Evidence map›Paper›PMID 36514109›Full record

ArticleBMC health services research2022

Using patient flow analysis with real-time patient tracking to optimize radiation oncology consultation visits.

Shane Mesko, Julius Weng, Prajnan Das, Albert C Koong, Joseph M Herman, Dorothy Elrod-Joplin, Ashley Kerr, Thomas Aloia, John Frenzel, Katy E French and 6 more

Open access · goldAbstract read
In one paragraph

Article in BMC health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.9field-weighted citation impact, top 10% 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

3 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

16 authors at 2 institutions in 1 country.

Shane MeskoScripps MD Anderson Cancer Center, Division of Radiation Oncology, San Diego, California, USA.
Julius WengDepartment of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA.
Prajnan DasDepartment of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA.
Albert C KoongDepartment of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA.
Joseph M HermanDepartment of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA.
Dorothy Elrod-JoplinDepartment of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA.
Ashley KerrDepartment of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA.
Thomas AloiaInstitute for Cancer Care Innovation, MD Anderson Cancer Center, Houston, TX, USA.
John FrenzelInstitute for Cancer Care Innovation, MD Anderson Cancer Center, Houston, TX, USA.
Katy E FrenchChair, Patient Informatics, MD Anderson Cancer Center, Houston, TX, USA.
Wendi MartinezInstitute for Cancer Care Innovation, MD Anderson Cancer Center, Houston, TX, USA.
Iris RecinosInstitute for Cancer Care Innovation, MD Anderson Cancer Center, Houston, TX, USA.
Abdulaziz AlshaikhInstitute for Cancer Care Innovation, MD Anderson Cancer Center, Houston, TX, USA.
Utpala DaftaryInstitute for Cancer Care Innovation, MD Anderson Cancer Center, Houston, TX, USA.
Amy C MorenoDepartment of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA.
Quynh-Nhu NguyenDepartment of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA. qnnguyen@mdanderson.org.
The University of Texas MD Anderson Cancer Center · USScripps MD Anderson Cancer Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeClinical efficiency is a key component of the value-based care model and a driver of patient satisfaction. The purpose of this study was to identify and address inefficiencies at a high-volume radiation oncology clinic. METHODS AND MATERIALS: Patient flow analysis (PFA) was used to create process maps and optimize the workflow of consultation visits in a gastrointestinal radiation oncology clinic at a large academic cancer center. Metrics such as cycle times, waiting times, and rooming times were assessed by using a real-time patient status function in the electronic medical record for 556 consults and compared between before vs after implementation of the PFA recommendations.

resultsThe initial PFA revealed four inefficiencies: (1) protracted rooming time, (2) inefficient communications, (3) duplicated tasks, and (4) ambiguous clinical roles. We analyzed 485 consult-visits before the PFA and 71 after the PFA. The PFA recommendations led to reductions in overall median cycle time by 21% (91 min vs 72 min, p < 0.001), in cumulative waiting times by 64% (45 min vs 16 min; p < 0.001), which included waiting room time (14 min vs 5 min; p < 0.001) and wait for physician (20 min vs. 6 min; p < 0.001). Slightly less than one-quarter (22%) of consult visits before the PFA lasted > 2 h vs. 0% after implementation of the recommendations (p < 0.001). Similarly, the proportion of visits requiring < 1 h was 16% before PFA vs 34% afterward (p < 0.001).

conclusionsPFA can be used to identify clinical inefficiencies and optimize workflows in radiation oncology consultation clinics, and implementing their findings can significantly improve cycle times and waiting times. Potential downstream effects of these interventions include improved patient experience, decreased staff burnout, financial savings, and opportunities for expanding clinical capacity.

Indexed as

Radiation OncologyAmbulatory Care FacilitiesEfficiency, OrganizationalHumansPatient Identification SystemsPatient SatisfactionReferral and ConsultationClinical efficiencyClinical workflowCycle timePatient flow analysisRadiation oncologyRooming timeWaiting time

Identifiers

PMID36514109
PMCPMC9745696
OpenAlexW4311373077

What OpenQuestion holds

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LicenceCC BY
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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.