Where Are the Patients and PIs?

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Overcoming Sluggish Clinical Trial Enrollment with Heat Mapping

There is no lack of concerning information about clinical trial enrollment: in 90% of trials the timeline for enrollment needs to be doubled, 11% of research sites are unable to enroll a single patient, delays are extremely costly and it all got a lot worse since the COVID pandemic hit in early 2020.

Global Data reports that since early March 2020 around 1,000 organizations have experienced disruptions to planned and ongoing clinical trials. The main issue: slow enrollment [1]. One of main reasons is that patients, especially those in high risk groups, decided to stay away from hospitals and other healthcare facilities to avoid infection. 

As clinical trials are resuming, enrollment will continue to be a challenge that trial sponsors, clinical collaborators and CROs have to address. Our case study shows how using a data-driven approach to identifying not just principal investigators (PIs) and sites but also previously unknown patients can help meet enrollment goals.

The Challenge: Where Are the Patients Going?

The oncology trial of one of our clients suffered from sluggish enrollment. Many patients were too unwell or unwilling to come to one of the major treatment centers selected as clinical trial sites. As a consequence, two PIs received too few referrals and failed to fulfil their recruitment metrics.

But the patients had to be treated somewhere. Where did they go for treatment if not to the cancer centers the trial sponsor expected? To answer that question, H1 in collaboration with our client used a new strategy to identify additional sites that see more relevant patients.

The Solution: Heat Mapping

Different data sets hold the answer to that puzzle and combined allowed us to develop a heat map that showed where patients are actually being treated and helped the sponsor identify additional sites with the potential to enroll patients. 

To develop the heat map we used the following process:

  • Identify physicians that treat a significant number of relevant patients using diagnostic and procedural codes as the data source. 
  • Identify physicians who have already participated in clinical trials and therefore have experience working on trials with commercial partners. The data source for this step comes from published clinical trial information.
  • Consolidate patients and providers by geography to get a good understanding where the patients and providers are located
  • Identify in-common provider names and addresses to find out where the patients are going for treatment
  • Cross-reference that information against the current site list and sites that were not on the list. 

The resulting heat map was used as the basis for the sponsor to decide whether and if so how their clinical site selection needed to change.

The Outcome

The heat map exercise showed clearly that patients did not behave as expected. Instead of seeking treatment in large national cancer centers they preferred smaller, regional centers. The heatmap also uncovered areas of the country with a significant number of relevant patients that the client didn’t know about.

On the physician side, cross-referencing physicians who see relevant patients with those who have clinical trial experience helped identify possible new PIs. Additional data about each individual medical expert, such as their publications, different ways they engage in the digital space, their referral networks and academic influence via citation count provided a holistic picture of each PI’s work and professional interests and with that valuable background information for successful engagement.

Based on the information revealed by the heat map, the sponsor added a new clinical trial site and has a deeper understanding of each PI that allows them to create a long-term engagement strategy.

Developing a patient heat map for trial site selection does not have to take several professionals working through various databases for days or weeks. The H1 database contains all the data needed in an easily searchable way. Data can be filtered and sorted to hone in on healthcare providers with very specific characteristics and experiences.

To explore who H1 can help your organization with identifying specialized professionals, please contact us here.  


[1] https://www.clinicaltrialsarena.com/comment/disrupted-clinical-trials-slow-recruitment/ 

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