Evidence map›Paper›PMID 33497439›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2021

The design and evaluation of a novel algorithm for automated preference card optimization.

David Scheinker, Matt Hollingsworth, Anna Brody, Carey Phelps, William Bryant, Francesca Pei, Kristin Petersen, Alekhya Reddy, James Wall

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Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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5citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

David ScheinkerDepartment of Management Science and Engineering, Stanford School of Engineering, Stanford University, Stanford, California, USA.ORCID 0000-0001-5885-8024
Matt HollingsworthGraduate School of Business, Stanford University, Stanford, California, USA.
Anna BrodyGraduate School of Business, Stanford University, Stanford, California, USA.
Carey PhelpsDepartment of Management Science and Engineering, Stanford School of Engineering, Stanford University, Stanford, California, USA.
William BryantMontefiore Medical Center, Bronx, New York, USA.
Francesca PeiLucile Packard Children's Hospital Stanford, Stanford University, Stanford, California, USA.
Kristin PetersenLucile Packard Children's Hospital Stanford, Stanford University, Stanford, California, USA.
Alekhya ReddyCarta Healthcare Inc., San Mateo, California, USA.
James WallLucile Packard Children's Hospital Stanford, Stanford University, Stanford, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInaccurate 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.

methodsThe 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.

resultsThe 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).

conclusionThe 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.

Indexed as

AlgorithmsCost SavingsHospital CostsDecision Support Systems, ClinicalHospital Information SystemsHumansSurgical EquipmentSurgical Procedures, Operativeclinical systems and informaticsdata mining and data analyticsdecision support systemshealth information technology quality and evaluation

Identifiers

PMID33497439
PMCPMC8661396

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.