| Primary Author: | Georganne Gairhan, RN, BC, MSN |
| Co-Principal Investigators/Collaborators: | Paul Cornell, PhD, Carol Thetford, RN, BS |
| Organization | Baptist Memorial Hospital for Women |
Abstract
Purpose
To create a common understanding of the pre-op process, quantify its flow, identify means to accelerate it while maintaining performance standards, and test the efficacy of those interventions through a series of experiments.
Background
Surgical suites require the communication, coordination and collaboration of multiple human, technological and architectural resources. The human resources can be particularly thorny. Not only are they multidisciplinary, they have varied organizational allegiances, making command and control difficult.
Materials & Methods
Pre-op processes were examined at the surgical suite of a suburban women’s hospital. Three rounds of direct observation were conducted, each involving measurement of the timing, sequence and variability in the process. Following baseline observations changes were implemented, including coordination with anesthesiology. Hypotheses were tested in a second observation. The findings led to more changes, including surgeon arrival. These were tested in a third observation.
Results
Changing the arrival of anesthesiology resulted in a reduction in pre-op time (99 minutes to 92), less competition for the patient chart, and a reduction in process variability. Average Wheels In improved from 6.8 minutes late to 3.9 minutes, but failed an ANOVA. Several gains were lost in the third observation. Wheels In increased to 6.5 minutes late, and the coordination between anesthesiology and pre-op nurses declined.
Conclusion
Three lessons derived from these early results. First, improved process clarity reduced variability in performance. In addition, participants became aware of their impact on others. Second is the measurement of process. Outcome and process measures are complementary and both should be monitored continually. And the most important lesson—evidence transformed the dialog. Data won out over opinion and anecdote. The best example was the correlation analysis, where the strongest correlate to on-time performance was the arrival time of the surgeon (0.66). This provided irrefutable evidence of the impact of surgeon behavior. This changed their perspective about their influence on system performance. The project continues with additional process and technology improvements planned.
Bibliogrpahy
Bardram J. (2000) Temporal coordination. Computer Supported Cooperative Work, 9, 157-187.
Cima R, et al. (2011) Use of lean and six sigma methodology to improve operating room efficiency in a high-volume tertiary-care academic medical center. Journal of American College of Surgeons, 213, 83-92.
Fischman D. (2010) Applying lean six sigma methodologies to improve efficiency, timeliness of care, and quality of care in an internal medicine residency clinic. Quality Management in Healthcare, 19, 210-210.
Nelson-Peterson, D & Leppa C. (2007) Creating an environment for caring using lean principles of the Virgina Mason production system. Journal of Nursing Administration, 37, 287-294.
Parks J, et al. (2008) Dissecting delays in trauma care using corporate lean six sigma methodology. The Journal of Trauma, 65(5), 1098-1104.
Pelayo S, et al. (2010) Does CPOE actually disrupt physician-nurse communication? Studies in Health Technology and Informatics, 160(Pt 1), 173-177.
Reddy M & Dourish P. (2002) Finger on the pulse: Temporal rhythms and information seeking in medical work. CSCW“02”; Nov 16–20, 2002; New Orleans, Louisiana, 344–353.
Vats A, et al. (2012) The impact of a lean rounding process in a pediatric intensive care unit. Critical Care Medicine, 40(2), 608-17.
Young G. et al. (1998) Patterns of coordination and clinical outcomes: A study of surgical services. Health Services Research, 33(5), 1211-1236.
Zhang K. et al. (2010) Quantifying the impact of health IT implementations on clinical workflow: A new methodological perspective. Journal of the Medical Informatics Association, 17, 454-461.
© Improvement Science Research Network, 2012
The ISRN published this as received and with permission from the author(s).