Healthcare data is of two main types: structured and unstructured. Structured data is organized and stored in Electronic Health Records (EHRs). It includes patient information like age, diagnosis, lab results, medications, and billing codes. This type of data is easier to find and analyze because it fits into set fields.
Unstructured data makes up about 80% of healthcare information. It includes things like clinical notes written in free text, radiology reports, scanned images, and doctor narratives. This data contains important details about a patient’s history, symptoms, and treatment, but it is harder to study because it is not organized in a fixed way.
Doctors and medical staff need to use both types of data to get a full picture of a patient’s health. For example, lab results may show high blood sugar, but notes from doctors may explain the patient’s diet or medicine issues.
Artificial intelligence (AI) depends on good data to make correct predictions and smart medical advice. In clinics, AI helps with diagnosis, risk assessment, and treatment suggestions. Using both structured and unstructured data together helps AI work better in a few ways:
Though combining these data types has clear advantages, there are problems to solve:
These combined data methods are already being used in some key medical areas in the U.S.:
AI and integrated data also improve how medical offices work day to day. Automation helps reduce errors and saves time for staff:
In the United States, using both structured and unstructured data is important. It helps meet rules, improve patient care, and control costs. Laws like the HITECH Act have pushed digital records, but without combining data and using AI well, much information stays unused.
Medical leaders should invest in technology that merges data types and uses AI for both patient care and office work. They also need to follow laws like HIPAA and FDA rules to keep data safe. Some platforms already show how this can work securely.
Using combined data also prepares practices for future trends like personalized medicine and remote monitoring. It lets them join research and quality programs that can improve payments and care quality.
Mixing structured and unstructured healthcare data helps AI models become more accurate. Patient profiles created this way lead to better predictions, treatments, and office efficiency. Medical administrators, owners, and IT managers in the U.S. should learn about and use these data and AI tools to handle today’s healthcare challenges.
Ahavi is a real-world data platform developed by UPMC Enterprises that provides primary source-verified, de-identified healthcare data. Its purpose is to enable researchers, scientists, and developers to create curated datasets for accelerating research, clinical trial design, and AI development in healthcare.
Ahavi applies a rigorous six-step process including data acquisition, cohort definition, data augmentation, de-identification, honest broker validation, and researcher portal access, ensuring all patient data is de-identified and privacy-compliant before being made available.
Ahavi offers both structured data (like allergies, labs, medications, procedures) dating back to 2019, and unstructured data (ambulatory documents, ED/inpatient reports, radiology, transcription) dating back to 2012, covering comprehensive patient health information.
The platform provides access to data from over 5 million patients treated at more than 24 hospitals within Pennsylvania, ensuring diverse and representative patient populations across various care settings.
Ahavi achieves over 80% linkage between structured and unstructured data, enabling a holistic view of patient health journeys, which is crucial for robust AI training and accurate clinical insights.
Ahavi primarily serves pharmaceutical companies, clinical trial partners, AI developers, and academic researchers who require high-quality, de-identified healthcare data to support research, AI model training, and clinical development.
Ahavi offers a secure, compliant environment with streamlined workflows that deliver comprehensive, de-identified datasets in as little as four weeks, enabling AI teams to train, validate, and fine-tune models efficiently without compromising data privacy.
Ahavi offers advanced real-world data analytics services that enable scalable, cost-effective exploration of both structured and unstructured data. These services help uncover clinical insights, optimize treatment pathways, and support epidemiological and retrospective research.
Third-party certification ensures that Ahavi’s data processing pipelines meet regulatory-grade standards, guaranteeing primary source verification, data integrity, privacy compliance, and publication readiness essential for trustworthy AI and clinical research.
Ahavi tracks longitudinal patient health journeys by providing access to data that goes back to 2012 for unstructured sources and 2019 for structured data, allowing researchers to analyze long-term health outcomes and trends for AI model development and clinical studies.
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