Leveraging Real-World Evidence to Drive Therapeutic Insights and Support Clinical Decision-Making in Healthcare

Real-World Evidence is information about how medical products are used and what effects they have in everyday life. This information comes from sources like electronic health records, insurance claims, patient surveys, and health studies. Unlike clinical trials, which follow strict rules and include selected patients, Real-World Evidence shows how treatments work in many different healthcare settings and among various types of patients. This makes it useful for healthcare leaders who want to improve services and patient care.

In the United States, healthcare groups use Real-World Evidence programs to find differences in treatments, check how resources are used, and see how well treatments work outside of trials. For those who manage medical practices and IT systems, real-world data helps shape strategies to make care better and more efficient.

The Importance of Real-World Evidence in Clinical Decision-Making

Doctors need good information about how treatments work and how patients respond to make the best decisions. By studying Real-World Evidence, providers learn about:

• Treatment Patterns: Knowing how treatments are actually used helps doctors see which work best for certain patients and in specific situations.
• Patient Outcomes: Long-term data on health changes and survival rates help doctors create better care plans.
• Safety and Effectiveness: Real-world data can show side effects or benefits that might not appear in clinical trials because those trials have fewer or less diverse patients.
• Health Equity: Real-World Evidence helps find differences in care and results among different groups, encouraging efforts to make care fairer.

For example, the iPROPEL platform from Aptitude Health collects detailed information on cancer patients, from screening to treatment and survivorship, from over 5,500 oncology providers. This kind of broad data helps reveal how patients do at different stages and points out where care can improve.

Healthcare groups can use these insights to match treatment guidelines with what patients really need, improve following best practices, and keep making care better.

Real-World Evidence Accelerating Clinical Research and Drug Development

Pharmaceutical companies, clinical trial teams, and healthcare providers use Real-World Evidence to help develop new medicines and meet regulations. Making new drugs today costs a lot, sometimes more than two billion dollars each. Real-World Evidence can save money by helping design better clinical trials, find patients more easily, and provide extra proof for regulatory approval.

In cancer research, companies like IQVIA offer services that use Real-World Evidence to speed up trial processes for targeted treatments. This helps avoid delays from testing biomarkers and gets therapies to the right patients faster. Knowing details about tumors and how patients respond is very important for precise cancer care.

ConcertAI uses artificial intelligence tools combined with Real-World Evidence to improve clinical trials and data analysis. They help make studies faster and support better decisions, which can lead to quicker approval of new treatments.

The Value of Standardized Data Models in Real-World Evidence

A big challenge with Real-World Evidence is that data come from many sources like health records, claims, registries, and patient reports. These often use different codes and formats, making it hard to combine and study the data.

The Observational Medical Outcomes Partnership (OMOP) Common Data Model solves this by offering a standard way to organize various types of data. OMOP helps healthcare groups across the country work with consistent data, which supports strong research and better evidence.

Government agencies such as the FDA and EMA accept evidence made with OMOP for checking the safety and effectiveness of medical products. For hospital leaders and IT staff, using these standards makes sure their data meet national and global rules and help research efforts.

Practical Applications of Real-World Evidence in U.S. Medical Practices

In community hospitals and clinics, Real-World Evidence is useful in several ways:

• Clinical Decision Support: Adding Real-World Evidence tools to daily work helps doctors give more personalized treatments and stick to guidelines.
• Health Economics and Outcomes Research (HEOR): By studying claims and treatment records, managers can check how cost-effective treatments are and plan budgets better. For instance, Boston Strategic Partners used EHR and claims data with statistics to prove the value of continuous patient monitoring. This kind of research helps decide if new devices or treatments are worth investing in.
• Addressing Health Disparities: Finding where care is uneven across areas or groups lets health programs focus on those who need more help.
• Trial Recruitment and Design: Understanding patient groups helps organize better recruitment and create trials that reflect real patients.

Organizations like Mathematica use AI and statistics to analyze Medicare, Medicaid, and other data sets. Their work helps healthcare providers design treatment paths that improve patient care.

AI Integration and Workflow Automation: Enhancing Real-World Evidence Use in Clinical Settings

New technology like artificial intelligence (AI) and automation are changing how Real-World Evidence is gathered, studied, and used in healthcare. These tools speed up data handling, reduce human errors, and give useful results to support doctors and staff.

• AI-Driven Analytics: AI programs can quickly study large amounts of health records and claims to find disease trends, patient progress, and treatment effects much faster and more accurately than people alone. For example, Boston Strategic Partners used machine learning to study how well patients followed CPAP therapy for sleep apnea, finding both health and cost benefits tied to patient traits.
• Automation of Data Collection and Processing: Automated steps lessen the work needed to gather and clean data. This helps keep information up-to-date and ready for quick clinical decisions.
• Decision Support Tools: AI systems can add Real-World Evidence into clinical work by creating patient risk profiles, treatment tips, or warnings about possible problems. ConcertAI’s PrecisionExplorer uses AI and real-world data together to help doctors make faster, better decisions right inside their workflow.
• Phone System and Front-Office Automation: Companies like Simbo AI provide AI-powered phone and answering services. These services reduce missed patient calls, handle appointments, and direct questions efficiently, which leads to smoother patient experiences and frees staff to focus on other work. Using AI here improves office operations and patient communication.
• Clinical Trial Support: AI helps find and enroll patients for trials by scanning medical records for eligibility. Combining AI with Real-World Evidence also helps sponsors simulate trials or create external comparison groups, cutting costs and reducing ethical issues.

Healthcare IT managers in the U.S. should consider using AI and automation to make better use of Real-World Evidence. These tools can save time for clinical and administrative workers and improve both patient care and facility operations.

Embracing Real-World Data for Improved Healthcare Delivery

Many U.S. healthcare systems and life sciences companies are investing in Real-World Evidence to help patient care and decision-making. Teams including data analysts, AI developers, and healthcare leaders work together on these efforts. For example, partnerships like ConcertAI with NVIDIA, AbbVie, and Bristol Myers Squibb show growing use of AI-enhanced Real-World Evidence tools for better cancer care and faster clinical trials.

Real-World Evidence now includes patient-reported outcomes, social factors, and data from diverse community care providers. Aptitude Health’s network of over 5,500 community oncology providers collects data from everyday care settings, adding to knowledge about how treatments work outside big academic centers.

Summary

In U.S. healthcare, using Real-World Evidence is becoming an important way to improve clinical choices and drug development. Data from regular patient care adds to what is learned from clinical trials by showing how treatments work, their safety, and patient results across many groups.

Standard data models like OMOP help make this information consistent, supporting teamwork and strong research. AI and automation help analyze data faster and bring results into clinical work, making care more reliable and efficient.

Medical practice leaders, owners, and IT managers in the U.S. can gain a lot by adopting Real-World Evidence strategies combined with AI-powered automation. These tools can help improve patient care while controlling costs and meeting healthcare rules in a complex system.