Risk of Frequent Emergency Department Use Among an Ambulatory Care Sensitive Condition Population: A Population-based Cohort Study.


Journal

Medical care
ISSN: 1537-1948
Titre abrégé: Med Care
Pays: United States
ID NLM: 0230027

Informations de publication

Date de publication:
03 2020
Historique:
entrez: 13 2 2020
pubmed: 13 2 2020
medline: 10 5 2020
Statut: ppublish

Résumé

A small fraction of patients use a disproportionately large amount of emergency department (ED) resources. Identifying these patients, especially those with ambulatory care sensitive conditions (ACSC), would allow health care professionals to enhance their outpatient care. The objectives of the study were to determine predictive factors associated with frequent ED use in a Quebec adult population with ACSCs and to compare several models predicting the risk of becoming an ED frequent user following an ED visit. This was an observational population-based cohort study extracted from Quebec's administrative data. The cohort included 451,775 adult patients, living in nonremote areas, with an ED visit between January 2012 and December 2013 (index visit), and previously diagnosed with an ACSC but not dementia. The outcome was frequent ED use (≥4 visits) during the year following the index visit. Predictors included sociodemographics, physical and mental comorbidities, and prior use of health services. We developed several logistic models (with different sets of predictors) on a derivation cohort (2012 cohort) and tested them on a validation cohort (2013 cohort). Frequent ED users represented 5% of the cohort and accounted for 36% of all ED visits. A simple 2-variable prediction model incorporating history of hospitalization and number of previous ED use accurately predicted future frequent ED use. The full model with all sets of predictors performed only slightly better than the simple model (area under the receiver-operating characteristic curve: 0.786 vs. 0.759, respectively; similar positive predictive value and number needed to evaluate curves). The ability to identify frequent ED users based only on previous ED and hospitalization use provides an opportunity to rapidly target this population for appropriate interventions.

Sections du résumé

BACKGROUND
A small fraction of patients use a disproportionately large amount of emergency department (ED) resources. Identifying these patients, especially those with ambulatory care sensitive conditions (ACSC), would allow health care professionals to enhance their outpatient care.
OBJECTIVE
The objectives of the study were to determine predictive factors associated with frequent ED use in a Quebec adult population with ACSCs and to compare several models predicting the risk of becoming an ED frequent user following an ED visit.
RESEARCH DESIGN
This was an observational population-based cohort study extracted from Quebec's administrative data.
SUBJECTS
The cohort included 451,775 adult patients, living in nonremote areas, with an ED visit between January 2012 and December 2013 (index visit), and previously diagnosed with an ACSC but not dementia.
MEASURES
The outcome was frequent ED use (≥4 visits) during the year following the index visit. Predictors included sociodemographics, physical and mental comorbidities, and prior use of health services. We developed several logistic models (with different sets of predictors) on a derivation cohort (2012 cohort) and tested them on a validation cohort (2013 cohort).
RESULTS
Frequent ED users represented 5% of the cohort and accounted for 36% of all ED visits. A simple 2-variable prediction model incorporating history of hospitalization and number of previous ED use accurately predicted future frequent ED use. The full model with all sets of predictors performed only slightly better than the simple model (area under the receiver-operating characteristic curve: 0.786 vs. 0.759, respectively; similar positive predictive value and number needed to evaluate curves).
CONCLUSIONS
The ability to identify frequent ED users based only on previous ED and hospitalization use provides an opportunity to rapidly target this population for appropriate interventions.

Identifiants

pubmed: 32049947
doi: 10.1097/MLR.0000000000001270
pii: 00005650-202003000-00009
doi:

Types de publication

Journal Article Observational Study Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

248-256

Références

Medical Council of Canada. Report of the Incidence and Prevalence of Diseases and Other Health Related. Issues in Canada. 2013.
Health Council of Canada. Population Patterns of Chronic Health Conditions in Canada: A Data Supplement to Why Health Care Renewal Matters: Learning From Canadians with Chronic Health Conditions. Toronto: Health Council; 2007.
Statistics Canada. 2014. Health Trends. Statistics Canada Catalogue No. 82-213-XWE. Ottawa. 2014. Available at: http://www12.statcan.gc.ca/health-sante/82-213/index.cfm?Lang=ENG. Accessed July 18, 2018.
Public Health Agency of Canada. How healthy are Canadians? A trend analysis of the health of Canadians from a healthy living and chronic disease perspective. 2016. Available at: https://www.canada.ca/en/public-health/services/publications/healthy-living/how-healthy-canadians.html. Accessed December 2, 2019.
Canadian Institute for Health Information. Health Indicators 2008. Ottawa: CIHI; 2008.
Billings J, Zeitel L, Lukomnik J, et al. Impact of socioeconomic status on hospital use in New York city. Health Aff. 1993;12:162e73.
Purdy S, Griffin T, Salisbury C, et al. Ambulatory care sensitive conditions: terminology and disease coding need to be more specific to aid policy makers and clinicians. Public Health. 2009;123:169e73.
Ansari Z. The concept and usefulness of ambulatory care sensitive conditions as indicators of quality and access to primary health care. Aust J Prim Health. 2007;13:91e110.
Johnson PJ, Ghildayal N, Ward AC, et al. Disparities in potentially avoidable emergency department (ED) care: ED visits for ambulatory care sensitive conditions. Med Care. 2012;50:1020–1028.
Hunt KA, Weber EJ, Showstack JA, et al. Characteristics of frequent users of emergency departments. Ann Emerg Med. 2006;48:1–8.
LaCalle E, Rabin E. Frequent users of emergency departments: the myths, the data, and the policy implications. Ann Emerg Med. 2010;56:42–48.
Pines JM, Asplin BR, Kaji AH, et al. Frequent users of emergency department services: gaps in knowledge and a proposed research agenda. Acad Emerg Med. 2011;18:e64–e69.
Soril LJJ, Leggett LE, Lorenzetti DL, et al. Reducing frequent visits to the emergency department: a systematic review of interventions. PLoS One. 2015;10:e0123660.
Moe J, Kirkland SW, Rawe E, et al. Effectiveness of interventions to decrease emergency department visits by adult frequent users: a systematic review. Acad Emerg Med. 2017;24:40–52.
Poole S, Grannis S, Shah NH. Predicting emergency department visits. AMIA Jt Summits Transl Sci Proc. 2016;2016:438–445.
Frost DW, Vembu S, Wang J, et al. Using the electronic medical record to identify patients at high risk for frequent emergency department visits and high system costs. Am J Med. 2017;130:601.e17–601.e22.
Pereira M, Singh V, Hon CP, et al. Predicting future frequent users of emergency departments in California State. Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (BCB ‘16). ACM. New York, NY: 2016; 603–610.
Wu J, Grannis SJ, Xu H, et al. A practical method for predicting frequent use of emergency department care using routinely available electronic registration data. BMC Emerg Med. 2016;16:12.
Brennan JJ, Chan TC, Hsia RY, et al. Predicting frequent use of emergency department resources. Ann Emerg Med. 2014;64:S118–S119.
Das LT, Abramson EL, Stone AE, et al. Predicting frequent emergency department visits among children with asthma using EHR data. Pediatr Pulmonol. 2017;52:880–890.
Grinspan ZM, Patel AD, Hafeez B, et al. Predicting frequent emergency department use among children with epilepsy: a retrospective cohort study using electronic health data from 2 centers. Epilepsia. 2018;59:155–169.
Grinspan ZM, Shapiro JS, Abramson EL, et al. Predicting frequent ED use by people with epilepsy with health information exchange data. Neurology. 2015;85:1031–1038.
Moons KG, Altman DG, Reitsma JB, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015;162:W1–W73.
Soong C, Bell C. Identifying preventable readmissions: an achievable goal or waiting for Godot? BMJ Qual Saf. 2015;24:741.e3.
[No authors listed]. Recommendations for the management of rural, remote, and isolated emergency health care facilities in Canada. Canadian Association of Emergency Physicians. Canadian Association of Emergency Physicians. J Emerg Med. 1997;15:741–747.
Statistics Canada. Census Dictionary. Statistical Area Classification (SAC). Available at: www12.statcan.gc.ca/census-recensement/2011/ref/dict/geo010-eng.cfm.
Zhu CW, Cosentino S, Ornstein K, et al. Use and cost of hospitalization in dementia: longitudinal results from a community-based study. Int J Geriatr Psychiatry. 2015;30:833–841.
Dewing J, Dijk S. What is the current state of care for older people with dementia in general hospitals? A literature review. Dementia (London). 2016;15:106–124.
Krieg C, Hudon C, Chouinard M-C, et al. Individual predictors of frequent emergency department use: a scoping review. BMC Health Serv Res. 2016;16:594.
Dendukuri N, McCusker J, Bellavance F, et al. Comparing the validity of different sources of information on emergency department visits: a latent class analysis. Med Care. 2005;43:266–275.
Simard M, Sirois C, Candas B. Validation of the combined comorbidity index of Charlson and elixhauser to predict 30-day mortality across ICD-9 and ICD-10. Med Care. 2018;56:441–447.
Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373–383.
Elixhauser A, Steiner C, Harris DR, et al. Comorbidity measures for use with administrative data. Med Care. 1998;36:8–27.
Sylvie Provost. Affiliation à un médecin de famille: Une mesure à; partir des banques de données médico-administratives [Affiliation to a family doctor: a measurement from medical-administrative data banks]. Direction de santé publique de l’Agence de la santé et des services sociaux de Montréal et Institut national de santé publique du Québec, Centre de recherche du Centre hospitalier de l’Université de Montréal. 2013. Available at: https://www.inspq.qc.ca/sites/default/files/publications/1681_affilmdfamille_mesurebdmedicoadmin.pdf. Accessed December 2019.
Romero-Brufau S, Huddleston JM, Escobar GJ, et al. Why the C-statistic is not informative to evaluate early warning scores and what metrics to use. Crit Care. 2015;19:285.
Okuyemi KS, Frey B. Describing and predicting frequent users of an emergency department. J Assoc Acad Minor Phys. 2001;12:119–123.
Ko M, Lee Y, Chen C, et al. Prevalence of and predictors for frequent utilization of emergency department: a population-based study. Medicine (Baltimore). 2015;94:e1205.

Auteurs

Catherine Hudon (C)

PRIMUS Research Group, Centre de recherche du Centre hospitalier universitaire de Sherbrooke (CRCHUS).
Department of Family and Emergency Medicine, Université de Sherbrooke, Sherbrooke.

Josiane Courteau (J)

PRIMUS Research Group, Centre de recherche du Centre hospitalier universitaire de Sherbrooke (CRCHUS).

Yohann M Chiu (YM)

Department of Family and Emergency Medicine, Université de Sherbrooke, Sherbrooke.

Maud-Christine Chouinard (MC)

Department of Health Sciences, Université du Québec à Chicoutimi, Chicoutimi.

Marie-France Dubois (MF)

Department of Community Health Sciences, Université de Sherbrooke.

Nicole Dubuc (N)

School of Nursing, Université de Sherbrooke, Sherbrooke, QC, Canada.

Nicolas Elazhary (N)

Department of Family and Emergency Medicine, Université de Sherbrooke, Sherbrooke.

Francois Racine-Hemmings (F)

Department of Family and Emergency Medicine, Université de Sherbrooke, Sherbrooke.

Isabelle Dufour (I)

Department of Family and Emergency Medicine, Université de Sherbrooke, Sherbrooke.

Alain Vanasse (A)

PRIMUS Research Group, Centre de recherche du Centre hospitalier universitaire de Sherbrooke (CRCHUS).
Department of Family and Emergency Medicine, Université de Sherbrooke, Sherbrooke.

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