Utilizing Multimodal Real-World Data: The Future of Patient Insights in Precision Medicine

Multimodal real-world data shows different types of patient information collected outside clinical trials. Unlike controlled trials that use selected patient groups under strict rules, real-world data comes from everyday medical care.

The three main parts of multimodal real-world data are:

• Clinical data: Electronic health records with patient info like age, diagnoses, lab results, treatments, and outcomes.
• Molecular and genomic data: Data from gene sequencing and lab tests that show genetic differences and molecular details.
• Pathology and imaging data: Digital pictures of tissues and organs, such as slide images and scans like MRIs and CTs.

Putting these together helps doctors and researchers get a full view of a patient’s health and how a disease changes over time. One type of data alone can’t give this complete picture.

The Role of Multimodal Real-World Data in Precision Medicine

Precision medicine uses detailed patient data to give treatments made just for that person. Multimodal data helps with this by offering lots of useful information needed for diagnosis, choosing treatments, predicting outcomes, and checking progress.

For example, Tempus is a U.S. company linked to many academic medical centers and used by many cancer doctors. It collects gene, clinical, behavior, and other data to help doctors make better treatment choices. This also helps find patients who fit clinical trials. Over 30,000 patients have been matched through Tempus, speeding up research and giving patients more treatment options.

Another company, Proscia, uses whole slide images of pathology samples along with gene and clinical data to help develop new drugs. They have over 10 million slide images with detailed data. This helps researchers study changes in cells and predict how treatments will work.

These examples show how multimodal data helps understand diseases better, especially complex ones like cancer where every case is different.

Why Multimodal Data is Critical for Medical Practices

Hospital leaders and IT managers find multimodal data useful for several reasons:

• Better patient results: Combining data helps doctors understand each patient’s disease and pick better treatments.
• Faster clinical trials: Real-world data cuts down the time it takes to find patients and start trial sites. For example, TriHealth increased trial enrollment by 64% after working with Tempus’ trial network, reducing site setup to 10 business days.
• Spotting care gaps: AI tools can study data to find patients who may not get enough care or have hidden health issues.
• Streamlined diagnosis: Using molecular and imaging data together speeds up diagnosis by showing important markers that might be missed otherwise.

These benefits rely on good data collection, joining data sources, and analysis—tasks where IT management is very important.

Addressing Challenges in Multimodal Data Integration

Using multimodal real-world data in clinics comes with some challenges:

• Data quality and standardization: Data comes in many types and sizes. It must be cleaned and changed into the same format to be useful. For example, raw pathology images need AI tools to turn them into measurable data.
• Interoperability: IT systems must connect smoothly across health records, gene databases, imaging, and labs. Without this, data stays separate and less useful.
• Privacy and compliance: Protecting patient privacy is key, especially with gene and health data. Systems must follow laws like HIPAA and allow sharing only to authorized users.
• Clinician trust and use: Doctors need AI results to be clear, explainable, and fit into their normal work to trust and use them.

Companies like Tempus and PathAI are making user-friendly platforms that help overcome these problems and support wide use of multimodal data.

Enhancing Workflows with AI-Enabled Automation and Clinical Decision Support

AI is also used with multimodal data to help medical work run smoother and support doctors’ decisions. AI can handle complex data faster than humans.

Ways AI helps clinical and office work include:

• Automated data processing: AI turns raw data like pathology images and health notes into organized data for quick review. For example, PathAI analyzes slides and measures over 300 tissue features to help assess tumors.
• Clinical decision support systems (CDSS): AI studies combined data to suggest diagnoses, risks, and best treatments. Tempus has an algorithm that predicts how well patients respond to immunotherapy, helping plan cancer care.
• Managing calls and appointments: AI tools, like Simbo AI, automate phone answering and scheduling, reducing wait times and freeing staff to focus on patients.
• Natural language processing (NLP): AI looks through clinical notes to find patients with risks for conditions needing attention, improving early care.
• Matching patients to clinical trials: AI finds trial candidates based on clinical and molecular data, speeding up enrollment.

By automating these tasks, staff and doctors have more time for patient care and communication.

The Impact of Multimodal Real-World Data on Clinical Trials and Drug Development

Clinical trials often face problems such as slow patient recruitment, high costs, and lack of diversity. Multimodal real-world data helps by including more types of patients than traditional trials that cover only about 5% of people.

Drug companies and health providers use multimodal data to speed up trial starts and find patients quickly. AstraZeneca uses this data to pick better patients and biomarkers in Phase 3 cancer trials, increasing the chance of success.

TriHealth’s work with Tempus TIME program shows real results. Their trial enrollments rose a lot, with quick site openings in 4 business days and patient treatments starting in 10 days. This shows how multimodal data and AI can lower barriers in trials.

Also, combining RNA sequencing with liquid biopsies lets doctors monitor diseases without invasive tests. This helps make new drugs for tough cancers like pancreatic and breast cancer.

The Growing Role of Pharmacogenomics and Digital Pathology

Pharmacogenomics studies how genes affect how a person reacts to medicine. It benefits from multimodal data and AI. For example, Tempus uses gene tests to help mental health patients, such as those with bipolar disorder and ADHD, get better drug choices. Combining gene and clinical data can reduce bad drug reactions and improve care.

Digital pathology is also important. AI tools turn big histology images into organized data, helping analyze tumor tissues in detail. This info helps with diagnosis, predicting disease course, and treatment decisions, especially in cancer.

Operational Considerations for U.S. Medical Practices and IT Managers

Medical leaders and IT managers in the U.S. should plan carefully to adopt multimodal data and AI. This includes:

• Upgrading systems and linking data: Health IT must support different data types like gene and imaging info together with clinical records.
• Data rules and privacy: Policies must protect privacy, security, and follow federal laws, especially for genetic data.
• Staff training: Both medical and office workers need training on new AI tools and workflows to make adoption smooth.
• Working with tech providers: Partnerships with companies like Tempus, Proscia, and Simbo AI give access to advanced tools and expert help.
• Checking costs and benefits: Practices should weigh long-term patient care improvements and efficiency against initial costs.

Final Thoughts on the Integration of Multimodal Data and AI Automation

The U.S. healthcare system is moving toward using detailed patient data and AI for better care. Practices that use these tools can improve patient care, run more efficiently, and join important research.

Groups linked to companies like Tempus and Proscia lead the way with large data sets that combine clinical, genetic, and imaging information. AI systems from providers like Simbo AI help by easing office work and keeping patients engaged.

As this field changes, medical managers and IT teams have a big role to make these tools work well. Doing so will improve precision medicine and make healthcare better for many patients.