Background
The use of quality improvement registries is widespread in surgical care.1 2 Over 700 hospitals participate in the American College of Surgeon’s National Surgical Quality Improvement Program (ACS NSQIP) and can benefit through improvement in specified quality measures.3–5 For example, in an analysis of 118 participating centres, ACS NSQIP was found to have potentially prevented 200–500 surgical complications and 12–36 deaths annually.4 Multicentre QI registries also exist focusing on specific surgical subspecialties, intervention types or patient populations. Examples are the Society for Vascular Surgery’s Vascular Quality Initiative (SVS VQI), the ACS Trauma Quality Improvement Program (ACS TQIP), the National Health Service National Emergency Laparotomy Audit and the Australian Orthopaedic Association National Joint Replacement Registry.6–9
These platforms support an individual hospital’s quality improvement efforts through external benchmarking. Patient and procedure characteristics and process measures and outcomes are collected in accordance with a data dictionary. These data are then used to define a hospital’s risk-adjusted performance relative to other participating hospitals.2 Of course, the utility of these results relies on the quality and scope of data inputs. Participating hospitals must therefore employ trained clinical reviewers and rely on, or require, surgeon input to accurately capture procedure characteristics and perioperative outcomes. Often, external QI platforms are hosted on third-party software and accordingly require dedicated data extraction and input on top of standard clinical documentation. Furthermore, no single QI registry captures the full scope of emergency and scheduled surgical care at a given institution; therefore, many hospitals participate in multiple QI registries.2 At the authors’ institution, for example, we have simultaneously contributed to ACS NSQIP, ACS TQIP, Society for Thoracic Surgery Registry and SVS VQI.
Hospitals collect abundant patient data. However, these data generally exist in different formats across various platforms (eg, time-stamped administrative data on patient encounters, ambulatory clinic records, inpatient ward vital signs, laboratory data, pharmacy medication dispensing, imaging results, etc). Some of these data may not be captured within the primary electronic medical record (EMR) software in a functional format for data analysis (eg, PDF document of pulmonary function test that can be downloaded from EMR with values of the test such as forced expiratory volume in one second that cannot be searched via EMR). A healthcare analytics team at our institution was brought together to harness the untapped potential of internally generated data and created what is known as an Enterprise Data Warehouse (EDW). Built on IBM’s PureData for Analytics system, the EDW is created and updated from automated algorithms that clean raw patient data and, following deterministic linkage, repackage them for more efficient queries.10 In other words, raw data from various hospital sources are first cleaned by standardising the format and removing any redundant information. Next, these refined data are linked together using a patient’s unique medical record number (deterministic linkage rather than probabilistic). These linked data are then organised to accurately reflect a patients’ course within and across multiple healthcare visits. Thus, stagnant and disarrayed data are converted into a normalised and optimised format that sets the ground for powerful healthcare analytics, including the use of machine learning.10 With the inception of the EDW, our author group saw an opportunity to develop a novel platform for surgical quality improvement. This article aims to describe the conceptualisation, development and use of this platform for vascular surgery quality improvement.