07 Jan Healthcare IT Today: How Do I Know if My Health Data Is Bad?

Healthcare organizations often assume their data is reliable until something breaks. In a recent Healthcare IT Today guest article, Mark Coetzer, VP of Business Development at IMAT Solutions, explains why poor data quality often goes undetected, how it quietly undermines analytics and AI initiatives, and why a formal health data quality assessment is becoming a critical first step for healthcare leaders.

Data is at the center of nearly every healthcare priority today, from quality improvement and care coordination to analytics and AI. Yet despite its importance, many organizations lack confidence in the data they rely on to make clinical and financial decisions. The challenge is not that leaders underestimate data. It is that they often do not realize when their data is incomplete, inconsistent, or quietly working against them.

In a recent guest article published by Healthcare IT Today, Mark explores a simple but critical question facing healthcare executives today, which is how do you know if your health data is bad?

When Data Problems Stay Hidden
One of the most dangerous aspects of poor data quality is that it rarely shows up as a single failure. Instead, it appears gradually through small issues that compound over time. Missing lab feeds, inconsistent coding, delayed claims files, and documentation workflows that quietly break are a few examples. And each issue may seem manageable on its own, but together they create blind spots that weaken analytics, reporting, and AI performance.

Many organizations only uncover these issues after an audit fails, a quality metric stalls, or an AI model underperforms. By that point, leaders are reacting instead of steering.

The Warning Signs Leaders Should Not Ignore
In the article, Mark outlines several common indicators that data quality has drifted. Teams spending more time chasing data than analyzing it. Conflicting answers across dashboards. Quality improvement efforts that surface gaps only during audit season. AI pilots that never move beyond early testing. These are not operational inconveniences but are signals that decisions are being made on unstable ground.

Why Health Data Quality Assessments Matter
Assumptions are not a strategy. Without a clear baseline, organizations cannot measure progress or determine whether investments in interoperability, analytics, or AI are actually delivering value.

A structured health data quality assessment helps leaders understand where their data is strong, where it is weak, and where risk exists. It creates clarity by answering essential questions about completeness, timeliness, consistency, and readiness to support advanced analytics and AI. As AI adoption accelerates, this step becomes even more important. AI does not correct bad data but amplifies it. If inputs are flawed, outputs become misleading at scale.

Moving From Uncertainty to Confidence
Healthcare organizations cannot build predictive, equitable, and accountable care models without a strong data foundation. Knowing the true condition of your data is the most reliable way to build trust in analytics and AI initiatives.

We encourage all healthcare leaders to read the full article in Healthcare IT Today and consider whether their organization truly understands the health of its data.

Read the full article here. If you want to take the next step, IMAT Solutions offers a Health Data Quality Assessment designed to establish a clear baseline and help organizations move forward with confidence.