The Complete Patient Journey: Unlocking the Value of Unstructured Health Care Data

How the Ahavi data platform combines multiple data sources to generate actionable insights.

Health care organizations have no shortage of data. What they are often challenged by is turning fragmented clinical information into usable evidence quickly enough to drive research, product development, and business decisions.

That challenge is especially clear when it comes to unstructured data, which contains some of the richest clinical information available but remains notoriously difficult to access and analyze. Physician notes, pathology reports, radiology reports, discharge summaries, scanned documents, and PDFs often contain the clinical context needed to understand disease progression, treatment decisions, and patient outcomes.

Ahavi, UPMC Enterprises’ real-world data platform, was built to solve that challenge.

Rather than simply aggregating data, Ahavi combines clinically dense, longitudinal data, industry-leading linkage capabilities, governed de-identification and validation processes, and a team of experts who work directly with AI developers and life sciences companies to generate insights, accelerate model development, and support scientifically rigorous research.

The result is a platform that helps innovators move from a clinical or business question to a trusted cohort, dataset, analysis, or AI-ready workflow.

The Clinical Context Hidden in Unstructured Data

Despite years of digitization, much of the information that is needed to understand a complete patient journey still lives outside structured fields of the electronic health record.

Physician notes, pathology and radiology reports, discharge summaries, scanned documents, and PDFs often contain critical details about patient health journeys, disease progression, treatment responses, and clinical decision-making. These documents can explain why a treatment changed, how symptoms evolved, what clinicians considered, and how a patient responded over time — details that may not appear in diagnoses, procedure codes, medications, or laboratory results alone.

“Much of the clinically rich information remains embedded in unstructured formats,” said Stuart Ingram, vice president at UPMC Enterprises. “That information is essential for generating advanced insights, supporting real-world evidence studies, and enabling robust model development and validation.”

Ahavi ingests and normalizes clinical documents from more than 60 real-time feeds across 20+ digital formats, representing more than 22,000 source document classifications from UPMC’s clinical care settings.

Drawing on UPMC Enterprises’ more than 15 years of experience managing unstructured health care data, the platform transforms these diverse sources into analyzable formats while maintaining data quality, consistency, and governance. The result is the platform contains more than 700 million documents, across UPMC’s multi-specialty care settings, with unstructured history extending to 2012.

Structured data in Ahavi is standardized following the Observational Medical Outcomes Partnership (OMOP) common data model, designed to enable large scale observational analytics across multiple data sources. Researchers can use that standardized foundation to define cohorts and retrieve the relevant unstructured documents needed to add clinical context.

But collecting data is only the beginning.

Connecting Structured Data with the Full Patient Story

One of the biggest obstacles facing researchers and life sciences companies is linking data across disparate systems.

Structured data such as claims, lab results, and other medical records often exist separately from unstructured clinical content. When those datasets cannot be reliably connected, researchers are left with an incomplete picture of the patient journey.

Ahavi addresses this challenge through a sophisticated enterprise master patient index and identity resolution framework that enables approximately 98% linkage between structured and unstructured data sources.

That level of connectivity, combined with the rich diversity of unstructured documentation, creates a comprehensive and deep longitudinal view of patients that can support everything from real-world evidence generation and predictive modeling to biomarker discovery and treatment effectiveness studies.

“Structured data alone provides an incomplete view of the patient journey,” Ingram said. “The true value comes from integrating structured and unstructured data to create a more complete and actionable understanding of patient cohorts.”

The Difference Isn’t Just the Data

Many organizations are offering access to data. But fewer can help researchers and developers determine what questions to ask and how to design studies that generate meaningful, clinically defensible results.

According to Ingram, that is one of Ahavi’s biggest differentiators.

“We have experienced business and data and analytics teams that will sit down with clients and help figure out what they want to learn and how to ask the right questions that deliver the most value,” he said. “That partnership helps ensure studies are clinically robust and aligned with business objectives and impact.”

This white-glove approach combines business, data scientists, analysts, clinical experts, and research professionals who are engaged throughout the project lifecycle. Rather than simply delivering datasets, the team helps organizations develop research strategies, validate hypotheses, identify meaningful outcomes, and translate findings into business value.

For companies developing new health care technologies, therapies, or AI models, that expertise can significantly accelerate time to market.

“Our goal is to help customers deliver business value and get to their desired outcome faster and with greater confidence,” Ingram said.

From Data to Credibility

The value of this approach is perhaps best illustrated through Ahavi’s work with emerging health care companies.

Whether helping digital health innovators validate their solutions or supporting AI development through access to clinically rich datasets, Ahavi provides both the data foundation and expert support needed to demonstrate real-world impact throughout the engagement life cycle.

In one example, Ahavi worked with Penguin Ai to evaluate and validate its technology using real-world data, allowing the company to assess performance without creating additional burden for partners.

Turning Clinical Evidence into Market Validation

The platform also has supported Glimmer Health, a UPMC Enterprises portfolio company. Ahavi’s data and analytics expertise helped generate evidence for the company’s go-to-market strategy.

The Glimmer platform is built on UPMC’s innovative chronic pain management care model, an approach designed to address the complex needs of individuals living with chronic pain through longitudinal, multimodal care. While the care model had demonstrated promise in practice, Glimmer needed rigorous evidence to quantify its impact and establish credibility with prospective customers and partners.

Ahavi’s data and analytics team worked closely with Glimmer to design and execute a clinically robust validation study. By leveraging Ahavi’s rich longitudinal dataset, including both structured and unstructured clinical information, the team evaluated outcomes across a large patient population and generated meaningful evidence of the care model’s effectiveness.

The study demonstrated significant reductions in health care utilization among patients participating in the chronic pain program, including decreases in emergency department visits and inpatient admissions. These findings provided data-driven validation of the model and helped establish an evidence base for Glimmer. The partnership also demonstrated the power of Ahavi’s analytics capabilities and services offering.

The Ahavi team partnered with Glimmer throughout the process, helping to refine research questions, design the analysis, interpret findings, and ensure the results would resonate with key stakeholders.

“We likely would not have been able to enter the market with the same level of confidence and credibility without the evidence generated through our work with Ahavi,” said Alissa Meade, CEO of Glimmer Health.

The experience highlights a core benefit of the platform: helping organizations move beyond hypotheses and anecdotes to develop the rigorous evidence needed to accelerate adoption, strengthen commercial conversations, and bring proven innovations to more patients.

Speed Matters

In today’s health care environment, waiting months to access data or answer research questions can mean missed opportunities.

Ahavi was designed with speed in mind.

Its extensive network of more than 60 clinical data feeds, mature data infrastructure, and dedicated quality management processes enable organizations to move from question to insight far more quickly than traditional approaches.

“Time to market is critical for many of our clients,” Ingram said. “Through our investments in data infrastructure, data quality, and expert services, we’re able to help organizations access the information they need faster than many alternative approaches.”

That accelerated timeline can be especially valuable for pharmaceutical companies conducting real-world evidence studies, organizations evaluating clinical trial feasibility, and healthcare innovators seeking to validate new products.

Unlocking the Future of Health Care Insights

As AI and advanced analytics become increasingly central to health care innovation, the importance of unstructured data and access to comprehensive patient journeys will only continue to grow.

The organizations that can successfully analyze populations using rich longitudinal multi-modal data, including structured data sources, will be positioned to unlock and generate deeper insights, build more effective solutions, and deliver better outcomes where time-to-business impact is becoming increasingly important.

For Ahavi, the competitive advantage is more than having access to data. It’s having the expertise to turn that data into answers that matter.

Next Steps

• Explore how Ahavi can support real-world evidence, AI model validation, cohort discovery, or commercial proof generation.
• Contact the Ahavi team to discuss your research question, data needs, or validation strategy.
• Read more Insights articles on how Ahavi turns complex clinical data into actionable evidence.

Note: UPMC has financial interests in Penguin Ai and Glimmer Health.