Clinical Trial Enrollment

Cancer trials are getting more complex and identifying eligible patients has been increasingly challenging. GenomOncology’s clinical trial capabilities optimize patient identification to increase trial enrollments.

The field of oncology is advancing at an unprecedented pace. As a result, oncology clinical trials are becoming increasingly complex which makes a manual approach to managing the entire screening process ineffective and inefficient. In addition, an effective system must be able to recognize and handle genomics and the array of biomarkers both present and future for patient decision support.

Key challenges our solution addresses...

  • Pre-screening typically done manually in excel by research staff
  • Difficulty in managing large number of inclusion & exclusion criteria across multiple trials and cancer types
  • Inability to keep up with changes in the trials or changes to the patients given the volume and level of complexity of these trials
  • Limited knowledge of missing biomarker(s) for patients which can cause low match rates to trials
  • Wait-list status and management of Clinical Trial enrollment
  • Opening a trial which is a bad fit for your patient population which results in underperformance
  • Un-optimal trial design - specific inclusion & exclusion criteria and their thresholds causing trial under-performance

  • Clinical Trial Enrollment

    GenomOncology’s clinical trial enrollment capabilities saves time filtering through trials for research staff, provides a more accurate lists of qualifying clinical trials for a given patient, and enables more strategic recruitment/design of future institutional clinical trials. All of these capabilities are designed improve performance in patient accruals.

    These tools can be accessed through our Virtual Molecular Tumor Board or they can be integrated into existing user-interfaces including the EHR.

    Key features:

    Clinical Trial Pre-Screen

    • Patient characteristics can be automatically parsed or manually entered into our clinical trial pre-screen modal, which stores information in perpetuity and continually matches against available trials
    • Once specific trial eligibility criteria has been entered, each criteria is remembered in the system in order to enable matching against all eligible patients
    • Our pre-screen module can store and track changes over time for both the trial eligibility criteria and patient criteria values. It keeps a history (date/time/user) of any criteria changes for audit-ability

    Clinical Trial Matching

    • Our trial database is refreshed nightly from and any new or changed clinical trials are flagged and curated on a weekly basis by our partner the MyCancerGenome team (Vanderbilt University)
    • Our tools are integrated with OnCore for the most accurate list and status of institution specific trials
    • We have the ability to handle all types of molecular tests and panel types through built-in parsers and genomic panels of all types and sizes (50 gene hotspots, FoundationOne, to tumor normal pair), cytogenetics, FISH, IHC, etc.
    • Integration and ingestion of clinical data from your EHR or other data sources is possible
    • Match results can be accessed through the GenomOncology Tumor Board and pushed to the EHR or other institution specific user-interface tool
    • Results can be filtered by patient’s zip-code, phase of trial, treatment context (e.g. metastatic, neo-adjuvant, etc), and more

    Clinical Trial Partial Match

    • For a given patient, GenomOncology’s system can identify a set of trials the patient may be eligible for but is missing a critical biomarker for final determination (e.g. androgen receptor status)
    • The system will notify the oncologist and research staff to alert them of this “partial match” list of trials and suggest testing for androgen receptor

    Clinical Trial Enrollment Management Tool

    • Able to see their accrual metrics in real-time including characteristics of patients who did not match
    • Determine suitability of trials under consideration for your institution using historical patient data (i.e. do you have the numbers for this trial to perform well at your institution)
    • Inform trial design by understanding the impact of key inclusion and exclusion criteria using historical patient data. These values can be fine tuned by evaluating different cut-offs or thresholds
    • Evaluate all pre-screened patient data at the aggregate or individual patient level – for both those patients who were successfully enrolled and those who were not

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