AI Article Synopsis

  • Clinical trials often face cancellations due to insufficient participants, leading to the development of Clinical Trial Recruitment Support Systems like ATLAS, which helps identify suitable subjects by utilizing medical classification systems.
  • The study assessed ATLAS's effectiveness in managing 223 eligibility criteria from 30 recent trials, revealing that 51% of inclusion criteria were fully implementable, with 10% of trials entirely portrayable in the software.
  • The research highlighted the need for collaboration with clinical personnel to enhance the accuracy of criteria definitions and expand the range of implementable criteria in clinical studies.

Article Abstract

Clinical trials are essential parts of a medical study process, but studies are often cancelled due to a lack of participants. Clinical Trial Recruitment Support Systems are systems that help to increase the number of participants by seeking more suitable subjects. The software ATLAS (developed by Observational Health Data Sciences and Informatics) can support the launch of a clinical trial by building cohorts of patients who fulfill certain criteria. The correct use of medical classification systems aiming at clearly defined inclusion and exclusion criteria in the studies is an important pillar of this software. The aim of this investigation was to determine whether ATLAS can be used in a Clinical Trial Recruitment Support System to portray the eligibility criteria of clinical studies. Our analysis considered the number of criteria feasible for integration with ATLAS and identified its strengths and weaknesses. Additionally, we investigated whether nonrepresentable criteria were associated with the utilized terminology systems. We analyzed ATLAS using 223 objective eligibility criteria from 30 randomly selected trials conducted in the last 10 years. In the next step, we selected appropriate ICD, OPS, LOINC, or ATC codes to feed the software. We classified each criterion and study based on its implementation capability in the software, ensuring a clear and logical progression of information. Based on our observations, 51% of the analyzed inclusion criteria were fully implemented in ATLAS. Within our selected example set, 10% of the studies were classified as fully portrayable, and 73% were portrayed to some extent. Additionally, we conducted an evaluation of the software regarding its technical limitations and interaction with medical classification systems. To improve and expand the scope of criteria within a cohort definition in a practical setting, it is recommended to work closely with personnel involved in the study to define the criteria precisely and to carefully select terminology systems. The chosen criteria should be combined according to the specific setting. Additional work is needed to specify the significance and amount of the extracted criteria.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10725886PMC
http://dx.doi.org/10.1038/s41598-023-49560-wDOI Listing

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