Application of Information Technology to Clinical Trial Evaluation and Enrollment: A Review
Open accessAbstract
IMPORTANCE: As cancer treatment has become more individualized, oncologic clinical trials have become more complex. Increasingly numerous and stringent eligibility criteria frequently include tumor molecular or genomic characteristics that may not be readily identified in medical records, rendering it difficult to best match clinical trials with clinical sites and to identify potentially eligible patients once a clinical trial has been selected and activated. Partly because of these factors, enrollment rates for cancer clinical trials remain low, creating delays and increased costs for drug development. Information technology (IT) platforms have been applied to the implementation and conduct of clinical trials to improve efficiencies in several medical fields, and these platforms have recently been introduced to oncologic studies. OBSERVATIONS: This review summarizes cancer and noncancer studies that used IT platforms for assistance with clinical trial site selection, patient recruitment, and patient screening. The review does not address the use of IT in other aspects of clinical research, such as wearable physical activity monitors or telehealth visits. A large number of IT platforms (which may be patient facing, site or investigator facing, or sponsor facing) are now commercially available. These applications use artificial intelligence and/or natural language processing to identify and summarize protocol eligibility criteria, institutional patient populations, and individual electronic health records. Although there is an expanding body of literature examining the role of this technology, relatively few studies to date have been performed in oncologic settings. CONCLUSIONS AND RELEVANCE: This review found that an increasing number and variety of IT platforms were available to assist in the planning and conduct of clinical trials. Because oncologic clinical care and clinical trial protocols are particularly complex, nuanced, and individualized, published experience with this technology in other fields may not be fully applicable to cancer settings. The extent to which these services will overcome ongoing and increasing challenges in cancer clinical research remains unclear.
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AI systems for clinical trial operations can parse biomarker requirements, prior therapies, and performance status from EHRs, identify potentially eligible patients, and flag candidates for screening.
“Systems parse biomarker requirements, prior therapies, and performance status from EHRs, identify potentially eligible patients, and flag candidates for screening [56,57,58,59,60,61,62].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [57]
Programs using real-time matching from automatically extracted criteria have reported up to a 50% increase in enrollment in precision oncology trials.
“Programs using real-time matching from automatically extracted criteria have reported up to a 50% increase in enrollment in precision oncology trials [57].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [57]
Clinical trial matching is usually framed as a natural language processing challenge in which patient medical records, usually unstructured clinical notes, are matched against inclusion and exclusion criteria.
“clinical trial matching is usually framed as a natural language processing (NLP) challenge, where patient medical records-usually only available as unstructured clinical notes-are matched against inclusion and exclusion clinical trial criteria (148).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [148]
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References 67 · Cited by 36
- Authors
- von Itzstein, M. S., Hullings, M., Mayo, H., Beg, M. S., Williams, E. L., & Gerber, D. E.
- Affiliations
- Department of Internal Medicine, Division of Hematology-Oncology, The University of Texas Southwestern Medical Center, Dallas
- Harold C. Simmons Comprehensive Cancer Center (US)
- The University of Texas at Dallas (US)
- Venue
- JAMA Oncology
- Publisher
- American Medical Association (AMA)
- Published
- 2021-10-01
- Volume
- 7
- Issue
- 10
- Pages
- 1559
- ISSN
- 2374-2437 (ISSN-L)
- OpenAlex
- W3178417386
- Cited by
- 36
- References
- 67
- Citations by year
- 2026: 11 · 2025: 5 · 2024: 6 · 2023: 6 · 2022: 7 · 2021: 1
- FWCI
- 0.41
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- Locations
- JAMA Oncology (publishedVersion)
- PubMed (publishedVersion)
- Griffith Research Online (submittedVersion, other-oa)
- Funders
- National Cancer Institute (K24CA201543)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics in Clinical Research
- Keywords
- IT platforms, clinical trial recruitment, patient recruitment, clinical trial site selection, eligibility criteria, electronic health records, artificial intelligence, natural language processing, clinical trial matching, clinical cancer research
- MeSH terms
- Information Technology, Artificial Intelligence, Humans, Natural Language Processing, Neoplasms, Patient Selection
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