title: A monitoring tool for performance improvement in plastic surgery at the individual level.
abstract: BACKGROUND
  The assessment of performance in surgery is expanding significantly. Application of relevant frameworks to plastic surgery, however, has been limited. In this article, the authors present two robust graphic tools commonly used in other industries that may serve to monitor individual surgeon operative time while factoring in patient- and surgeon-specific elements.
  
  
  METHODS
  The authors reviewed performance data from all bilateral reduction mammaplasties performed at their institution by eight surgeons between 1995 and 2010. Operative time was used as a proxy for performance. Cumulative sum charts and exponentially weighted moving average charts were generated using a train-test analytic approach, and used to monitor surgical performance. Charts mapped crude, patient case-mix-adjusted, and case-mix and surgical-experience-adjusted performance.
  
  
  RESULTS
  Operative time was found to decline from 182 minutes to 118 minutes with surgical experience (p < 0.001). Cumulative sum and exponentially weighted moving average charts were generated using 1995 to 2007 data (1053 procedures) and tested on 2008 to 2010 data (246 procedures). The sensitivity and accuracy of these charts were significantly improved by adjustment for case mix and surgeon experience.
  
  
  CONCLUSIONS
  The consideration of patient- and surgeon-specific factors is essential for correct interpretation of performance in plastic surgery at the individual surgeon level. Cumulative sum and exponentially weighted moving average charts represent accurate methods of monitoring operative time to control and potentially improve surgeon performance over the course of a career.
authors: Boston and Jamaica Plain, Mass.; and Lyon, France From Brigham and Women's Hospital Center for Surgery and Public Health; Hospices Civils de Lyon, Pôle Information Médicale Evaluation Recherche, Université de Lyon; and Division of Plastic Surgery, Brigham and Women's Hospital/Faulkner Hospital.
authors: Mahiben
authors: M
authors: Maruthappu
authors: Antoine
authors: A
authors: Duclos
authors: Dennis
authors: D
authors: Orgill
authors: Matthew J
authors: MJ
authors: Carty
mesh_terms: D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D005260:Female; D006801:Humans; D016462:Mammaplasty / Q000592:standards* / Q000706:statistics & numerical data; D061646:Operative Time; D017063:Outcome Assessment, Health Care; D011183:Postoperative Complications / Q000453:epidemiology / Q000517:prevention & control; D011785:Quality Assurance, Health Care / Q000379:methods*; D012189:Retrospective Studies; D020379:Risk Adjustment / Q000592:standards; D013518:Surgery, Plastic / Q000592:standards* / Q000706:statistics & numerical data
references: Dimick JB, Weeks WB, Karia RJ, Das S, Campbell DA Jr. Who pays for poor surgical quality? Building a business case for quality improvement. J Am Coll Surg. 2006;202:933–937
references: Hersh WR. Medical informatics: Improving health care through information. JAMA. 2002;288:1955–1958
references: Carty MJ, Chan R, Huckman R, Snow D, Orgill DP. A detailed analysis of the reduction mammaplasty learning curve: A statistical process model for approaching surgical performance improvement. Plast Reconstr Surg. 2009;124:706–714
references: Kusamura S, Baratti D, Deraco M. Multidimensional analysis of the learning curve for cytoreductive surgery and hyperthermic intraperitoneal chemotherapy in peritoneal surface malignancies. Ann Surg. 2012;255:348–356
references: Duclos A, Lifante JC, Ducarroz S, Soardo P, Colin C, Peix JL. Influence of intraoperative neuromonitoring on surgeons’ technique during thyroidectomy. World J Surg. 2011;35:773–778
references: Williams SM, Parry BR, Schlup MM. Quality control: An application of the cusum. BMJ. 1992;304:1359–1361
references: Cook JA, Ramsay CR, Fayers P. Statistical evaluation of learning curve effects in surgical trials. Clin Trials. 2004;1:421–427
references: Page E. Continuous inspection schemes. Biometriks. 1954;41:100–115
references: Roberts SW. Control chart tests based on geometric moving averages. Technometrics. 1959;42:97–102
references: Biau DJ, Resche-Rigon M, Godiris-Petit G, Nizard RS, Porcher R. Quality control of surgical and interventional procedures: A review of the CUSUM. Qual Saf Health Care. 2007;16:203–207
references: Duclos A, Carty MJ, Peix JL, et al.CATHY Study Group. Development of a charting method to monitor the individual performance of surgeons at the beginning of their career. PLoS One. 2012;7:e41944
references: Cook DA, Duke G, Hart GK, Pilcher D, Mullany D. Review of the application of risk-adjusted charts to analyse mortality outcomes in critical care. Crit Care Resusc. 2008;10:239–251
references: Cook DA, Coory M, Webster RA. Exponentially weighted moving average charts to compare observed and expected values for monitoring risk-adjusted hospital indicators. BMJ Qual Saf. 2011;20:469–474
references: Carty MJ, Ashley SW. Too much of a good thing or operating at the fringe. Arch Surg. 2011;146:1203
references: Horsch A, Hapfelmeier A, Elter M. Needs assessment for next generation computer-aided mammography reference image databases and evaluation studies. Int J Comput Assist Radiol Surg. 2011;6:749–767
references: Ramsay CR, Grant AM, Wallace SA, Garthwaite PH, Monk AF, Russell IT. Statistical assessment of the learning curves of health technologies. Health Technol Assess. 2001;5:1–79
references: Steiner SH, Cook RJ, Farewell VT, Treasure T. Monitoring surgical performance using risk-adjusted cumulative sum charts. Biostatistics. 2000;1:441–452
references: Lucas JM, Saccucci MS. Exponentially weighted moving average control schemes: Properties and enhancements. Technometrics. 1990;32:1–29
references: Woodall WH, Adams BM. The statistical design of CUSUM charts. Qual Eng. 1993;5:559–570
references: Biau DJ, Porcher R, Salomon LJ. CUSUM: A tool for ongoing assessment of performance. Ultrasound Obstet Gynecol. 2008;31:252–255
references: Cunningham BL, Gear AJ, Kerrigan CL, Collins ED. Analysis of breast reduction complications derived from the BRAVO study. Plast Reconstr Surg. 2005;115:1597–1604
references: Chun YS, Schwartz MA, Gu X, Lipsitz SR, Carty MJ. Body mass index as a predictor of postoperative complications in reduction mammaplasty. Plast Reconstr Surg. 2012;129:228e–233e
references: Hu YY, Peyre SE, Arriaga AF, et al. Postgame analysis: Using video-based coaching for continuous professional development. J Am Coll Surg. 2012;214:115–124
references: Duclos A, Peix JL, Colin C, et al.CATHY Study Group. Influence of experience on performance of individual surgeons in thyroid surgery: Prospective cross sectional multicentre study. BMJ. 2012;344:d8041
references: Carty MJ, Duclos A, Gu X, Elele N, Orgill D. Patient satisfaction and surgeon experience: A follow-up to the reduction mammaplasty learning curve study. Eplasty. 2012;12:e22
references: Lucas JM, Saccucci MS. Exponentially weighted moving average control schemes: Properties and enhancements. Technometrics. 1990;32:1–29
references: Crowder SV. A simple method for studying run-length distributions of exponentially weighted moving average charts. Technometrics. 1987;29:401–408
references: Gan FF. An optimal design of CUSUM quality control charts. J Qual Tech. 1991;23:279–286
references: Lucas JM, Crosier RB. Fast initial response for CUSUM quality control schemes: Give your CUSUM a head start. Technometrics. 1982;24:199–205
references: Biau DJ, Landreau P, Graveleau N. Monitoring surgical performance: An application of industrial quality process control to anterior cruciate ligament reconstruction. Knee Surg Sports Traumatol Arthrosc. 2010;18:1263–1268
references: Lim TO, Soraya A, Ding LM, Morad Z. Assessing doctors’ competence: Application of CUSUM technique in monitoring doctors’ performance. Int J Qual Health Care. 2002;14:251–258