According to the National Institutes of Health (NIH), approximately 80% of research studies fail to meet their enrollment goals within the stated timeframes. Recruitment and trial design challenges can prolong the timeline for phase III studies, driving up costs and delaying market entry.
While many sponsors use aggregated data and predictive analytics to improve the trial process, one data source is often overlooked: lab data. By analyzing normalized lab data sourced from both commercial and inpatient labs, pharma companies can optimize clinical trial design and feasibility, streamline the recruitment process, and reduce overall spend—both in the development stage and beyond. Here’s how:
- Assist in site selection: Lab data is ideally suited to helping manufacturers identify optimal study sites for new trials, as well as rescue sites for at-risk trials. Prescriber-level lab data indicates which physicians and facilities are ordering a high volume of tests for a particular biomarker or testing sequence. Analyzing this patient density data can help manufacturers narrow down the geographic areas with access to a sizable patient population. Lab-informed real-world evidence (RWE) can also help pharma companies make smart decisions about the feasibility of a clinical trial, based on an assessment of planned eligibility criteria.
- Improve recruitment and enrollment: Most importantly, lab data can help pharma companies achieve their patient recruitment goals once a trial is underway. Physician-level lab data can reveal which physicians are ordering a specific set of lab tests that are likely to lead to a given diagnosis. As lab data includes not only test orders but also actual results, this data can also help manufacturers confirm clinical diagnoses and identify specific patient populations. By educating target providers about the benefits of the drug under study, manufacturers can encourage them to promote trial participation to applicable patients.Transaction-level patient lab data can also help manufacturers determine how many patients are likely to be eligible for their clinical trial in a particular zip code. Armed with this information, manufacturers can focus their programmatic advertisements on key geographical areas, boosting enrollment.As deidentified patient lab data includes real-time results from various lab tests, including genetic, genomic and biomarker tests, manufacturers have a comprehensive view of patients who have been or are likely to be diagnosed with the disease in question. When paired with other data sources, lab data increases screening accuracy, identifying candidates who are likely to clear trial inclusion/exclusion criteria.
- Monitor disease progression and adverse events: By linking normalized lab data to other datasets—including claims data and clinical data from the EHR—manufacturers can gain full visibility into the patient’s journey along the care continuum. Combining other RWE datasets with lab data provides insights into a patient’s condition, showing their testing history and results in conjunction with prescribed therapies.Tracking certain tests over time can indicate if a patient has experienced an adverse event, or if their condition is deteriorating. For manufacturers of second- or third-line therapies, this data is useful for identifying target patients who may be eligible for treatment. Lab datasets can also reveal patients who have become ineligible for treatment due to recent results, events, or therapies.For some manufacturers, adding the variable of time to the lab data equation provides unprecedented visibility into a target patient population. For example, a drug may only be applicable for patients who experience a gradual decline in a particular hormone level. Without longitudinal lab data, manufacturers would be unable to proactively track patients who could potentially benefit from a clinical trial.
- Aid evidence generation and outcomes studies: Lab data can also provide the key to understanding a new therapy’s clinical, economic, and experiential benefits. When paired with other datasets, lab-informed data can give manufacturers the RWE they need to support a drug’s value proposition—and prove that value to physicians and health plans. Manufacturers can also deploy lab data in comparative effectiveness analyses, demonstrating their drug’s superior results.Incorporating lab data into health economics and outcomes research (HEOR) also yields valuable results. For example, by mapping lab data to claims datasets, manufacturers can see when the onset of therapy begins in relation to diagnostic testing. Analyzing the patient access timeline can reveal the impact of specific payer policies, testing patterns and utilization management restrictions on outcomes. By comparing patients who completed a companion diagnostic test to those who didn’t, for example, a manufacturer could assess the effect of that omission on clinical outcomes.
Throughout the drug life cycle, from the pre-clinical phase to commercialization, lab data provides valuable insights into patient and provider activity that pharma companies can’t get elsewhere. In this competitive pharma marketplace, companies that are working to improve and accelerate their clinical trials are more likely to get their products to market faster, which can make all the difference to long-term success.
Learn how Panalgo’s IHD and lab data offering helps you improve patient recruitment and enrollment.
Ritupriya Yamujala is Director, Solution Engineering at Norstella