Evidence map›Paper›PMID 25155098›Full record

ArticleHealth care management science2016

Improving patient flow at a family health clinic.

Jonathan F Bard, Zhichao Shu, Douglas J Morrice, Dongyang Ester Wang, Ramin Poursani, Luci Leykum

Abstract read
PubMed Publisher
In one paragraph

Article in Health care management science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 pooled it
1.6field-weighted citation impact, top 17% 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

14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 34 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
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  4. Article
  5. Article
  6. Optimizing neurosurgery clinic operations: a comparative study of interventions in Finland's public healthcare system.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2024
    Article
  7. Article
  8. Article
  9. Discrete-Event Simulation Modeling in Healthcare: A Comprehensive Review.International journal of environmental research and public health · 2021
    Review
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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

6 authors at 2 institutions in 1 country.

Jonathan F BardThe University of Texas, Austin, TX, 78712, USA. jbard@mail.utexas.edu.
Zhichao ShuThe University of Texas, Austin, TX, 78712, USA.
Douglas J MorriceThe University of Texas, Austin, TX, 78712, USA.
Dongyang Ester WangThe University of Texas, Austin, TX, 78712, USA.
Ramin PoursaniSchool of Medicine, The University of Texas Health Science Center, San Antonio, TX, 78229, USA.
Luci LeykumSchool of Medicine, The University of Texas Health Science Center, San Antonio, TX, 78229, USA.
The University of Texas at Austin · USThe University of Texas Health Science Center at San Antonio · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper presents an analysis of a residency primary care clinic whose majority of patients are underserved. The clinic is operated by the health system for Bexar County and staffed primarily with physicians in a three-year Family Medicine residency program at The University of Texas School of Medicine in San Antonio. The objective of the study was to obtain a better understanding of patient flow through the clinic and to investigate changes to current scheduling rules and operating procedures. Discrete event simulation was used to establish a baseline and to evaluate a variety of scenarios associated with appointment scheduling and managing early and late arrivals. The first steps in developing the model were to map the administrative and diagnostic processes and to collect time-stamped data and fit probability distributions to each. In conjunction with the initialization and validation steps, various regressions were performed to determine if any relationships existed between individual providers and patient types, length of stay, and the difference between discharge time and appointment time. The latter two statistics along with resource utilization and closing time were the primary metrics used to evaluate system performance.The results showed that up to an 8.5 % reduction in patient length of stay is achievable without noticeably affecting the other metrics by carefully adjusting appointment times. Reducing the no-show rate from its current value of 21.8 % or overbooking, however, is likely to overwhelm the system's resources and lead to excessive congestion and overtime. Another major finding was that the providers are the limiting factor in improving patient flow. With an average utilization rate above 90 % there is little prospect in shortening the total patient time in the clinic without reducing the providers' average assessment time. Finally, several suggestions are offered to ensure fairness when dealing with out-of-order arrivals.

Indexed as

Appointments and SchedulesEfficiency, OrganizationalAcademic Medical CentersAmbulatory Care FacilitiesComputer SimulationHumansLength of StayModels, OrganizationalOutpatientsPhysician-Patient RelationsPrimary Health CareRegression AnalysisTexasTime FactorsFamily medicinePatient flowPerformance analysisResidency outpatient clinicSimulation

Identifiers

PMID25155098
OpenAlexW2036612011

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.