Healthcare is changing every day. New therapies enter the market, prescribing patterns shift, safety questions emerge, and guidelines begin to reshape clinical practice. Yet the way organizations study these changes has historically lagged well behind the pace of care.
Several developments are beginning to close that gap. Electronic health records have created a more complete view of patient care. Real-world data have become an important complement to clinical trials and published research. AI is making healthcare data easier to explore and helping more people investigate complex questions without specialized programming or analytics expertise. These changes are all contributing to a broader shift in how organizations learn from healthcare.
Many organizations now use the term healthcare intelligence to describe the ability to explore questions using real-world healthcare data, analytics, and AI. In practice, healthcare intelligence helps organizations monitor change, investigate emerging questions, and identify where deeper research may be needed.
Instead of relying on a series of one-off projects, organizations can continuously learn from healthcare as care evolves. Truveta Intelligence is built for this new way of working. Powered by the most complete and real-time view of US patient care, it helps healthcare, life science, and government teams ask better questions, examine the reasoning behind each answer, and move faster to evidence, enabling us to learn from every patient every day.
What is healthcare intelligence?
Healthcare intelligence combines healthcare data, analytics, and AI to help organizations understand what is happening across patient care.
Unlike many healthcare AI tools, healthcare intelligence is grounded in real-world healthcare data. Some healthcare AI applications help clinicians document visits, summarize patient charts, or search medical literature. Healthcare intelligence helps organizations investigate questions using real-time patient care data so that what is learned about one patient can help the next.
For example, organizations may want to understand how a newly approved therapy is being adopted, whether treatment patterns are changing, or which patient populations are receiving care. These questions often cannot be answered through published literature alone because the changes are still unfolding.
Healthcare intelligence complements clinical trials, peer-reviewed research, and real-world evidence by helping teams understand what is happening between those milestones and identify questions that warrant deeper study.
What healthcare intelligence enables
Healthcare intelligence helps organizations answer questions that traditionally required multiple teams, custom analyses, or months of research.
Understanding therapy adoption
When a new therapy launches, organizations want to know who is receiving treatment, how prescribing patterns differ across clinicians, and whether adoption is occurring as expected. Healthcare intelligence can provide an early view of treatment uptake, switching patterns, and patient populations while longer-term outcomes research is still underway.
Supporting clinical trial and evidence decisions
Researchers use healthcare intelligence to understand patient populations, evaluate study feasibility, identify potential sites, and refine study design. These insights can help inform clinical trials, observational studies, and evidence-generation strategies before significant time and resources are invested.
Detecting emerging safety and treatment signals
Healthcare intelligence can surface unexpected treatment patterns, changes in utilization, and potential safety signals that warrant further investigation. These early observations help organizations prioritize where deeper analysis, formal studies, or additional monitoring may be needed.
Healthcare intelligence in practice: Early Foundayo adoption
When oral orforglipron (Foundayo) was approved for obesity in April 2026, researchers already had data from clinical trials describing its efficacy and safety. What they did not yet know was how the therapy would be adopted in routine clinical practice.
Using Truveta Data, researchers identified 5,723 patients with a prescription or medication dispense for Foundayo between April 1 and July 6, 2026.
Among patients initiating Foundayo, 40.3% had no evidence of prior GLP-1 medication use, indicating the medication may be reaching a new patient population. General practice clinicians accounted for 60.0% of prescriptions, while advanced practice providers accounted for another 31.9%. Only 0.1% of patients had evidence of recent oral semaglutide use.
Three months after approval is not enough time to understand long-term outcomes or treatment persistence, but it is enough time to understand who is receiving treatment and how adoption is taking shape. In this case, the analysis suggested that Foundayo was reaching many patients who had not previously used a GLP-1 medication and that adoption was occurring mostly through primary care. Those observations provided an early picture of how oral GLP-1 therapies were entering routine obesity care and identified questions for future research.
The standard healthcare intelligence should meet
AI has made healthcare questions easier to ask. It has also made it easier to receive answers without understanding where they came from. When evaluating healthcare intelligence, organizations need to consider the data, methods, transparency, and evidence behind the results.
Timely data
Many questions involve changes taking place now, including the adoption of a new therapy or the emergence of a potential safety signal. Daily refreshed data allow teams to study recent care without waiting for claims to mature or new research to be published.
Clinical depth
Claims data provide useful information about utilization and cost, but many questions require details found in electronic health records (EHRs). Laboratory results, medications, clinical notes, imaging, and longitudinal treatment histories help researchers understand which patients received care and what happened over time.
Verifiable answers
Healthcare intelligence should not operate as a black box. Users should be able to understand how a patient population was defined, which data were included, how outcomes were measured, and what limitations apply.
A path to evidence
Early findings may lead to safety analyses, observational studies, peer-reviewed research, or regulatory-grade evidence. Healthcare intelligence should make it possible to carry a useful question into deeper analysis rather than beginning again in a separate workflow.
How Truveta approaches healthcare intelligence
Truveta Intelligence helps healthcare, life science, and government teams explore questions using natural language and real-world healthcare data. It is designed to help users investigate emerging questions, examine the reasoning behind each answer, and monitor meaningful changes across patient care.
Those answers are grounded in Truveta Data, a daily refreshed view of patient care representing more than 140 million patients. Truveta Data includes electronic health records, clinical notes, images, linked claims, mortality data, social determinants of health, and more than 10 years of longitudinal patient history.
When a question requires deeper validation, organizations can build on those findings with Truveta Evidence. Rather than starting over in a separate workflow, teams can move from exploration to formal analysis using the same underlying data foundation.
Together, these capabilities help organizations answer questions about healthcare today while supporting the rigorous evidence generation required for healthcare decisions.
Frequently asked questions about healthcare intelligence
What is healthcare intelligence?
Healthcare intelligence combines healthcare data, analytics, and AI to help organizations investigate questions using real-world patient care data. It can help teams understand how care is changing, monitor emerging trends, and identify questions that require deeper research.
How is healthcare intelligence different from healthcare AI?
Healthcare AI is a broad category that includes tools for tasks such as clinical documentation, patient chart summarization, and medical literature search. Healthcare intelligence focuses on helping organizations learn from healthcare data and understand what is happening across patient populations, treatments, and outcomes.
How is healthcare intelligence different from healthcare analytics?
Healthcare analytics are the methods used to analyze data. Healthcare intelligence is the ability to use data, analytics, and AI to answer questions, explore emerging issues, and support decision-making.
Why do real-time data matter?
Many healthcare questions involve changes that are happening now, including therapy adoption, treatment patterns, and potential safety signals. Access to timely data can help organizations understand those changes while they are occurring rather than months later.
What makes healthcare intelligence trustworthy?
Trustworthy healthcare intelligence is built on current, clinically deep data, transparent methods, and answers that can be verified. Users should be able to understand how a result was generated, what data were included, and what limitations apply.
Is healthcare intelligence the same as real-world evidence?
No. Healthcare intelligence helps organizations explore questions, identify signals, and understand emerging trends. Real-world evidence requires more formal study design, validation, and methodological rigor appropriate for the decision being made.
How can healthcare intelligence support life science organizations?
Healthcare intelligence can help life science teams understand therapy adoption, evaluate clinical trial opportunities, monitor safety signals, characterize disease populations, and identify areas for further research.
