Data Discovery in Healthcare: Improve Decision-Making, Compliance, and Innovation

Updated February 11th, 2025

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Healthcare institutions manage vast and sensitive datasets, including patient records, diagnostic data, and operational information. However, these datasets are often siloed across different departments, systems, and locations, making it difficult to access and use effectively.
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Through effective data discovery in healthcare, organizations can identify, catalog, and understand their data assets, enabling better clinical decisions, operational efficiency, and compliance with regulations like HIPAA and GDPR.

This article explores the role of data discovery in healthcare, its benefits and challenges, and how a metadata control plane can help.

Table of contents #

  1. What is data discovery in healthcare?
  2. The business benefits of data discovery in healthcare
  3. Top challenges of data discovery in healthcare
  4. Data discovery in healthcare: How a metadata control plane can help
  5. Data discovery in healthcare is central to unlocking the full potential of data
  6. Data discovery in healthcare: Related reads

What is data discovery in healthcare? #

Data discovery in healthcare is the process of finding, exploring, and understanding data from multiple sources—such as electronic health records (EHRs), medical imaging systems, patient feedback, insurance claims, and clinical trials—to identify trends, patterns, and insights.

In healthcare organizations, data is often fragmented across multiple systems, departments, and even different healthcare facilities.

By consolidating and making sense of disparate healthcare data, organizations can enhance patient care, improve operational efficiency and interoperability, support regulatory compliance, and enable data-driven decision-making for better health outcomes.

“Clinical data have immense potential to drive progress in health care by providing the means to measure and track care processes and outcomes, and to enable rapid discovery and innovation. [As this data can] be easily stored, aggregated, and shared, these data can enable rapid learning and continuous improvement.” - National Library of Medicine, US

The business benefits of data discovery in healthcare #

Data discovery helps healthcare organizations unlock value from their data and gain several key benefits, such as:

  • Faster and more effective searches: Quickly locate patient records, treatment histories, or lab results without sifting through multiple systems, thereby reducing wait times and speeding up decision-making.
  • Enhanced visualization of the data estate: A unified view of healthcare data allows hospitals to track patient flows, monitor resource usage, and identify trends in disease outbreaks. A large healthcare network, for instance, can visualize emergency room occupancy in real time to allocate staff and resources efficiently.
  • Easier understanding of data with proper context: With all the relevant metadata in one place, healthcare organizations can see where data originates and how it has been used. Complete context helps you ensure that you’re working with the latest, most relevant data, reducing the risk of errors.
  • Better cross-team collaboration: Data discovery enables different departments—such as radiology, oncology, and pharmacy—to access and share critical patient information seamlessly. A cancer treatment center, for example, can ensure oncologists, radiologists, and pharmacists work from the same data set to create personalized treatment plans.
  • Improved regulatory compliance: Effective data discovery supports end-to-end transparency of your data estate, which simplifies audit trails. As a result, healthcare organizations can ensure they meet data security and privacy requirements. A health insurer, for example, can use a data discovery platform to quickly verify that sensitive patient information is stored and processed in line with regulations like HIPAA.
  • Opportunities for innovation: Effective data discovery helps uncover hidden patterns in data, which can drive medical advancements and operational improvements. A biotech company using data discovery to analyze genetic research can accelerate the development of personalized medicine, leading to more effective treatments.

Top challenges of data discovery in healthcare #

Data discovery in healthcare is complex with numerous challenges and constraints, such as:

  • Fragmented data across multiple systems and departments
  • Lack of a unified metadata management framework
  • Poor data quality with incomplete or inconsistent records
  • Lack of interoperability between systems
  • High regulatory demands for data security and privacy

Fragmented data across multiple systems and departments #


Healthcare data is often spread across electronic health records (EHRs), radiology systems, pharmacy databases, insurance portals, and more. This fragmentation makes it challenging to get a holistic view of a patient’s medical history.

For example, if a patient is treated at different facilities, their medical records may not be easily accessible, leading to duplicate or unnecessary tests, delays in diagnosis, or missed critical health information.

Lack of a unified metadata management framework #


Effective data discovery relies on accurate and consistent metadata tagging to enable searchable and retrievable datasets. Without proper metadata, organizations struggle to understand where data resides, how it is classified, and how it is being used.

A hospital may have different definitions for “patient visit” across its billing, clinical, and scheduling systems, creating inconsistencies in reporting and decision-making.

Moreover, the hospital could receive lab results from multiple vendors, each using different tags for the same data – one vendor uses “lab_test” while another uses “test_results”. This makes it difficult to locate or group similar datasets.

Poor data quality with incomplete or inconsistent records #


Inaccurate, outdated, or missing patient data can severely impact care delivery. If a patient’s records are incomplete, clinicians might prescribe medications that cause adverse reactions.

Inconsistent records also affect health insurance claims – incorrect coding in insurance claims can lead to claim denials (frustrating customers) and revenue losses for healthcare providers.

Lack of interoperability between systems #


Many healthcare systems use proprietary software that does not communicate well with other platforms. This lack of interoperability makes it difficult to exchange patient records between hospitals, clinics, and insurers.

For example, if a patient moves to a new provider, their previous medical records may not transfer seamlessly, forcing them to undergo unnecessary tests or treatments.

High regulatory demands for data security and privacy #


Healthcare organizations must comply with strict regulations like HIPAA, GDPR, and other regional privacy laws. Ensuring data security while maintaining accessibility for authorized users is a constant challenge. For example, a research hospital collecting patient data for clinical trials must ensure de-identified data remains compliant while still being usable for medical advancements.

Data discovery in healthcare: How a metadata control plane can help #

Effective data discovery in healthcare requires a structured approach to managing and accessing data across fragmented systems. A metadata control plane provides a single platform where healthcare organizations can search, discover, access, and govern data assets across all systems—ensuring that clinicians, researchers, and administrators work with accurate, trusted information.

A metadata control plane also enables end-to-end visibility, automation, and governance—critical for healthcare institutions dealing with vast amounts of patient records, clinical trial data, operational metrics, and regulatory requirements.

The control plane seamlessly integrates with legacy and modern systems, ensuring that all data–from patient records and clinical trial information to operational metrics–is available and governed uniformly across the organization.

Also, read → The unified control plane in action

Essential capabilities to look for in such a unified control plane include:

  • Google-like search with natural language processing: Search for patient records, reports, or datasets across multiple platforms using natural language search. You should get results for synonyms related to your keyword, find assets linked to business metrics, and discover data through ‘db.schema’ – regardless of typos and other keyword errors.
  • Metadata filters: Filter data assets based on ownership, classifications, certification status, data source, last updated timestamps, and other metadata attributes.
  • Auto-profiling for data quality assurance: Understand the structure, completeness, and accuracy of healthcare data, highlighting missing values, inconsistencies, or anomalies.
  • 360-degree visibility with embedded collaboration: Provide a unified view of data assets, supporting contextual discussions, ticketing, and real-time issue resolution within the platform – without switching apps.
  • Cross-system, column-level actionable data lineage: Trace the flow of patient, billing, and operational data—from its origin, through transformations, to its final usage in dashboards, reports, or analytics models.
  • Granular access control and governance: Set customized permissions for viewing, editing, or sharing data assets depending on user role, data domain, projects, and more. This maintains confidentiality while democratizing access across teams.
  • Real-time monitoring and automated alerts: Continuously track data usage, detect anomalies, and trigger alerts for potential compliance violations, security threats, or data integrity issues.

Also, read → Data search and discovery in Atlan | Using filters to refine your search results | How to automate data profiling | Asset profiles for complete context | What is data lineage? Tracking the journey of your data

Data discovery in healthcare is central to unlocking the full potential of data #

Data discovery in healthcare is essential to identifying, cataloging, and understanding healthcare data assets across systems.

A metadata control plane can enhance data discoverability, governance, and collaboration, thereby supporting faster insights, self-service analytics, and real-time compliance monitoring.

As healthcare continues to digitize, adopting a metadata control plane is essential for secure, scalable, and AI-ready data discovery. This ensures that healthcare organizations can unlock value, enhance decision-making, and improve patient outcomes.

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