Harnessing Multimodal Real-World Data for Comprehensive Patient Insights in Oncology

Real-world data means information gathered outside of regular clinical trials. It includes patient details from electronic health records (EHRs), insurance claims, cancer registries, molecular and genetic data, imaging tests, lab results, and reports directly from patients. Multimodal data mixes all these different types, offering a fuller view of a patient’s condition and treatment progress.
Unlike clinical trials, which have strict rules about who can join, real-world data shows what happens in everyday medical care. It includes patients of all ages, backgrounds, and with different health problems. This data helps doctors understand how treatments work in many kinds of people. It also helps spot side effects or care problems that may not appear in controlled studies.

Importance of Multimodal RWD in Oncology Practice

Using clinical, genetic, imaging, and lab data together lets cancer care teams study cases more deeply. For example, clinical data records a patient’s diagnosis and treatments. Molecular data gives details about tumor genes. Imaging data shows if tumors are growing or shrinking over time. These combined data types help doctors make care plans tailored to each patient.
In the U.S., more oncology groups use platforms that bring these data together in one place. For example, Tempus uses AI to look at data from over 250,000 patients. They also use about seven million anonymous patient records to find new ways to treat cancer and improve clinical trials. Many hospital centers and cancer doctors in the U.S. use similar technology to get better clinical information and make smarter choices.

Applications of Multimodal Data in Clinical Trials and Treatment

One important use of multimodal data is matching patients to clinical trials. Even though many patients could join trials, less than 3% actually do. AI-based platforms review patient data to find who might fit a trial and notify their doctors. This helps increase trial participation. For medical offices, it means making the most of trial options. For patients, it might mean access to newer treatments.
Multimodal data also helps find biomarkers, which are signs that guide precise cancer treatments. Biomarkers like tumor mutational burden (TMB), microsatellite instability (MSI), and human leukocyte antigen loss of heterozygosity (HLA LOH) have been found using AI applied to real-world data. These biomarkers help doctors predict how well patients will respond to certain immune therapies or targeted drugs.
Tempus’ Immune Profile Score (IPS) combines DNA and RNA gene patterns to predict immune responses. Many platforms keep developing similar biomarkers to help doctors make faster decisions and support drug approvals.

Real-World Data Supporting Drug Development and Regulatory Processes

Drug development in the U.S. takes a long time and costs a lot. It often takes more than ten years to bring a drug from discovery to use. Cancer drugs face extra challenges because many candidates compete for the same target. AI and multimodal data help speed this up by quickly finding drug targets, checking biomarkers, and making trials more efficient.
Pharmaceutical companies partner more with data platforms. For example, over 95% of the top 20 cancer drug companies work with groups like Tempus. These partnerships use real-world data to better understand how well treatments work and how safe they are in everyday care.
Some AI tools approved by the FDA now help find patients at risk for conditions. For example, Tempus ECG-AF checks for atrial fibrillation or flutter risk. This shows how AI helps meet both medical and regulatory needs.

AI and Workflow Automation in Oncology Practices

AI and workflow automation can make cancer clinics run more smoothly and help patients get better care. AI looks at large, mixed data quickly and gives reports that doctors can use to decide care without waiting for long manual reviews.
One key use is in matching patients to clinical trials. AI scans patient files, including genetic tests, imaging, and medical history, to find matches for current trials. It sends alerts to doctors or staff, so fewer chances are missed and less paperwork is needed.
AI also finds care gaps like missing important genetic tests or lab work. For example, Tempus Next is an AI tool that notices these gaps and alerts care teams. This helps ensure care follows medical guidelines and lets administrators check performance.
In the front office, AI helps manage calls and appointment schedules. For example, Simbo AI uses conversational AI to answer patient questions quickly, reduce wait times, and improve workflows. This lets office staff focus more on direct patient care and important admin tasks.
On the IT side, combining AI tools with current electronic records and practice software improves data accuracy and consistency. Automated data handling reduces mistakes and saves time, which is important in busy cancer clinics handling complex information.

Case Studies Illustrating Multimodal Data Impact

Some big health systems and cancer centers in the U.S. show how multimodal data and AI work in real life. The Cleveland Clinic used AI to raise clinical trial enrollment by quickly matching patients to relevant studies. Their results show what is possible when good data and AI are used together.
International projects like SOPHiA GENETICS’ DEEP-Lung-IV combined genetic, imaging, and clinical data to find how lung cancer patients respond to treatments. These projects set an example for how multimodal data supports research in many places, which can also happen in the U.S.

Considerations for Medical Practice Administrators, Owners, and IT Managers in the U.S.

Using multimodal data and AI requires planning and teamwork among clinical, admin, and tech staff. Practice administrators and owners should check vendors carefully for data capabilities and follow privacy laws like HIPAA. The new tools should work well with existing systems to avoid disrupting daily work.
IT managers are important for keeping data secure, making sure systems work together, and confirming AI tools are trained and tested properly. Staff should get ongoing training so they can trust AI recommendations.
Investing in multimodal data and AI automation can improve patient scheduling, reduce admin work, raise trial enrollment, and support more exact treatments. These benefits can lead to happier patients and better care results.

Summary

Using multimodal real-world data with AI is becoming a key part of cancer care in the U.S. It helps improve matching patients to trials, speeds up drug development, and automates workflows. These tools help doctors give care that is faster and more accurate. As more health groups start using them, administrators, owners, and IT managers need to learn how to choose and use these tools well.