From the 2019 HVPAA National Conference
Dr. Rawn Salenger (University of Maryland), Ms. Rebecca Sandler (University of Maryland Saint Joseph Medical Center), Ms. Kate Knott (University of Maryland Saint Joseph Medical Center), Ms. Jennifer Zeller (University of Maryland Saint Joseph Medical Center), Ms. Jayne Knuth (University of Maryland Saint Joseph Medical Center), Ms. Rachel Born (University of Maryland Saint Joseph Medical Center), Ms. Elizabeth Holderness (University of Maryland Saint Joseph Medical Center), Dr. Bradley Taylor (University of Maryland)
Background
Enhanced recovery after surgery (ERAS) programs have demonstrated decreased postoperative complications, decreased length of stay, and reduced readmission rates, leading to better care at lower cost (1-3). Our health system developed a cardiac ERAS program with specific care bundles designed to fundamentally shift the way we care for our patients peri-operatively. We concurrently sought to develop an automated audit system to track and measure our program compliance and key outcomes.
Objective
We sought to develop an effective way to measure our compliance with ERAS care bundles and the clinical effect on our cardiac surgery outcomes.
Methods
A multi-disciplinary, system-wide panel of cardiac surgery experts met routinely over eight months to review the ERAS literature and apply the evidence-based practice to our cardiac surgery program. This group included informatics experts to advise and build automated tracking systems which would pull data directly from the electronic health record (EHR).
Results
We successfully implemented a cardiac ERAS program after eight months of planning and training front-line staff. The audit tools developed were able to pull data from flowsheet documentation in the EHR. There were two separate tools employed. The first was a real-time patient list which tracked eleven metrics. This created a closed-loop feedback system of all patients undergoing cardiac surgery and tracked key metrics for compliance and outcomes (figure 1). Some of the metrics tracked included patient ambulation, incentive spirometry use, pain score, and postoperative nausea. Data on the patient list updated throughout the day as care was documented in the EHR. Green, yellow, and red indicators on the patient list alerted the care team if patients were at goal for each metric. This real time data allowed care givers to adjust care throughout the day to meet key ERAS goals. Also developed, was a dashboard which aggregated and saved these same data over time. This dashboard had the ability to sort and tabulate data, as well as interrogate individual patient records (figure 2). This aggregate dashboard utilized Tableau© software and also auto-harvested data from the EHR.
Conclusions
An automated system to track ERAS metrics allows programs to measure compliance with ERAS protocols as well as measure impact on patient outcomes. This can assist with ongoing quality improvement and sustainability of clinical gains.
Clinical Implications
The ability to track compliance and outcomes resulting from new clinical protocols is critical to the process of continuous quality improvement. Many medical centers lack the resources for ongoing collection and analysis of clinical data to guide pathway development. Most centers, however, have abundant clinical data available in the EHR. Effective methods to auto-harvest these data can help quantitate the effect of new clinical programs and inform providers of opportunities for future improvement.
References
1. Williams JB, McConnell G, Allender JE, Woltz P, Kane K, Smith PK, Engelman DT, Bradford WT. One-year results from the first US-based enhanced recovery after cardiac surgery program. Jo Thorac and Cardiovasc Surg 2019; 157(5): 1881-1888.
2. Ljungqvist O, Scott M, Fearon KC. Enhanced Recovery After Surgery. JAMA Surg 2017; 152(3): 292–298.
3. Fleming O, Garratt C, Guha R, Desai J, et al. Aggregation of Marginal Gains in Cardiac Surgery: Feasibility of a Perioperative Care Bundle for Enhanced Recovery in Cardiac Surgical Patients. Journal of Cardiothoracic and Vascular Anesthesia 2016; 30(3): 665-70.
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