Why Your Internal Hospital Quality Metrics Are Likely To Be Inaccurate and How Dexur’s Data Audit Can Help Validate Them

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This article is part of the Dexur Insight, which is focused on Hospital Quality and Safety topics and is read by CEOs, CMOs, CNOs, CQOs, Quality, Safety, Compliance, Analytics & Performance teams. Please email dexurqualitysights@dexur5.com to join this newsletter and receive updates (not more than once per week).

Imagine you're in a routine leadership meeting, presenting your hospital's latest quality metrics from your internal dashboard—readmission rates, and mortality metrics, all seemingly well under control. The team is satisfied and confident in the data's accuracy and the hospital’s performance. Fast forward to the release of the Centers for Medicare & Medicaid Services (CMS) final results, and the situation unfolds quite differently. Suddenly, there’s a stark contrast—CMS data indicates a decline in performance. The board is stunned, the C-suite is questioning the data, and you're left wondering where the discrepancy lies.

This scenario is all too common in healthcare settings. Often, when hospitals first compare their internally tracked metrics to those presented by Dexur, a startling revelation occurs: the numbers don’t match. Firstly, Dexur informs Hospitals that they are comparing apples to oranges since Dexur has the full patient journey i.e. Dexur has access to out-of-hospital readmissions and Mortality. Secondly, even while comparing In-Hospital data to Dexur's In-Hospital data, it becomes evident through Dexur’s data audit that the hospital has been tracking incorrect metrics. This discovery is the first step in unraveling the mystery behind the unexpected CMS results.

Common Issues in Hospital Quality Measure Calculation

Accurate measurement of quality metrics is crucial for hospital management and compliance with healthcare standards. However, several pitfalls can skew these metrics, leading to discrepancies in reported performance and actual outcomes. Understanding these common issues can help in identifying and rectifying errors effectively:

  • Inappropriate Coding Logic in Denominators: The foundation of many quality metrics lies in the correct application of coding rules for patient inclusion and exclusion. Misinterpretations or errors in implementing these rules can drastically alter the denominator, leading to incorrect overall metrics.

  • Errors in Numerator Exclusions: Accurately defining which cases should be excluded from the numerator is crucial for precise metrics. Common mistakes include misinterpretation of exclusion criteria, leading to either an inflated or deflated numerator and, consequently, skewed performance indicators.

  • Misidentification of Patient Populations: Relying on administrative data, like the 837 healthcare claim forms, often results in misclassification of patient groups. A typical error is coding all patients under generic categories like Medicare, which does not distinguish between Medicare Advantage and traditional Medicare patients, crucial for certain metrics.

  • Faulty Implementation of Scoring Algorithms: Algorithms such as those calculating HCAHPS Linear Mean scores or SIR risk adjustments must be applied meticulously. Incorrect implementation can distort quality scores, affecting both internal evaluations and external benchmarks.

  • Compromised Data Engineering Pipelines: Even with robust IT support, the integrity of data pipelines can be compromised by unnoticed errors or breakdowns in the data flow. These can stem from flawed data collection methods, integration errors, or software malfunctions, leading to inaccurate data feeding into quality measure calculations.

  • Inadequate Validation Processes: Without stringent data validation checks, inaccuracies are more likely to remain in the system. Regular audits and cross-verifications against trusted external sources are essential to maintain data integrity and reliability.

  • Unaligned Metric Definitions: Differences in metric definitions between what hospitals use internally and those defined by oversight bodies like CMS can result in significant discrepancies. It’s crucial for hospitals to continually align their metrics with those of standard-setting organizations.

  • How Dexur Assists in Enhancing Hospital Quality Measure Accuracy

    Dexur plays a pivotal role in supporting hospitals to identify and correct inaccuracies in their quality metrics calculations. Leveraging a suite of sophisticated tools and methodologies, Dexur offers comprehensive services that ensure data integrity and accuracy.

    Here’s how Dexur assists hospitals in overcoming common data-related challenges:

  • Implementation of AI-Driven Data Test Harness: Dexur employs an AI-driven data test harness designed to identify potential issues in data handling and metric calculations automatically. By analyzing trends and inconsistencies in large datasets, the AI identifies patterns that may indicate underlying problems, providing an early warning system to preemptively address these issues.

  • Comprehensive Data Audits: Dexur conducts extensive audits comparing hospital’s internal data with multiple external benchmarks, including CMS-published data and Dexur's proprietary Medicare claims data. This helps identify discrepancies and provides a clear direction for necessary adjustments.

  • Advanced Data Engineering Solutions: Dexur supports hospitals in redesigning their data architecture to enhance the accuracy and timeliness of data collection and integration. This includes optimizing data flows, enhancing data warehouse structures, and implementing robust data validation processes to prevent future errors.

  • Real-Time Dashboard Implementation: Dexur develops sophisticated, real-time dashboards that allow hospitals to continuously monitor their performance against key quality measures. These dashboards are integrated with alerts that notify hospital staff of anomalies or deviations from expected metrics, enabling immediate corrective action.

  • To maintain the credibility and accuracy of its services, Dexur applies its data validation frameworks internally as well. This involves regular audits and cross-verifications of its own analytical tools and databases against external data sources and benchmarks. By doing so, Dexur not only ensures the integrity of the data it provides to clients but also continuously enhances its methodologies and algorithms based on real-world data and feedback.

    Dexur’s Free Quality Measure Data Audit Offer

    To address potential discrepancies and improve the reliability of hospital metrics, Dexur offers a free audit to validate internal data. This service is designed to help hospitals identify and rectify inaccuracies in their metric tracking, ensuring that their internal reports are aligned with external standards and expectations.

    Hospitals interested in improving their quality metrics and ensuring compliance with CMS standards are encouraged to take advantage of this free audit by emailing dexur@dexur.com . By doing so, they can gain a clearer understanding of their performance and implement more effective strategies for patient care and operational excellence.