Plastic Surgery Research Council

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Application Of LEAN Methodology Reveals Patient Modifiable Factors To Be The Prime Source of Delay in “First Start” Cases
Srikanth Kurapati, MD MBA CPHQ, Ali Kilic, MD MSHA, Jorge De La Torre, MD MSHA FACS.
UAB, Birmingham, AL, USA.

Purpose
Surgical care represents one of the greatest sources of revenue and cost in healthcare. Increasing surgical throughput offers the benefits of simultaneously expanding profit margins while increasing patient satisfaction. Cima et al in 2011 demonstrated LEAN strategies can be extremely effective in reducing waste and increasing efficiency in the operating room (OR). The purpose of this project is to demonstrate the utility of applying LEAN philosophy to the process of OR scheduling.
Methods
This study represents scheduling data gathered from a single institution's OR scheduling times. “First starts” are defined as surgeries scheduled as the first case in a particular operating room. Surgeon “call in” is the surgeon notifying the OR staff he or she is in the building. Data gathered represents 238 scheduled first starts over 1 year. Included were all surgical specialties with first start times. Delays in “first start” cases were attributed to one of the following: “2nd Room” (start time intentionally staggered to allow for two simultaneous cases with first start times) “Anesthesia related” (such as nerve blocks causing delay, or delay in anesthesia pre-operative evaluation), “Patient Modifiable Delay” (such as patient not having ride, not going to pre-op clinic as previously instructed, or patient arriving late), “Patient Non-modifiable Delay” (patient acutely ill on day of surgery), “Surgeon Modifiable Delay” (surgeon not calling in, surgeon late for arrival, office mis-scheduling, or industry rep late with necessary equipment), “Surgeon Non-modifiable Delay (one surgeon helping another due to acutely decompensated patient), or “unknown” (no reason listed for delay). Once data was gathered, a statistical analysis was performed.
Results
The surgeon “calling in” was weakly associated with on-time starts (r=0.18). The surgeon “calling in” did not relate to all-cause delays (r=-0.04). Moreover, the greatest delays were caused by Patient Modifiable Factors (mean 35mins, Stdev 25mins). Additionally, 80% of delays were caused by Patient Modifiable factors.
Conclusions
Variability represents the most significant threat to OR scheduling efficiency. Steps to reduce variability lead to reduction in waste and increase in throughput. By focusing proactively on delays attributed to Patient Modifiable Factors, one source of waste can be eliminated. Future intervention will focus on modifying these factors in the preoperative setting. For example, additional reminders can be built in to the scheduling system to remind patients they will need a ride, be NPO, or to attend their pre-op Anesthesia appointment. Another intervention could be to require Pre-op Anesthesia appointment the same day as surgery scheduling appointment. Continuing to cull waste in surgical scheduling will result in noticeable gains in making surgical care economically sustainable while enhancing quality.
Figure 1: Pareto Chart Revealing Source of Delays


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