ArticleJournal of medical systems2011
Understanding performance and behavior of tightly coupled outpatient systems using RFID: initial experience.
Article in Journal of medical systems, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Real-time locating systems to improve healthcare delivery: A systematic review.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- RFID-enabled healthcare applications, issues and benefits: an archival analysis (1997-2011).Journal of medical systems · 2012Review
- ECC-based grouping-proof RFID for inpatient medication safety.Journal of medical systems · 2012Article
- Increasing the reliability of production schedules in a pharmaceutical packaging department.Journal of medical systems · 2012Article
- On the designing of a tamper resistant prescription RFID access control system.Journal of medical systems · 2012Article
- The adoption and implementation of RFID technologies in healthcare: a literature review.Journal of medical systems · 2012Review
- Patient safety through RFID: vulnerabilities in recently proposed grouping protocols.Journal of medical systems · 2012Article
- Simulation Analysis and Comparison of Point of Care Testing and Central Laboratory Testing.MDM policy & practiceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding how clinical systems actually behave in an era of limited medical resources is critical. The purpose of this study was to determine if a radiofrequency-identification-based indoor positioning system (IPS) could objectively and unobtrusively capture outpatient clinic behavior. Primary outcomes were flowtime, wait time and patient/clinician face time. Two contrasting clinics were evaluated: a primary care clinic (PC) with templated scheduling and an urgent care clinic (UC) with unconstrained visit time and first-in, first-out scheduling. All staff wore transponders throughout the study period. Patients carried transponders from check in to check out. All patients and staff were allowed to opt out. The study was approved by hospital IRB. Standard descriptive and analytic statistical methods were used. Five hundred twenty-six patients (309 patients (PC), 217 patients (UC)) and 38 clinicians (eight (PC) and 30 (UC)) volunteered between April 30 and July 1, 2008. Total FT was not significantly different across clinics. PC wait time was significantly shorter (7.6 min [SD 15.8]) vs. UC (19.7 min [SD 25.3], p < 0.0001), and PC Face time was significantly longer (29.9 min, [SD 19.1] vs. UC (9.8 min [SD 8.5], p < 0.0001). PC Face time distributions reflected template scheduling structure. In contrast, face time distributions in UC had a smooth log normal distribution with a lower mean value. Our study seems to indicate that an IPS can successfully measure important clinic process measures in live clinical outpatient settings and capture behavioral differences across different outpatient organizational structures.
Indexed as
Identifiers
20703561What OpenQuestion holds
Registered trials
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.