June 25, 2026
How Healthcare Organizations Unlock Value from Unstructured Clinical Data
Executive Summary
- More than 80 percent of healthcare data is unstructured and difficult to use
- Valuable clinical information remains trapped in notes, reports, and documents
- Traditional search tools cannot reliably interpret clinical context
- Clinical-grade AI helps healthcare organizations transform unstructured data into actionable insights
- Organizations that improve data accessibility gain operational, clinical, and research advantages
What is unstructured healthcare data?
Unstructured healthcare data includes physician notes, discharge summaries, radiology reports, pathology reports, referral documents, scanned PDFs, and other narrative clinical content.
While healthcare organizations have invested heavily in electronic health records, much of the most valuable clinical information still exists as free text.
Industry estimates suggest that more than 80 percent of healthcare data is unstructured. That creates a major challenge. Healthcare teams often have access to the data, but they cannot easily analyze, search, or operationalize it.
Why is unstructured data still a healthcare challenge?
Healthcare organizations have made significant progress toward interoperability. Federal initiatives such as the HITECH Act and the 21st Century Cures Act expanded access to healthcare information and encouraged data sharing across systems.
Yet, accessibility is not the same as usability. A clinician may have access to a patient record, but finding a specific diagnosis, medication history, adverse event, or care gap often requires manual review. This creates challenges across:
- Clinical operations
- Population health programs
- Quality reporting
- Risk adjustment
- Clinical research
- Care management
The result is lost time, increased administrative burden, and missed opportunities to improve outcomes.
Why traditional search tools fall short
Many healthcare organizations still rely on keyword searches to find information inside clinical records. Keyword searches work for simple tasks, but struggle with clinical complexity. For example:
- “No history of diabetes” may incorrectly return diabetes-related results.
- Historical conditions may be interpreted as active diagnoses.
- Related concepts may not be linked together.
Healthcare data requires contextual understanding, systems must recognize:
- Negation
- Temporality
- Clinical relationships
- Synonyms and abbreviations
Without that context, organizations miss valuable information.
How clinical AI unlocks healthcare data
Clinical AI uses technologies such as natural language processing (NLP) to interpret healthcare language and convert narrative text into structured data. At emtelligent, Dr. Tim O’Connell and the team behind the Medical Language Engine focus on helping healthcare organizations transform unstructured clinical information into structured, actionable insights that support real-world workflows.
Instead of manually reviewing hundreds of pages of documentation, healthcare teams can extract relevant information automatically. This helps organizations:
- Reduce chart review time
- Improve coding accuracy
- Identify care gaps
- Support quality reporting
- Accelerate clinical research
- Improve operational efficiency
A 2024 review found that NLP has become a widely used method for extracting information from electronic health records and supporting clinical decision-making.
https://pmc.ncbi.nlm.nih.gov/articles/PMC11474138/
Comparison: Manual Review vs Clinical AI
Capability | Manual Review | Clinical AI |
|---|---|---|
| Speed | Slow | Fast |
| Scalability | Limited | High |
| Consistency | Variable | Consistent |
| Clinical Data Extraction | Manual | Automated |
| Population Health Analysis | Resource Intensive | Scalable |
| Research Cohort Identification | Time Consuming | Accelerated |
| Care Gap Detection | Manual Review Required | Automated Identification |
| Operational Efficiency | Limited | Improved |
What role does interoperability play?
Interoperability is an important foundation. Healthcare organizations need systems that can exchange information, however interoperability alone does not create value.
Value comes from understanding and acting on the information being exchanged. Organizations that combine interoperability with clinical AI can move beyond data sharing and begin generating actionable insights. That shift allows healthcare teams to spend less time searching for information and more time using it.
Best practices for unlocking value from unstructured data
Start with a clear use case. Examples include:
- Risk adjustment
- Clinical workflow optimization
- Quality reporting
- Cohort identification
- Care management
Build trust through transparency. Users need to understand where insights originate. Traceability is critical. Healthcare teams must be able to connect outputs back to source documentation. Measure outcomes. Successful programs focus on measurable improvements such as:
- Reduced review time
- Improved coding accuracy
- Increased operational efficiency
- Faster research recruitment
- Better patient outcomes
Why this matters now
Healthcare organizations continue to generate enormous amounts of data. At the same time, AI adoption is accelerating.
The organizations that succeed will not be the ones with the most data. They will be the organizations that can access, understand, and act on that data effectively.
Unlocking unstructured clinical data represents one of the largest opportunities in healthcare today.
Why Experience and Expertise Matter in Healthcare AI
Healthcare AI requires more than technical expertise. It requires a deep understanding of how clinicians document care, how healthcare organizations operate, and how data moves across systems.
Dr. Tim O’Connell’s experience as a practicing radiologist, healthcare technology founder, and member of the Forbes Technology Council has shaped emtelligent’s approach to clinical AI. The company’s Medical Language Engine and Clinical Workflow solutions were built specifically to address the challenges of accessing and operationalizing unstructured healthcare data at enterprise scale.
By combining clinical expertise with advances in artificial intelligence and natural language processing, healthcare organizations can move beyond data access and begin generating meaningful, measurable outcomes from their information assets.
Key Takeaways
- Unstructured healthcare data contains critical information for patient care, operations, and research
- Interoperability alone does not solve healthcare’s data accessibility problem
- Clinical AI and natural language processing help organizations extract value from narrative data
- Structured, traceable outputs improve trust and adoption
- Healthcare organizations need scalable strategies for turning data into action
About the Author
Dr. Tim O’Connell is a practicing radiologist, Founder and CEO of emtelligent, and a member of the Forbes Technology Council. For more than 20 years, he has worked at the intersection of healthcare, medical informatics, and artificial intelligence. Before founding emtelligent, he served as Clinical Assistant Professor and Vice Chair of Medical Informatics in the Department of Radiology at the University of British Columbia.
Connect with Dr. O’Connell on LinkedIn or read his insights on the Forbes Technology Council.
References
HITECH Act:
https://www.healthit.gov/topic/laws-regulation-and-policy/hitech-act
21st Century Cures Act Final Rule:
https://www.healthit.gov/curesrule/
Clinical Decision Support and NLP Review:
https://pmc.ncbi.nlm.nih.gov/articles/PMC11474138/
From Strategy To Implementation: Leveraging Unstructured Health Data in Forbes Technology Council:
Original Source Article:
https://www.citybiz.co/article/740300/forbes-technology-council-welcomes-dr-tim-oconnell-founder-and-ceo-of-emtelligent/