Types and examples of data in healthcare

Aug 20, 2025 7 min read

Healthcare data comes in many forms — clinical, administrative, patient-generated, and financial. With the rise of EHRs, connected devices, and AI tools, healthcare data has exploded in both volume and variety. But, sadly, more data doesn’t automatically mean better care. In fact, 47% of healthcare data is still underutilized in decision-making. That’s like paying for a three-course meal and only eating the main dish, while the rest goes to waste.

And the impact isn’t just theoretical. 77% of healthcare professionals say they lose clinical time due to incomplete or inaccessible patient data. For 1 in 3, that adds up to over 45 minutes per shift, roughly 23 full days a year spent chasing information that should’ve been there in the first place. My team at Innowise spends a lot of time fixing problems exactly like this. To do it right, you need to understand what kinds of data healthcare organizations deal with, and I’ll walk you through them in this article.

Key takeaways

Here are four main healthcare data types: clinical, administrative, patient-generated, and financial.
Clinical data is the most directly tied to patient outcomes and enables use cases like personalized medicine, predictive analytics, and clinical decision support.
Without proper standards like HL7, FHIR, and DICOM, data integration becomes a bottleneck rather than a benefit.
Using healthcare data effectively can improve patient care, reduce operational inefficiencies, and open the door to smarter, data-driven healthcare.

What is healthcare data?

Healthcare data is everything that is recorded, exchanged, or analyzed when delivering care. This includes clinical data such as lab results and imaging, administrative data like hospital staffing and resource use, financial data like billing records and insurance claims, and patient-generated data from wearables and health apps.

When utilized properly, this data can improve care in real and lasting ways. Doctors are able to spot early signs of deterioration, avoid duplicate tests, and adjust treatments based on real-world outcomes. On a larger scale, healthcare data supports clinical research, policy decisions, forecasting flu seasons, and modeling population risk in chronic disease.

Let’s break down the different types of healthcare data we work with and see how they’re used in real clinical settings.

Entrust your healthcare data to IT experts

Types of healthcare data

Healthcare organizations deal with broad types of data, each serving a different purpose, whether it’s direct patient care, hospital operations, or administrative workflows. Let’s explore the four main healthcare data types.

1. Clinical data

Operating as the backbone of patient care, clinical data includes everything captured during patient assessment, diagnostics, treatment, and follow-up care. This type of healthcare data is the most directly tied to patient outcomes: it tells healthcare professionals what’s going on with the patient, what’s been done, and how they responded to care.

Examples of clinical data:

Electronic health records (EHRs)
Medical history and patient demographics
Lab tests, pathology results, and genomic data
Diagnostic medical images, like MRI, X-rays, CT scans, etc.
Medication plans and prescriptions
Clinical notes and discharge summaries
Treatment and care plans
Clinical registry data
Clinical trial records
Patient outcomes
Population health data for epidemiological tracking

2. Administrative data

Administrative data details how the healthcare organization runs behind the scenes. It doesn’t directly impact diagnoses or treatments, but it plays a huge role in care delivery efficiency. It tells both management and executives how resources are being used, how patients are being treated, and where bottlenecks occur. Hospitals rely on administrative data to make strategic decisions, like buying new equipment or building a new wing.

Examples of administrative data:

Patient appointment schedules and waitlists
Medical staff schedules and shift planning
Bed utilization and patient flow statistics
Asset tracking, like equipment, medication, and supplies
Resource performance and throughput metrics
Staffing levels across departments

3. Patient-generated data

This type of healthcare data helps healthcare professionals fill in the gaps between clinical visits. It comes directly from the patient, often via wearables, mobile apps, or online surveys. Patient-generated data reflects real-world lifestyle choices, behaviors, and health metrics. When used effectively, it gives providers a clearer picture of what’s happening in the day-to-day lives of their patients, beyond the often short appointment windows.

Examples of patient-generated data:

Vital signs, like heart rate, glucose levels, blood pressure, and oxygen saturation
Patient symptoms, moods, and overall health state
Activity tracking, like step count, exercise frequency, and sleep patterns
Lifestyle inputs, like diet, alcohol consumption, and medication adherence
Pre- and post-visit patient satisfaction or health status surveys

4. Financial data

This data helps healthcare providers keep track of the budget and the organization’s financial health. It captures all cost-related information, from insurance reimbursements to hospital revenue and operating expenses. This data is essential for budgeting, forecasting, and optimizing resource allocation. What I find most important, it also helps align financial performance with quality-of-care goals under value-based care models.

Examples of financial data:

Insurance coverage details and reimbursement status
Insurance claims and authorization data
Treatment and procedure costs
Hospital stay and outpatient visit costs
Billing records and collections
Revenue cycle management data
Operating expenses and capital investments
Cash flow and financial performance indicators
Out-of-pocket care costs for patients

But how do the different types of clinical data actually work in practice and strengthen medical decision-making? Let’s dig deeper into clinical data examples and take a look at the real-world use cases it powers.

“You can have all the clinical data in the world, but if it’s scattered across systems and stored in incompatible formats, it’s practically useless. Healthcare organizations need consistent formats, shared protocols, and clean handoffs between systems. Interoperability standards like HL7, FHIR, and DICOM are what turn scattered records into actionable information. Without them, it’s just more chaos.”

Anastasia Ilkevich

Portfolio Manager in Healthcare and Medical Technologies

What clinical data makes possible

When you connect the dots of clinical data into one, clear picture, it becomes the foundation for everything from personalized treatments to population-level insights. Here are some of the most powerful ways clinical data is driving better care and smarter systems today:

Personalized medicine. Clinical data makes it possible to tailor care to the individual, not blindly following general protocols for the diagnosis. Providers can use intelligent software that combines patient history, multiomics data, lab test results, imaging, and real-time vitals to identify the treatment that’s most likely to work for this patient (not what more or less works for most people).
Predictive analytics. By analyzing trends in EHRs, lab results, and population health data, healthcare software can flag patients at risk before problems escalate. For example, identifying early signs of sepsis using vitals and clinical notes, or predicting hospital readmissions by analyzing discharge summaries and comorbidity patterns. Recognizing these patterns based on clinical signals helps providers prepare and adapt care.
Clinical trials management. Recruiting the right participants for trials has always been a challenge. With structured clinical data (like diagnosis codes, medication history, lab results, and patient demographics), researchers can match trial criteria with people faster. Clinical data also helps monitor trial outcomes more effectively and feed real-world data back into research.
Remote patient monitoring (RPM). Clinical data from wearable devices, such as heart rate, oxygen saturation, or blood pressure, can be monitored in real-time and integrated into patient records. It opens new opportunities in home and nursing care. Thanks to RPM systems, medical teams are alerted if clinical data indicates an emergency and deliver early care interventions.

Introduce healthcare innovations powered by clinical data

Conclusion

Healthcare data is one of the most powerful tools clinicians have, but only if they put it to use. Understanding the different types of data in healthcare, be it clinical, administrative, or financial, is what helps doctors deliver better care, researchers push medical breakthroughs, and healthcare systems run more efficiently. When data is accurate and connected, it becomes a foundation for smarter decisions at every level of care.

If you’re exploring how to put your healthcare data to work or struggling with fragmented apps that don’t exchange information, Innowise can help. We’ve built data-driven healthcare IT solutions for businesses across the US and EU, so if you’re ready to take the next step, let’s talk.

Aleh Yafimau

Senior Technical Delivery Manager in Healthcare and MedTech

Aleh has a strong grasp of what makes healthcare and MedTech software truly work. He leads with both technical clarity and sector knowledge, making sure every project delivers long-term value — not just code that runs, but systems that matter.

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