Considerations for Integration of Perioperative Electronic Health Records Across Institutions for Research and Quality Improvement: The Approach Taken by the Multicenter Perioperative Outcomes Group

Abstract

Use of the electronic health record (EHR) has become a routine part of perioperative care in the United States. Secondary use of EHR data includes research, quality, and educational initiatives. Fundamental to secondary use is a framework to ensure fidelity, transparency, and completeness of the source data. In developing this framework, competing priorities must be considered as to which data sources are used and how data are organized and incorporated into a useable format. In assembling perioperative data from diverse institutions across the United States and Europe, the Multicenter Perioperative Outcomes Group (MPOG) has developed methods to support such a framework. This special article outlines how MPOG has approached considerations of data structure, validation, and accessibility to support multicenter integration of perioperative EHRs. In this multicenter practice registry, MPOG has developed processes to extract data from the perioperative EHR; transform data into a standardized format; and validate, deidentify, and transfer data to a secure central Coordinating Center database. Participating institutions may obtain access to this central database, governed by quality and research committees, to inform clinical practice and contribute to the scientific and clinical communities. Through a rigorous and standardized approach to ensure data integrity, MPOG enables data to be usable for quality improvement and advancing scientific knowledge. As of March 2019, our collaboration of 46 hospitals has accrued 10.7 million anesthesia records with associated perioperative EHR data across heterogeneous vendors. Facilitated by MPOG, each site retains access to a local repository containing all site-specific perioperative data, distinct from source EHRs and readily available for local research, quality, and educational initiatives. Through committee approval processes, investigators at participating sites may additionally access multicenter data for similar initiatives. Emerging from this work are 4 considerations that our group has prioritized to improve data quality: (1) data should be available at the local level before Coordinating Center transfer; (2) data should be rigorously validated against standardized metrics before use; (3) data should be curated into computable phenotypes that are easily accessible; and (4) data should be collected for both research and quality improvement purposes because these complementary goals bolster the strength of each endeavor.

Figure 1.

Overview of information contained within MPOG and its uses: conceptual overview of types of data held by MPOG and their uses. Current data types and uses are within the solid lines. Emerging data types and uses are indicated within the dashed lines. MPOG indicates Multicenter Perioperative Outcomes Group.

Figure 2.

The lifecycle of perioperative EHR information: data become knowledge that informs practice—illustration of flow of information as part of involvement in the MPOG process. At each participating site: creation during the patient–physician encounter, documentation in the EHR, extraction from EHR and other systems, standardization, validation, PHI removal, and upload to MPOG. At the coordinating center: this figure indicates the automated handling steps which make data available at the coordinating center. Finally, data are accessible for specified projects and purposes on the approval of the Quality Committee or PCRC or Publications Committee. AIMS indicates Anesthesia Information Management Systems; EMR, electronic medical record; MPOG, Multicenter Perioperative Outcomes Group; PCRC, Perioperative Clinical Research Committee; PHI, protected health information.

Figure 3.

MPOG Concept mapping utility: using the MPOG Concept mapping utility, a site is able to match terminology present in their local AIMS or EHR systems to standardized MPOG Concepts. Concepts offered in the right hand pane are ordered based on probability of match based on text selection in the left hand pane. Color coding in the left hand pane indicates the status of the match. The Figure has been edited to remove identifying information. AIMS indicates Anesthesia Information Management Systems; EHR, electronic health record; MPOG, Multicenter Perioperative Outcomes Group.

Figure 4.

MPOG Data Diagnostics and Case Validation Tools. A, The output of one of multiple data diagnostic control charts is demonstrated. Charts are color coded and prioritized to focus work on improving highest priority data elements. B, The case-by-case review is demonstrated, whereby the extracted record is compared against the source medical record and clinician attests to the accuracy of the detailed extraction. Both panels have been edited for clarity and to remove identifying information. MPOG indicates Multicenter Perioperative Outcomes Group; U-OR 31, operating room 31.