TY - JOUR
T1 - Where is my infusion pump? Harnessing network dynamics for improved hospital equipment fleet management
AU - Martinez, Diego A.
AU - Cai, Jiarui
AU - Oke, Jimi B.
AU - Jarrell, Andrew S.
AU - FEIJOO PALACIOS, FELIPE ANDRES
AU - Appelbaum, Jeffrey
AU - Klein, Eili
AU - Barnes, Sean
AU - Levin, Scott R.
N1 - Funding Information:
This work was supported by the Johns Hopkins Health System. The sponsor had no role in the design or conduct of this study; the collection, management, analysis, or interpretation of data; or the preparation, review, or approval of the manuscript. DM is also supported by the Centers for Disease Control and Prevention (CDC) and the Agency for Healthcare Research and Quality (AHRQ). EK is supported by the CDC and AHRQ. FF is supported by the Comisión Nacional de Investigación Científica y Tecnológica of Chile. SB is supported by the CDC. SL is supported by the National Science Foundation, National Institutes of Health, CDC, and AHRQ.
Publisher Copyright:
© 2020 The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/6/1
Y1 - 2020/6/1
N2 - 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.
AB - 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.
KW - efficiency
KW - electronic health records
KW - machine learning
KW - organizational
KW - radio frequency identification device
KW - systems analysis
UR - http://www.scopus.com/inward/record.url?scp=85086052284&partnerID=8YFLogxK
U2 - 10.1093/jamia/ocaa033
DO - 10.1093/jamia/ocaa033
M3 - Article
C2 - 32337588
AN - SCOPUS:85086052284
VL - 27
SP - 884
EP - 892
JO - Journal of the American Medical Informatics Association
JF - Journal of the American Medical Informatics Association
SN - 1067-5027
IS - 6
ER -