Only 13.8% of drug development programs lead to FDA approval, according to a 2018 MIT Sloan School of Management study. More than eight out of ten clinical trials don’t finish on time – even with technology designed to help researchers plan and conduct effective clinical trials. Many studies with complex criteria, especially in oncology, continue falling short of enrollment goals. Why?

If you don’t have the right data, you can’t find the right patients. If you can’t find the right patients, you can’t run the trial. Matching patients with appropriate trials continues as the most time-consuming element of clinical research. Not finding the right patients means missed recruitment goals, which leads to failed trials.

Searching “Many Haystacks for Perfect Needles”
With more than 292,000 active research studies open for patients, and 33,000 of these being oncology studies, matching patients to studies remains the most time-consuming element of the process. The manual process used takes a significant amount of work and long periods of time – made even longer and more complex by complicated inclusion/exclusion criteria.

The complexity of clinical trial matching in oncology has significantly increased as new classes of therapeutics, such as targeted therapeutics and immune checkpoint inhibitors, have emerged. In many cases, patient inclusion into a trial is predicated on the presence, or absence, of a single molecular alteration. Some trials require combinations of alterations to be present for a patient to qualify. This complexity, in addition to the usual inclusion/exclusion criteria, have made accurate technology critical in identifying these necessary data points. Because this molecular data is often “buried” in the patient’s electronic record, matching patients manually is no longer a truly viable option. Is it really surprising that more than eight out of ten clinical trials don’t finish on time and do not meet recruitment goals?

Matching patients with appropriate trials continues as the most time-consuming element of clinical research. Not finding the right patients means missed recruitment goals, which leads to failed trials.

Many Types of Data Are Key = The Many “Perfect Needles”
Finding more of the right patients starts with mining – and automating the process of mining – data. Without it, all of the state-of-art technology (AI, NLP, analytic engines) is useless. Such an approach will bring about a sea of change in the planning, design, and implementation of trials, which can improve the process and get the best clinical care outcomes. This approach:

  • Extracts, cleans, and standardizes data, so it can be used to automate the process of identifying, evaluating, and enrolling patients.
  • Screens out false positives, so finding the right patients is an “easier lift, especially for hard-to-enroll trials.
  • Helps research sites better understand patient cohorts, treatment options, and outcome trends, so sites can better focus on finding the right trials for their patient population.

Extracting and mining data from many sources can have a positive effect, as Inteliquet outlined in abstracts presented at ASCO 2019. Researchers can leverage new automation technology to maximize data by extracting it from disparate electronic systems and standardizing it into a repository. Historically, data necessary for trial matching was often missed because manual searching had access to one, or at best a few, sources of patient data. Now that technology is available to draw data from all available sources, the chances of successful inclusion, or indeed exclusion, are maximized. This is a great boon for not only trial accrual, but ultimately improved patient care.

This approach can dramatically improve the user experience for searching, reporting, and analyzing patient data for a myriad of tasks, including:

  • Patient cohorts
  • Clinical decision support
  • Treatment options
  • Patient outcomes trends
  • Benchmarks for active measurement
  • Monitoring for quality, trends, and issues to resolve during a trial

This approach enables connectivity to new sources of healthcare data in days and weeks – as opposed to several months.

Once you have high-quality comprehensive data, the proper technology, insights, and expertise can help harness that data and provide insights and knowledge to dramatically improve this clinical trial process for everyone, from sponsors to patients.

Only 3% to 6% percent of cancer patients who are eligible for clinical trials participate; this slows the clinical development process significantly, and means that more than 90% of cancer patients may be missing out on potentially life-saving new treatments. It is vital to fix this issue, and ensure every patient gets access to a trial if they need one.

The patients are out there. Using the right data and technology will help you find more of them.

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