Multimodal real-world data means combining different kinds of patient information from many sources. This includes electronic health records (EHRs), genetic and molecular tests, lab results, imaging scans, and even data reported by patients themselves. Instead of using only one type of data, multimodal data gives a wider view of a patient\u2019s condition by linking clinical facts with genetic and behavior details.
\nFor example, usual cancer care might only look at a tissue biopsy or symptoms. But multimodal data connects genetic sequencing results, protein analysis, and patient history to make a fuller picture of the disease. Using these data types together helps doctors make better diagnoses, find new treatment targets, and pick the right clinical trials for patients.<\/p>\n
Precision medicine depends on carefully using different data to stop using the \u201cone-size-fits-all\u201d method. When real-world clinical data is combined with molecular data like genomic and transcriptomic profiles, doctors can tailor treatments based on unique tumor mutations or an individual\u2019s genes.
\nA clear example in the U.S. is in cancer care. Tools like the Tempus AI platform help medical centers give better cancer treatment. About 65% of academic medical centers and more than half of U.S. cancer doctors use platforms that bring together multimodal data to make smarter treatment choices. Tempus has over 8 million anonymous patient records used to find new treatment targets and close gaps in care.
\nAt Ochsner Health in New Orleans, Dr. Marc Matrana showed that using RNA sequencing with DNA tests found lung cancer markers that regular DNA tests missed. This discovery led to new treatment options. It shows how multimodal data can increase care options that were not available before.
\nAlso, drug companies use this data a lot. AstraZeneca, for example, applies multimodal real-world data in its Phase 3 cancer trials to better predict how patients respond to new treatments. This helps make drug development more focused, lowers trial failures, and improves chances of giving patients effective medicines.<\/p>\n
All in all, multimodal data helps move healthcare from standard treatments to plans made just for each patient\u2019s specific condition.<\/p>\n
Many U.S. healthcare centers use multimodal real-world data approaches. About 65% of academic medical centers use platforms like Tempus for cancer care. Also, over half of U.S. cancer doctors use these tools to get detailed genetic sequencing, match clinical trials, and join research.
\nDrug companies also work widely with these platforms. Ninety-five percent of the top 20 cancer drug firms team up to use genomic and clinical data. These partnerships support more than 200 projects aimed at making new drugs and doing clinical research.
\nThe amount of data is huge. For example, Tempus handles over 300 petabytes of data from many sources like clinical records and molecular profiles. This big data storage is key for AI systems to find useful insights for personalized care.<\/p>\n
Most people think of AI for clinical decisions and data analysis. But AI is also growing in automating front-office work in medical practices. For healthcare administrators, owners, and IT managers, AI tools that manage calls and admin tasks can reduce work and improve patient contact.
\nAI tools like Simbo AI handle phone calls by automating responses and booking appointments. This cuts call wait times, lowers no-shows by sending reminders, and frees staff from repeating tasks like checking insurance or registering patients. Studies show AI systems linked to electronic health records automate everyday front-office work, making offices run smoother and patients happier.
\nAlso, some platforms used for multimodal data are changed to manage workflows. For instance, AI patient screening tools can prequalify patients for clinical trials while managing scheduling. This lets clinical staff focus more on direct patient care. These tools have doubled clinical trial screening speeds. ConcertAI\u2019s CARA system signed up 120 patients in 25 hours compared to 58 patients in 40 hours with old methods.
\nPlus, AI health apps like Tempus\u2019 Olivia help patients and caregivers manage health data. By improving communication between patients and doctors, these apps help patients follow treatment and keep care steady.<\/p>\n
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