Data streams converging into a glowing hospital, symbolizing improved healthcare through data quality.

Is Your Hospital Data Ready for the Future? How a Data Quality Program Can Help

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Lena Voss in Health & Wellness October 2026 • 4 min read.

"Unlock the potential of your hospital's electronic health records (EHR) with a robust data quality program. Learn how to ensure accuracy, improve research, and enhance patient care."


In today's healthcare landscape, Electronic Health Records (EHRs) have become the cornerstone of medical practice. As hospitals increasingly rely on EHRs for everything from patient care to research, the quality of this data becomes paramount. Imagine making critical decisions based on inaccurate or incomplete information – the consequences could be significant.

The Learning Health System (LHS), envisioned by the Institute of Medicine, strives for continuous improvement in healthcare through data-driven insights. However, the success of LHS hinges on the reliability of the data it utilizes. EHR data, often generated from routine electronic transactions, may contain errors that can negatively impact research findings and clinical outcomes.

This article delves into the importance of data quality programs within hospitals, drawing insights from a real-world case study at Assistance Publique – Hôpitaux de Paris (AP-HP), Europe's largest hospital system. We'll explore how AP-HP tackled the challenge of ensuring data accuracy in their vast EHR system, and what lessons can be applied to healthcare organizations worldwide.

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Public Data for Measuring Care

The Agency for Healthcare Research and Quality’s National Healthcare Quality and Disparities Report Data Tools help users identify populations experiencing disparities in care and areas for improvement in healthcare delivery. AHRQ also offers the Healthcare Cost and Utilization Project and the Medical Expenditure Panel Survey to examine care quality, safety, and access. Separately, the CDC’s National Center for Health Statistics Data Query System provides estimates of healthcare access and quality based on the National Health Interview Survey, the National Hospital Ambulatory Medical Care Survey, and other sources.

No Universal Data-Quality Definition

A 2025 review describes healthcare data quality as a complex concept whose definition varies by context, with no universally accepted definition and assessment made challenging by diverse dimensions and methods. A 2023 review likewise notes extensive research on healthcare data quality and emphasizes that electronic medical records underpin data-driven analysis intended to improve clinical decisions and care quality. One discussion of health data standards reports that ontological data modelling can provide high expressivity and formalism, with standard-value-set-linked artefacts offering comprehensive representation for lifetime support, patient safety, and electronic communication.

Why Data Quality Matters: From Research to Patient Care

Data streams converging into a glowing hospital, symbolizing improved healthcare through data quality.

Think of your hospital's EHR as a vast library. If the books are misfiled, pages are missing, and information is inaccurate, finding the right answers becomes nearly impossible. Similarly, poor data quality in EHRs can have far-reaching consequences:

EHR data is increasingly used for clinical research, driving innovation in digital health and quality improvement initiatives. But if the data is flawed, research results can be compromised, leading to incorrect conclusions and potentially harmful treatments.

  • Compromised Research: Flawed data leads to unreliable results, hindering medical advancements.
  • Inefficient Operations: Inaccurate data wastes time and resources, impacting hospital efficiency.
  • Risked Patient Safety: Poor data quality can lead to misdiagnosis and incorrect treatment plans, jeopardizing patient well-being.
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Methods and Dimensions in Recent Reviews

A systematic literature review of data quality–driven improvement in healthcare reports that semiautomated assessment often used scheduled, ad hoc scripts curated manually and written in R, SAS, or SQL. The review also notes that approaches to defining and assessing data-quality dimensions varied. A separate scoping-review preprint reports that Completeness was the most frequently reported dimension, appearing in 78 documents, followed by Accuracy (66), Consistency (47), Compliance (43), and Reliability (42); it also notes the lack of a standardized lexicon.

Operational Barriers to Data Quality

Healthcare data-quality challenges include accuracy, privacy, and integration, according to iXsight’s overview of common issues and proposed solutions. Acceldata identifies data silos as a source of inconsistent formats that hinder analysis, and says outdated technologies and governance problems—including unclear ownership or insufficient oversight—make quality harder to maintain. These accounts point to organizational and technical obstacles that can complicate efforts to use healthcare data effectively.

The promise of Learning Health Systems (LHS) – where data continuously informs and improves healthcare – relies on the availability of high-quality data. Without it, the LHS vision remains unrealized. That's why implementing a robust data quality program is not just a best practice, it's a necessity for modern healthcare organizations.

Taking Control of Your Data: Key Steps and Future Directions

The AP-HP's experience provides a valuable blueprint for other healthcare organizations seeking to improve their data quality. By focusing on patient identification and healthcare services, AP-HP was able to identify key data quality issues and implement targeted solutions.

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Preparing for Data-Driven, AI-Enabled Care

Oracle describes the future of healthcare as data-driven, AI-enabled, and patient-centered, with AI, cloud computing, genomics, and virtual care converging to support risk prediction, personalized treatment, and earlier intervention. Its account emphasizes that deploying technology alone is not the goal. Point B similarly frames a central challenge for healthcare organizations as unlocking the potential of their data and acting on insights, while building enterprise data capabilities to optimize operations, engage patients, and prepare for an AI-driven future.

Bias and Quality in Secondary EHR Use

Research on secondary analysis of electronic health records for clinical research identifies significant challenges from data-quality issues in records collected during routine care. The study also points to biases associated with standard healthcare practices as a source of difficulty. These limitations matter when using observationally collected clinical data for research beyond its original care context.

While AP-HP's initial efforts focused on non-clinical domains, the future lies in expanding data quality initiatives to encompass clinical data, validating automated diagnosis algorithms, and assessing unstructured data. This requires a combination of manual and automated checks, along with a commitment to data governance and ongoing monitoring.

Ultimately, investing in data quality is an investment in the future of healthcare. By ensuring the accuracy and reliability of EHR data, hospitals can unlock its full potential to improve research, enhance patient care, and build a smarter, more effective healthcare system for all.

About this Article -

Written with AI assistance from published research, and reviewed by the Mystum team. See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1016/j.cmpb.2018.10.016, Alternate LINK

Title: Initializing A Hospital-Wide Data Quality Program. The Ap-Hp Experience.

Subject: Health Informatics

Journal: Computer Methods and Programs in Biomedicine

Publisher: Elsevier BV

Authors: Christel Daniel, Patricia Serre, Nina Orlova, Stéphane Bréant, Nicolas Paris, Nicolas Griffon

Published: 2019-11-01

Everything You Need To Know

1

What is a data quality program?

A data quality program is a structured approach to ensure the accuracy, completeness, and reliability of data within a hospital's Electronic Health Records (EHR) system. It involves identifying and addressing data quality issues, implementing data quality checks, and establishing processes to maintain data integrity. This is critical because hospitals rely on EHRs for various purposes, including patient care, research, and operational efficiency. Without a data quality program, the potential of the EHR data remains locked, limiting the ability to improve patient outcomes or drive innovation.

2

Why is data quality so important for hospitals?

Inaccurate data within Electronic Health Records (EHRs) can compromise research, leading to incorrect conclusions and potentially harmful treatments. This is because researchers rely on EHR data to identify patterns, trends, and insights that can inform medical advancements. Flawed data can also lead to inefficient hospital operations, wasting time and resources. Perhaps most critically, poor data quality can jeopardize patient safety by contributing to misdiagnosis, incorrect treatment plans, and adverse patient outcomes. This is why it is essential to have a robust data quality program.

3

How does data quality affect the Learning Health System (LHS)?

The Learning Health System (LHS) is a model for healthcare that continuously improves through data-driven insights. The concept envisions a system where data informs decision-making, driving better patient outcomes and operational efficiency. However, the LHS relies heavily on the reliability of data. Inaccurate or incomplete data from Electronic Health Records (EHRs) can undermine the LHS vision, as it can lead to incorrect conclusions and decisions. Therefore, a data quality program is vital for the success of the LHS model, as it ensures the data used for analysis and decision-making is accurate and reliable.

4

What role do Electronic Health Records (EHRs) play in healthcare, and why is data quality so important?

Electronic Health Records (EHRs) have become the foundation of modern healthcare, serving as the primary source of patient information. The data within EHRs is used for patient care, research, and operational efficiency. As hospitals rely more on EHRs, the quality of the data within them becomes increasingly important. A data quality program directly addresses issues such as inaccuracies, inconsistencies, and missing data, ensuring that the information used for patient care and research is reliable. This program becomes essential as the EHR is a crucial component of modern healthcare.

5

What are the main benefits of implementing a data quality program in a hospital setting?

Implementing a data quality program offers several benefits, including improved research outcomes, enhanced patient safety, and increased operational efficiency. When EHR data is accurate, research findings are more reliable, leading to more informed clinical decisions. Patient safety is improved as data accuracy reduces the risk of errors in diagnosis and treatment. Further, a robust data quality program allows for more efficient hospital operations. The implementation involves identifying key data quality issues and implementing targeted solutions to address them. The experience of Assistance Publique – Hôpitaux de Paris (AP-HP) highlights the potential benefits of improved data quality, offering a valuable blueprint for other healthcare organizations seeking to improve their data quality.

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