How can we improve health equity and build diverse clinical trials without a holistic view of patients? ConvergeHEALTH is bridging that gap by linking Komodo's Healthcare Map™, one of the most complete views of patient journeys in the US, with HealthPrism™, one of the largest social determinants of health data sources, to create insights that inform drug development and treatments and drive health equity.
If you can’t measure it, you can’t improve it. The health care industry generates a tremendous amount of real-world data (RWD) that provides valuable insights on patients, their disease, and care delivery. But up to 80% of health outcomes can be driven by nonclinical factors, such as access to transportation, education, job opportunities, nutritious food, and safe housing.1 This nonclinical data—referred to as social determinants of health (SDoH)—aren’t typically captured in traditional RWD. SDoH are the environmental conditions where people live, learn, work, play, and worship that affect a wide range of health and quality-of-life outcomes and risks.2
While the availability of RWD sources continues to grow, challenges still remain with understanding each factor (medical and nonmedical) that impacts patients and their health outcomes. Traditional RWD includes anonymized information, such as medical and pharmacy claims, electronic health records, lab data, and registries. This data has served as the foundation for observational research within the health care community for years to better understand cost, safety, and the real-world effectiveness of treatments. RWD can also inform drug development by providing insights into the natural history of disease, unmet needs, disease burden, clinical trial design, and site selection to enroll a diverse set of patients. Unfortunately, there have been known gaps in this data for years.
The COVID-19 pandemic shed light on the long-standing inequities that are present in today’s society, which underscores the importance of addressing SDoH to help advance health equity in clinical trials. For example, access to transportation can affect a person’s ability to get to a health care provider, resulting in missed or delayed diagnosis, increased health care expenditures, and overall worse health outcomes.3
This is where integrating traditional RWD with Deloitte's HealthPrism™ unlocks new insights. HealthPrism’s SDoH data provides de-identified information like race and ethnicity, access to transportation, neighborhood demographics, and other nonmedical information that potentially impacts a person’s health.
By linking Komodo’s Healthcare Map with our HealthPrism SdoH data set, we are helping clients address some of their biggest challenges, create a more comprehensive view of their patients, and ultimately improve equitable access to medicines, increase diversity in clinical trials, and understand health disparity.
Patient privacy is absolutely critical, and our de-identification approach helps ensure privacy and Health Insurance Portability and Accountability Act (HIPAA) compliance. Patient-level data is linked across datasets using advanced de-identification and tokenization technology to ensure protected health information (PHI) or personally identifiable information (PII) is never exposed.
Across the product life cycle, our robust dataset drives value throughout the patient journey for both patients and health care organizations. These use cases in research, trial design, and trial recruitment highlight open areas of opportunity, the type of data used for each scenario, and the value delivered.
Use case: Understand the potential disparities and social vulnerabilities in health care utilization or outcomes related to socioeconomic factors.
Linked data: Claims (diagnosis, labs); SDoH (socioeconomic status, health care accessibility); behavioral data (lifestyle choices, purchase behavior).
Value: Improve health outcomes in chronically ill and underserved patient populations.
Use case: Increasing diversity in clinical trials is a top priority for pharma and the Food and Drug Administration (FDA), but traditional RWD lacks accurate and complete race, ethnicity, and SDoH data to include minorities.
Linked data: Claims, EHR (diagnosis, provider); SDoH (health care accessibility, economic stability, etc.); demographics (race and ethnicity).
Value: Identify diverse populations to include in your next clinical trial.
Use case: Successful recruitment and retention require access to patients’ nonmedical information, such as insurance coverage, transportation, health literacy, or choice of technology/social media channels.
Linked data: Claims, EHR (diagnosis, insurance coverage); SDoH (neighborhood, health care accessibility, etc.); behavioral (purchase behavior, social media preferences, etc.).
Value: Segment and effectively engage and retain diverse patient populations.
Use case: Deep health care provider (HCP) profiling to identify the right investigators and trial sites is challenging because of siloed understanding of patient characteristics, HCPs treating those patients, diagnosis and prescription patterns, and patients’ SDoH factors.
Linked data: Claims (diagnosis, medications, insurance coverage); national provider identifier (NPI, including physician name, specialty, address, patients treated), SDoH (neighborhood, health care accessibility, etc.).
Value: Identify qualified investigators treating an abundance of patients with the desired phenotype to boost recruitment rates for trials.
1 Manatt, Phelps & Philips, LLP, “Medicaid’s role in addressing social determinants of health,” Robert Wood Johnson Foundation, February 1, 2019.
2 Healthy People 2030, US Department of Health and Human Services, Office of Disease Prevention and Health Promotion, “Social determinants of health,” accessed February 26, 2024.
3 Health Research & Educational Trust, Social determinants of health series: Transportation and the role of hospitals (Chicago, IL: Health Research & Educational Trust, 2017).
