Rohleder, Thomas R.

Objective : Practice analysis of cardiac surgical service lines suggested that, for patients having routine cardiac surgery, standardization of care processes in the ICU could reduce cost and improve outcomes. We tested this hypothesis in a large surgical practice. Methods : Patients eligible for a standardized care pathway were prospectively identified preoperatively. ICU protocol development and practice redesign occurred in 2009 with implementation in 2010. In the ICU, all major process steps to recovery were managed by tiered and integrated protocols whereby care advancement is the default if clinical criteria are met. We compared outcomes of patients managed by protocol to those managed by standard care. ICU metrics (time intubated, drip weaning, length of stay) were benchmarked against a sample of 65 CABG patients from 2008. Return to ICU and to OR for standardized care patients were compared to concurrent patients under routine care. We report on all patients having cardiac surgery for 12 months beginning March 2010. Findings : Over 12 months, the ICU care of 1289 patients followed standardized tiered protocols, while 1309 patients were managed by standard care. After implementation of standardized protocols, ICU metrics improved in protocol patients relative to 2008 (2010 versus 2008): mean intubation time in hours (7.4 vs 11.6), mean time on hemodynamic drips in hours (16.2 vs 25.3), average ICU length of stay in hours (25.1 vs 35.9). The safety of protocol based care is demonstrated by lower rates of return to ICU and to OR among patients under protocol vs non-protocol: return to OR (1.8% vs 8.9%), return to ICU (1.6% vs 6.2%). Conclusions: With proper patient selection and rigorous protocols that make care advancement the default, cardiac surgical care in the ICU can be standardized and clinical metrics improved. The safety of this strategy is demonstrated by very low rates of return to OR or ICU. This practice model is translatable to other ICU practices.

Spine surgeries tend to be lengthy (mean time of 4 hours) and highly variable (with some surgeries lasting 18 hours or more). This variability along with patient preferences driving scheduling decisions resulted in both low operating room (OR) utilization and significant overtime for surgical teams at Mayo Clinic. In this paper we discuss the development of an improved scheduling approach for spine surgeries over a rolling planning horizon. First, data mining and statistical analysis was performed using a large data set to identify categories of surgeries that could be grouped together based on surgical time distributions and could be categorized at the time of case scheduling. These surgical categories are then used in a hierarchical optimization approach with the objective of maximizing a weighted combination of OR utilization and net profit. The optimization model is explored to consider trade-offs and relationships among utilization levels, financial performance, overtime allowance, and case mix. The new scheduling approach was implemented via a custom Web-based application that allowed the surgeons and schedulers to interactively identify best surgical days with patients. A pilot implementation resulted in a utilization increase of 19% and a reduction in overtime by 10%.

Discrete-event simulation (DES) is an effective tool to for analyzing and improving healthcare processes. In this chapter we discuss the use of simulation to improve patient flow at an outpatient procedure center (OPC) at Mayo Clinic. The OPC addressed is the Pain Clinic, which was faced with high patient volumes in a new, untested facility. Simulation was particularly useful due to the uncertain patient procedure and recovery times. We discuss the simulation process and show how it helped reduce patient waiting time while ensuring the clinic could meet its target patient volumes.