Abstract
Introduction:Health systems can be supported by collaborative networks focused on data sharing and comparative analytics to identify and rapidly disseminate promising care practices. Standardized data collection, quality assessment, and cleansing is a necessary process to facilitate meaningful analytics for operations, quality improvement, and research. We developed a framework for aligning data from health care delivery systems using the High Value Healthcare Collaborative central registry.Framework:The centralized data registry model allows for multiple layers of data quality assessment. Our framework uses an iterative approach, starting with clear specifications, maintaining ongoing dialogue with diverse stakeholders, and regular checkpoints to assess data conformance, completeness, and plausibility.Lessons Learned:We found that an iterative communication process is critical for a central registry to ensure: 1) clarity of data specifications, 2) appropriate data quality, and 3) thorough understanding of data source, purpose, and context. Engaging teams from all participating institutions and incorporating diverse stakeholders of clinicians, information technologists, data analysts, operations managers, and health services researchers in all decision making processes supports development of high quality datasets for comparative analytics across multiple institutions.Conclusion:A standard data specification and submission process alone does not guarantee aligned data for a collaborative registry. Implementing an iterative data quality improvement framework with extensive communication proved to be effective for aligning data from multiple institutions to support meaningful analytics.
Highlights
Health systems can be supported by collaborative networks focused on data sharing and comparative analytics to identify and rapidly disseminate promising care practices
We developed a framework for aligning data from health care delivery systems using the High Value Healthcare Collaborative central registry
When on Member’s Intensive Care Unit Length of Stay (ICU LOS) reported to High Value Healthcare Collaborative (HVHC) appeared notably different than found in CMS claims, the Program Management Office (PMO) convened a group of analysts, informaticists, billing specialists, and quality improvement professionals at that organization to understand the data sources and workflows contributing to HVHC data reporting; this is explored further in the accompanying article by von Recklinghausen et al [12]
Summary
Health care delivery systems have a long history of using their own internal data to assess and improve performance. In cases where it was not possible for Members to submit updated data, the multidisciplinary Project Team developed statistical approaches based on available data and site-specific context Examples of this include imputation of data points for Members missing one or more quarters’ worth of encounter data, Members missing encounters for patients who were never transferred to the intensive care unit (ICU), and Members with identified discrepancies between clinical data submitted to HVHC and data observed in CMS claims. Each of these examples required significant root cause analysis by PMO staff, the Project Team, and Member representatives before determining the appropriate adjustment methodology to meet the analytic needs of Phase II deliverables. Further details explaining the analytic approaches used to addressing data inconsistencies are reviewed in the accompanying paper by Welch et al [13] within this issue
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