The National Academy of Medicine has estimated that less than half of all medical care in the US is supported by adequate evidence of effectiveness. The proliferation of treatment options in some disease states and the rising costs of many of those treatments have spurred many decision-makers to demand information on comparative effectiveness to support coverage and patient care decisions.

However, comparative information is often not readily available, either due to the difficulties, costs, and timelines associated with gathering these data. This presents challenges for providers trying to make evidence-based clinical decisions, as well as for life sciences companies seeking to differentiate their therapies.

Since clinical trials are run in highly controlled environments, the results are not readily generalizable. Life sciences companies must generate clinically specific data on drug or device performance outside of trials to stand out – particularly as therapies become more targeted.

EHR data enables precise comparative effectiveness research

Electronic health record (EHR) data contains a tremendous amount of rich, clinical insight that can be used to fuel timely comparative effectiveness research. Relative to other real-world databases such as claims or registries, EHR data enables researchers to define real-world populations with a high degree of clinical specificity.

Because EHRs capture nearly all the major clinical factors clinicians consider when making therapy choices, access to clean, normalized EHR data at scale (including insights captured in free-text clinical notes) can enable identification of precise populations with sufficient sample sizes for evaluating outcomes. EHR data sourced from many health systems can also ensure data are representative.

Truveta Data comprises complete EHR data for more than 100 million patients, enabled by a collective of 30+ leading health systems that provide more than 18% of all clinical care in the US. These complete medical records – including notes and images – are linked with claims and patient-level SDOH and mortality data for a more complete view of patient journeys, and a representative look at real-world care delivery patterns.

These data enable life sciences companies to:

Generate evidence of differentiated clinical outcomes

Robust real-world data from EHRs enable researchers to assess a product’s performance in diverse populations and clinical scenarios. With longitudinal patient records and full clinical context, researchers can answer questions such as:

      • What is the long-term effectiveness of a given product compared to alternatives?
      • How do different treatments impact quality of life or patient-reported outcomes?
      • How does the product perform considering specific comorbidities, concomitant medications, and variations in patient demographics?

These insights can be used to inform market access decisions and healthcare provider (HCP) and patient education, ensuring the right patients have access to the therapies with demonstrated evidence of real-world effectiveness.

Understand real-world product performance for specific subpopulations

In addition to differentiating one treatment from another, it is important to understand how a given treatment performs for specific subpopulations. However, racial and ethnic minorities are frequently underrepresented in clinical trials, despite having a disproportionate burden for certain diseases.

Access to real-world data representative of the US population enables researchers to understand the performance of therapies or interventions based on factors including age, sex, pregnancy status, race, ethnicity, genetic variation, and more. This enables researchers to understand real-world clinical care patterns and associated outcomes, and answer questions such as:

      • Which patients are prescribed specific treatments?
      • Are specific patient subpopulations more responsive to particular treatments?
      • Do demographic factors, comorbidities, or SDOH variables impact treatment outcomes?
      • How is a given therapy performing in a group routinely excluded from clinical trials, such as pregnant or breastfeeding women?

These insights can inform both product optimization, as well as efforts to ensure access for patients most likely to benefit from the therapy.

Ensure the right patients have access to needed therapies

In addition to understanding performance of therapies in specific populations, Truveta Data enables researchers to identify potential disparities in treatment access. By linking patient-level SDOH data to EHR data, researchers can explore the impact of income, education, transportation access, social support, housing stability, and other SDOH factors on treatment access and outcomes. Specifically, researchers can answer questions such as:

      • Are there demographic disparities in the utilization of specific treatments?
      • How do treatment access patterns vary across geographic regions?
      • How do SDOH factors or the presence of specific comorbidities impact treatment access?

These insights can inform both product optimization, as well as efforts to ensure access for patients most likely to benefit from the therapy. At the patient level, this information can fuel shared decision making and patient-centered care decisions.

Driving product innovation and clinical advancements

Collectively, Truveta’s patient-level health data empowers researchers to generate evidence to drive product innovation and clinical advancements and support evidence-based decision making, personalized medicine, and equitable healthcare access. Whether looking to study a rare or common disease, Truveta Data’s clinical depth, scale, longitudinality, and representativeness make Truveta the ideal data source for comparative effectiveness research, as well as other real-world data study designs.

Contact us to learn more about how we’re supporting life sciences companies to advance their comparative effectiveness research or discuss your own research questions.

Next, check out examples of comparative effectiveness research conducted using Truveta Data: