title: Estimating surgical case durations and making comparisons among facilities: identifying facilities with lower anesthesia professional fees.
abstract: Consumer-driven health care relies on transparency in cost estimates for surgery, including anesthesia professional fees. Using systematic narrative review, we show that providing anesthesia costs requires that each facility (anesthesia group) estimate statistics, reasonably the mean and the 90% upper prediction limit of case durations by procedure. The prediction limits need to be calculated, for many procedures, using Bayesian methods based on the log-normal distribution. Insurers and/or governments lack scheduled durations and procedures and cannot practically infer these estimates because of the large heterogeneities among facilities in the means and coefficients of variation of durations. Consequently, the insurance industry cannot provide the cost information accurately from public and private databases. Instead, the role of insurers and/or governments can be to identify facilities with significantly briefer durations (costs to the patient) than average. Such comparisons of durations among facilities should be performed with correction for the effects of the multiple comparisons. Our review also has direct implications to the potentially more important issue of how to study the association between anesthetic durations and patient morbidity and mortality. When pooling duration data among facilities, both the large heterogeneity in the means and coefficients of variation of durations among facilities need to be considered (e.g., using "multilevel" or "hierarchical" models).
authors.affiliation: From the Division of Management Consulting, Department of Anesthesia, University of Iowa, Iowa City, Iowa;†Department of Anesthesiology, Jefferson Medical College, Philadelphia, Pennsylvania; and Departments of Anesthesia and Biostatistics and Management Sciences, University of Iowa, Iowa City, Iowa.
authors.forename: Franklin
authors.initials: F
authors.lastname: Dexter
authors.forename: Richard H
authors.initials: RH
authors.lastname: Epstein
authors.forename: Emine O
authors.initials: EO
authors.lastname: Bayman
authors.forename: Johannes
authors.initials: J
authors.lastname: Ledolter
mesh_terms: D000758:Anesthesia / Q000191:economics* / Q000706:statistics & numerical data; D001499:Bayes Theorem; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D005251:Fees, Medical / Q000706:statistics & numerical data*; D005544:Forecasting; D013502:General Surgery / Q000191:economics* / Q000458:organization & administration; D006268:Health Facilities / Q000191:economics* / Q000706:statistics & numerical data; D017052:Hospital Mortality; D006801:Humans; D007348:Insurance, Health; D018803:Models, Economic; D011183:Postoperative Complications / Q000191:economics / Q000453:epidemiology / Q000401:mortality
references.citation: Dexter F, Epstein RH, Lee JD, Ledolter J. Automatic updating of times remaining in surgical cases using bayesian analysis of historical case duration data and “instant messaging” updates from anesthesia providers. Anesth Analg. 2009;108:929–40
references.citation: Ledolter J, Dexter F, Epstein RH. Analysis of variance of communication latencies in anesthesia: comparing means of multiple log-normal distributions. Anesth Analg. 2011;113:888–96
references.citation: Dexter F, Wachtel RE, Sohn MW, Ledolter J, Dexter EU, Macario A. Quantifying effect of a hospital’s caseload for a surgical specialty on that of another hospital using multi-attribute market segments. Health Care Manag Sci. 2005;8:121–31
references.citation: Dexter F, Birchansky L, Bernstein JM, Wachtel RE. Case scheduling preferences of one Surgeon’s cataract surgery patients. Anesth Analg. 2009;108:579–82
references.citation: Dexter F, Epstein RH, Traub RD, Xiao Y. Making management decisions on the day of surgery based on operating room efficiency and patient waiting times. Anesthesiology. 2004;101:1444–53
references.citation: Dexter F, Wachtel RE, Epstein RH. Event-based knowledge elicitation of operating room management decision-making using scenarios adapted from information systems data. BMC Med Inform Decis Mak. 2011;11:2
references.citation: Dexter F, Macario A, Epstein RH, Ledolter J. Validity and usefulness of a method to monitor surgical services’ average bias in scheduled case durations. Can J Anaesth. 2005;52:935–9
references.citation: McIntosh C, Dexter F, Epstein RH. The impact of service-specific staffing, case scheduling, turnovers, and first-case starts on anesthesia group and operating room productivity: a tutorial using data from an Australian hospital. Anesth Analg. 2006;103:1499–516
references.citation: Dexter F, Macario A, Ledolter J. Identification of systematic underestimation (bias) of case durations during case scheduling would not markedly reduce overutilized operating room time. J Clin Anesth. 2007;19:198–203
references.citation: Dexter F, Weih LS, Gustafson RK, Stegura LF, Oldenkamp MJ, Wachtel RE. Observational study of operating room times for knee and hip replacement surgery at nine U.S. community hospitals. Health Care Manag Sci. 2006;9:325–39
references.citation: Berry M, Berry-Stölzle T, Schleppers A. Operating room management and operating room productivity: the case of Germany. Health Care Manag Sci. 2008;11:228–39
references.citation: Sulecki L, Dexter F, Zura A, Saager L, Epstein RH. Lack of value of scheduling processes to move cases from a heavily used main campus to other facilities within a health care system. Anesth Analg. 2012;115:395–401
references.citation: Cassera MA, Zheng B, Martinec DV, Dunst CM, Swanström LL. Surgical time independently affected by surgical team size. Am J Surg. 2009;198:216–22
references.citation: Dexter F, Dexter EU, Masursky D, Nussmeier NA. Systematic review of general thoracic surgery articles to identify predictors of operating room case durations. Anesth Analg. 2008;106:1232–41
references.citation: Stepaniak PS, Heij C, de Vries G. Modeling and prediction of surgical procedure times. Statistica Neerlandica. 2010;64:1–18
references.citation: Eijkemans MJ, van Houdenhoven M, Nguyen T, Boersma E, Steyerberg EW, Kazemier G. Predicting the unpredictable: a new prediction model for operating room times using individual characteristics and the surgeon’s estimate. Anesthesiology. 2010;112:41–9
references.citation: Stepaniak PS, Vrijland WW, de Quelerij M, de Vries G, Heij C. Working with a fixed operating room team on consecutive similar cases and the effect on case duration and turnover time. Arch Surg. 2010;145:1165–70
references.citation: Gillespie BM, Chaboyer W, Fairweather N. Factors that influence the expected length of operation: results of a prospective study. BMJ Qual Saf. 2012;21:3–12
references.citation: Dexter F, Traub RD. Sequencing cases in the operating room: predicting whether one surgical case will last longer than another. Anesth Analg. 2000;90:975–9
references.citation: Weiss EN. Models for determining estimated start times and case orderings in hospital operating rooms. IIE Transactions. 1990;22:143–50
references.citation: Wachtel RE, Dexter F. A simple method for deciding when patients should be ready on the day of surgery without procedure-specific data. Anesth Analg. 2007;105:127–40
references.citation: Wachtel RE, Dexter F. Influence of the operating room schedule on tardiness from scheduled start times. Anesth Analg. 2009;108:1889–901
references.citation: Smallman B, Dexter F. Optimizing the arrival, waiting, and NPO times of children on the day of pediatric endoscopy procedures. Anesth Analg. 2010;110:879–87
references.citation: Dexter F, Macario A, O’Neill L. A strategy for deciding operating room assignments for second-shift anesthetists. Anesth Analg. 1999;89:920–4
references.citation: Dexter EU, Dexter F, Masursky D, Kasprowicz KA. Prospective trial of thoracic and spine surgeons’ updating of their estimated case durations at the start of cases. Anesth Analg. 2010;110:1164–8
references.citation: Dalton JE, Kurz A, Turan A, Mascha EJ, Sessler DI, Saager L. Development and validation of a risk quantification index for 30-day postoperative mortality and morbidity in noncardiac surgical patients. Anesthesiology. 2011;114:1336–44
references.citation: Dexter F, Dexter EU, Ledolter J. Influence of procedure classification on process variability and parameter uncertainty of surgical case durations. Anesth Analg. 2010;110:1155–63
references.citation: Barber JA, Thompson SG. Analysis of cost data in randomized trials: an application of the non-parametric bootstrap. Stat Med. 2000;19:3219–36
references.citation: Strum DP, May JH, Vargas LG. Modeling the uncertainty of surgical procedure times: comparison of log-normal and normal models. Anesthesiology. 2000;92:1160–7
references.citation: Dexter F, Ledolter J. Bayesian prediction bounds and comparisons of operating room times even for procedures with few or no historic data. Anesthesiology. 2005;103:1259–167
references.citation: Spangler WE, Strum DP, Vargas LG, May JH. Estimating procedure times for surgeries by determining location parameters for the lognormal model. Health Care Manag Sci. 2004;7:97–104
references.citation: Stepaniak PS, Heij C, Mannaerts GH, de Quelerij M, de Vries G. Modeling procedure and surgical times for current procedural terminology-anesthesia-surgeon combinations and evaluation in terms of case-duration prediction and operating room efficiency: a multicenter study. Anesth Analg. 2009;109:1232–45
references.citation: He B, Dexter F, Macario A, Zenios S. The timing of staffing decisions in hospital operating room: incorporating workload heterogeneity into the newsvendor problem. Manuf Serv Op. 2012;14:99–114
references.citation: Dexter F, Traub RD. Statistical method for predicting when patients should be ready on the day of surgery. Anesthesiology. 2000;93:1107–14
references.citation: Bayman EO, Chaloner K, Cowles MK. Detecting qualitative interaction: a Bayesian approach. Stat Med. 2010;29:455–63
references.citation: Finney DJ. On the distribution of a variate whose logarithm is normally distributed. J R Stat Soc Series B Stat Methodol. 1941;7:155–61
references.citation: Zhou XH. Estimation of the log-normal mean. Stat Med. 1998;17:2251–64
references.citation: Zellner A. Bayesian and non-Bayesian analysis of the log-normal distribution and log-normal regression. J Am Stat Assoc. 1971;66:327–30
references.citation: Wachtel RE, Dexter F. Reducing tardiness from scheduled start times by making adjustments to the operating room schedule. Anesth Analg. 2009;108:1902–9
references.citation: Kaushik R. Bleeding complications in laparoscopic cholecystectomy: Incidence, mechanisms, prevention and management. J Minim Access Surg. 2010;6:59–65
references.citation: Zhou J, Dexter F. Method to assist in the scheduling of add-on surgical cases–upper prediction bounds for surgical case durations based on the log-normal distribution. Anesthesiology. 1998;89:1228–32
references.citation: Clopper CJ, Pearson ES. The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika. 1934;26:404–13
references.citation: Hall P, Rieck A. Improving coverage accuracy of nonparametric prediction intervals. J R Stat Soc Ser B. 2001;63:717–25
references.citation: Dexter F. Application of prediction levels to OR scheduling. AORN J. 1996;63:607–15
references.citation: Li Y, Zhang S, Baugh RF, Huang JZ. Predicting surgical case durations using ill-conditioned CPT code matrix. IIE Transactions. 2010;42:121–35
references.citation: Zhou J, Dexter F, Macario A, Lubarsky DA. Relying solely on historical surgical times to estimate accurately future surgical times is unlikely to reduce the average length of time cases finish late. J Clin Anesth. 1999;11:601–5
references.citation: Silber JH, Rosenbaum PR, Zhang X, Even-Shoshan O. Estimating anesthesia and surgical procedure times from medicare anesthesia claims. Anesthesiology. 2007;106:346–55
references.citation: Jones HE, Spiegelhalter DJ. The identification of “unusual” health-care providers from a hierarchical model. Am Stat. 2011;65:154–63
references.citation: Silber JH, Rosenbaum PR, Zhang X, Even-Shoshan O. Influence of patient and hospital characteristics on anesthesia time in medicare patients undergoing general and orthopedic surgery. Anesthesiology. 2007;106:356–64
references.citation: Dexter F, Davis M, Egger Halbeis CB, Halbeis CE, Marjamaa R, Marty J, McIntosh C, Nakata Y, Thenuwara KN, Sawa T, Vigoda M. Mean operating room times differ by 50% among hospitals in different countries for laparoscopic cholecystectomy and lung lobectomy. J Anesth. 2006;20:319–22
references.citation: Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21:1539–58
references.citation: Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, Clarke M, Devereaux PJ, Kleijnen J, Moher D. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J Clin Epidemiol. 2009;62:e1–34
references.citation: Dexter F, Bayman EO, Epstein RH. Statistical modeling of average and variability of time to extubation for meta-analysis comparing desflurane to sevoflurane. Anesth Analg. 2010;110:570–80
references.citation: Wachtel RE, Dexter F, Epstein RH, Ledolter J. Meta-analysis of desflurane and propofol average times and variability in times to extubation and following commands. Can J Anaesth. 2011;58:714–24
references.citation: Ledolter J, Dexter F. Analysis of interventions influencing or reducing patient waiting while stratifying by surgical procedure. Anesth Analg. 2011;112:950–7
references.citation: DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7:177–88
references.citation: Spiegelhalter DJ. Handling over-dispersion of performance indicators. Qual Saf Health Care. 2005;14:347–51
references.citation: Strum DP, Sampson AR, May JH, Vargas LG. Surgeon and type of anesthesia predict variability in surgical procedure times. Anesthesiology. 2000;92:1454–66
references.citation: Wright IH, Kooperberg C, Bonar BA, Bashein G. Statistical modeling to predict elective surgery time. Comparison with a computer scheduling system and surgeon-provided estimates. Anesthesiology. 1996;85:1235–45
references.citation: Banik S, Kibria BMG. Comparison of some parametric and nonparametric type one sample confidence intervals for estimating the mean of a positively skewed distribution. Commun Stat Simulat. 2010;39:361–89
references.citation: Krishnamoorthy K, Mathew T. Inferences on the means of lognormal distributions using generalized p-values and generalized confidence intervals. J Stat Plan Inference. 2003;115:103–121
references.citation: Kuntz L, Vera A. Transfer pricing in hospitals and efficiency of physicians: the case of anesthesia services. Health Care Manage Rev. 2005;30:262–9
references.citation: Schuster M, Standl T, Reissmann H, Kuntz L, Am Esch JS. Reduction of anesthesia process times after the introduction of an internal transfer pricing system for anesthesia services. Anesth Analg. 2005;101:187–94
references.citation: Jones HE, Ohlssen DI, Spiegelhalter DJ. Use of the false discovery rate when comparing multiple health care providers. J Clin Epidemiol. 2008;61:232–40
references.citation: Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B. 1995;57:289–300
references.citation: Dexter F, Dexter EU, Ledolter J. Importance of appropriately modeling procedure and duration in logistic regression studies of perioperative morbidity and mortality. Anesth Analg. 2011;113:1197–201
references.citation: Allen BT, Anderson CB, Rubin BG, Thompson RW, Flye MW, Young-Beyer P, Frisella P, Sicard GA. The influence of anesthetic technique on perioperative complications after carotid endarterectomy. J Vasc Surg. 1994;19:834–42
references.citation: Serletti JM, Higgins JP, Moran S, Orlando GS. Factors affecting outcome in free-tissue transfer in the elderly. Plast Reconstr Surg. 2000;106:66–70
references.citation: Farwell DG, Reilly DF, Weymuller EA Jr, Greenberg DL, Staiger TO, Futran NA. Predictors of perioperative complications in head and neck patients. Arch Otolaryngol Head Neck Surg. 2002;128:505–11
references.citation: Penel N, Mallet Y, Roussel-Delvallez M, Lefebvre JL, Yazdanpanah Y. Factors determining length of the postoperative hospital stay after major head and neck cancer surgery. Oral Oncol. 2008;44:555–62
references.citation: Greenblatt DY, Rajamanickam V, Mell MW. Predictors of surgical site infection after open lower extremity revascularization. J Vasc Surg. 2011;54:433–9
references.citation: Glaser D, Hastings RH. An introduction to multilevel modeling for anesthesiologists. Anesth Analg. 2011;113:877–87
references.citation: Muralidhar K, Zanakis SH. A simple minimum-bias percentile estimator of the location parameter for the gamma, weibull, and log-normal distributions. Decision Sciences. 1992;23:862–79
references.citation: Shapiro SS, Wilk MB, Chen HJ. A comparative study of various tests for normality. J Am Stat Assoc. 1968;63:1343–72
references.citation: Shapiro SS, Wilk MB. An analysis of variance test for normality. Biometrika. 1965;52:591–611
references.citation: Lilliefors HW. On the Kolmogorov-Smirnov test for normality with mean and variance unknown. J Am Stat Assoc. 1967;62:399–402