Did you know that AI-assisted mammography screening has been shown to increase breast cancer detection rates by 17.6%? This breakthrough is only possible if the underlying data is clean and reliable. 

AI is already reshaping healthcare workflows, from diagnostics and predictive analytics to operational efficiency. However, its success depends on the integrity of the data it processes. Without structured governance, fragmented or incomplete data can lead to misdiagnoses, compliance risks, and unreliable AI models.

Mark Your Calendar – On April 9, 2025, Secoda is hosting the Data Leaders Forum, an online event for leaders and innovators in data governance. The second panel, "Balancing data, privacy, and AI in healthcare," will bring together industry experts to explore how structured data management strengthens compliance and powers AI-driven innovations in healthcare.

Register Now

How data fragmentation disrupts healthcare

Healthcare data often exists in silos, spread across electronic health records (EHRs), billing systems, and patient management tools. Without standardized definitions, discrepancies arise, leading to inefficiencies and compliance risks.

Same metric, three different definitions of "Number of Patients Treated in a Day":

• Emergency department: Counts discharges from 12:00 AM - 11:59 PM
• Billing department: Counts patients billed during the calendar day
• Admissions team: Counts patients from 8:00 AM - 8:00 AM the next day

When metrics are misaligned, AI-driven analytics don’t fix the problem—they make it worse. For healthcare data leaders, addressing foundational governance issues is crucial to ensuring AI enhances rather than disrupts decision-making.

Risks of poor data governance in AI implementation

When healthcare organizations deploy AI without addressing governance fundamentals, several risks emerge:

• AI models trained on inconsistent data lead to misleading diagnoses.
• Predictive analytics fail when data is incomplete.
• Lack of governance creates regulatory and ethical risks, increasing exposure to HIPAA and GDPR violations.

Strong data governance ensures AI models are trained on clean, reliable data—preventing misleading predictions. In healthcare, accuracy is only part of the equation. Organizations must also comply with strict regulations like HIPAA, where governance gaps can lead to significant fines and data breaches.

For global healthcare organizations, the compliance landscape is even more complex. The EU’s General Data Protection Regulation (GDPR) imposes stricter requirements around consent and data protection. In 2023, a multinational healthcare technology provider was fined €1.2 million after failing to safeguard cross-border data transfers.

With these challenges addressed, healthcare organizations are unlocking AI's potential to transform patient care.

Transforming patient care through data

Healthcare organizations are increasingly using AI to improve outcomes across key areas:

Diagnostic assistance

• Increases radiologist accuracy and reduces review time by 30%
• Detects conditions like diabetic retinopathy and certain cancers earlier
• Lowers diagnostic error rates through advanced pattern recognition

Predictive analytics

• Optimizes patient flow to reduce emergency department wait times
• Forecasts admission rates for better resource planning
• Identifies high-risk patients for preventative care

Operational efficiency

• Automates routine administrative tasks, giving clinicians more time with patients
• Improves supply chain management and inventory forecasting
• Enhances scheduling to minimize no-shows

However, these AI applications are only as effective as the data they rely on. For healthcare data leaders, robust governance isn’t optional—it’s the foundation for delivering safe, effective AI that improves patient outcomes.

Why healthcare needs purpose-built data governance tools

Modern governance tools help healthcare organizations maintain regulatory compliance, detect data quality in real time, and standardize critical metrics to prevent inconsistencies across teams.

Key benefits of healthcare-focused governance tools:

• Standardize metrics – Maintain consistent definitions across departments
• Enhance interoperability – Enable seamless data exchange between clinical, operational, and billing systems
• Automate compliance – Continuously monitor and document standards to meet HIPAA and GDPR requirements
• Improve data quality – Detect and resolve inconsistencies before they affect patient care or AI outputs
• Enable full data lineage – Track data from its source to its use in analytics for complete transparency
• Reduce operational costs – Eliminate duplicate records and streamline data access
• Accelerate decision-making – Provide fast, reliable insights for resource planning and patient care

Effective governance isn’t just about regulatory checklists—it’s key to building patient trust, delivering high-quality care, and ensuring AI systems produce accurate, actionable insights. As patients grow more aware of how their data is used, organizations that prioritize security and transparency are quickly becoming providers of choice.

Essential capabilities for effective healthcare data governance

Successfully implementing data governance in healthcare requires more than just commitment—it demands specific technical capabilities that address the unique challenges of medical data. Healthcare organizations should prioritize these essential components:

• Data cataloging and discovery – Deploy enterprise-wide cataloging solutions that automatically identify and classify data across all systems, making clinical and operational information findable and usable 
• Comprehensive metadata management – Implement systems that capture and organize all metadata types, from clinical documentation standards to operational metrics and administrative classifications
• Data agreements and responsibilities – Establish formal contracts between data producers (like clinical departments) and consumers (such as analytics teams) to define quality standards and usage parameters
• Policy automation – Leverage AI to continuously monitor compliance with governance policies, automatically flagging potential issues before they impact patient care
• Governance visibility – Implement dashboards that provide real-time visibility into policy coverage, compliance rates, and governance maturity across the organization
• Proactive incident management – Deploy systems that alert stakeholders to potential governance violations as they occur, enabling immediate remediation

Organizations that invest in these capabilities create a foundation for both regulatory compliance and AI innovation, positioning themselves to leverage emerging technologies while maintaining data integrity.

Case studies

The link between governance, compliance, and AI effectiveness becomes clear when examining real-world case studies:

Lifespan Health System’s HIPAA Violation

HIPAA extends beyond cybersecurity; strong governance policies are key to protecting patient data and ensuring compliance.

In 2020, Lifespan Health System paid $1.04 million after an unencrypted laptop containing PHI of 20,431 patients was stolen. The investigation revealed governance failures:

• No encryption for PHI on laptops
• Poor inventory management of devices storing PHI
• Weak device and media controls

This case illustrates how robust data governance helps organizations meet HIPAA requirements while building a secure and scalable AI foundation.

Fullscript’s data transformation journey

Fullscript, a healthcare platform serving 90,000+ practitioners, faced governance challenges in managing healthcare data across 20,000+ products and 300+ brands. By implementing structured governance, they achieved:

• 300% improvement in data pipeline efficiency
• 10x increase in reporting performance
• Integration of 100 new data models within a month of a major merger

Using governance tools like Secoda, Fullscript standardized metrics, ensured compliance, and scaled operations effectively—demonstrating how proper governance enables innovation.

Why healthcare data leaders should attend our forum

The Data Leaders Forum event is specifically designed for healthcare data professionals, compliance officers, and AI strategy teams who face the dual challenge of regulatory compliance and innovation. Attendees will learn:

• How to implement data governance in healthcare
• Strategies for balancing AI innovation with HIPAA compliance
• Moving from defensive compliance to proactive governance
• Automating governance to enhance compliance and innovation

Take action now

As we've seen throughout this guide, health data represents perhaps the most valuable strategic asset in modern healthcare. Organizations that implement robust governance frameworks not only ensure regulatory compliance but also uphold the ethical standards that patients increasingly expect. The future of healthcare belongs to organizations that recognize data's strategic value and govern it with both compliance and ethics at the forefront.

Struggling with data fragmentation, reporting inconsistencies, or balancing HIPAA compliance with AI? You're not alone. 

Join us at the Data Leaders Forum to learn how leading healthcare organizations are tackling these challenges head-on.

Register Now