Alert burden in pediatric hospitals: a cross-sectional analysis of six academic pediatric health systems using novel metrics.
alert fatigue
benchmarking
clinical burnout
decision support systems
electronic health records
health personnel
professional
Journal
Journal of the American Medical Informatics Association : JAMIA
ISSN: 1527-974X
Titre abrégé: J Am Med Inform Assoc
Pays: England
ID NLM: 9430800
Informations de publication
Date de publication:
25 11 2021
25 11 2021
Historique:
received:
26
04
2021
revised:
02
07
2021
accepted:
10
09
2021
pubmed:
20
10
2021
medline:
22
1
2022
entrez:
19
10
2021
Statut:
ppublish
Résumé
Excessive electronic health record (EHR) alerts reduce the salience of actionable alerts. Little is known about the frequency of interruptive alerts across health systems and how the choice of metric affects which users appear to have the highest alert burden. (1) Analyze alert burden by alert type, care setting, provider type, and individual provider across 6 pediatric health systems. (2) Compare alert burden using different metrics. We analyzed interruptive alert firings logged in EHR databases at 6 pediatric health systems from 2016-2019 using 4 metrics: (1) alerts per patient encounter, (2) alerts per inpatient-day, (3) alerts per 100 orders, and (4) alerts per unique clinician days (calendar days with at least 1 EHR log in the system). We assessed intra- and interinstitutional variation and how alert burden rankings differed based on the chosen metric. Alert burden varied widely across institutions, ranging from 0.06 to 0.76 firings per encounter, 0.22 to 1.06 firings per inpatient-day, 0.98 to 17.42 per 100 orders, and 0.08 to 3.34 firings per clinician day logged in the EHR. Custom alerts accounted for the greatest burden at all 6 sites. The rank order of institutions by alert burden was similar regardless of which alert burden metric was chosen. Within institutions, the alert burden metric choice substantially affected which provider types and care settings appeared to experience the highest alert burden. Estimates of the clinical areas with highest alert burden varied substantially by institution and based on the metric used.
Sections du résumé
BACKGROUND
Excessive electronic health record (EHR) alerts reduce the salience of actionable alerts. Little is known about the frequency of interruptive alerts across health systems and how the choice of metric affects which users appear to have the highest alert burden.
OBJECTIVE
(1) Analyze alert burden by alert type, care setting, provider type, and individual provider across 6 pediatric health systems. (2) Compare alert burden using different metrics.
MATERIALS AND METHODS
We analyzed interruptive alert firings logged in EHR databases at 6 pediatric health systems from 2016-2019 using 4 metrics: (1) alerts per patient encounter, (2) alerts per inpatient-day, (3) alerts per 100 orders, and (4) alerts per unique clinician days (calendar days with at least 1 EHR log in the system). We assessed intra- and interinstitutional variation and how alert burden rankings differed based on the chosen metric.
RESULTS
Alert burden varied widely across institutions, ranging from 0.06 to 0.76 firings per encounter, 0.22 to 1.06 firings per inpatient-day, 0.98 to 17.42 per 100 orders, and 0.08 to 3.34 firings per clinician day logged in the EHR. Custom alerts accounted for the greatest burden at all 6 sites. The rank order of institutions by alert burden was similar regardless of which alert burden metric was chosen. Within institutions, the alert burden metric choice substantially affected which provider types and care settings appeared to experience the highest alert burden.
CONCLUSION
Estimates of the clinical areas with highest alert burden varied substantially by institution and based on the metric used.
Identifiants
pubmed: 34664664
pii: 6401984
doi: 10.1093/jamia/ocab179
pmc: PMC8633657
doi:
Banques de données
Dryad
['10.5061/dryad.5mkkwh769']
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2654-2660Informations de copyright
© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.
Références
CMAJ. 2010 Mar 23;182(5):E216-25
pubmed: 20212028
Am J Epidemiol. 2019 Apr 1;188(4):709-723
pubmed: 30535131
J Am Med Inform Assoc. 2015 Mar;22(2):361-9
pubmed: 25318641
Ann Intern Med. 2012 Jul 3;157(1):29-43
pubmed: 22751758
J Biomed Inform. 2020 Jul;107:103421
pubmed: 32407878
J Am Med Inform Assoc. 2014 Jul-Aug;21(4):602-6
pubmed: 24821737
NPJ Digit Med. 2020 Aug 19;3:109
pubmed: 32864472
J Am Med Inform Assoc. 2012 May-Jun;19(3):346-52
pubmed: 21849334
J Am Med Inform Assoc. 2006 Mar-Apr;13(2):138-47
pubmed: 16357358
Congenit Heart Dis. 2009 Sep-Oct;4(5):318-28
pubmed: 19740186
JAMA Pediatr. 2020 Feb 1;174(2):162-169
pubmed: 31860017
J Am Med Inform Assoc. 2017 Mar 1;24(2):409-412
pubmed: 27274015
Pediatrics. 2013 Jun;131(6):e1970-3
pubmed: 23713099
JAMA Netw Open. 2020 Jun 1;3(6):e207385
pubmed: 32515799
Nat Biotechnol. 2015 Apr;33(4):360-3
pubmed: 25850061
J Hosp Med. 2015 Jun;10(6):345-51
pubmed: 25873486
Int J Med Inform. 2012 Nov;81(11):733-45
pubmed: 22819199
Clin Diabetes. 2020 Apr;38(2):141-151
pubmed: 32327886
N Engl J Med. 2003 Jun 19;348(25):2526-34
pubmed: 12815139
AMIA Annu Symp Proc. 2012;2012:281-90
pubmed: 23304298
Clin Pharmacol Ther. 2020 Apr;107(4):834-842
pubmed: 31869442
Appl Clin Inform. 2017 Jul 05;8(3):686-697
pubmed: 28678892
Appl Clin Inform. 2014 Sep 03;5(3):802-13
pubmed: 25298818
AMIA Annu Symp Proc. 2018 Dec 05;2018:348-357
pubmed: 30815074
Appl Clin Inform. 2020 Jan;11(1):46-58
pubmed: 31940671
Kidney Int. 2020 Mar;97(3):580-588
pubmed: 31980139
JAMA Intern Med. 2019 Jan 1;179(1):11-15
pubmed: 30535345
BMJ Qual Saf. 2019 Dec;28(12):987-996
pubmed: 31164486