The following is a guest article by Mark Coetzer, VP of Business Development at IMAT Solutions
Everyone in healthcare agrees that data is critical to quality improvement, care coordination, analytics, and AI. However, very few organizations have full confidence in the data they are working with today. The challenge is not that leaders do not value data, but it’s that most do not know when their data is quietly working against them.
Most executives discover their data problems only after an audit goes poorly, a quality metric stalls without explanation, or an AI model underperforms. By that point, the damage has already been done, and the bigger opportunity is to get ahead of the issue by recognizing the early warning signs of poor data quality.
The Hidden Cost of “Bad” Data
Bad data in healthcare rarely shows up as a single catastrophic failure. Rather, it creeps in over time with missing lab feeds here, and loosely mapped codes there, or a delay in a claims file. A documentation workflow that quietly breaks. Each issue by itself may look small, but together they create blind spots that undermine analytics.
When data is fragmented or unverified, health systems and payers make decisions based on partial truth. This affects risk scoring, quality measurement, care gap closure, and even member satisfaction efforts. The organization believes it is working from insight when it is actually working from approximation.
Five Signs Your Health Data Has a Quality Problem
You do not need a complex model to know when your data is drifting. In most environments, the symptoms are already visible:
- Teams Spend More Time Hunting for Data than Using It: If analysts or quality teams are constantly reconciling spreadsheets or chasing records, the issue is not analytics capacity; it is foundational data fragmentation
- Quality Improvement is Reactive Instead of Proactive: When gaps are discovered during audit season instead of during care delivery, the problem is not performance effort; it is a lack of visibility
- Results Vary Depending on Which System is Queried: If leadership receives different answers from two internal dashboards, trust is already compromised
- AI Pilots Stall After Initial Testing: Models cannot perform if the training data is incomplete, unnormalized, or stale
- Documentation from Providers Arrives Too Late, or Not at All: What looks like a “provider engagement” issue is often a symptom of missing or mismatched clinical data flows
These are not operational inconveniences but are signals that the organization is making strategic decisions on ground that is not stable.
Why Health Data Assessments Matter
Many healthcare leaders assume they have data quality issues. Fewer have quantified them. And even fewer have a baseline to measure improvement against. Without a baseline, there is no way to know whether investments in interoperability, analytics, or AI are moving the needle.
A structured quality assessment helps answer fundamental questions such as:
- How complete is our data across clinical, claims, and encounter feeds
- How current is our data when viewed in a care delivery or quality reporting context
- Where are the gaps, duplications, or drift patterns that create downstream risk
- Does our environment have the integrity required for advanced analytics or AI
- Is the data reliable enough to use in provider performance programs or contractual incentive structures
An assessment is not about pointing to a problem, but is all about establishing the truth. Once a baseline exists, leadership can act with clarity rather than speculation.
From Silence to Signal
In other sectors, continuous data auditing is standard practice. In healthcare, data is often assumed to be accurate unless something breaks. But as AI adoption accelerates, that assumption is no longer safe.
AI does not fix bad data, but it actually amplifies it. If the input is skewed, the output becomes misleading at a faster scale. That is why assessments are becoming a critical first step for organizations preparing for analytics modernization or responsible AI deployment.
The Path Forward
Healthcare cannot become more predictive, equitable, or efficient without a strong data foundation. Knowing where your data stands today is the most reliable way to build trust in your analytics tomorrow.
A formal health data assessment does more than evaluate quality. It creates a roadmap for confidence. It tells an organization: here is where your data is strong, here is where it is weak, and here is what needs to change to support the outcomes you intend to deliver.
Before we ask whether AI can transform healthcare, we must ask a simpler question: which is can our data be trusted to support it? For many organizations, the smartest next step is not another analytics tool. It is clarity. And that begins with understanding the health of the data you already have.
About Mark Coetzer
Mark Coetzer is VP of Business Development at IMAT Solutions, with more than 30 years of technology experience and a decade dedicated to healthcare. He brings deep expertise in clinical data integration, interoperability, and population health, and is passionate about helping organizations build trusted data foundations for better care and smarter outcomes.
