Why Unstructured Data Management Helps Bridge the Healthcare Funding Gap 

“We may see a 30% funding cut this year,” I heard from an IT infrastructure director at one of our healthcare customers this week.  Sadly, their situation is not unique: many U.S. hospitals are facing a financial crisis due to funding uncertainty, rising operational costs, global supply chain constraints related to tariffs and growing demand for healthcare services.  

For instance, Medicare reimbursement continues to lag behind inflation, covering just 83 cents for every dollar spent by hospitals in 2023. This results in over $100 billion in underpayments, according to the AHA. 

This is the tall order we hear from every healthcare IT leader: how can we invest in transformational opportunities such as AI to improve outcomes and productivity when we are struggling to simply make ends meet?   

Let’s look at a few of the top pressure points happening right now in the U.S. healthcare sector and why unstructured data lies at the heart of these: 

Data-driven patient care could save the U.S. healthcare system

Patients aren’t getting any happier with the quality of the care they get along with the high price tag they pay to receive it.  

• The U.S. ranked last among the 11 highest-income countries for healthcare delivery across 71 performance metrics, according to the ACS Journal.  
• Writes James K. Elsey, MD in the same article: “The U.S. healthcare delivery system is the leading cause of personal bankruptcy.”  
• Declining Medicare and Medicaid reimbursements this year and in coming years is making the situation worse for healthcare providers and their clientele alike.  
• Widespread reforms are necessary, yet data-driven care is the linchpin for addressing high costs, poor quality and suboptimal patient outcomes. 

Rampant unstructured data growth and its AI potential 

The healthcare industry has come a long way in the past 20 years with digitalization, especially from the widespread use of electronic medical record systems. The growth of telemedicine, advanced imaging techniques and digital pathology, analytics and the proliferation of devices like wearable monitors have dramatically changed how healthcare is delivered.  

• Yet all these applications and devices have created an explosion of unstructured data. Some healthcare organizations are storing, at no small price tag, 50+petabytes of data since this data is retained for decades to meet compliance requirements.    
• IT leaders have a new mandate to efficiently and cost-effectively leverage clinical notes, machine data and images for AI analysis to hopefully improve operations and clinical practice.  
• It seems that with AI, we’re on the cusp of change, according to J.P. Morgan: “Generative AI offers tremendous cost-cutting capabilities by helping providers with documentation and analysis and synthetization of large amounts of data.” 

Unstructured data poses cyber and compliance risks 

Heavy compliance requirements around the collection, storage and protection of PII and PHI are expensive while healthcare data breaches cost $11M per incident in 2024. These costs and requirements further erode competitiveness and innovative practices. 

• A recent survey from Netwrix found a 400% increase in healthcare cyberattacks costing $200k or more in 2025 over 2024.  Unstructured data is most vulnerable to attacks due to its sheer volume and variety. 
• Healthcare PII data breaches are not only costly but add legal risk if compliance violations occur. 
• Sensitive corporate data can also get leaked in a data breach, threatening competitive advantage. 

3 Ways Unstructured Data Management is a Weapon for Addressing the Healthcare Financial Crisis  

Unstructured data is both the problem and the solution to healthcare’s list of woes. This file and object data consumes 30% of IT budgets or more and its large volume, massive growth and inherent inefficiencies present untapped opportunity for cost optimization.  In a positive light, AI is the most promising innovation in recent years to battle the many barriers facing healthcare organization survival today. Implemented with the right safeguards, AI can discover unknown value trapped in data silos to deliver competitive differentiation and more precise, personalized patient care.   

Here are three ways that healthcare organizations can better manage unstructured data to lower costs and prepare for AI: 

Save 70% of Storage, Ransomware and Backup costs with Cold Data Tiering 

As much as 80% of the unstructured data in a hospital is cold, meaning it has not been accessed in over six months. Typically, this data lives on expensive storage and backup resources.   

• A major hospital in the southeast found they could save $2.5M per year by using data management to identify and tier cold files from their on-premises file storage to the cloud.   
• Another hospital cut its ransomware attack surface by 75% using intelligent tiering to immutable storage.  File-level tiering, unlike proprietary block-level tiering from storage vendors, removes the entire cold data footprint from the ransomware attack surface. This also reduces backup costs.  The best part is that file tiering of cold data is transparent and non-disruptive. Users and applications continue to access the tiered files from the original location.   

Avoid Costly Sensitive Data Leakage with Automated Classification and Remediation 

A sound data management strategy always begins with visibility. It’s hard to prevent data breaches if you don’t know how much data you have, how it is being used, or where sensitive data lurks. 

• Automated data classification is the next step, entailing granular search and metadata enrichment tools using built-in content scanners or integrations with third-party AI scanners.  
• This allows IT to search, tag and confine sensitive data so it is protected from misuse, theft and LLM exposure. Look for unstructured data management solutions that can ingest data into AI with proper sensitive data handling and automated auditing. 

Empower Users to Participate in AI Data Management Practices  

Most healthcare organizations today operate IT as a service where a central IT organization caters to several departments. In this structure, building trust and collaborating on data management with departmental users can maximize savings and help prepare their data for AI workflows. 

• A major research hospital tripled their savings by increasing the data tiering rate through giving researchers capabilities to search and tag data on completed projects for archiving.  
• AI can enhance the user experience by inspecting file contents and providing richer tags for data classification.  For instance, a regional medical system is using data management workflows to run AI on specific pathology images to speed up tumor identification with metadata tagging and enrichment. The workflow also includes tools to identify and mitigate data containing PII. Such collaboration between IT and users can improve patient outcomes, reduce research cycles, and reduce costs.   

Unstructured data is an obvious opportunity for cost optimization and AI innovation given its massive footprint in healthcare. Data-centric healthcare organizations can alleviate the financial crisis by leveraging unstructured data management to cut cold data costs, reduce sensitive data security and compliance risks and leverage AI for faster patient diagnosis.