Evidence map›Paper›PMID 38295191›Full record

ArticleJournal of applied clinical medical physics2024

Using standardized workflows and quantitative data-driven management to reduce time interval from simulation to treatment initiation.

Naichang Yu, Danielle LaHurd, Anthony Mastroianni, Anthony Magnelli, Rahul Tendulkar, Samuel T Chao, John H Suh, Ping Xia

Abstract read
In one paragraph

Article in Journal of applied clinical medical physics, 2024. 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
–field-weighted citation impact
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.

  1. Waiting time interpretations: Complexity and consequences for radiotherapy delays.Technical innovations & patient support in radiation oncology · 2026
    Article
  2. Article
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

8 authors.

Naichang YuDepartment of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Danielle LaHurdDepartment of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Anthony MastroianniDepartment of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Anthony MagnelliDepartment of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Rahul TendulkarDepartment of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Samuel T ChaoDepartment of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio, USA.
John H SuhDepartment of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Ping XiaDepartment of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeExternal beam radiotherapy is a complex process, involving timely coordination among multiple teams. The aim of this study is to report our experience of establishing a standardized workflow and using quantitative data and metrics to manage the time-to-treatment initiation (TTI). METHODS AND MATERIALS: Starting in 2014, we established a standard process in a radiation oncology-specific electronic medical record system (RO-EMR) for patients receiving external beam radiation therapy in our department, aiming to measure the time interval from simulation to treatment initiation, defined as TTI, for radiation oncology. TTI data were stratified according to the following treatment techniques: three-dimensional (3D) conformal therapy, intensity-modulated radiotherapy (IMRT), and stereotactic body radiotherapy (SBRT). Statistical analysis was performed with the Mann-Whitney test for the respective metrics of aggregate data for the initial period 2012- 2015 (PI) and the later period 2016-2019 (PII).

resultOver 8 years, the average annual number of treatments for PI and PII were 1760 and 2357 respectively, with 3D, IMRT, and SBRT treatments accounting for 53, 29, 18% and 44, 34, 22%, respectively, of the treatment techniques. The median TTI for 3D, IMRT, and SBRT for PI and PII were 1, 6, 7, and 1, 5, 7 days, respectively, while the 90th percentile TTI for the three techniques in both periods were 5, 9, 11 and 4, 9, 10 days, respectively. From the aggregate data, the TTI was significantly reduced (p = 0.0004, p < 0.0001, p < 0.0001) from PI to PII for the three treatment techniques.

conclusionEstablishing a standardized workflow and frequently measuring TTI resulted in shortening the TTI during the early years (in PI) and maintaining the established TTI in the subsequent years (in PII).

Indexed as

RadiosurgeryRadiotherapy, ConformalRadiotherapy, Intensity-ModulatedHumansRadiotherapy Planning, Computer-AssistedWorkflowdata-driven managementstandardizationtime-to-treatment initiationworkflow

Identifiers

PMID38295191
PMCPMC10930000

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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.