| Autor: |
Seif MA; Department of Urology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA., Kruse BC; Institute for Cancer Care Innovation, The University of Texas MD Anderson Cancer Center, Houston, TX, USA., Keramati CA; Institute for Cancer Care Innovation, The University of Texas MD Anderson Cancer Center, Houston, TX, USA., Aloia TA; Institute for Cancer Care Innovation, The University of Texas MD Anderson Cancer Center, Houston, TX, USA., Amaku RA; Institute for Cancer Care Innovation, The University of Texas MD Anderson Cancer Center, Houston, TX, USA., Bhavsar S; Anesthesiology and PeriOperative Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA., DeCarlo KR; EHR Analytics and Reporting, The University of Texas MD Anderson Cancer Center, Houston, TX, USA., Erfe RJD; Department of Anesthesia, Critical Care, and Pain Management, The University of Texas MD Anderson Cancer Center, Houston, TX, USA., Eska JS; Institute for Cancer Care Innovation, The University of Texas MD Anderson Cancer Center, Houston, TX, USA., Iniesta MD; Gynecology Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA., Prakash LR; Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA., Zhang T; EHR Analytics and Reporting, The University of Texas MD Anderson Cancer Center, Houston, TX, USA., Gottumukkala V; Anesthesiology and PeriOperative Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. |
| Abstrakt: |
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. |
