Where is my infusion pump? Harnessing network dynamics for improved hospital equipment fleet management. (26th April 2020)

• Record Type: Journal Article
• Authors: Martinez, Diego A; Cai, Jiarui; Oke, Jimi B; Jarrell, Andrew S; Feijoo, Felipe; Appelbaum, Jeffrey; Klein, Eili; Barnes, Sean; Levin, Scott R
• Is Part Of: Journal of the American Medical Informatics Association. Volume 27:Number 6(2020)
• Page Start: 884
• Page End: 892
• Publication Date: 2020-04-26
• Subjects: efficiency -- organizational -- systems analysis -- machine learning -- radio frequency identification device -- electronic health records; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocaa033
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: Timely availability of intravenous infusion pumps is critical for high-quality care delivery. Pumps are shared among hospital units, often without central management of their distribution. This study seeks to characterize unit-to-unit pump sharing and its impact on shortages, and to evaluate a system-control tool that balances inventory across all care areas, enabling increased availability of pumps. Materials and Methods: A retrospective study of 3832 pumps moving in a network of 5292 radiofrequency and infrared sensors from January to November 2017 at The Johns Hopkins Hospital in Baltimore, Maryland. We used network analysis to determine whether pump inventory in one unit was associated with inventory fluctuations in others. We used a quasi-experimental design and segmented regressions to evaluate the effect of the system-control tool on enabling safe inventory levels in all care areas. Results: We found 93 care areas connected through 67, 111 pump transactions and 4 discernible clusters of pump sharing. Up to 17% (95% confidence interval, 7%-27%) of a unit's pump inventory was explained by the inventory of other units within its cluster. The network analysis supported design and deployment of a hospital-wide inventory balancing system, which resulted in a 44% (95% confidence interval, 36%-53%) increase in the number of care areas above safe inventory levels. Conclusions: Network phenomena are essential inputs to hospital equipment fleet management. Consequently, benefits of improved inventory management in strategic unit(s) are capable of spreading safer inventory levels throughout the hospital.