Building Better Healthcare AI with Structured and Unstructured Data

Healthcare is often described as data-rich but insight-poor. Every blood pressure reading, every medication fill, every hospital discharge produces a trail of digital breadcrumbs. Yet much of this information sits trapped in incompatible formats, handwritten notes, or siloed systems. Analysts estimate that 80% of healthcare data remains unstructured, meaning that it’s difficult to share, compare, or interpret in real time.¹

In healthcare, the difference between insight and illusion begins with the quality of the data.

AI in healthcare is often framed as a revolution, but beneath the algorithms lies something more elemental: data. The way we gather it, the discipline with which we structure it, and the choices we make about how to apply it will determine not only the credibility of the technology, but the care patients ultimately receive. Health systems and policymakers see AI as the engine that will drive more efficient, predictive, and equitable care, but without useful data, it cannot meaningfully improve care or outcomes.² This level of precision may sound tedious, but it is the invisible scaffolding on which modern healthcare and future AI applications depend.

Structured vs. unstructured healthcare data

At its simplest and best, clean healthcare data is accurate, consistent, and complete. Structured data is information organized in standardized formats like coded diagnoses, lab values in numeric ranges, or medications tied to drug databases rather than free-text notes. It is structured data in machine-readable, interoperable, and analyzable at scale.

Unstructured data, like clinical notes, PDFs, faxes, and scanned images, make up the bulk of healthcare information today. Advances in natural language processing (NLP) and large language models (LLMs) are unlocking far more value from these sources than ever before. Free-text notes, radiology reports, and discharge summaries can now be mined for subtle clinical insights that structured fields might miss, such as nuance in patient history, clinician reasoning, or social context.

But this progress comes with caveats:³

• Cost and complexity: NLP and LLM tools require significant computing power and careful tuning to avoid hallucinations or misinterpretations.
• Error risk: Unlike structured fields, free-text data often contains ambiguities, abbreviations, or inconsistencies that can distort outputs.
• Validation challenges: Unstructured-derived insights are harder to standardize and benchmark.

Clean Data Builds Better AI for Healthcare

Clean, reliable data is what allows AI to operate with precision in practice. Clean data is foundational, and without it, an AI system is fragile, incapable of producing reliable recommendations or trustworthy predictions, a weakness that is especially consequential in healthcare.

Clean data isn't measured by a single property but a composite of several qualities: accuracy, consistency, completeness, timeliness, governance, and provenance.

The qualities of clean data

Clean data isn't measured by a single property but a composite of several qualities:⁴

• Accuracy: Information reflects clinical reality without transcription errors, missing fields, or duplications. Without accurate data, AI inherits the distortions of the record.
• Consistency: Values are recorded in standardized formats across systems, whether blood pressure in mmHg, medications tied to RxNorm codes, or diagnoses mapped to ICD-10. Consistency enables comparability and interoperability.
• Completeness: Partial datasets mislead algorithms as surely as biased ones. Clean data requires capturing the full scope of encounters, medications, labs, and relevant social determinants.
• Timeliness: Stale data undermines predictive modeling. Clean data must also be current, feeding AI systems in ways that reflect the present, not just the past.
• Governance and provenance: Beyond technical formatting, clean data also demands transparent origins - knowing where it came from, who entered it, and how it has been transformed. Provenance is what makes data auditable and trustworthy.

Each of these qualities matters because AI systems today calculate as much as they reason. Their fidelity to reality is entirely dependent on the integrity of their inputs. A model trained on inconsistent or incomplete data is systematically flawed: biases in source data translate to inequities, and data errors compound into false predictions. By contrast, when data meet these standards, AI systems shift from being a liability to becoming not only a trustworthy tool but also one that generates the outcomes and ROI you intended.

Building Healthcare Systems for the Future

Using clean data today isn't only about solving present inefficiencies. It's about laying the foundation for a system that can evolve responsibly. Increasingly, synthetic data is being used alongside real-world structured data, allowing healthcare teams to test and build AI applications at scale, explore rare patient populations, and analyze complex health data, all without compromising patient privacy.

Infrastructure readiness

Clean, structured data creates the conditions for interoperability, making it possible for tomorrow’s systems to exchange and act on information seamlessly. Synthetic data, generated from clean, structured datasets, builds on this foundation by providing realistic but de-identified data that can be shared across institutions, accelerating interoperability testing and system integration without exposing sensitive patient records.

Scalability

Models trained on high-quality data are easier to validate, monitor, and adapt as new therapies, policies, and technologies emerge. Synthetic data can play a crucial role, allowing systems to test algorithms at scale, simulate rare clinical scenarios, and refine models safely.

Accountability

Clean data enable audit trails and transparent reporting, both of which are essential for aligning AI with regulatory oversight and public trust. Synthetic data adds another layer: it makes it possible to release shareable datasets for independent review, benchmarking, and validation.

Equity by design

Standardizing how social, demographic, and clinical variables are recorded today ensures that future AI systems do not reproduce blind spots that have long plagued healthcare. For example, consistently capturing social determinants of health like housing, transportation, and food security can help AI address inequities rather than reinforce them. Synthetic data can further advance equity by generating representative samples from under-documented populations, helping researchers stress-test algorithms for fairness even when real-world data is limited.

Healthcare AI depends on strong data and technology foundations designed to meet the split-second complexity of real care.

AI is beginning to give us clarity, turning vast warehouses of health data into patterns we can finally act on. Structured data provides the stability, while unstructured data adds context and richness. Both are indispensable. Clean, complete, and trustworthy data cannot be a technical afterthought - it must be treated as a critical building block of every technology we design for healthcare.