Evidence map›Paper›PMID 36527239›Full record

ArticleJournal of applied clinical medical physics2023

Frequency of errors in the transfer of treatment parameters from the treatment planning system to the oncology information system in a multi-vendor environment.

William P Donahue, Emily Draeger, Dae Yup Han, Zhe Chen

Open access · goldAbstract read
In one paragraph

Article in Journal of applied clinical medical physics, 2023. 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
1.1field-weighted citation impact, top 24% 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, 5 citations in OpenAlex.

  1. Review
  2. Article
  3. 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

4 authors at 1 institution in 1 country.

William P DonahueDepartment of Therapeutic Radiology, Yale University School of Medicine, New Haven, Connecticut, USA.
Emily DraegerDepartment of Therapeutic Radiology, Yale University School of Medicine, New Haven, Connecticut, USA.
Dae Yup HanDepartment of Therapeutic Radiology, Yale University School of Medicine, New Haven, Connecticut, USA.
Zhe ChenDepartment of Therapeutic Radiology, Yale University School of Medicine, New Haven, Connecticut, USA.
Yale University · US

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
NCATS NIH HHS UL1 TR001863
6 · The paper itself

Abstract

backgroundTechnological advancements have made it possible to improve patient outcomes in radiotherapy, sparing both normal tissues and increasing tumour control. However, these advancements have resulted in an increase in the number of software systems used, which each require data inputs to function. For institutions with multiple vendors for their treatment planning systems and oncology information systems, the transfer of data between them is potentially error prone and can lead to treatment errors. PURPOSE: The goal of this work was to determine the frequency of errors in data transfers between the Varian Eclipse treatment planning system and the Elekta Mosaiq oncology information system.

methodsAn in-house program was used to quantify the number of errors for 2700 unique plans over an 8-month period. Using this information, the frequency of the errors were calculated. A risk priority number was calculated using the calculated frequencies to determine the impact on the clinic.

resultsThe most common errors discovered were backup timer settings (10.7%), Field label (8.5%), DRR associations (3.3%), imaging field types (3.1%), dose rate (1%), Field Id (0.8%), imaging isocenter (0.7% and SSD (0.7%). Based on the risk priority numbers, the DRR association error was ranked as having the highest potential impact on the patient.

conclusionsThe results of the work show that the most effort should be focused on checking the manual steps performed in the transfer process, while items that are imported directly from DICOM-RT without modification are highly likely to be transferred accurately. The data can be used to help guide the implementation of future automated tools and process improvement in the clinic.

Indexed as

NeoplasmsRadiotherapy, Intensity-ModulatedHumansRadiotherapy DosageRadiotherapy Planning, Computer-AssistedSoftwaredata transferinitial plan check

Identifiers

PMID36527239
PMCPMC10113690
OpenAlexW4311822520

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.