Real-world data comes from places outside of regular clinical trials. This includes electronic medical records (EMR), imaging data, lab results, patient reports, and even genetic information. Multi-modal data means using these different types of information together. This gives a better and fuller picture of a patient’s health than using just one type of data.<\/p>\n
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In cancer studies, for example, joining imaging tests, pathology reports, and genetic details helps scientists see how a patient reacts to treatment more clearly. Multi-modal RWD lets researchers study health results more deeply and find the best treatments for each patient. This kind of data makes clinical trials more precise and helps researchers think about different factors that affect patient results.<\/p>\n
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Jeff Elton, PhD, CEO of ConcertAI, points out that multi-modal data is important for figuring out cause and effect in clinical trials. He says mixing different data types makes it easier to understand trial results better because it helps remove confusing factors. This is very useful in cancer research, where patient groups and treatment results vary a lot.<\/p>\n
Causal inference means deciding if a treatment, like a new drug, actually causes a result, such as making a tumor smaller or helping a patient live longer. Usual clinical trial methods sometimes struggle to prove cause and effect because they use limited data and face confusing factors.<\/p>\n
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By using multi-modal real-world data, scientists can do more thorough and detailed studies. For example, the CARA AI platform from ConcertAI helps manage different types of data and uses AI tools. This system helps teams connect health results with patient details like genetic changes, imaging results, or blood markers. This way, researchers can better understand complex treatment effects and have more trust in their findings.<\/p>\n
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Another step forward is AI tools like TriaLinQ, which help with clinical trial screening. Usually, researchers spend a lot of time checking patient records. In one study, staff checked 58 patients in 40 hours using old methods but could screen 120 patients in just 25 hours using AI. TriaLinQ cut the time per patient from 41.4 minutes to 12.5 minutes. This speeds up the whole trial process and helps find the right patients more accurately.<\/p>\n
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CancerLinQ, a network set up by the American Society of Clinical Oncology, uses strong data collection and AI to match patients with new treatments better. Its RxLinQ tool finds patients for new targeted therapies, using more than just traditional biomarker data. This makes the clinical trial system more efficient and helps patients who might have been missed.<\/p>\n
Healthcare administrators and IT managers have an important job in adding these data tools to everyday clinical research. They need to understand how useful multi-modal RWD is and put in place systems that collect, manage, and study different types of data.<\/p>\n
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By using platforms like CARA AI, medical centers can support teamwork between research groups, doctors, and data experts. This teamwork makes sure data moves smoothly between systems and AI results are used the right way when making decisions. This helps pick clinical trial patients faster and more accurately and improves treatment plans.<\/p>\n
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IT managers need to prepare systems that handle large data files, like images, genetic sequences, and detailed clinical records. Building safe and compatible systems that can manage this kind of data is a technical challenge and needs careful planning.<\/p>\n
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Medical practice owners who invest in AI and automation tools are not just trying to speed things up. They are trying to improve research quality and patient care. AI screening helps move patients through trials faster, lowers costs, and gives more patients a chance to join studies. This fits with wider goals in the US to make healthcare fair and accessible.<\/p>\n
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