As healthcare organisations accelerate toward AI-driven digital transformation, there’s one foundational step that simply can’t be overlooked: preparing healthcare data to be AI-ready.

Without clean, complete, standardised, and secure data, even the most promising AI initiatives will fall short. AI is only as powerful as the quality of data it learns from—and right now, much of that data is unfit for purpose.

The Untapped Potential of Healthcare Data

AI has the potential to transform patient care, from predictive analytics and early disease detection to personalised treatment plans. But to achieve this, we must overcome some significant roadblocks.

Currently, up to 80% of healthcare data exists in unstructured formats like clinician notes, PDF documents, and imaging metadata. This makes it largely unusable without heavy preprocessing.

Graph: Data Transformation Process
Source: Wolters Kluwer – 5 Tips to Ensure Your Data is Analytics-Ready

On top of that, health data is fragmented across providers, payers, systems, and devices, making interoperability a major hurdle.

Key Challenges in Preparing AI-Ready Healthcare Data

1. Fragmented Systems and Data Silos

Many organisations still operate in isolated EHR systems that can’t communicate across institutions. The absence of standardised data exchange frameworks limits the flow of information—essential for both effective care and AI applications.

Graph depicting the many challenges of data analytics in healthcare.
Graph: Challenges of Data Analytics in Healthcare
Source: Kodjin – Data Analytics in Healthcare: Challenges & Solutions

2. Time-Consuming Data Preparation

Data scientists in healthcare spend 50–80% of their time just cleaning and preparing data for use. Errors, missing values, duplicate entries, and inconsistent coding (e.g., ICD-10, SNOMED, LOINC) all reduce data quality and introduce bias into AI outputs.

3. Cybersecurity Risks

Healthcare data is one of the most targeted assets for cyberattacks. Legacy infrastructure, poor patch management, and device vulnerabilities remain persistent threats. Any AI system must sit on a secure and compliant foundation, aligned with frameworks such as HIPAA, ISO 27001, and HITRUST.

Graph: Healthcare Data Growth (2018–2025 CAGR)
Source: RBC Capital Markets – The Healthcare Data Explosion

Strategies to Make Data AI-Ready

Embrace Interoperability Standards

Adopt modern protocols like HL7® FHIR® and participate in Health Information Exchanges (HIEs) to create a single source of truth. National-level data liquidity also depends on governance and trust frameworks like HISO (NZ) and TEFCA (US).

Learn more about HL7 and it’s importance in healthcare in this blog.

Use AI to Prepare Data for AI

We can also use AI-assisted tools to identify and eliminate duplicate records, fill in missing values, and convert unstructured formats into standardised, structured datasets. Natural language processing, optical character recognition, and terminology management systems are essential tools in this transformation​.

Embed Data Governance Throughout the Organisation

Healthcare data governance is not just an IT responsibility. It must be woven into clinical and operational workflows, supported by:

  • Transparent data quality metrics
  • Comprehensive data dictionaries
  • Routine audits

Governance must evolve alongside changes to medical standards and terminologies.

Prioritise Security and Privacy from the Ground Up

A secure AI deployment starts with:

  • Encryption of patient data at rest and in transit
  • Zero-trust architecture
  • Multi-factor authentication (MFA)
  • Role-based access controls
  • Ongoing vulnerability assessments

AI without data readiness is a non-starter

Digital transformation in healthcare without high-quality, AI-ready data is dead on arrival. As healthcare data grows in both volume and complexity, organisations must act swiftly to prioritise data readiness—not as a technical side project but as a core strategic investment.

It’s not a matter of if your data should be AI-ready, but how quickly you can get there.

References:

Digital Guardian. 2023. Healthcare Data Security: Challenges & Solutions. June 14, 2023. https://www.digitalguardian.com/blog/healthcare-data-security.

LinkedIn. 2025. Beyond Raw Data: How to Build AI-Ready, High-Value Healthcare Data Assets. SynapseHealthTech. February 13, 2025. https://www.linkedin.com/pulse/beyond-raw-data-how-build-ai-ready-high-value-healthcare-guq8c/.

Oracle New Zealand. 2024. Interoperability in Healthcare Explained. June 24, 2024. https://www.oracle.com/nz/health/interoperability-healthcare/.

Wolters Kluwer. 2024. 5 Tips to Ensure Your Data is Analytics-Ready. https://www.wolterskluwer.com/en-nz/expert-insights/5-tips-to-ensure-your-data-is-analytics-ready.