JMIR Publications

ABSTRACT

Background:

Enhanced Recovery After Surgery (ERAS) programs bundle evidence-based interventions to standardize care, expedite recovery, and improve outcomes. As ERAS programs have expanded it has become clear that a major challenge is monitoring compliance of bundle elements and outcomes to feedback performance to stakeholders and guide changes. Manual data abstraction is onerous and not feasible. Reliance on receiving new reports from busy health system information technology (IT) groups is challenging. Therefore, we sought to address this unmet need at our hospital by developing a novel ERAS Datamart system.

Objective:

Our objectives were to develop a novel Datamart and Tableau dashboard: 1) to enable continuous analysis of data, harvested directly from the EMR, to measure compliance and outcomes, and 2) to enable end-users, e.g. ERAS coordinator, to create reports customized based on surgical procedure types, requested data variables, and custom date ranges.

Methods:

After “buy-in” from hospital leadership and other stakeholders, data metrics were identified and categorized according to phase of care, i.e. pre-operative, intra-operative, and post-operative. A multi-disciplinary team reviewed ICD-10 procedure codes to capture EMR data for patients undergoing ERAS procedures. IT was given a master list with metric names, definitions, and screenshots of the discrete field in the EMR to assist with building the metrics. Validations of the novel Datamart were done against known ERAS patient populations maintained by the surgery clinic.

Results:

The Datamart and Tableau dashboard has been built, is functional, and contains over 17,000 patients across 5 ERAS service lines: colorectal n=1,742, joint replacement n=4,235, surgical oncology n=941, bariatric n=1,130, and c-section n=9,390. Currently, 56 metrics spanning the perioperative period have been validated across these populations. Reports can be tailored according to patients, timeframes, and metrics. If desired, patient level raw data can be exported for statistical analyses. Two use cases (total joint replacement and surgical oncology ERAS programs) are presented showing how the Datamart can be used.

Conclusions:

Discrete fields within an EMR can be successfully captured into a novel Datamart and visualized using a custom Tableau dashboard for providing stakeholder feedback, facilitating quality improvement analyses and auditing pathways.