David Scheinker1,2,3, Matt Hollingsworth4,5, Anna Brody4,5, Carey Phelps1, William Bryant6, Francesca Pei3, Kristin Petersen3, Alekhya Reddy5,7, James Wall3,8. 1. Department of Management Science and Engineering, Stanford School of Engineering, Stanford University, Stanford, California, USA. 2. Clinical Excellence Research Center, Stanford School of Medicine, California, USA. 3. Lucile Packard Children's Hospital Stanford, Stanford University, Stanford, California, USA. 4. Graduate School of Business, Stanford University, Stanford, California, USA. 5. Carta Healthcare Inc., San Mateo, California, USA. 6. Montefiore Medical Center, Bronx, New York, USA. 7. Massachusetts Institute of Technology, Cambridge, Massachusett, USA. 8. Department of Surgery, Stanford University School of Medicine, Stanford, California, USA.
Abstract
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.
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.
Keywords:
clinical systems and informatics; data mining and data analytics; decision support systems; health information technology quality and evaluation
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