J.P. Morgan Healthcare Conference - TriNetX

J.P. Morgan Healthcare Conference

Biotech Showcase

January 13-16, 2025  |  San Francisco, CA

Meet the team that is fueling innovation and opportunity with real-world data.

Book a Meeting

Pushing the Boundaries of What’s Possible in Biotech and Healthcare Innovation

As we look to the new year, a key question on our clients’ minds—and likely yours as well—is: What trends are likely to have a major impact on biotech and healthcare innovation in 2025?

TriNetX experts, at the forefront of these discussions, are closely tracking emerging trends in real-world data (RWD) and real-world evidence (RWE), artificial intelligence (AI) applications in drug discovery, personalized medicine, regulatory frameworks, and more.

Our insights help our partners navigate these shifts, ensuring they’re prepared to leverage new opportunities and drive impactful business outcomes in a rapidly evolving healthcare landscape.

Pre-Book Meetings with TriNetX

If you’re attending Biotech Showcase or the J.P. Morgan Healthcare Conference on January 13-16, 2025, now’s your chance to connect with us. Schedule a meeting to learn how we can push the boundaries of what’s possible in biotech and healthcare, together. 

Why Engage with TriNetX

RWD/RWE

Fueling clinical research and evidence generation with a globally federated network of deeply enriched, deidentified EHR data.

Countries

Healthcare Organizations

Sites

Patients

Speed

Accelerating clinical trials and decision-making through AI and real-world insights.

%

fewer protocol amendments, saving both time and money

Diversity

Empowering life sciences organizations to meet health equity goals by ensuring diverse clinical trials with representative data.

For patients who had a visit in the last year:

%

known race values

%

known ethnicity values

Collaboration

Fostering a global, collaborative research ecosystem that bridges the gap between healthcare providers and pharmaceutical companies.

Site identification and outreach outcomes for one large pharma:

%

Response Rate

average response time in days

%

response in 3 days

%

acceptance rate

Patient Centricity

Supporting the development of precision medicine and personalized therapies by leveraging comprehensive, de-identified patient data.

TriNetX non-US oncology RWD to fuel chimeric antigen receptor (CAR) T-cell therapy research to better understand personalized treatment pathways and follow-up protocols

Explore the possibilities

Book a meeting with TriNetX at the J.P. Morgan Healthcare Conference today to discover how real-world data and analytics can accelerate your clinical trials, streamline study design, and improve market access strategies. Together, we can shape smarter, data-driven outcomes for 2025 and beyond.

The TriNetX Impact

TriNetX is a critical enabler of modern clinical research, offering life sciences companies the tools needed to overcome the industry’s most pressing challenges while advancing patient care globally. Scan three of our most impactful use cases, demonstrating the power of TriNetX RWD combined with AI to optimize clinical trials, improve patient care, and investigate disease pathways.

Let’s connect so we can tell you more.

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Automating Clinical Trial Protocol Diversity with Machine Learning (ML)

Part of the challenge in how clinical trial protocols are developed is that they are built on historical criteria, which are usually restrictive and/or tailored for specific populations. With a large pharmaceutical company, TriNetX developed an ML-based diversity protocol evaluation model.

Based on three existing protocols, the team trained a diversity optimization model which looks at different combinations of criteria and how they impact diversity and representation. The goal was an optimal set of criteria that did not meaningfully impact the profile of the clinical trial protocol but did consider different epidemiologies, demographics, and diversity inclusion (DI) recommendations.

Building these models into protocol development is a move in the right direction as it provides a data-driven approach to improving design and planning. Biopharmas can find patient populations that meet their diversity enrollment targets while not compromising the scientific criteria of the trial. As regulators increase the requirements for documentation, ML to predict different optimization scenarios will become more commonplace.

AI-based cancer screening

This AI modeling project targeted individuals from the general patient population at increased risk of developing pancreatic cancer that are currently being overlooked by traditional screening guidelines. These guidelines primarily rely on genetic or familial risk factors for eligibility, which account for only about 10 percent of the overall pancreatic cancer population. Early detection can lead to survival rates of over 80 percent within five years in some cases.

While many predictive models have been developed, this project was unique due to the large volume of geographically and ethnically diverse patients accessed from TriNetX’s network. Thirty-five thousand patients with pancreatic cancer and about 1.5 million without from 55 different health systems were analyzed. The model identified 87 top predictive features for Pancreatic Ductal Adenocarcinoma (PDAC) development from demographics, labs, medications, and diagnoses from EHR entries.

The next step in the project is currently ongoing and involves prospective model validation through deployment of the model on the data of approximately 6 million patients to tag those predicted to be at risk of pancreatic cancer within the TriNetX network. This project marks not only the development of a compelling predictive model built on a large swath of the U.S. population, but also, it explores how this model can be deployed into the healthcare system with prospective following of patients.

RWD and ML to understand disease progression

A second line therapy drug from a top pharma company was going into third line therapy for relapsed follicular lymphoma. The TriNetX team pulled a large electronic health record (EHR) data set to study the factors associated with transitioning from one line of therapy to the next.

The model showed that certain labs and abnormalities within those labs were significantly correlated to patient progression. Some patient and treatment pathways were also positively associated with progression. The team categorized these and then created a scoring algorithm for the company that showed the patients in their target population that will progress into the next line of therapy.

Meet the TriNetX Experts

Gadi Lachman

Gadi Lachman

President and CEO

Shields Carstarphen

Shields Carstarphen

Chief Commercial Officer

Steve Kundrot

Steve Kundrot

Chief Technology and Solutions Officer

Let's Connect

Reserve your time with TriNetX experts live at J.P. Morgan. Space is limited.

Our Mission is Our Promise

At TriNetX, our mission is to operate the world’s broadest federated network of real-world data in partnership with healthcare providers and apply intelligence that accelerates innovation across the healthcare ecosystem.

Access and Analyze Rich, Secure, Real-Time Data

TriNetX is a global network of healthcare organizations and life science companies, driving real-world research to accelerate the development of life-saving therapies.

From trial operations to evidence generation, we put you at the forefront of discovery. ​

 

Clinical Trial Design & Optimization

Datasets & Analytics

Non-US Oncology

Real-World Evidence Generation

Pharmacovigilance & Drug Safety