The healthcare industry is undergoing a digital revolution, driven by the explosion of data across the care continuum. From patient check-ins to advanced genomic testing, data is being collected, analyzed, and leveraged in ways never seen before. But to harness the full potential of this information, it’s crucial to understand the different types of healthcare data—how they’re generated, how they’re used, and why they matter.
Healthcare data comes from a wide range of sources, each offering a unique piece of the patient care puzzle. When integrated effectively, these data types enable providers to make informed decisions, researchers to uncover new insights, and life science companies to develop more personalized and effective treatments.
1. Electronic Health Records (EHRs)
Electronic Health Records are the most foundational and widely used form of healthcare data. They contain comprehensive patient information gathered during clinical encounters, such as diagnoses, medications, treatment plans, immunization dates, allergies, and test results. EHRs are structured for both clinical use and administrative purposes and are critical in supporting day-to-day patient care. They also form the basis for population health studies and long-term care tracking.
2. Administrative and Claims Data
Administrative data is generated during billing and insurance processes. It includes patient demographics, diagnoses (ICD codes), procedures (CPT codes), hospital admissions, discharges, and cost data. Although primarily used for operational and reimbursement purposes, claims data can offer valuable insights into care patterns, health system efficiency, and disease prevalence. Researchers and payers often use it to track utilization trends and assess treatment effectiveness at scale.
3. Genomic and Molecular Data
This type of data is derived from tests that analyze genes, proteins, and other biomolecules. Genomic data plays a vital role in precision medicine, where treatments are tailored to an individual’s genetic profile. It includes sequencing results, genetic variants, and expression patterns. Handling and interpreting genomic data requires robust infrastructure, due to its high volume and complexity.
One leading organization leveraging this type of data is NashBio, a data solutions provider affiliated with Vanderbilt University Medical Center. NashBio combines longitudinal clinical data with genomic information through its BioVU® platform—one of the largest such repositories in the world. By making this data accessible via their powerful self-service platform, TOTUM, NashBio enables researchers and pharmaceutical companies to conduct large-scale studies that advance drug development and personalized care. Their datasets are diverse, ethically sourced, and linked across clinical and molecular layers—making them especially valuable for translational research.
4. Imaging and Radiology Data
Medical imaging—such as X-rays, MRIs, CT scans, and ultrasounds—produces vast amounts of unstructured data. These images are typically stored in Picture Archiving and Communication Systems (PACS) and require specialized tools to analyze. Radiology reports often accompany these files, adding context for diagnosis or tracking disease progression. AI tools are increasingly being used to extract patterns from imaging data, enabling earlier and more accurate diagnoses.
5. Patient-Generated Health Data (PGHD)
PGHD refers to health-related data created, recorded, or gathered by patients themselves. This includes data from fitness trackers, mobile health apps, and patient-reported outcomes. It covers everything from daily steps and heart rate to medication adherence and pain levels. PGHD helps providers better understand patients’ lifestyles and manage chronic diseases more effectively. It also empowers patients to take a more active role in their health.
6. Wearable and Sensor Data
Wearable technology and Internet of Medical Things (IoMT) devices are becoming a significant source of real-time health monitoring. These devices can continuously collect metrics such as blood pressure, glucose levels, oxygen saturation, and more. Sensor data is particularly useful for remote patient monitoring, especially for individuals with chronic illnesses or those recovering from surgery at home. When integrated with EHRs, this data supports more responsive and proactive care models.
7. Real-World Evidence and Behavioral Data
Beyond the clinic, data collected from everyday interactions—such as social determinants of health (housing, income, education), behavioral patterns, and lifestyle habits—contributes to the broader picture of patient health. Real-world evidence (RWE) combines multiple types of healthcare data to evaluate how treatments perform outside of clinical trials. This is essential for regulators, payers, and life science companies seeking to demonstrate value and improve outcomes in real-world settings.
8. Clinical Trial Data
Clinical trial data is generated during research studies that assess new treatments, procedures, or medical devices. This data includes patient demographics, trial protocols, lab results, adverse events, and outcomes. High-quality trial data is essential for securing regulatory approvals and advancing medical innovation. Integrating trial data with EHR and genomic information can help identify patient cohorts and optimize future study designs.
Why Healthcare Data Types Matter
Each of these healthcare data types tells a different part of a patient’s story. When analyzed together, they provide a holistic view that supports better decision-making, improves outcomes, and drives innovation. For example, a diabetic patient’s EHR might show a history of elevated glucose levels, while wearable data could reveal poor sleep and low activity. Combining these with genomic data might indicate a genetic predisposition, guiding a more targeted and effective intervention plan.
Organizations like NashBio are at the forefront of this data integration, bridging the gap between raw information and real-world impact. Their work exemplifies how understanding and utilizing different healthcare data types can lead to major breakthroughs in drug development, population health, and personalized medicine.
Final Thoughts
As healthcare continues its digital transformation, the ability to navigate and utilize different types of healthcare data will be a defining factor for success. From clinicians and researchers to data scientists and policymakers, understanding the nuances and strengths of each data type is essential for unlocking the full potential of data-driven healthcare.
Whether it’s enabling earlier diagnosis, driving precision therapies, or streamlining clinical trials, healthcare data—when collected, connected, and analyzed properly—has the power to revolutionize the future of medicine.
