Let’s talk about the quality of healthcare data. This is our primary focus and a favorite topic of discussion at Clinical Architecture. From a technology standpoint, many of the challenges healthcare organizations face with interoperability, quality measures, clinical decision support, and AI have a common root: poor data quality. When the information we rely on isn’t accurate, complete, or trustworthy, even the most advanced tools can’t deliver their full potential. It is important to lay a solid foundation to ensure success.
Through conversations with CHIME-member healthcare leaders and insights from our annual Healthcare Data Quality Report, we’ve seen a clear pattern emerge. Providers are increasingly concerned that poor data quality is limiting their ability to care for patients and operate efficiently.
Symptoms of Poor Data Quality within a Health System
Across health systems, poor data quality reveals itself in different ways. Operational teams face data onboarding delays, manual workarounds, and disconnected workflows. For leadership, it shows up as inconsistent reports, stalled initiatives, and eroding trust in performance metrics. The symptoms may vary, but the diagnosis is the same.
82% of Healthcare Professionals are Concerned about the Quality of Data Received from External Sources
The 2025 Healthcare Data Quality Report highlighted that most healthcare professionals have concerns about the quality of the information they receive from others. This sentiment was also shared by fellow CHIME members who said that external data is often reviewed but not reconciled. And this information is stored separately rather than being integrated into their systems. This is not surprising as it is often heard in discussions about data quality “I barely trust mine. I don’t trust yours”. Information in the report supports this as only 17% of healthcare professionals said they are currently integrating patient information from external sources. It is important to note that the Healthcare Data Quality Report includes input from various industry segments including providers, payers, government, life sciences, academia, consultants, analytics, and vendors.
An Increased Concern about Provider Fatigue from Too Much Data
The healthcare industry faces a significant challenge in the volume of data that is being produced. It’s been estimated that a single patient generates about 80 megabytes of data per year and a single hospital creates about 137 terabytes per day. Having a strong data governance strategy in place is essential to managing this data to ensure it is usable by downstream systems, population tools, and enterprise reporting. However, many health systems share that their organizations are challenged by legacy systems and informal data teams without clear ownership which create silos of information. This can cause issues with leveraging patient data in a meaningful way downstream.
Inconsistent data governance places a burden on the enterprise as it can result in an unreliable combination of mastered and unmastered data which produces uncertain results as non-standard data is invisible to standard-based reports and metrics. Add to that numerous concerns over the data quality and usability of patient information from external sources and it comes as no surprise that clinicians are fatigued by the amount of data pouring in. The Healthcare Data Quality Report indicated 66% of survey participants were concerned about provider fatigue related to the amount of external data being integrated into their systems. This was a 7% increase in concern from the previous year’s findings.
High Quality Data is Necessary for Effective Artificial Intelligence
Health system leaders we talk with agree that having a consistent data governance policy in place is a necessity for ensuring data is trustworthy and reliable. This foundation is essential for AI intelligence initiatives. Without it, AI becomes unreliable or in some cases dangerous. Many organizations shared they have a lack of trust in AI-generated outputs and are double and triple-checking the information before acting on it.
The Future of Healthcare Data Quality
Effective data governance requires an on-going commitment. It is not a one-time project. Ensuring data quality involves putting the right data governance, editorial policies and tooling in place to maintain the quality of data. The right tools and practices make consistent data quality at scale a reality.
Interested in learning more about the trends uncovered in the 2025 Healthcare Data Quality Report? Download it now to see what the healthcare industry has to say about the perceptions around trustworthiness and usability of patient information, and the impact data quality has on patient care and decision-making.
What is your organization’s data quality strategy? What policies have you put in place to ensure the data you are creating and collecting is trustworthy and reliable? Clinical Architecture has been serving the healthcare industry for nearly two decades and helped many of the largest health systems, payer organizations, government agencies, public health agencies, health information exchanges and others solve challenges around data quality and usability. Contact us to discuss your data quality needs.