Development and implementation of an institutional enhanced recovery program data process

Mohamed A. Seif, Brittany C. Kruse, Cameron A. Keramati, Thomas A. Aloia, Ruth A. Amaku, Shreyas Bhavsar, Kenneth R. DeCarlo, Rose Joan D. Erfe, Jarrod S. Eska, Maria D. Iniesta, Laura R. Prakash, Tao Zhang, Vijaya Gottumukkala

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Background: With increasing implementation of enhanced recovery programs (ERPs) in clinical practice, standardised data collection and reporting have become critical in addressing the heterogeneity of metrics used for reporting outcomes. Opportunities exist to leverage electronic health record (EHR) systems to collect, analyse, and disseminate ERP data. Objectives: (i) To consolidate relevant ERP variables into a singular data universe; (ii) To create an accessible and intuitive query tool for rapid data retrieval. Method: We reviewed nine established individual team databases to identify common variables to create one standard ERP data dictionary. To address data automation, we used a third-party business intelligence tool to map identified variables within the EHR system, consolidating variables into a single ERP universe. To determine efficacy, we compared times for four experienced research coordinators to use manual, five-universe, and ERP Universe processes to retrieve ERP data for 10 randomly selected surgery patients. Results: The total times to process data variables for all 10 patients for the manual, five universe, and ERP Universe processes were 510, 111, and 76 min, respectively. Shifting from the five-universe or manual process to the ERP Universe resulted in decreases in time of 32% and 85%, respectively. Conclusion: The ERP Universe improves time spent collecting, analysing, and reporting ERP elements without increasing operational costs or interrupting workflow. Implications: Manual data abstraction places significant burden on resources. The creation of a singular instrument dedicated to ERP data abstraction greatly increases the efficiency in which clinicians and supporting staff can query adherence to an ERP protocol.

Original languageEnglish (US)
Pages (from-to)151-156
Number of pages6
JournalHealth Information Management Journal
Volume52
Issue number3
DOIs
StatePublished - Sep 2023

Keywords

  • data management
  • data storage and retrieval
  • electronic health records
  • enhanced recovery after surgery
  • health information management
  • quality improvement

ASJC Scopus subject areas

  • Leadership and Management
  • Health Policy
  • Health Informatics
  • Health Information Management

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