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Private user Manager   •   about 2 years ago

TRACK 2: Accelerating Clinical Trials - Open Source Tools and Resources

Hello Innovators! This is the main discussion board for patients, providers, researchers, developers, and all health tech stakeholders focused on accelerating clinical trials (National Cancer Institute overview of clinical trials: https://youtu.be/dsfPOpE-GEs?feature=shared).

*We would like to share a few resources that may help you design and develop an innovative solution to expedite clinical trials. For example, below are implementations to inspire your work:

1) A team of students from Columbia University created OwnYourData, a cancer trial enrollment platform that aggregates medical records, automatically filters records for trial inclusion criteria, and connects patients with trial coordinators. https://www.ownyourdata.health

2) Health systems like the Medical University of South Carolina have designed a system whereby their 1.7 million patients are default eligible for clinical studies. Their opt-out process has been well received by both patients and researchers. https://www.cambridge.org/core/journals/journal-of-clinical-and-translational-science/article/enhancing-study-recruitment-through-implementation-of-an-optout-cold-contact-process-with-consideration-for-autonomy-beneficence-and-justice/D91621F3EE98F812D3500B85A67706B5

3) OneStudyTeam has created an enrollment platform that helps study sites manage and onboard their eligible patients, and gives study sponsors data and visibility on the enrollment progress. https://www.onestudyteam.com/resources-ebooks-clinical-trial-enrollment

*Additionally, here are a few open source modules that may accelerate your development timeline:

1) Spezi Modules
Open-source ecosystem of many different modules based on international health data standards such as the HL7 Fast Healthcare Interoperability Resources (FHIR). Spezi can aid in rapid development of modern health applications. https://spezi.sites.stanford.edu

2) EPI Info by CDC
Public domain suite of interoperable software tools designed for the global community of public health practitioners and researchers. It provides for easy data entry, construction, and analyses with statistics, maps, and graphs. https://www.cdc.gov/epiinfo/index.html

*Finally, tell us about yourself and what you would like to see!

  • 2 comments

  • Manager   •   about 2 years ago

    Trial Planning
    TRIAL PLANNING examples of categories of technology solutions that could help accelerate trials:
    * Predicting success of clinical trials: predicting clinical trial operating characteristics e.g. enrollment speed or dropout rate or overall success rates, using attributes of clinical trials such as those found on clinicaltrials.gov entries
    * Clinical trial design: biomedical literature (e.g. articles avaialble on PubMed.gov) and clinical trial records (e.g. on clinicaltrials.gov) can be manually searched and reviewed to help clinical scientists and others come up with clinical trial designs. Newer AI/ML-based systems may be able to help automate and improve upon the way that different clinical trial design elements are catalogged and suggested, including:
    1. Inclusion and exclusion criteria for clinical trial patients
    2. Endpoints i.e. the primary outcomes being assessed or measured by the clinical trial.
    3. Run-in period. Run-in periods are not always used, but sometimes used in placebo-controlled trials to establish baseline observations and assess and eliminate placebo responders.
    4. Adaptive Design. These allow for modifications to the trial or statistical procedures during its conduct, such as updating the recruited population to those most responsive to the study drugs.
    * Automatically extracting structured protocol detail from clinical trial-related documents and publications. Especially for industry-sponsored trials, modern systems can increasingly make use of structured, machine-readable versions of clinical trial protocols, including a Schedule of Events, a part of the protocol that describes what procedures or assessments will happen with what timing, usually by day or week of the protocol. These structured, machine-readable versions can then be used for other downstream uses like automatically building an EDC/eCRF for data collection, or a visualization of the protocol to help research site personnel and/or patients more easily understand the journey. In some cases, algorithms can assess aspects like clinical trial complexity or aspects like patient burden based on weightings of burden for each procedure (e.g. blood draw or imaging test) and their frequency / number of times the procedure is done during the trial.
    * Site selection: not all doctors do clinical research; site selection involves identifying clinical research sites and investigators who are capable of and ideally experienced doing high-quality clinical trials, who also have a known patient population that is likely to meet the clinical trial eligiblity criteria (also known as inclusion/exclusion criteria or "I/E" criteria), and patients who are likely to participate in clinical trials

  • Manager   •   about 2 years ago

    Hi all! The Clinical Trials Transformation Initiative, or CTTI, is a great resource to understand needs and opportunities to improve how study sponsors and investigators bring new medicines to patients.
    Here's their main web site:
    https://ctti-clinicaltrials.org/

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