Aligning data for the perfect match.
Recruiting the right candidates is one of the biggest hurdles in clinical trials. In fact, 80% of trials face delays or cancellations because of recruitment challenges. And here’s another staggering stat: about half of trial sites struggle with under-enrollment due to a lack of participants.
The truth is, recruitment is a numbers game. Finding enough potential matches is crucial to keeping trials on track. The good news? There’s a smarter way to do it. According to a study in Contemporary Clinical Trials Communications, using EHR-based searches identified over 170% more candidates compared to manual methods.
However, EHR data isn’t always readily available to trial coordinators. Let’s take a look at how Snowflake not only helps match candidates from EHR data, but from across the entire healthcare tech stack.
Challenge
Finding clinical trial candidates that match on siloed data from across multiple hospital systems
Solution
Snowflake
Results
Faster candidate matching, larger patient populations, improved patient safety, and higher reliability
The Problem
The challenge of matching candidates for clinical trials starts with bad data. Most hospital systems don’t talk to each other, and roughly 80% of healthcare data is unstructured. To build the right patient matches, you need a full picture, including demographics, conditions, medications, medical events, and more. But with data scattered across EHR platforms, lab systems, claims databases, and external registries, those crucial details often get lost in the shuffle.
And let’s not forget. Safety is the backbone of a solid clinical trial…and exclusionary criteria are just as crucial as inclusionary ones. Take allergies, for instance. If a trial involves a specific drug, patients with potential adverse reactions need to be filtered out. The same goes for comorbidities, which are critical details that directly impact patient safety.
For hospitals without integrated data systems, matching candidates often means endless chart reviews, phone calls, and in-person meetings. It’s slow, exhausting, and leads to the same outcomes: delayed trials and enrollment struggles.
The Solution
Better candidate matching starts with smart data integration. That’s why we use Snowflake as our data lakehouse. Snowflake brings structured, semi-structured, and unstructured data together under one roof. We pull data from wherever it lives, including EHRs, lab systems, marketing tools, ERPs, and wearable devices. Even unstructured data like images, audio, and PDFs? Snowflake can handle that as well. Machine learning models transform written notes, discharge summaries, and other results into actionable insights. And with Snowflake’s AI-powered entity resolution, everything ties back to a single patient profile.
Now, your clinical teams need an easy way to query this data. At Penrod, we build an easy-to-use interface on top of Snowflake, giving clinical trial teams the power to filter patients based on key criteria like:
- Demographics
- Medical Conditions
- Medical Events
- Medications
- Allergies
- Risk Categories
- Location
With a single, data-rich patient profile, you can run queries to zero in on candidates who fit your needs, while excluding those with incompatible co-morbidities or potential allergies. As you set criteria, the candidate pool updates in real time. This means you can stay focused on your ideal participants without wasting a second. It’s efficient, flexible, and designed to help you find ideal candidates smarter, faster, and more accurately.
The Results
Faster Candidate Matching
Larger Patient Populations
Improved Patient Safety
Request Free Consultation
Need faster candidate matching, larger patient populations, improved patient safety, and a partner who can help?
We're here for you. Fill out the form on the right for a free consultation!
Trusted by the leaders in healthcare and life sciences.
