Development and implementation of an institutional enhanced recovery program data process

Author:

Seif Mohamed A1ORCID,Kruse Brittany C2ORCID,Keramati Cameron A2ORCID,Aloia Thomas A2ORCID,Amaku Ruth A2,Bhavsar Shreyas3ORCID,DeCarlo Kenneth R4,Erfe Rose Joan D5,Eska Jarrod S2ORCID,Iniesta Maria D6ORCID,Prakash Laura R7ORCID,Zhang Tao4,Gottumukkala Vijaya3ORCID

Affiliation:

1. Department of Urology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA;

2. Institute for Cancer Care Innovation, The University of Texas MD Anderson Cancer Center, Houston, TX, USA;

3. Anesthesiology and PeriOperative Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA;

4. EHR Analytics and Reporting, The University of Texas MD Anderson Cancer Center, Houston, TX, USA;

5. Department of Anesthesia, Critical Care, and Pain Management, The University of Texas MD Anderson Cancer Center, Houston, TX, USA;

6. Gynecology Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA;

7. Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

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.

Publisher

SAGE Publications

Subject

Health Policy,Leadership and Management

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