The healthcare industry confronts numerous challenges, including rising costs, inefficiencies, and fragmented patient data scattered across disparate systems. In 2023, U.S. healthcare spending reached $4.9 trillion—$14,570 per person—representing 17.6% of the nation's GDP. Healthcare spending growth is expected to outpace GDP growth during the coming decade, with health's share of the economy projected to reach 19.7% by 2032 and 20.3% by 2033. Despite this massive investment, patient outcomes haven't improved proportionately, and healthcare organizations continue to struggle with delivering high-quality care while managing costs effectively.
Data silos represent a primary obstacle to progress. Patient information, clinical research, and operational data often remain confined within individual departments or systems, hindering healthcare providers' ability to access and utilize this valuable information for decision-making. This fragmentation leads to inefficiencies, duplicated efforts, and suboptimal patient care.
Data products offer a transformative solution by leveraging advanced analytics and artificial intelligence (AI) to address these challenges directly.
The global healthcare analytics market, valued at $52.98 billion in 2024, is projected to reach $198.79 billion by 2033, growing at a CAGR of 14.85%. This explosive growth underscores the increasing importance of data-driven solutions in healthcare and the transformative potential of data products across the industry.
What are data products for healthcare?
In the healthcare industry, data has become a cornerstone for enhancing patient care, managing costs, and improving operational efficiency. Yet raw datasets alone cannot address the intricate challenges healthcare organizations face, particularly as they relate to patient privacy. Data products bridge this gap, offering curated, actionable solutions that extend beyond basic analytics reports.
At their core, data products are more than just collections of information—they're structured, reusable assets designed to deliver measurable business value. Think of them as the difference between raw ingredients and a ready-to-eat meal: while raw data requires extensive preparation before use, data products come complete with the context, quality assurance, and documentation needed for immediate consumption.
Effective data products embody ten critical characteristics:
Value-first: Designed for measurable outcomes (e.g., reduced readmissions, cost savings).
Discoverable: Easily found and accessed through a data catalog.
Governed by clear ownership: Defined and enforced through data contracts.
Understandable: Documented with rich metadata and clear explanations.
Trustworthy: Backed by transparent lineage, quality checks, and governance.
Accessible: Delivered through modern data marketplaces, APIs, or self-service tools.
Reusable: Built once and leveraged across multiple teams or use cases.
Composable & interoperable: Structured with standardized schemas for easy integration.
Secure & compliant: Aligned with organizational and regulatory requirements.
Continuously improved: Evolving based on feedback, performance insights, and changing business needs.
The security and compliance attribute deserves special emphasis in healthcare. Data products incorporate role-based access control (RBAC) and data masking capabilities from the ground up—critical for HIPAA compliance and protecting Protected Health Information (PHI).
By implementing granular access controls, data products enable healthcare organizations to democratize analytics while maintaining strict privacy safeguards. Data masking techniques allow different user roles to access appropriate levels of detail: analysts might work with de-identified datasets, while clinicians access complete records for patient care. This balance between accessibility and security ensures that organizations can leverage the full potential of data without compromising patient privacy or regulatory compliance.
What truly distinguishes data products is their ability to dismantle data silos, enabling interoperability between disparate systems to create holistic views of patient health. Built with user accessibility in mind, they cater to a broad spectrum of stakeholders—from clinicians to administrators—presenting insights in clear, actionable formats that empower healthcare professionals to make informed, data-driven decisions.
Benefits of data products for healthcare
Data products are transforming healthcare by improving patient outcomes, reducing costs, and strengthening operational performance. Here’s what that looks like:
Better outcomes through proactive, risk-based care: Predictive data products—such as readmission risk scores—help clinicians identify high-risk patients before complications occur. By unifying clinical, demographic, and social data, they reveal patterns traditional tools miss, enabling targeted outreach, tailored discharge plans, and interventions that reduce avoidable readmissions and emergency visits.
Lower costs through clearer visibility into utilization and spend: Claims and utilization data products standardize messy datasets and make cost drivers easier to understand. They highlight unnecessary services, detect billing inconsistencies, and reveal gaps in preventive care. When combined with clinical data, these products support more accurate forecasting, smarter resource allocation, and stronger value-based care strategies.
Greater efficiency through interoperability and automation: Interoperability data products create unified patient records that reduce redundant testing, improve medication safety, and streamline care transitions. Automated products—like NLP-enhanced documentation or real-time EHR dashboards—reduce manual burden and support faster, more informed decisions.
A foundation for enterprise-wide trust and consistency: Governed data products, as illustrated in the case study above, establish shared definitions and make analytical assets discoverable. This improves data literacy, accelerates insight delivery, and ensures teams across clinical, commercial, and operational functions work from the same reliable information.
Healthcare data products: 3 examples
Modern healthcare generates enormous volumes of data—but data alone doesn’t improve care. Data products do. The best healthcare data products take raw clinical, operational, or financial data and turn it into a governed, trustworthy asset that clinicians, care managers, payers, and operations teams can actually use.
Below are three practical examples of healthcare data products—what they do, why they matter, and how organizations are using them today.
1. Patient readmission risk score
Hospital readmissions are costly—for patients, providers, and payers. A patient readmission risk score is one of the clearest demonstrations of how governed healthcare data products can drive measurable impact.
A readmission risk data product pulls together data from the EHR (diagnoses, procedures, vitals, labs, medications, notes), demographics, social determinants of health, and sometimes claims. Predictive models analyze this information to flag patients at risk of being readmitted within 30, 60, or 90 days.
Why it matters:
Prevents avoidable readmissions and improves quality metrics
Helps care teams intervene proactively before a problem escalates
Lowers total cost of care by reducing expensive readmissions
Improves performance under value-based care programs
Ensures care managers and discharge teams can prioritize who needs attention most
What it looks like in practice: A regional health system implemented a readmission risk product for cardiac patients. By acting on the score—via follow-up calls, home health visits, and more personalized discharge plans—readmissions fell by 19% in six months, saving $2.8M.
How it’s delivered:
Integrated into the EHR or care management platform
Available in real time before discharge
Delivered via dashboards or APIs
Updated throughout a hospital stay as new data arrives
Who uses it: care managers, discharge planners, physicians, population health teams, and administrators.
2. EHR interoperability API
Patient data is often scattered across hospitals, clinics, labs, and pharmacies that use different EHR systems. This fragmentation slows down care and creates clinical blind spots.
An EHR interoperability data product uses standards like HL7 and FHIR to securely exchange patient data across systems. Instead of relying on faxes, phone calls, or incomplete charts, clinicians can see a unified patient history—no matter where the patient received care.
Why it matters:
Reduces duplicate testing and unnecessary procedures
Improves patient safety with visibility into allergies, meds, and past diagnoses
Enhances care coordination across providers and care settings
Streamlines operations and removes manual data-sharing work
Supports research and public health with de-identified datasets
What it looks like in practice: A multi-hospital system used an interoperability API to link records across inpatient and outpatient facilities. Clinicians accessed 27% more external patient histories, eliminating redundant labs and improving medication reconciliation—resulting in a 15% reduction in duplicate lab orders.
How data products enable this:
Governed, secure APIs expose standardized patient records
Metadata management ensures data quality and lineage
Access controls maintain HIPAA compliance
Unified patient views help clinicians get critical context quickly
Who uses it: providers, HIEs, app developers, public health agencies, and patients through portals.
3. Claims data analytics dataset
Claims data is one of the richest sources for understanding healthcare utilization, costs, and trends—but it’s notoriously messy. A claims analytics data product standardizes claims from medical, pharmacy, and dental sources and enriches them with diagnoses, procedure codes, provider IDs, and billed/paid amounts.
Why it matters:
Identifies high-cost conditions and services
Highlights care utilization patterns across regions or populations
Flags waste, fraud, or unusual billing
Supports value-based care programs and reimbursement models
Enables provider and plan benchmarking
What it looks like in practice: A regional health insurer used a claims dataset to analyze spending for diabetic patients. The product showed that 34% of members weren’t receiving annual eye exams—leading to a targeted outreach program that improved compliance by 19% and projected $2.1M in avoided complication costs.
How data products help:
Claims datasets are curated and standardized for easy analysis
Trends can be analyzed across plans or populations
Linked clinical + claims data yields richer insights
Governance ensures HIPAA compliance and trustworthy access
Who uses it: payers, providers, researchers, employers, and public agencies.
Here is the revised section with softened, future-focused language that reflects work in progress rather than completed outcomes:
Key components of data products for healthcare
Building effective healthcare data products requires a strong foundation of integrated data, governed processes, advanced analytics, and user-friendly delivery.
Comprehensive data integration and interoperability
Effective healthcare data products begin with the ability to integrate data from many sources—EHRs, lab and radiology systems, pharmacy and billing platforms, and even patient-generated data from wearables. True interoperability requires adherence to standards like HL7 FHIR, consistent clinical terminology such as SNOMED CT, and strong semantic alignment across systems.
When done well, interoperability enables seamless care coordination. Clinicians gain a complete view of a patient’s medications, lab results, and recent visits, reducing duplicate tests, preventing adverse drug interactions, and ensuring treatment plans work together rather than in isolation.
Robust data governance and quality assurance
Data products are only as reliable as the data behind them. High-quality data products embed quality checks throughout ingestion, transformation, and delivery. Governance frameworks define ownership, track lineage, enforce data quality rules, and maintain documentation. In healthcare, governance also ensures HIPAA compliance, manages consent, and protects patient privacy from end to end.
Advanced analytics capabilities
Modern data products apply descriptive (what happened), diagnostic (why it happened), predictive (what will happen), and prescriptive (what to do next) analytics. Combining these capabilities allows healthcare organizations to move from reporting on past outcomes to anticipating future events and recommending optimal interventions.
User-centric design and delivery
Even the most advanced data product fails if it is not usable. Successful healthcare data products deliver insights in the right context and at the right moment—whether through intuitive dashboards, embedded EHR workflows, or API-driven applications. Clear visualizations, plain-language explanations, and thoughtful UX ensure insights translate into action.
Together, these elements form the backbone of any effective healthcare data product—ensuring data is integrated, trustworthy, insightful, and usable by the people who need it most. But while the principles are universal, their real value emerges when applied in practice. The following example shows how one global life sciences organization is putting these foundations to work as it builds its next generation of healthcare data products with Alation.
How an immunology company is building healthcare data products with Alation
A rapidly growing biopharmaceutical organization operating recently began transforming its data ecosystem to support expanded commercial and medical operations. With commercial launches spanning the U.S., Japan, and EMEA, the organization needed a unified, governed approach to data—one capable of supporting rapid growth, complex operations, and diverse analytics and field teams.
The challenge: Fragmented data and custom, brittle workflows
Before adopting Alation, every insight required a custom-built pipeline. Data arrived from multiple external vendors, each slicing and structuring patient, provider, and market data differently. Metrics rarely aligned—“good, better, indifferent, the numbers needed to match” became a common refrain—and teams lacked trust in the reports they received.
The company wanted to own more of its data supply chain, accelerate access to insights, and enable true self-service. Instead of relying on Tableau developers or analysts to interpret conflicting datasets, they needed governed, reusable data products that anyone could find, understand, and trust.
The solution: Governed data products across the commercial value chain
With Alation as its data intelligence layer, the organization is now creating a portfolio of standardized healthcare data products spanning core business functions:
1. Patient 360 data product: A governed master and dimension table designed to power patient metrics across specialty pharmacy feeds, PMS platforms, EHR data, and IQVIA sources. This will help teams reliably track patient journeys—from enrollment to persistency—and support demand forecasting.
2. HCP experience data product: A consolidated view of healthcare providers, including patient counts, outreach history, and engagement tiers. Commercial analytics teams can generate consistent HCP rankings, while business insights teams rely on Alation to keep definitions aligned across reporting.
3. Market access data products: Account-level dashboards (e.g., for national payers) help market access teams monitor coverage, product performance, and utilization trends. Governed data products reduce information overload and establish consistent monthly reporting.
4. Medical affairs data products: Using data from Veeva, Komodo, and field activity systems, medical affairs teams are gaining access to governed insights without needing a dedicated analytics function.
The impact
By standardizing definitions, consolidating data origins, and making analytical assets discoverable, the organization is laying the groundwork for faster insights, greater data literacy, and a scalable foundation for global commercialization—powered by data products they can finally trust.
Conclusion: The future of data products in healthcare
Data products and artificial intelligence are reshaping the future of healthcare, driving transformative changes in patient care, operational efficiency, and cost optimization. Predictive analytics are reducing readmission rates while EHR interoperability enhances care coordination. The potential for data-driven improvement is vast.
Success begins with targeted pilot projects demonstrating measurable impact. By focusing on specific use cases and utilizing the right tools, healthcare providers can build momentum and gradually scale their data initiatives. Robust data governance, quality assurance, and user-centric design form the foundation for sustainable transformation.
The future of healthcare is promising, with data products leading the charge. By adopting effective strategies, implementing the right tools, and fostering a forward-thinking mindset, organizations can harness the power of data to improve patient outcomes, reduce costs, and thrive in a competitive landscape.
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- What are data products for healthcare?
- Benefits of data products for healthcare
- Healthcare data products: 3 examples
- Key components of data products for healthcare
- How an immunology company is building healthcare data products with Alation
- Conclusion: The future of data products in healthcare
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