Real-world data means patient health information collected outside of traditional clinical trials. This kind of data comes from electronic health records (EHRs), insurance claims, patient registries, and other sources. It shows how diseases appear and are treated in everyday healthcare, giving a wider and more realistic picture.
In the United States, clinical research relies a lot on accurate and complete data. But combining data from many sources while keeping patient privacy safe is a hard task. This is where AI-powered platforms, like IQVIA’s Analytics Research Accelerator (ARA), become important.
This platform lets healthcare groups analyze over 1.2 billion anonymous patient records collected from more than 150 databases in over 20 countries, including the U.S. For medical practice administrators and IT managers, having this access means they can quickly find and use data needed for research. Being able to work with such large global data helps people understand disease patterns, how treatments work, and how patients follow their care plans in different groups of people.
Evidence gaps happen when there is not enough scientific information to support medical decisions or rules, especially for rare diseases. Rare diseases affect small numbers of people, so normal clinical trials are hard because there are fewer patients and less data.
AI tools in platforms like IQVIA’s ARA help researchers find these gaps faster by using advanced data mining and analysis. An AI-powered cohort builder in the platform helps select groups of patients by suggesting related diagnoses, treatments, and procedures for the disease being studied. This makes picking patients more accurate and takes less manual work.
These tools are very useful when studying rare diseases. They allow quick checks and data analysis using real-world data from different places and health centers in the U.S. With AI, research teams can find patients who are not well represented, learn about their health experiences, and collect treatment results quicker than before.
Running clinical research while caring for patients is a big challenge for medical centers. Many administrators know how long it takes to find the right datasets, match patients to study rules, and do group analyses. These tasks can slow down studies and cost more money.
Platforms like IQVIA’s ARA provide self-service tools for medical practices. These tools let researchers and data experts do many feasibility checks and group profiles fast, often in near real-time. Using AI, studies can work at the same time across many local and global databases without complicated manual effort.
For U.S. health organizations, this means clinical staff can spend less time on paperwork and more time caring for patients. These smoother workflows help support faster and wider clinical research, especially for population health and value-based care plans.
The main goal of clinical research is to improve patient health. AI analyzing real-world data gives a better view of disease impact, treatment habits, and how well care works. This data helps doctors make better evidence-based choices.
For example, AI can find issues with patients not following their medication plans by linking demographic, clinical, and treatment information. With this knowledge, doctors can make better plans to help patients stick to their treatments and improve their health.
Also, AI platforms enable studies that compare different groups of people. This helps show how treatments work differently for each group. This is very important for managing long-term illnesses and rare diseases where personalized care matters.
AI-powered workflow automation helps reduce the manual work needed to handle health data, write clinical documents, and run research tasks. Platforms with AI can automatically process large datasets, do medical coding, and create reports without constant human help.
In the U.S., medical practices face growing demands for administrative tasks like data entry, coding, and billing. About 25% of health costs are linked to these admin tasks. Using AI to automate work can lower these costs by improving accuracy and speeding up processes.
Besides billing, AI automation helps with fast data extraction, cleaning, and preparation, making data ready for analysis sooner. For IT managers, this means smoother operations and better use of staff resources.
By adding AI to the daily work of clinical research, medical practice owners can improve the quality and speed of data for studies. These improvements help give faster diagnosis, more accurate treatment plans, and better tracking of outcomes.
Healthcare workers in the U.S. face pressure due to a predicted shortage of 90,000 doctors by 2025 and burnout rates between 40% and 60%. These problems affect clinical research and patient care.
AI real-world data platforms are tools medical practice administrators, owners, and IT managers can use to help with these problems. Better data access and fast analysis help make clinical workflows more efficient by cutting down manual work for study setups and management.
For example, using AI-driven cohort builders and analytic accelerators lets research teams quickly start multi-site studies without sorting data by hand. This is very important for rare disease research where patients are few and spread out across places.
Also, AI tools help support value-based care by giving detailed views of patient experiences and clinical results. This information helps shape payment models and policies in the U.S. healthcare system.
Medical practices in the U.S. that want to start or upgrade AI tools for clinical research need systems that work well with existing electronic health record (EHR) systems and cloud platforms. The IQVIA platform can connect with cloud providers like AWS and Snowflake to move data smoothly and allow custom analytics.
Keeping data private and secure is very important under U.S. health laws like HIPAA. AI platforms made for healthcare include features to hide patient identity and use standard data formats like OMOP to keep data useful while protecting privacy.
This means IT managers can add AI real-world data analysis tools confidently, knowing they meet legal rules and institutional policies.
Medical practice administrators, owners, and IT managers in the United States are leading the use of these AI advances. By using AI-powered real-world data tools, they can support quicker, higher-quality research that directly helps improve patient care and better manage rare diseases. Clinical research in the U.S. will rely more on these technologies to meet the needs of growing patient groups and changing healthcare challenges.
The main goal is to integrate generative AI into healthcare workflows to address urgent challenges like clinician burnout, staffing shortages, and operational inefficiencies, thereby improving patient care, clinician productivity, and health system financial integrity.
The collaboration increases productivity through AI-assisted note summarization, suggested text for faster documentation, rapid review, and embedded ambient clinical documentation using Nuance Dragon Ambient eXperience (DAX), enabling seamless, efficient workflows for physicians and nurses.
The integration includes Microsoft’s Azure OpenAI Service and Nuance DAX Express, enabling conversational, ambient, and generative AI capabilities for clinical insights, administrative tasks, natural language queries, and interactive data analysis within Epic’s EHR modules.
Generative AI streamlines labor-intensive administrative processes such as revenue cycle management by automating tasks like medical coding suggestions based on clinical documentation, increasing accuracy and speeding up billing workflows.
AI supports advancing medicine by using real-world data analysis through tools like SlicerDicer, enabling exploration of clinical evidence gaps, studying rare diseases, and improving patient outcomes through data-driven insights.
The initiative targets critical issues including a predicted shortage of 90,000 physicians, high clinician burnout rates (40-60%), financial pressures, and inefficiencies in clinical and operational workflows requiring scalable AI-driven solutions.
AI reduces administrative costs, which are about a quarter of U.S. national health expenditure, by automating repetitive, manual tasks, improving accuracy in coding and billing, and optimizing operational processes to decrease unnecessary labor expenditures.
Health systems focus AI investments on operational optimization, disease management and prediction, diagnostic imaging, population health management, value-based care, patient engagement, and clinical research over the next two years.
Nuance DAX provides AI-powered ambient clinical documentation embedded within Epic’s platform, allowing real-time speech-to-text capturing of patient encounters to reduce clinician documentation burden and improve workflow efficiency.
They collaborate closely with healthcare providers, embed AI within existing clinical workflows for seamless adoption, emphasize secure AI solutions, and leverage scalable cloud infrastructure to responsibly and swiftly address healthcare challenges.
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