Understanding Multimodal Real-World Data and Its Impact on Treatment Personalization in Oncology

Multimodal real-world data is the collection and joining of different kinds of patient information from many sources. These include:

• Electronic Health Records (EHRs): Patient details, doctor notes, lab results, medicines taken, and treatment history.
• Genetic and Molecular Tests: DNA and RNA tests that show tumor mutations and gene activity.
• Imaging Data: Scans from radiology and tissue slides, including digital images of tissue.
• Patient-Reported Data: Information directly given by patients about their symptoms, side effects, and how they feel.
• Claims and Billing Data: Insurance claims that show how healthcare is used and its results.

In cancer care, combining these different types of data gives a clearer view of a patient’s health, tumor details, and how they respond to treatments. This is better than older methods that used only one type of data or a few clinical details.

Companies like Tempus and Caris Life Sciences handle huge amounts of this data. Tempus connects with about 65% of academic medical centers and over half of U.S. oncologists, keeping anonymous records of more than 8 million patients. Caris Life Sciences has a database with over 600,000 matched clinical and genetic profiles and more than 18 million pathology slides. These databases help AI systems understand tumors at a molecular level to guide precise treatment choices.

The Role of AI in Multimodal Data Analysis

Artificial intelligence (AI) is important for studying and understanding multimodal data. Cancer care produces a lot of complex information. Each biopsy or clinic visit makes thousands of data points from genetics, images, clinical factors, and patient history. Doctors cannot look over all this data manually during short appointments.

AI models can quickly handle this large and complex data. They find important markers that predict how a patient might respond to treatment or their outlook. For example, explainable AI methods studied 350 markers—including clinical details and tumor profiles—across thousands of cancer patients. In studies with more than 15,000 patients, AI found markers that usual clinical tools missed. This led to better predictions about survival and treatment options.

AI also helps match patients to clinical trials by quickly checking multimodal data for trial requirements. Some platforms identify over 30,000 eligible patients from their data network. This speeds up enrollment and gives patients more chances to try new treatments.

AI tools can watch for rare genetic changes and tumor type changes over time. For example, breast cancer can change and resist certain therapies. Knowing this helps doctors change treatment plans faster or find new trial options.

How Multimodal Data Improves Personalized Cancer Care

Personalized medicine means tailoring cancer treatment based on each patient and tumor type. Multimodal real-world data helps in many ways:

• Improved Diagnosis: Combining gene tests with images and records helps find cancer markers earlier that might be missed otherwise.
• Targeted Treatment Decisions: Knowing gene mutations and tumor types lets doctors pick treatments best for the patient’s tumor. For example, RNA-based tests using large datasets help predict response to certain drugs like KRAS inhibitors.
• Better Clinical Trial Matching: These data platforms quickly analyze patient information to connect patients with suitable trials. This speeds up joining trials and lowers failure rates.
• Early Detection of Resistance: Tracking genetic data over time lets doctors see if tumors become more aggressive or resistant. This guides changes in treatment plans.
• Reducing Side Effects: Genetics can help predict how patients will react to medicines. This reduces bad side effects and helps patients handle treatments better.

Medical practices need systems that manage large data, keep it safe, and add AI tools into patient care smoothly.

Adoption and Trends in the U.S. Oncology Care Sector

More oncology practices in the U.S. are using multimodal real-world data. Academic medical centers and cancer clinics especially use it. About 65% of academic centers and over half of U.S. oncologists use platforms like Tempus. These connect doctors, researchers, and drug companies.

These platforms support over 200 biopharmaceutical partnerships. They help develop new cancer drugs by sharing data and analyses.

Tempus manages more than 300 petabytes of clinical, genetic, and behavior data. Caris Life Sciences has one of the largest clinical-genetic databases. They help pharmaceutical companies and researchers with AI tools for analysis.

Health systems like Providence Health System work with technology companies like Microsoft. They build AI tools that combine clinical and genetic data to make patient profiling and treatment matching easier. Their tools process millions of images to support real-time personalized care.

AI-Supported Workflow Improvements in Oncology Practices

AI technology is not only helping with treatment but also improving how oncology offices work. This includes:

• Front-Office Call Automation: Services like Simbo AI use AI to answer phones and book appointments. This lowers wait times and reduces missed appointments. It helps front desk staff focus on harder tasks.
• Faster Clinical Trial Screening: AI can double the speed of finding patients who fit trial rules by quickly reviewing multimodal data.
• Data Integration and Summarization: AI gathers scattered patient data from different records into one clear summary during visits. This saves time usually spent looking for information.
• Patient Communication and Management Apps: Apps like Tempus’ Olivia help patients and caregivers manage health data, take medicines on time, and talk to care teams.
• Better Accuracy and Fewer Errors: By automating tasks and standardizing data entry, AI reduces mistakes. This helps make better treatment choices and keeps records correct.

Oncology practices in the U.S. use these AI tools to solve problems like limited staff, many patients, and the challenge of managing complex care plans.

Data Privacy and Compliance

Handling multimodal real-world data needs strict following of privacy laws like HIPAA. Most leading AI and data platforms in oncology use strict methods to remove patient names and details. This lets them use data safely for research and care without risking privacy.

Practice managers and IT staff must make sure all AI and data tools follow federal and state laws. This protects patient information while still gaining benefits from technology.

Challenges in AI Integration for Oncology Care

Though AI and multimodal data can help, there are challenges:

• Data Fragmentation: Patient data is often stored separately in different systems, making it hard to combine. AI has to work with mixed and unorganized data formats.
• Limited Long-Term Data: Many AI models only have short-term patient data instead of tracking changes over time. Long-term data is needed to see how tumors evolve.
• Ethical and Clinical Issues: Patient trust and choice must be kept. AI should help doctors, not replace them. Care decisions must stay focused on patients.
• Infrastructure and Cost: Setting up AI systems and data management needs money for technology, training, and security.
• Model Transparency: Doctors need AI models that explain treatment suggestions clearly. This helps make smart decisions and talk with patients.

The Outlook for Oncology Practices in the U.S.

As cancer care changes, multimodal real-world data and AI will be more important for personalizing treatments. Practice administrators, owners, and IT managers need to learn about these changes. This helps them guide their teams in using new technology, improving workflows, and giving better patient care.

By adding AI-driven data tools to daily work, oncology centers can be more efficient, support precise treatments, and make patients happier. Using these tools also helps make better use of resources and keep up with changing cancer care standards.