Utilizing Multimodal Real-World Data in Precision Medicine to Elevate Patient Care and Treatment Options

Multimodal real-world data combines many types of patient information. Traditional clinical trial data usually comes from very controlled tests. But real-world data comes from regular healthcare places like electronic health records (EHRs), scans, lab tests, genetic tests, and reports from patients themselves. Putting these different data types together helps doctors understand a patient’s health better.

For example, Tempus is a company that uses multimodal data—including genetic, transcriptomic, protein, and clinical information—to improve cancer care. Their system helps hospitals and cancer doctors by giving detailed molecular information. This information guides treatment choices and helps match patients to clinical trials.

In the United States, about 65% of academic hospitals use Tempus. More than half of oncologists there also use their services for gene sequencing and trials. This shows that many medical professionals depend on multimodal data to make decisions.

Applying Multimodal Data to Enhance Oncology Care

Cancer treatment is one area where multimodal real-world data helps a lot. Usually, doctors rely on biopsy results and scans to diagnose cancer. But by combining different data types, they understand cancer and treatments better.

A clear example is RNA sequencing, which works with DNA sequencing to find markers that normal tests miss. At Ochsner Health, Dr. Marc Matrana found new important markers in a lung cancer patient using RNA sequencing. DNA tests did not find these markers. This discovery led to new treatment options. It shows how using many data types can fill important knowledge gaps.

AstraZeneca also uses multimodal data to make Phase 3 cancer trials more successful. By mixing genetic, protein, imaging, and clinical data, scientists can better predict how patients will respond to treatments. This helps design better trials.

The Role of AI in Real-World Data and Precision Medicine

Artificial intelligence (AI) is very helpful in handling lots of data from multimodal datasets. AI can find patterns and links in complex data that humans might miss. This helps improve treatment choices, matching patients to trials, and keeping track of patients.

ConcertAI is a healthcare company with a platform called CARA AI. It manages multimodal data and supports AI models for prediction and generation. Their trial screening tool, TriaLinQ, shows how AI helps enroll patients faster. Compared to traditional methods, TriaLinQ screens over twice as many patients in less time (120 patients in 25 hours vs. 58 patients in 40 hours). Hospitals and clinics in the U.S. can use such AI tools to lower research staff workload and speed up trials.

Real-World Applications Beyond Oncology

Although cancer care is a main focus, multimodal real-world data and AI are useful in other diseases too. In Alzheimer’s disease, early diagnosis and treatment personalization are tough problems. Real-world evidence and biomarkers can help.

Harald Hampel’s research shows that mild cognitive impairment (MCI), an early sign of Alzheimer’s, is often missed in primary care. Detection rates are only between 6% and 15%. Combining EHRs with AI screening tools can find at-risk patients earlier. This helps doctors provide timely treatments. Also, the FDA supports trials that use real-world data and biomarkers to create prediction models. These models improve personal treatment plans for Alzheimer’s patients.

How Multimodal Data Supports Personalized Medicine

Personalized medicine means tailoring treatment based on individual genetic and molecular details. Tests like next-generation sequencing (NGS), RNA sequencing, and liquid biopsy give a full view of tumor mutations and disease progress.

The Vanderbilt-Tempus partnership uses genomics combined with real-world data to give precise results to doctors. This supports therapy choices based on tumor mutations and biomarkers. It helps decide if treatments like immunotherapy are suitable.

Pharmacogenomics is another key area. It uses genetic data to guide medicine choices and dosages. Dr. Brian Forsythe says this is helpful in mental health clinics. It makes medicines work better and causes fewer side effects. Advances in AI and data tools make using genomics and patient information easier than before.

AI and Workflow Integration for Medical Practices: Enhancing Front Office Functions

While precision medicine mainly improves clinical care, medical practice administrators and IT managers can also benefit from AI in daily operations. This includes front-office tasks and managing calls.

Simbo AI creates AI tools to automate phone systems in healthcare. This reduces patient wait times, handles many calls efficiently, and makes appointment scheduling easier. Using AI answering services can help practices better engage with patients and keep them satisfied.

AI tools connected to EHR systems can also do routine jobs like patient registration, checking insurance, and sending appointment reminders automatically. This lowers the workload for staff and lets them spend more time on patient care.

Using AI for clinical trial recruitment also speeds up the process. As seen with ConcertAI’s TriaLinQ, AI tools quickly find and enroll eligible patients. Hospitals and medical offices linked to research groups can use AI workflows for both administration and clinical projects.

The Importance of Data Privacy and Synthetic Data

When using real-world data in healthcare, keeping patient privacy safe is very important. Laws like HIPAA require strong security and confidentiality.

To keep data safe but still useful, synthetic data is used. Synthetic data looks like real patient data but does not include actual patient details. Researchers can use this data to build and improve AI models without risking privacy.

A recent review shows that about 73% of synthetic data studies use deep learning methods, mostly in Python. Synthetic data helps lower costs and shorten clinical trial times. This is important for rare diseases with few patients. It also helps create fair AI models that give unbiased treatment advice to different groups.

Collaboration and Challenges in Data Integration

Making good multimodal data systems needs teamwork between doctors, researchers, and tech companies. One big challenge is making different data types work together. These can be clinical notes, lab results, images, and genetic test data.

Companies like Tempus and ConcertAI work to bring these data kinds together using AI and machine learning. Tempus links clinical and molecular data and offers tools to search and use this data well. Their system has over 8 million research records without patient names. This helps science and patient care get better.

It is important to bring these advances to regular community medical offices, not just big hospitals. AI apps like Tempus’ Olivia help patients manage their health data. This can improve communication with their doctors.

Supporting Clinical Trials with Multimodal Data and AI

Finding patients for clinical trials has been a major obstacle in drug research and approval. Real-world data combined with AI can find suitable patients more accurately and faster than old methods.

For example, Tempus has identified more than 30,000 patients who can join trials by analyzing complex data. This improves trial diversity and speeds up research, helping new treatments reach patients sooner.

Using multimodal data in early research also helps find new treatment targets. AI supports this by linking patient-based organoids with clinical data, making lab research faster.

Final Remarks for Medical Practice Leadership in the United States

For administrators, owners, and IT staff in U.S. medical practices, learning about multimodal real-world data and AI is important. These tools help doctors make better decisions, lower extra work, and support research involvement.

Investing in AI platforms to automate tasks like phone answering, scheduling, and clinical trial matching can reduce staff workload and improve patient experience. Working with groups that provide multimodal data systems can help offer more personalized and evidence-based care.

As healthcare moves toward data-driven precision medicine, practices that know and prepare for these technology changes will be better able to help patients and improve results in a complex healthcare world.