ArticleCurrent oncology (Toronto, Ont.)2026
Assessing Quality Gaps and Clinician Perspectives on AI Integration in Colorectal Cancer NGS Pathways.
Article in Current oncology (Toronto, Ont.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
19 authors.
Funding
Abstract
Next-generation sequencing (NGS) is integral to colorectal cancer (CRC) care, but its value depends on results being available when treatment decisions are made. We conducted a qualitative study to identify quality gaps in CRC NGS pathways and assess perspectives on digital and artificial intelligence (AI)-enabled workflow support. Semi-structured interviews were conducted with 22 medical oncologists and pathologists from 14 Ontario hospitals, and transcripts were analyzed thematically. Five themes emerged: (1) clinical indications and test ordering, where initiation was inconsistent because of unclear reflex-testing criteria, reliance on upstream clinicians, staging uncertainty, evolving indications, and funding constraints; (2) tissue, pathology, and laboratory workflow, where participants attributed bottlenecks and turnaround variability to send-out testing, staffing, infrastructure, and capacity; (3) interdisciplinary communication and tracking, where informal communication, unclear ownership, and manual tracking highlighted the need for closed-loop confirmation and alerts; (4) reporting and clinical integration, where report clarity, interpretability, and timing affected use; and (5) electronic health record integration and AI integration, where fragmented systems and limited interoperability delayed result access. Participants supported clinician-supervised digital or AI-enabled tools for case identification, triage, tracking, and overdue-result alerts. CRC NGS optimization requires standardized reflex criteria, clearer ownership, closed-loop tracking, reliable routing, structured reporting, and interoperable infrastructure.
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Registered trials
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.