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What are emergency-sensitive conditions? A survey of Canadian emergency physicians and nurses

Published online by Cambridge University Press:  01 April 2015

Simon Berthelot ,
Eddy S. Lang ,
Hude Quan  and
Henry T. Stelfox
Simon Berthelot*
Affiliation:
Département de médecine d’urgence du CHU de Québec, Québec, QC Department of Community Health Sciences, University of Calgary, Calgary, AB
Eddy S. Lang
Affiliation:
Department of Emergency Medicine, University of Calgary, Calgary, AB
Hude Quan
Affiliation:
Department of Community Health Sciences, University of Calgary, Calgary, AB
Henry T. Stelfox
Affiliation:
Department of Community Health Sciences, University of Calgary, Calgary, AB Department of Critical Care, University of Calgary, Calgary, AB
*
Correspondence to: Dr. Simon Berthelot, Department of Emergency Medicine, CHU de Québec–CHUL, 2705 Boul. Laurier, Québec, QC G1V 4G2; [email protected].

Abstract

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Objective: In a previous study, we assembled a multidisciplinary Canadian panel and identified 37 International Classification of Diseases-10-Canada Diagnosis Groups (DGs) for which emergency department (ED) management may potentially reduce mortality (emergency-sensitive conditions). Before using these 37 DGs to calculate a hospital standardized mortality ratio (HSMR) specific to emergency care, we aimed to test their face validity with ED care providers.

Methods: We conducted a self-administered web survey among Canadian emergency physicians and nurses between November 22 and December 31, 2012. All members (N=2,507) of the Canadian Association of Emergency Physicians and the National Emergency Nurses Association were surveyed. They were asked to agree or disagree (binary response) with the panel classification for each of the 37 DG emergency-sensitive conditions identified and provide free text responses to identify missing entities.

Results: A total of 719 ED providers (719 of 2,507, 29%) completed the survey, of whom 470 were physicians (470 of 1,407, 33%) and 232 were nurses (232 of 1,100, 21%). Information on professional status was not provided for 17 respondents. Of 37 DGs, 32 (e.g., A41 sepsis) were rated by more than 80% of respondents to be emergency-sensitive conditions. The remaining five DGs (e.g., E11 type 2 diabetes mellitus) were rated by 68.5 to 79.7% of the respondents to be emergency-sensitive conditions. Respondents suggested an additional 31 emergency-sensitive diagnoses.

Conclusion: We identified 37 emergency-sensitive DGs that had high face validity with emergency physicians and nurses, which will enable the calculation of an ED-HSMR.

Résumé

Objectif: Dans une étude antérieure, un groupe d’étude pluridisciplinaire, canadien a relevé 37 groupes de diagnostics (GD) selon la Classification internationale des maladies–10–CA (adaptation canadienne), pour lesquels la prise en charge au service des urgences (SU) pouvait réduire la mortalité (maladies en phase critique au service des urgences [SU]). Toutefois, avant d’utiliser ces 37 GD pour calculer un ratio normalisé de mortalité hospitalière (RNMH) propre aux soins d’urgence, nous voulions en vérifier la validité apparente parmi les fournisseurs de soins d’urgence.

Méthode: Une enquête autoadministrée en ligne a été menée au sein du personnelmédical et infirmier d’urgence, au Canada, entre le 22 novembre et le 31 décembre 2012. Tous les membres (N=2507) de l’Association canadienne des médecins d’urgence et de la National Emergency Nurses Association ont été invités à y répondre. On leur a demandé d’indiquer, pour chacun des 37 GD de maladies en phase critique au SU, s’ils étaient d’accord ou non (réponse binaire) avec le groupe d’étude sur la sélection proposée, et de donner des réponses en formulation libre pour l’ajout d’autres diagnostics.

Résultats: Au total, 719 (719 sur 2507; 29%) fournisseurs de soins d’urgence, dont 470 médecins (470 sur 1407; 33%) et 232 infirmières/infirmiers (232 sur 1100; 21%), ont répondu au questionnaire. Dix-sept répondants n’ont pas fourni de renseignements quant à leur statut professionnel. Sur les 37 GD de maladies en phase critique au SU, 32 (ex.: A41–Sepsis) ont recueilli l’appui de plus de 80% des répondants; pour ce qui est des 5 autres GD de maladies en phase critique au SU (ex.: E11–Diabe` te sucréde type 2), l’appui des répondants variait de 68.5% à 79.7%. Enfin, les participants ont suggéré l’ajout de 31 diagnostics de maladies en phase critique au SU sur la liste préétablie.

Conclusion: La validité apparente de 37 GD de maladies en phase critique au SU a fait l’objet d’un large consensus au sein du personnel médical et infirmier d’urgence, ce qui a permis de calculer un RNMH-SU.

Type
Original Research
Information
Canadian Journal of Emergency Medicine , Volume 17 , Issue 2 , March 2015 , pp. 154 - 160
Copyright
Copyright © Canadian Association of Emergency Physicians 2015 

References

1. Schull, MJ, Guttmann, A, Leaver, CA, et al. Prioritizing performance measurement for emergency department care: consensus on evidence-based quality of care indicators. CJEM 2011;13(300-9):E328–43.CrossRefGoogle ScholarPubMed
2. Beattie, E, Mackway-Jones, K. A Delphi study to identify performance indicators for emergency medicine. Emerg Med J 2004;21:47–50, doi:10.1136/emj.2003.001123.CrossRefGoogle ScholarPubMed
3. Sibbritt, D, Isbister, GK, Walker, R. Emergency department performance indicators that encompass the patient journey. Qual Manag Health Care 2006;15(1):27–38, doi:10.1097/00019514-200601000-00004.CrossRefGoogle ScholarPubMed
4. Lindsay, P, Schull, M, Bronskill, S, et al.. The development of indicators to measure the quality of clinical care in emergency departments following a modified-Delphi approach. Acad Emerg Med 2002;9:1131–1139, doi:10.1111/j.1553-2712.2002.tb01567.x.Google Scholar
5. Jones, P, Harper, A, Wells, S, et al.. Selection and validation of quality indicators for the Shorter Stays in Emergency Departments National Research Project. Emerg Med Australas 2012;24:303–312, doi:10.1111/j.1742-6723.2012.01546.x.Google Scholar
6. Canadian Institute for Health Information. RNMH: Unenouvelle méthode de mesure des tendances relatives à la mortalité hospitalière au Canada. Ottawa ICIS; 2007, Available at: https://secure.cihi.ca/free_products/RNMH_hospital_mortality_trends_in_canada_f.pdf..Google Scholar
7. Berthelot, S, Lang, ES, Quan, H, et al. Identifying emergencysensitive conditions for the calculation of an emergency care inhospital standardized mortality ratio. Ann Emerg Med 2014;63:418–424, doi:10.1016/j.annemergmed.2013.09.016.Google Scholar
8. Burns, KE, Duffett, M, Kho, ME, et al. A guide for the design and conduct of self-administered surveys of clinicians. CMAJ 2008;179:245–252, doi:10.1503/cmaj.080372.Google Scholar
9. Cook, DJ, Guyatt, GH, Jaeschke, R, et al.. Determinants in Canadian health care workers of the decision to withdraw life support from the critically ill. Canadian Critical Care Trials Group. JAMA 1995;273:703–708, doi:10.1001/jama.1995.03520330033033.Google Scholar
10. Zhang, Y, Wildemuth, B. Qualitative analysis of content. In: Wildemuth B editor. Applications of social research methods to questions in information and library science. Santa Barbara (CA) Greenwood Press; 2009, p 308–319.Google Scholar
11. Rutstein, DD, Berenberg, W, Chalmers, TC, et al.. Measuring the quality of medical care. A clinical method. N Engl J Med 1976;294:582–588, doi:10.1056/NEJM197603112941104.Google Scholar
12. Billings, J, Zeitel, L, Lukomnik, J, et al.. Impact of socioeconomic status on hospital use in New York City. Health Aff (Millwood) 1993;12(1):162–173, doi:10.1377/hlthaff.12.1.162.Google Scholar
13. Carr, BG, Conway, PH, Meisel, ZF, et al.. Defining the emergency care sensitive condition: a health policy research agenda in emergency medicine. Ann Emerg Med 2010;56:49–51, doi:10.1016/j.annemergmed.2009.12.013.Google Scholar
14. Ben-Tovim, DI, Pointer, SC, Woodman, R, et al. Routine use of administrative data for safety and quality purposes—hospital mortality. Med J Aust 2010;193(8 Suppl):S100–3.Google Scholar
15. Pouw, ME, Peelen, LM, Lingsma, HF, et al.. Hospital standardized mortality ratio: consequences of adjusting hospital mortality with indirect standardization. PloS One 2013;8(4):e59160 doi:10.1371/journal.pone.0059160.Google Scholar
16. Jarman, B. In defence of the hospital standardized mortality ratio. Healthc Papers 2008;8(4):37–42; discussion 69-75 doi:10.12927/hcpap.2008.19974.Google Scholar
17. Grava-Gubins, I, Scott, S.. Effects of various methodologic strategies: survey response rates among Canadian physicians and physicians-in-training. Can FamPhysician 2008;54:1424–1430.Google Scholar
18. James, KM, Ziegenfuss, JY, Tilburt, JC, et al.. Getting physicians to respond: the impact of incentive type and timing on physician survey response rates. Health Serv Res 2011;46(1 Pt 1):232–242, doi:10.1111/j.1475-6773.2010.01181.x.Google Scholar