Data Quality Improvement: How Reliable Data Strengthens Patient Safety Performance

As hospitals navigate rising costs and staffing shortages, maintaining data quality becomes both more difficult and more critical. Electronic health records, registries, surveys, and other systems generate vast volumes of information, yet Quality and Safety leaders must still quantify reliability, embed it in governance, and hold teams accountable for how it is captured and used.

Why Data Quality Improvement Matters Now

In today’s environment, hospitals are facing persistent cost growth, inadequate reimbursement, and rising patient complexity, as detailed in AHA’s 2024 Costs of Caring report (April 2025). The cost of care is increasing while reimbursement lags, even as hospitals treat growing numbers of older, sicker, and more complex patients.

The AHA report highlights rising labor and drug costs, with labor expenses accounting for approximately 56% of hospital operating costs in 2024. The report also notes ongoing supply chain instability, increasing administrative burden, and widespread fatigue among clinical teams. In this environment, maintaining reliable data has become both more difficult and more essential to safe care. Streamlined data entry processes and validation tools can reduce documentation burden on already stretched clinical staff while improving data quality.

How Does Data Quality Affect Patient Safety?

With vast amounts of data flowing into hospitals today, Quality and Safety leaders have a tremendous opportunity to turn data into performance- and safety-improving initiatives. The stakes are particularly high as regulatory agencies and payers increasingly tie reimbursement and compliance standing to data accuracy and completeness.

Poor data quality has direct consequences: incomplete event reports delay root cause investigations, inconsistent coding obscures safety trends, and missing timestamps prevent timely intervention. Conversely, reliable data enables faster identification of patterns, more accurate risk stratification, and evidence-based allocation of safety resources.

Recent systematic reviews conclude that rigorous data-quality practices are also critical to valid clinical, research, and operational decisions, and to trustworthy reuse and compliance.

How to Use Data to Improve Quality and Patient Safety

Improving data quality requires deliberate strategy and sustained execution. It demands measurable standards, visible dashboards, embedded governance structures, continuous monitoring, and clear accountability at every level. To operationalize data reliability, hospitals can focus on five key areas:

#1. Define Measurable Metrics

Before Quality and Safety teams can improve data reliability, they need a shared language for what ‘reliable’ means. Hospital executives can establish clear indicators that quantify how trustworthy safety and quality data truly are. These reliability dimensions include:

• Percent of required fields with valid entries.
• Elapsed time from event to entry and to review.
• Internal agreement across fields and over time, monitored with rule-based checks.
• Concordance with authoritative sources or logic rules, using scheduled exception reports.

Modern reporting systems and analytics platforms, such as ADN’s Patient Safety Event Reporting Application, can automate exception reports and highlight overdue entries, flagging incomplete records and validating entries in real time to support both frontline staff and Quality and Safety leadership.

Once metrics are defined, the next step is making them visible across the organization.

Leadership takeaway: Start with one dimension where your organization struggles most, measure it consistently for 90 days, then expand.

#2. Visualize Reliability Through Patient Safety Event Reporting

Making data quality visible creates accountability and enables targeted improvement. Use a reliability dashboard, such as ADN’s Patient Safety Event Reporting Application, to surface delayed, incomplete, or inconsistent entries by unit, then compare results against publicly available benchmarks.

Example: Compare your internal patterns against AHRQ’s NPSD Dashboards, which chart aggregated reports by Common Formats version, report type, event type, and report completeness. Current Common Formats materials are available through the PSO Privacy Protection Center and AHRQ’s Common Formats overview.

AHRQ’s HCUP provides state and national databases, and HCUPnet generates on-demand statistics and trends for inpatient and emergency department (ED) care.

Leadership takeaway: Dashboard visibility alone won’t drive change. Pair it with regular review meetings where unit managers discuss trends and interventions.

#3. Embed Reliability in Governance Using Patient Safety Indicators

Hospital leaders can integrate reliability metrics into Quality and Safety Council or Board reports alongside other statistics, such as infection rates, falls, or HCAHPS outcomes. Embedding metrics around completeness, timeliness, consistency, and validity into the broader set of safety and quality statistics keeps reliability visible and reinforces it as part of daily operations.

AHRQ Quality Indicators are standardized, evidence-based measures that use hospital inpatient administrative data to measure and track clinical performance and outcomes. Patient Safety Indicators and other quality measures allow for robust trend and benchmark information.

The Joint Commission’s Responsible Use of Health Data Certification, launched in December 2023, provides a framework to mitigate risk and prioritize patient privacy when transferring data to third parties for secondary use.

Leadership takeaway: When reliability metrics sit alongside outcome measures in governance reports, data quality becomes a leadership priority, not just an IT issue.

#4. Track Data Quality Improvement Over Time

Embedding reliability metrics in governance is essential, but sustainable improvement requires tracking progress over time. Trend time-to-event-entry and data completeness by unit, set quarterly targets, and review misses in your Quality and Safety Council.

These metrics help leaders track how data integrity improvements lead to quicker event detection, more accurate root cause analyses, and more credible outcome reporting. Regular review allows teams to identify which units need additional support and recognize improvements that can be replicated across the organization.

Leadership takeaway: Declining trends in one unit often signal staffing stress, technology issues, or training gaps that warrant leadership attention.

#5. Close The Loop With Accountability

Quality and Safety team leads should provide feedback to those entering event data on timeliness and accuracy, reinforcing ownership for reliability at the point of entry. Effective accountability aligns with The Joint Commission’s three domains for advancing toward zero harm: leadership commitment, a strong safety culture, and Robust Process Improvement:

• Leadership commitment. Setting a clear direction for Quality and Safety teams and building accountability into day-to-day practices.
• Strong safety culture. Fostering a communicative environment where staff feel welcome to speak up about possible challenges.
• Robust Process Improvement. Encouraging staff to use, implement, and suggest process improvement strategies that produce enhanced outcomes.

Leadership takeaway: Accountability works both ways. Remove barriers to reliable data entry by investing in user-friendly systems, standardized workflows, and ongoing training.

Data Quality Improvement Drives Patient Safety Performance

When accountability mechanisms work as intended, the results reach far beyond compliance. Reliable data accelerates event detection, sharpens root cause analysis, strengthens PSI and QI reporting to regulators, and builds the case for safety investments. Quality and Safety leaders who make data integrity visible and measurable can transform it from a technical requirement into a strategic asset that drives safer care.