Evidence map›Paper›PMID 34790372›Full record

ArticleJournal of gastrointestinal oncology2021

Deploying an artificial intelligence-based online search tool to increase patients' access to and understanding of solid tumor gastrointestinal clinical trials.

Emily Rose Jordan, Luca Jahreiss, Pashtoon Murtaza Kasi

Abstract read
In one paragraph

Article in Journal of gastrointestinal oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024
    Article
  6. Review
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

3 authors.

Emily Rose Jordan *Ancora.ai AG, Zurich, Switzerland.
Luca Jahreiss *Ancora.ai AG, Zurich, Switzerland.
Pashtoon Murtaza KasiDivision of Internal Medicine, Department of Hematology and Oncology, Weill Cornell Medicine, New York, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe number and complexity of clinical trials is growing, making finding and accessing trials increasingly challenging. A key barrier to increasing recruitment efficiency is patients' low awareness of trials as an option. They rarely hear about trials from oncologists, unless these are based at a research center, which introduces trial population biases. Even if patients decide to search for trial information on their own, it is difficult for laypeople to understand. Trial search tools exist, but have multiple shortcomings: complex navigation, limited search functionality, data too complex for non-specialists, and next steps unclear.

methodsAccess to trials via a novel trial search tool using technology, including artificial intelligence (AI), to restructure trial information and match patients to trials was the focus of this study. We focused on 6 gastrointestinal cancers for evaluating the search tool's AI model in which their trial information was restructured based on key eligibility differentiators identified. Research participants were recruited with the support of patient associations leveraging social media. They were asked to complete a survey evaluating an established tool and the novel tool to find trials relevant for them. They assessed aspects of the tools' usability on 5-level Likert scales.

resultsSurvey respondents had a base level of awareness of trials, with patient associations and the internet as leading information sources. The novel tool made it easier to find trials, led to an improvement in ease of understanding information presented and provided more clarity on what the next steps towards enrollment would be. Overall, this led to higher patient satisfaction.

conclusionsNovel patient-focused tools can unlock trial access for all patients. Democratizing trial information will not only increase trial accrual, but also patient satisfaction while reducing disparities. This tool could also be of value for academic or community oncologists who face similar issues when navigating trials for patients.

Indexed as

Artificial intelligence (AI)cancer disparitiesclinical trialshealth equitypatient access

Identifiers

PMID34790372
PMCPMC8576209

What OpenQuestion holds

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Registered trials

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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.