Evidence map›Paper›PMID 34805621›Full record

ArticleAdvances in radiation oncology

An Investigation of Radiation Treatment Learning Opportunities in Relation to the Radiation Oncology Electronic Medical Record: A Single Institution Experience.

Y Jessica Huang, Vikren Sarkar, Adam Paxton, Hui Zhao, Frances Fan-Chi Su, Ryan Price, Bill J Salter

Abstract read
In one paragraph

Article in Advances in radiation oncology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Y Jessica HuangDepartment of Radiation Oncology, University of Utah, Salt Lake City, Utah.
Vikren SarkarDepartment of Radiation Oncology, University of Utah, Salt Lake City, Utah.
Adam PaxtonDepartment of Radiation Oncology, University of Utah, Salt Lake City, Utah.
Hui ZhaoDepartment of Radiation Oncology, University of Utah, Salt Lake City, Utah.
Frances Fan-Chi SuDepartment of Radiation Oncology, University of Utah, Salt Lake City, Utah.
Ryan PriceDepartment of Radiation Oncology, University of Utah, Salt Lake City, Utah.
Bill J SalterDepartment of Radiation Oncology, University of Utah, Salt Lake City, Utah.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeA modern radiation oncology electronic medical record (RO-EMR) system represents a sophisticated human-computer interface with the potential to reduce human driven errors and improve patient safety. As the RO-EMR becomes an integral part of clinical processes, it may be advantageous to analyze learning opportunities (LO) based on their relationship with the RO-EMR. This work reviews one institution's documented LO to: (1) study their relationship with the RO-EMR workflow, (2) identify best opportunities to improve RO-EMR workflow design, and (3) identify current RO-EMR workflow challenges. METHODS AND MATERIALS: Internal LO reports for an 11-year contiguous period were categorized by their relationship to the RO-EMR. We also identify the specific components of the RO-EMR used or involved in each LO. Additionally, contributing factor categories from the ASTRO/AAPM sponsored Radiation Oncology Incident Learning System's (RO-ILS) nomenclature was used to characterize LO directly linked to the RO-EMR.

resultsA total of 163 LO from the 11-year period were reviewed and analyzed. Most (77.2%) LO involved the RO-EMR in some way. The majority of the LO were the results of human/manual operations. The most common RO-EMR components involved in the studied LO were documentation related to patient setup, treatment session schedule functionality, RO-EMR used as a communication/note-delivery tool, and issues with treatment accessories. Most of the LO had staff lack of attention and policy not followed as 2 of the highest occurring contributing factors.

conclusionsWe found that the majority of LO were related to RO-EMR workflow processes. The high-risk areas were related to manual data entry or manual treatment execution. An evaluation of LO as a function of their relationship with the RO-EMR allowed for opportunities for improvement. In addition to regular radiation oncology quality improvement review and policy update, automated functions in RO-EMR remain highly desirable.

Identifiers

PMID34805621
PMCPMC8581278

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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.