Implementation of the CHA IPSO Collaborative at a Pediatric Academic Center - Mathur, Himi (Pediatr Qual Saf, 2018)
INTRODUCTION: Boston Children’s Hospital joined a national quality improvement collaborative to reduce the incidence of severe sepsis (SS) and associated mortality through early identification and treatment. We developed a scalable data infrastructure to retrospectively identify SS and non-severe sepsis (NSS) patients from the data warehouse (EDW) to identify opportunities for quality improvement by April 2017. METHODS: Developed a 3-stage scalable process based on techniques involved in retrospective identification of SS patients by using RedCap(TM), as the tool of choice (see Figure 1, Supplemental Digital Content 1, available at http://links.lww.com/PQ9/A22). 1. Retrospective Data Mining: SS and NSS patients are extracted from the EDW by SQL query based on the treatment criteria specified by IPSO (Fig. 1) 2. Data Verification: Data from the EDW is automatically extracted into a patient specific RedCap(TM) screening form for all patients filtered through the query logic into automated and manual fields, for data coordinator and clinical verification 3. Data submission: RedCap(TM) submission form is a completely automated extension of the Redcap(TM) screening form to extract data sepsis quality variables for all patients. An MS Excel version of this form is submitted to IPSO data portal RESULTS: The data mining accuracy improved by 46% and time spent per manual review decreased from 20 minutes to 6 minutes after implementing the 3-stage process. CONCLUSIONS: Continuously optimize data collection through an iterative process by minimizing manual review to improve the identification of pediatric sepsis patients. Lessons learned from our process are definitely transferable and can be customized to other IPSO participants.