Utilizing Multimodal Real-World Data to Enhance Insights in Oncology and Personalized Treatment

In recent years, advancements in artificial intelligence (AI), data analytics, and real-world data have played a significant role in transforming cancer care and personalized treatment in the United States.

For medical practice administrators, owners, and IT managers engaged in oncology and healthcare delivery, understanding the application and benefits of multimodal real-world data is increasingly important for improving patient outcomes and optimizing clinical operations.

This article provides a detailed overview of how multimodal real-world data is used to enhance oncology research and personalized medicine. It highlights key trends, statistics, and practical considerations, with a focus on developments in the United States healthcare system. Additionally, it discusses the role of AI and workflow automation in supporting these efforts.

What is Multimodal Real-World Data in Oncology?

Multimodal real-world data means combining and studying many types of patient and clinical information from different sources.

This data includes genomic sequences, clinical records, radiological images, pathology reports, laboratory results, and patient-reported outcomes.

When these different types of data are put together, they give a more complete picture of a patient’s cancer diagnosis, how it is changing, and how the patient responds to treatment.

Real-world data is different from data collected only in controlled clinical trials. It collects information from everyday clinical practice, electronic medical records (EMRs), and various diagnostic tools.

This variety helps researchers and healthcare providers learn about disease in a wider and more realistic group of patients.

The Scale of Multimodal Data in US Oncology

Tempus is a large AI-powered precision medicine company in the US. It has one of the biggest multimodal cancer data collections in the world.

Their database has over eight million de-identified research records. It includes two million imaging datasets, 1.5 million records that link clinical and genomic data, and about 300,000 whole-transcriptomic profiles.

This amount of data is about 60 times bigger than publicly available resources like The Cancer Genome Atlas.

This large amount of data helps with detailed molecular studies, early disease diagnosis, and guiding treatment choices for cancer patients all over the United States.

About 65% of US Academic Medical Centers and more than half of oncology practices use Tempus’ solutions. Also, over 95% of leading oncology drug companies in the country work with Tempus to support drug development and clinical trials.

One example is the Tempus xT platform. It mixes molecular and clinical data to find personalized treatment options.

This method goes beyond traditional tumor DNA tests by using many data points. It helps create treatment plans that better fit each patient’s needs.

Impact on Clinical Trial Enrollment and Research

AI-powered multimodal data has helped improve the process of recruiting patients for clinical trials in the US.

Using EMRs alone to screen patients can be slow and may not match patients to the right trials very well.

ConcertAI is another AI company focusing on cancer. It offers tools like TriaLinQ that use AI with real-world data to find clinical trial candidates three times faster than manual EMR reviews.

For example, screening 120 patients takes 25 hours with TriaLinQ. Using only EMRs, screening 58 patients took 40 hours.

This faster process lets more qualifying patients try experimental treatments and helps move clinical research along quicker.

The platform also uses many types of data like radiology, digital pathology, and clinical notes. This makes it easier to assess complex patient eligibility rules that used to take a lot of manual work.

Besides enrolling patients, the system helps with trial design, biomarker validation, and picking the right groups for studies. This helps drug companies and researchers improve their studies.

Enhancing Oncology Research Through Data Collaboration

Cooperation between biopharma companies and AI-powered precision medicine firms has increased quickly.

For instance, Tempus has teamed up with over 200 biopharma companies in the US. These include AstraZeneca, BioNTech, and Genmab.

They use multimodal data to find new treatment targets, predict how patients will respond, and sort patient groups better.

Susan Galbraith, Executive Vice President of Oncology R&D at AstraZeneca, said that detailed data helps understand tumor biology more and raises the chance of success in clinical trials.

Pharma and biotech firms get help from Tempus’ multimodal data for forming ideas, discovering biomarkers, and testing new treatment plans.

They also use AI tools to run simulations and analyze large patient groups quickly, helping with decisions during drug development.

Precision Medicine Powered by Real-World Data

Precision medicine means making medical treatment fit individual traits, genetic information, and specific disease details.

In cancer care, this means picking treatments aimed at the tumor’s unique molecular features instead of using the same method for all patients.

Real-world multimodal data helps precision medicine by giving clear patient information that can predict which treatments will work best and be safest.

For example, Tempus’ pan-cancer organoid platform uses neural networks to run many drug response tests fast. It predicts which treatments may work for a patient’s solid tumor.

Liquid biopsy technology is another tool helped by multimodal data. It mixes blood-based biopsy results with tissue testing to find genetic changes that guide treatment.

This helps doctors treat patients earlier and get better results.

AI systems can link molecular tests to clinical outcomes, giving doctors tools to make better treatment plans for cancer patients.

AI and Workflow Automation in Oncology Practices

For administrators and IT managers in oncology practices across the US, using AI tools to manage real-world data can make operations easier and support good patient care.

AI automation helps cut down paperwork, improve patient interaction, and use data more efficiently.

One way AI helps is through managing phone calls and front-office tasks.

Medical offices get many calls for appointments, prescription refills, and patient questions.

AI phone systems use language technology to answer calls quickly. This reduces waiting time and lets staff focus on medical work.

This makes patients happier and helps with better care coordination.

Inside clinical work, AI combines data from EMRs, imaging, and labs to give useful information directly to doctors.

AI can predict care gaps or flag patients who might not respond well to treatment, so doctors can act early.

AI tools also help match patients to clinical trials faster by screening records against detailed study rules.

This cuts down manual work and improves the number of patients joining trials.

IT managers need to plan carefully for data security, system compatibility, and follow US healthcare rules like HIPAA when adding AI tools.

Working with companies experienced in cancer data and AI helps make this change smooth.

Significance for US Medical Practices

The use of multimodal real-world data and AI in cancer care is growing in the US and changes how medical practices treat patients with cancer.

Practices working with data providers like Tempus and ConcertAI get access to the latest clinical information. This helps them give more exact and effective treatment.

Using this data, oncology practices can improve patient results, join clinical trials easier, and help raise treatment standards across the country.

Partnerships between data companies and drug makers make sure new treatments are based on strong real-world evidence.

Since more than 65% of academic medical centers and over half of US oncologists use these technologies, practice leaders should think about how multimodal data fits into their work, improves skills, and supports personalized care.

Practical Considerations for Adoption

• Data Integration: Being able to link different data types—clinical, molecular, imaging—is very important. Practices should pick platforms that handle big real-world data and follow US rules.
• User Interfaces: Data dashboards and AI tools must be easy for doctors and staff to use without needing advanced data training.
• Security and Privacy: Following HIPAA and privacy laws is required when managing genomic and clinical data.
• Clinical Validation: AI models and prediction systems should have good proof that they work well in cancer patients.
• Staff Training: New staff education should focus on understanding AI results and changing clinical work to fit.

Healthcare IT managers play an important role in managing these points while keeping systems safe and working well.

Closing Thoughts for Medical Practice Leaders

Multimodal real-world data is becoming a key part of modern cancer care in the US.

Combining molecular data, clinical records, and imaging helps precision medicine, speeds up clinical trials, and supports better treatment development.

Companies like Tempus and ConcertAI have built large AI-powered data systems used by many US healthcare centers. This shows how important data is in cancer care.

Practice administrators and owners who use these tools can improve patient involvement, make workflows smoother, and join more research efforts.

Medical IT managers are central to putting in place and running these advanced systems, making sure data works well together, stays private, and is easy to access in the clinical setting.

AI and multimodal data in US cancer care are not just ideas for the future. They are working today and helping patients and doctors.