Evidence map›Paper›PMID 32337588›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2020

Where is my infusion pump? Harnessing network dynamics for improved hospital equipment fleet management.

Diego A Martinez, Jiarui Cai, Jimi B Oke, Andrew S Jarrell, Felipe Feijoo, Jeffrey Appelbaum, Eili Klein, Sean Barnes, Scott R Levin, AHRQ Patient Safety Learning Laboratory Program and the CDC MinD Healthcare Program

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.8field-weighted citation impact, top 23% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Machine learning and artificial intelligence: applications in healthcare epidemiology.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2021
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 6 institutions in 2 countries.

Diego A MartinezDepartment of Emergency Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
Jiarui CaiDepartment of Operations, Johns Hopkins Health System, Baltimore, Maryland, USA.
Jimi B OkeDepartment of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Andrew S JarrellDepartment of Pharmacy, The Johns Hopkins Hospital, Baltimore, Maryland, USA.
Felipe FeijooEscuela de Ingeniería Industrial, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile.
Jeffrey AppelbaumDepartment of Operations, Johns Hopkins Health System, Baltimore, Maryland, USA.
Eili KleinDepartment of Emergency Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
Sean BarnesDepartment of Decision, Operations and Information Technologies, University of Maryland, College Park, Maryland, USA.
Scott R LevinDepartment of Emergency Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
AHRQ Patient Safety Learning Laboratory Program and the CDC MinD Healthcare Program
Johns Hopkins University · USCenter for Disease Dynamics, Economics & Policy · USJohns Hopkins Hospital · USPontificia Universidad Católica de Valparaíso · CLUniversity of Maryland, College Park · USUniversity of Massachusetts Amherst · US

Funding

NCEZID CDC HHS U01 CK000536NCEZID CDC HHS U01 CK000589
6 · The paper itself

Abstract

objectiveTimely 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

methodsA 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.

resultsWe 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.

conclusionsNetwork 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.

Indexed as

Bed OccupancyEquipment and Supplies, HospitalHospital UnitsHumansInfusion PumpsInventories, HospitalLogistic ModelsRetrospective Studiesefficiencyelectronic health recordsmachine learningorganizationalradio frequency identification devicesystems analysis

Identifiers

PMID32337588
PMCPMC7647311
OpenAlexW3019968152

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.