The design and evaluation of a novel algorithm for automated preference card optimization.
clinical systems and informatics
data mining and data analytics
decision support systems
health information technology quality and evaluation
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:
12 06 2021
12 06 2021
Historique:
received:
30
05
2020
accepted:
31
10
2020
pubmed:
27
1
2021
medline:
19
8
2021
entrez:
26
1
2021
Statut:
ppublish
Résumé
Inaccurate surgical preference cards (supply lists) are associated with higher direct costs, waste, and delays. Numerous preference card improvement projects have relied on institution-specific, manual approaches of limited reproducibility. We developed and tested an algorithm to facilitate the first automated, informatics-based, fully reproducible approach. The algorithm cross-references the supplies used in each procedure and listed on each preference card and uses a time-series regression to estimate the likelihood that each quantity listed on the preference card is inaccurate. Algorithm performance was evaluated by measuring changes in direct costs between preference cards revised with the algorithm and preference cards that were not revised or revised without use of the algorithm. Results were evaluated with a difference-in-differences (DID) multivariate fixed-effects model of costs during an 8-month pre-intervention and a 15-month post-intervention period. The accuracies of the quantities of 469 155 surgeon-procedure-specific items were estimated. Nurses used these estimates to revise 309 preference cards across eight surgical services corresponding to, respectively, 1777 and 3106 procedures in the pre- and post-intervention periods. The average direct cost of supplies per case decreased by 8.38% ($352, SD $6622) for the intervention group and increased by 13.21% ($405, SD $14 706) for the control group (P < .001). The DID analysis showed significant cost reductions only in the intervention group during the intervention period (P < .001). The optimization of preference cards with a variety of institution-specific, manually intensive approaches has led to cost savings. The automated algorithm presented here produced similar results that may be more readily reproducible.
Sections du résumé
BACKGROUND
Inaccurate surgical preference cards (supply lists) are associated with higher direct costs, waste, and delays. Numerous preference card improvement projects have relied on institution-specific, manual approaches of limited reproducibility. We developed and tested an algorithm to facilitate the first automated, informatics-based, fully reproducible approach.
METHODS
The algorithm cross-references the supplies used in each procedure and listed on each preference card and uses a time-series regression to estimate the likelihood that each quantity listed on the preference card is inaccurate. Algorithm performance was evaluated by measuring changes in direct costs between preference cards revised with the algorithm and preference cards that were not revised or revised without use of the algorithm. Results were evaluated with a difference-in-differences (DID) multivariate fixed-effects model of costs during an 8-month pre-intervention and a 15-month post-intervention period.
RESULTS
The accuracies of the quantities of 469 155 surgeon-procedure-specific items were estimated. Nurses used these estimates to revise 309 preference cards across eight surgical services corresponding to, respectively, 1777 and 3106 procedures in the pre- and post-intervention periods. The average direct cost of supplies per case decreased by 8.38% ($352, SD $6622) for the intervention group and increased by 13.21% ($405, SD $14 706) for the control group (P < .001). The DID analysis showed significant cost reductions only in the intervention group during the intervention period (P < .001).
CONCLUSION
The optimization of preference cards with a variety of institution-specific, manually intensive approaches has led to cost savings. The automated algorithm presented here produced similar results that may be more readily reproducible.
Identifiants
pubmed: 33497439
pii: 6120552
doi: 10.1093/jamia/ocaa275
pmc: PMC8661396
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1088-1097Informations 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
J Am Med Inform Assoc. 2013 Jan 1;20(1):144-51
pubmed: 22733976
Curr Probl Surg. 2016 Apr;53(4):165-205
pubmed: 27102336
JAMA. 2014 Dec 10;312(22):2401-2
pubmed: 25490331
J Clin Anesth. 2010 Jun;22(4):233-6
pubmed: 20522350
Med Care. 2013 Aug;51(8 Suppl 3):S30-7
pubmed: 23774517
J Am Med Inform Assoc. 2017 Mar 1;24(2):246-250
pubmed: 28011595
Health Aff (Millwood). 2015 Dec;34(12):2174-80
pubmed: 26561387
JAMA Surg. 2017 Mar 1;152(3):284-291
pubmed: 27926758
J Neurosurg. 2017 Feb;126(2):620-625
pubmed: 27153160
J Am Med Inform Assoc. 2003 Nov-Dec;10(6):523-30
pubmed: 12925543
JAMA Surg. 2018 Apr 18;153(4):e176233
pubmed: 29490366
Health Aff (Millwood). 2013 Jan;32(1):63-8
pubmed: 23297272
Health Syst (Basingstoke). 2018 Jul 18;8(2):134-151
pubmed: 31275574
J Am Med Inform Assoc. 2013 Jun;20(e1):e26-32
pubmed: 23396512
JAMA. 2019 Oct 15;322(15):1451-1452
pubmed: 31613351
J Pediatr Urol. 2018 Feb;14(1):20-24
pubmed: 28967607
J Pediatr. 2021 Jan;228:208-212
pubmed: 32920104
Ann Surg. 2018 Jul;268(1):48-57
pubmed: 29533265
J Pediatr Surg. 2015 Jun;50(6):919-22
pubmed: 25805009
Science. 2019 Oct 25;366(6464):447-453
pubmed: 31649194
Am J Surg. 2018 Jan;215(1):19-22
pubmed: 28676153