A Framework for Aligning Data from Multiple Institutions to Conduct Meaningful Analytics.

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.

The Truth is in the Data - Differences in the Same Measure Based on Different Sources among HVHC Members Using ICU Length of Stay as an Example.

INTRODUCTION: Intensive Care Unit (ICU) length of stay is a strong indicator of severity of illness and cost in the care of sepsis patients. In this case study, we examine the difference between an electronic health record (EHR) based submissions with Centers for Medicare and Medicaid Services (CMS) payment data. METHODS: Member submitted EHR data contained 26,733 unique patient's records. The CMS data contained demographics, diagnosis, and revenue codes. After linking EHR data to CMS data, we found a discrepancy in ICU days from CMS claims vs. EHR data. Our hypothesis was that removing intermediate ICU LOS would result in a closer match from CMS claims with EHR data. We suspected the use of Intermediate ICU stays in our CMS ICU definition contaminated our ICU LOS data. This resulted in a review of the sepsis specification, further investigation of the data, and follow up conversations with the Member organizations. RESULTS: Agreement between EHR and CMS data improved from 73 percent to 86 percent once the Intermediate ICU time had been removed. DISCUSSION AND CONCLUSIONS: The inclusion of Intermediate ICU in the analysis of severely ill sepsis patients from CMS data diluted the importance of using an ICU LOS for estimating the severity of illness and the cost to the healthcare system. We must ensure that clinical definitions are consistent between data sources that were built for different purposes. Additionally, we learned that engaging with clinicians, analysts, and clinical coders early in the process is required to fully understand the complexities from different sources.