The first step is recognizing there is a problem: a methodology for adjusting for variability in disease severity when estimating clinician performance.
Clinical medicine
Critical care
Data science
Performance measure
Social network analysis
Journal
BMC medical research methodology
ISSN: 1471-2288
Titre abrégé: BMC Med Res Methodol
Pays: England
ID NLM: 100968545
Informations de publication
Date de publication:
16 03 2022
16 03 2022
Historique:
received:
24
10
2021
accepted:
11
02
2022
entrez:
17
3
2022
pubmed:
18
3
2022
medline:
22
3
2022
Statut:
epublish
Résumé
Adoption of innovations in the field of medicine is frequently hindered by a failure to recognize the condition targeted by the innovation. This is particularly true in cases where recognition requires integration of patient information from different sources, or where disease presentation can be heterogeneous and the recognition step may be easier for some patients than for others. We propose a general data-driven metric for clinician recognition that accounts for the variability in patient disease severity and for institutional standards. As a case study, we evaluate the ventilatory management of 362 patients with acute respiratory distress syndrome (ARDS) at a large academic hospital, because clinician recognition of ARDS has been identified as a major barrier to adoption to evidence-based ventilatory management. We calculate our metric for the 48 critical care physicians caring for these patients and examine the relationships between differences in ARDS recognition performance from overall institutional levels and provider characteristics such as demographics, social network position, and self-reported barriers and opinions. Our metric was found to be robust to patient characteristics previously demonstrated to affect ARDS recognition, such as disease severity and patient height. Training background was the only factor in this study that showed an association with physician recognition. Pulmonary and critical care medicine (PCCM) training was associated with higher recognition (β = 0.63, 95% confidence interval 0.46-0.80, p < 7 × 10 We present a data-driven metric of clinician disease recognition that accounts for variability in patient disease severity and for institutional standards. Using this metric, we identify two unique physician populations with different intervention needs. One population consistently recognizes ARDS and reports barriers vs one does not and reports fewer barriers.
Sections du résumé
BACKGROUND
Adoption of innovations in the field of medicine is frequently hindered by a failure to recognize the condition targeted by the innovation. This is particularly true in cases where recognition requires integration of patient information from different sources, or where disease presentation can be heterogeneous and the recognition step may be easier for some patients than for others.
METHODS
We propose a general data-driven metric for clinician recognition that accounts for the variability in patient disease severity and for institutional standards. As a case study, we evaluate the ventilatory management of 362 patients with acute respiratory distress syndrome (ARDS) at a large academic hospital, because clinician recognition of ARDS has been identified as a major barrier to adoption to evidence-based ventilatory management. We calculate our metric for the 48 critical care physicians caring for these patients and examine the relationships between differences in ARDS recognition performance from overall institutional levels and provider characteristics such as demographics, social network position, and self-reported barriers and opinions.
RESULTS
Our metric was found to be robust to patient characteristics previously demonstrated to affect ARDS recognition, such as disease severity and patient height. Training background was the only factor in this study that showed an association with physician recognition. Pulmonary and critical care medicine (PCCM) training was associated with higher recognition (β = 0.63, 95% confidence interval 0.46-0.80, p < 7 × 10
CONCLUSIONS
We present a data-driven metric of clinician disease recognition that accounts for variability in patient disease severity and for institutional standards. Using this metric, we identify two unique physician populations with different intervention needs. One population consistently recognizes ARDS and reports barriers vs one does not and reports fewer barriers.
Identifiants
pubmed: 35296240
doi: 10.1186/s12874-022-01543-7
pii: 10.1186/s12874-022-01543-7
pmc: PMC8924737
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
69Subventions
Organisme : U.S. Army
ID : W911NF-14-1-0259
Organisme : NIGMS NIH HHS
ID : T32GM008152
Pays : United States
Organisme : NCATS NIH HHS
ID : 8UL1TR000150
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL140362
Pays : United States
Organisme : National Heart Lung and Blood Institute
ID : R01HL140362
Organisme : National Heart Lung and Blood Institute
ID : K23HL118139
Informations de copyright
© 2022. The Author(s).
Références
Lancet. 2015 Jul 11;386(9989):145-53
pubmed: 25952354
BMJ. 2012 Apr 05;344:e2124
pubmed: 22491953
Implement Sci. 2011 Sep 29;6:113
pubmed: 21958674
Intensive Care Med. 2016 May;42(5):725-738
pubmed: 27025938
Crit Care Med. 2016 Aug;44(8):1515-22
pubmed: 27035237
Am J Respir Crit Care Med. 2017 May 1;195(9):1253-1263
pubmed: 28459336
Implement Sci. 2013 Oct 01;8:116
pubmed: 24083343
Soc Sci Med. 2011 Mar;72(5):798-805
pubmed: 21306807
Crit Care Med. 2006 Feb;34(2):300-6
pubmed: 16424706
Respir Care. 2016 May;61(5):689-99
pubmed: 27121623
Addict Behav. 2002 Nov-Dec;27(6):989-93
pubmed: 12369480
N Engl J Med. 2000 May 4;342(18):1301-8
pubmed: 10793162
Minerva Anestesiol. 2014 Nov;80(11):1158-68
pubmed: 24569355
Nature. 2005 Feb 24;433(7028):895-900
pubmed: 15729348
Implement Sci. 2021 Jan 7;16(1):8
pubmed: 33413437
Health Serv Res. 2011 Oct;46(5):1592-609
pubmed: 21521213
Crit Care Med. 2004 Jun;32(6):1289-93
pubmed: 15187508
Implement Sci. 2019 Mar 27;14(1):34
pubmed: 30917844
Lancet Respir Med. 2016 Jul;4(7):547-548
pubmed: 27155768
Am J Respir Crit Care Med. 2008 Jun 1;177(11):1215-22
pubmed: 18356562
JAMA. 2016 Feb 23;315(8):788-800
pubmed: 26903337
Am J Public Health. 2015 Mar;105(3):513-6
pubmed: 25602895
Ann Am Thorac Soc. 2017 Nov;14(11):1682-1689
pubmed: 28771042
Implement Sci. 2018 Jul 28;13(1):101
pubmed: 30055629
Crit Care Med. 2008 May;36(5):1463-8
pubmed: 18434907
Yearb Med Inform. 2000;(1):65-70
pubmed: 27699347
Am J Respir Crit Care Med. 2003 May 15;167(10):1304-9
pubmed: 12574072
BMC Psychol. 2015 Sep 16;3:32
pubmed: 26376626
Crit Care Med. 2004 Jun;32(6):1260-5
pubmed: 15187503
R Soc Open Sci. 2014 Nov 19;1(3):140216
pubmed: 26064558
J Crit Care. 2018 Apr;44:72-76
pubmed: 29073535
PLoS One. 2015 Jun 25;10(6):e0131712
pubmed: 26110842
JAMA. 2012 Jun 20;307(23):2526-33
pubmed: 22797452
Am J Public Health. 1999 Sep;89(9):1322-7
pubmed: 10474547
Eur Respir J. 2020 Sep 24;56(3):
pubmed: 32747391
BMC Health Serv Res. 2013 Oct 22;13:429
pubmed: 24148207
Intensive Care Med. 2011 Dec;37(12):1932-41
pubmed: 21997128
Am J Public Health. 1995 Mar;85(3):367-72
pubmed: 7892921
J R Soc Interface. 2015 Mar 6;12(104):20140686
pubmed: 25631561
Implement Sci. 2015 Dec 03;10:166
pubmed: 26634923
Respir Care. 2008 Apr;53(4):455-61
pubmed: 18364057
BMC Health Serv Res. 2012 May 16;12:118
pubmed: 22591757
Implement Sci. 2011 Jul 03;6:67
pubmed: 21722400
Eval Health Prof. 2006 Mar;29(1):7-32
pubmed: 16510878
Health Educ Behav. 2007 Dec;34(6):881-96
pubmed: 17602096
PLoS One. 2019 Sep 20;14(9):e0222826
pubmed: 31539417
Sex Transm Dis. 2006 Jul;33(7 Suppl):S23-31
pubmed: 16794552
Am J Respir Crit Care Med. 2015 Jan 15;191(2):177-85
pubmed: 25478681