Evidence map›Paper›PMID 35659230›Full record

ArticleBMC medical informatics and decision making2022

Performance of a rule-based semi-automated method to optimize chart abstraction for surveillance imaging among patients treated for non-small cell lung cancer.

Catherine Byrd, Ureka Ajawara, Ryan Laundry, John Radin, Prasha Bhandari, Ann Leung, Summer Han, Stephen M Asch, Steven Zeliadt, Alex H S Harris and 1 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 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
0.5field-weighted citation impact, top 37% 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, 4 citations in OpenAlex.

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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

11 authors at 3 institutions in 1 country.

Catherine ByrdDepartment of Cardiothoracic Surgery, Stanford University School of Medicine, 300 Pasteur Dr., Falk Research Building, Stanford, CA, 94305, USA.
Ureka AjawaraDepartment of Veterans Affairs, VA Palo Alto Healthcare System, Health Services Research and Development, Palo Alto, USA.
Ryan LaundryDepartment of Veterans Affairs, VA Puget Sound Healthcare System, Health Services Research and Development, Seattle, USA.
John RadinVA Information Resource Center (VIReC), Health Services Research and Development Service, Hines, USA.
Prasha BhandariDepartment of Cardiothoracic Surgery, Stanford University School of Medicine, 300 Pasteur Dr., Falk Research Building, Stanford, CA, 94305, USA.
Ann LeungDepartment of Radiology, Stanford University School of Medicine, Stanford, USA.
Summer HanQuantitative Science Unit, Stanford University School of Medicine, Stanford, USA.
Stephen M AschDepartment of Veterans Affairs, VA Palo Alto Healthcare System, Health Services Research and Development, Palo Alto, USA.
Steven ZeliadtDepartment of Veterans Affairs, VA Puget Sound Healthcare System, Health Services Research and Development, Seattle, USA.
Alex H S HarrisDepartment of Veterans Affairs, VA Palo Alto Healthcare System, Health Services Research and Development, Palo Alto, USA.
Leah BackhusDepartment of Cardiothoracic Surgery, Stanford University School of Medicine, 300 Pasteur Dr., Falk Research Building, Stanford, CA, 94305, USA. Lbackhus@stanford.edu.
Stanford University · USHealth Services Research & Development · USVA Puget Sound Health Care System · US

Funding

HSRD VA I01 HX002456
6 · The paper itself

Abstract

backgroundWe aim to develop and test performance of a semi-automated method (computerized query combined with manual review) for chart abstraction in the identification and characterization of surveillance radiology imaging for post-treatment non-small cell lung cancer patients.

methodsA gold standard dataset consisting of 3011 radiology reports from 361 lung cancer patients treated at the Veterans Health Administration from 2008 to 2016 was manually created by an abstractor coding image type, image indication, and image findings. Computerized queries using a text search tool were performed to code reports. The primary endpoint of query performance was evaluated by sensitivity, positive predictive value (PPV), and F1 score. The secondary endpoint of efficiency compared semi-automated abstraction time to manual abstraction time using a separate dataset and the Wilcoxon rank-sum test.

resultsQuery for image type demonstrated the highest sensitivity of 85%, PPV 95%, and F1 score 0.90. Query for image indication demonstrated sensitivity 72%, PPV 70%, and F1 score 0.71. The image findings queries ranged from sensitivity 75-85%, PPV 23-25%, and F1 score 0.36-0.37. Semi-automated abstraction with our best performing query (image type) improved abstraction times by 68% per patient compared to manual abstraction alone (from median 21.5 min (interquartile range 16.0) to 6.9 min (interquartile range 9.5), p < 0.005).

conclusionsSemi-automated abstraction using the best performing query of image type improved abstraction efficiency while preserving data accuracy. The computerized query acts as a pre-processing tool for manual abstraction by restricting effort to relevant images. Determining image indication and findings requires the addition of manual review for a semi-automatic abstraction approach in order to ensure data accuracy.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsRadiologyElectronic Health RecordsHumansRadiographyChart abstractionImaging surveillanceLung neoplasmsNatural language processingNon-small cell lung carcinoma

Identifiers

PMID35659230
PMCPMC9166440
OpenAlexW4281624628

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.