Utilizing Multimodal Real-World Data: The Key to Advancing Personalized Treatment Strategies in Oncology

Multimodal real-world data means collecting many types of patient information from different places. This can include genetic data like DNA sequencing results, medical records, pathology reports, how well treatments worked, and how patients follow their medicine plans. When doctors mix these data types, they get a clearer view of a patient’s cancer and how treatments affect them.
In cancer care, using multimodal real-world data has helped doctors and researchers understand how tumors behave and differ. For example, putting together genetic data and medical records lets doctors learn about tumors at the smallest level. This helps them pick treatments based on specific genetic changes or how the patient might react, instead of just looking at general cancer types.
A company in the US called Tempus has helped push the use of multimodal real-world data in cancer care. They work with about 65% of university medical centers and over half of cancer doctors in the country. Tempus collects and looks at more than 8 million patient records that have been made anonymous. These records include tumor DNA, normal DNA, RNA sequencing, and even data from blood tests to give doctors useful details.

Importance of Tumor-Normal Matched Sequencing and Tumor Mutational Burden

One key use of multimodal real-world data in cancer care is tumor-normal matched sequencing. This means comparing genetic material from a patient’s tumor cells with their normal tissue. This helps doctors tell which mutations happen only in the tumor and which are inherited from birth. This filtering makes it possible to focus on mutations important for treatment.
Using this method, doctors can better measure tumor mutational burden, or TMB. TMB counts how many mutations a tumor has. It is a useful way to predict how well a patient might respond to immunotherapy. Immunotherapy helps the immune system attack cancer cells. Patients with higher TMB often respond better to these treatments. Tempus used multimodal data to prove that TMB results can guide immunotherapy choices and help patients.

AI and Machine Learning in Oncology Data Analysis

Artificial intelligence (AI) and machine learning are important for handling and understanding large amounts of multimodal data in cancer care. These tools study complex data to find patterns that people might miss. AI programs can identify signs of cancer growth or how a patient may respond to treatment and predict results more accurately.
For example, AI can quickly analyze next-generation sequencing (NGS) data to support doctors’ decisions. These methods combine several types of sequencing results, such as tumor DNA, RNA, and blood tests, to give an updated picture of the patient’s condition. This helps doctors create treatment plans tailored to the patient, including finding clinical trials that match their genetic profile.
Doctors like Dr. Jeffrey James and Dr. James Maher have said that using AI tools and combining data helps provide personalized cancer care. These tools increase clinical trial participation and improve treatment planning beyond older methods.

Pharmacogenomics and Its Expanding Role in Oncology and Mental Health

Pharmacogenomics (PGx) is another growing use of multimodal data and AI. PGx studies how genetic differences affect how people react to medicines. In cancer care, PGx helps find the best drugs and correct doses for each person to lower side effects and improve results. This is very useful for difficult cases like cancers that do not respond to usual treatments.
Tempus also uses pharmacogenomics in mental health problems such as ADHD, treatment-resistant depression, and major depression. By analyzing genetics and providing PGx reports, doctors get data-based help in choosing medicines. This improves care for many health issues.

Challenges in Integrating Multimodal RWD into Daily Oncology Practice

Even though multimodal real-world data has many benefits, health care places face problems when adding these tools to everyday work. Collecting data comes from many sources with different formats and quality. This needs advanced systems to combine and standardize it. Protecting patient privacy and following HIPAA rules is also very important.
Also, the large amount of data requires strong IT systems and trained workers to manage and understand it. Many clinics have to buy new software, secure ways to store data, and train staff to use multimodal data fully.
Still, there is growing demand for precise medicine. Partnerships between hospitals, data firms, and drug companies help solve these problems. For example, Tempus works with 95% of the top 20 drug companies in the US. This helps researchers, new drug discovery, and clinical trials improve.

AI and Workflow Automation: Enhancing Oncology Practice Efficiency

Besides data analysis, AI also helps with automating work that affects managing cancer care clinics. Many patient questions, appointment bookings, follow-ups, and team communication can overwhelm office staff. Using AI-based automation can reduce these burdens. This lets staff spend more time on patient care.
For example, AI-powered phone systems can handle scheduling calls, confirming visits, and giving patients updates. This lowers waiting and fewer missed appointments. It improves patient experience and clinic operations.
AI can also make it easier to handle referrals, lab results, and patient education. Clinics using these tools say they run better and patients are happier.
For IT workers and clinic managers, adding AI-driven automation matches goals of using up-to-date technology and improving health care with data. These tools cut time spent on paperwork and let clinics focus more on helping patients better.

Specific Considerations for Medical Administrators and IT Leaders in the United States

The US health care system is complex with many rules and insurance types. Medical managers must carefully plan when using multimodal data and AI tools. They need to follow federal and state laws like HIPAA to protect patient information while making good use of data.
Budget is important because buying advanced sequencing tools, data platforms, and AI software costs a lot at first. But these costs can pay off over time through better treatments and fewer hospital stays.
IT managers have a key job in selecting systems that work well with current electronic health records (EHR). They also provide ongoing tech help and keep data safe. At the same time, they make sure systems are easy for medical staff to use.
Working with outside partners such as Tempus and drug companies can give access to the latest data and scientific knowledge. This helps clinics join clinical trials and take part in precise cancer research.

In summary, multimodal real-world data is a main part of changing cancer care in US health institutions. The detailed genetic and clinical data combined with AI and machine learning make it possible to make treatment choices based on the patient. Automation tools supported by AI also improve clinic efficiency. This helps medical leaders and IT teams handle growing demands in cancer care. These tools support more targeted, effective, and patient-focused cancer services in medical centers across the country.