists common statistical tools used for analysing frequent users in emergency departments. It highlights the fact that some are well established while others are much less so. More research is needed to apply appropriate techniques to health data or to diversify statistical point of views.
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Affiliation(s)
- Yohann Chiu
- Department of Family Medicine and Emergency Medicine, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - François Racine-Hemmings
- Department of Family Medicine and Emergency Medicine, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Isabelle Dufour
- Department of Family Medicine and Emergency Medicine, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Alain Vanasse
- Department of Family Medicine and Emergency Medicine, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | | | - Mathieu Bisson
- Department of Family Medicine and Emergency Medicine, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Catherine Hudon
- Department of Family Medicine and Emergency Medicine, Université de Sherbrooke, Sherbrooke, Quebec, Canada
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