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. Issue 3 (15th December 2017)

• Record Type: Journal Article
• Authors: von Recklinghausen, Friedrich Maximilian; Taenzer, Andreas; Gorman, Chrissie; Knowlton, Jay; Kinslow, Allison; Russell, Ron
• Is Part Of: EGEMS. Volume 5:Issue 3(2017)
• Publication Date: 2017-12-15
• Subjects: data -- CMS -- electronic health record -- EHR -- sepsis -- ICU; Medical records -- Data processing -- Periodicals; Medical care -- Data processing -- Periodicals; Medical Records; Automatic Data Processing; Medical care -- Data processing; Medical records -- Data processing; Periodicals; Periodicals; 651.504261
• DOI: 10.5334/egems.194
• Languages: English
• ISSNs: 2327-9214

Abstract

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.