The hidden value in your healthcare organization’s data
What if the mountains of data your healthcare organization has accumulated over decades could become your most strategic asset? In today’s healthcare environment, organizations are sitting on vast treasures of information—clinical trials, medical research, laboratory logs, and patient records. This wealth of data holds the key to more strategic decision-making, yet it remains largely disconnected and underutilized in most organizations. The challenge isn’t a lack of data, but rather how to effectively harness its potential.
Healthcare organizations face a paradoxical situation: they are simultaneously data-rich and insight-poor. The raw material for transformation exists, but the structures to extract its value often don’t. “Healthcare organizations generate highly valuable data through clinical trials, medical research, and laboratory records,” explains Viktor Lazarevich, CTO and co-founder of Digiteum. “But this data is often disconnected across systems and isolated within departments, mainly because there’s no overarching data strategy or governance structure.” With over 20 years of experience helping organizations implement digital technologies and build effective data strategies, Lazarevich has seen firsthand how healthcare organizations struggle to utilize their data assets effectively. This disconnect creates practical challenges for organizations trying to make data-driven decisions. When information exists in separate silos—with laboratory data in one system, clinical trial results in another, and patient records in a third—drawing meaningful connections becomes nearly impossible. Strategic initiatives are hampered by incomplete information, and opportunities for innovation are missed entirely.
Even small improvements in how healthcare organizations leverage their data can yield remarkable results. As Lazarevich notes: “Even a modest improvement—say, a 1% increase in the success rate of drug discovery—could yield hundreds of new, potentially life-saving treatments. The cumulative impact of such gains is enormous.” This perspective shifts the conversation from seeing data as merely a record-keeping necessity to recognizing it as a strategic asset with untapped potential. Consider the implications across different healthcare sectors:
Pharmaceutical companies could accelerate drug development cycles and reduce costly late-stage failures
Healthcare providers might identify treatment patterns that lead to better outcomes for specific patient populations
Research institutions could discover connections between seemingly unrelated conditions
Public health organizations would gain clearer insights into emerging health trends before they become crises
Each of these improvements stems not from collecting more data, but from better utilizing what already exists.
A critical issue in healthcare data management is how data structures designed for one department become barriers for others. “The data might be structured for one specific department but is often not usable for others,” Lazarevich explains. “For instance, most diagnoses are entered as plain text. There are no separate fields for specific conditions, diseases, or treatment plans.”
This creates a situation where data perfectly serves its original purpose but becomes unsuitable for more advanced applications:
“It’s fine for archiving, since anyone can access and read the patient record. But when a research team wants to analyze specific conditions or compare treatment plans, they can’t do it effectively,” says Lazarevich.
This pattern repeats throughout healthcare organizations: radiology departments structure data for imaging retrieval, pharmacy departments optimize for medication management, and research teams collect information specific to their studies. Each approach makes perfect sense within its original context but creates barriers to organization-wide analysis.
The inability to effectively analyze cross-departmental data means organizations miss critical insights that exist at the intersection of different data types. The connections between medication response, genetic markers, and environmental factors might exist within the data, but remain undiscovered due to structural limitations.
The good news is that healthcare organizations don’t need to start from scratch. As Lazarevich emphasizes: “All existing data can be structured, improved, or transformed to meet new criteria. Nowadays, data is almost never thrown away—unless regulations require it.” This perspective shifts the conversation from data replacement to data transformation—an approach that preserves existing investments while unlocking new value. Modern data technologies make it possible to create structured frameworks on top of existing information. Natural language processing can extract specific conditions from narrative text. Machine learning algorithms can identify patterns across disparate datasets. Integration layers can connect previously isolated systems without replacing them entirely. These approaches allow organizations to preserve their existing data investments while making the information more accessible for advanced analytics and AI applications.
For healthcare organizations looking to extract greater value from their existing data, the first step is a comprehensive understanding of the current state. “The first priority is understanding your data landscape,” Lazarevich emphasizes. “You need someone who knows what data you have, where it comes from, where it’s stored, and how it flows between departments or partners.” This holistic understanding doesn’t happen automatically. Many healthcare organizations have evolved their data practices organically, resulting in fragmented knowledge: “If each department only understands their piece of the puzzle, and no one sees the full picture, it becomes very difficult to drive meaningful change.” Creating this comprehensive view requires bringing together stakeholders from across the organization to map data sources, formats, ownership, and flows. This collaborative mapping process often reveals surprising insights—duplicate data collections, untapped information sources, and unexpected connections between different systems. The mapping process itself delivers immediate value by identifying quick wins and long-term opportunities. Organizations frequently discover existing data that could answer pressing business questions or inform strategic decisions, if only it were properly structured and accessible.
By viewing data not just as records to be maintained but as strategic assets to be leveraged, healthcare organizations can unlock significant hidden value that already exists within their walls—transforming their operations, accelerating innovation, and ultimately delivering better patient outcomes. Ready to unlock the hidden value in your healthcare organization’s data? Download our comprehensive whitepaper: “How healthcare organizations can build an AI-ready data foundation” for a practical roadmap to transforming your data assets.
Find deeper insights inside the whitepaper.
Explore how to turn fragmented healthcare data into a strategic asset that powers real innovation. In this whitepaper, Digiteum outlines the practical steps healthcare organizations can take today to prepare for an AI-driven future.
Download Whitepaper
