Estimating Procedure Times for Surgeries by Determining Location Parameters for the Lognormal Model

We present an empirical study of methods for estimating the location parameter of the lognormal distribution. Our results identify the best order statistic to use, and indicate that using the best order statistic instead of the median may lead to less frequent incorrect rejection of the lognormal model, more accurate critical value estimates, and higher goodness-of-fit. Using simulation data, we constructed and compared two models for identifying the best order statistic, one based on conventional nonlinear regression and the other using a data mining/machine learning technique. Better surgical procedure time estimates may lead to improved surgical operations.

[1]  Satya D. Dubey,et al.  Some Percentile Estimators for Weibull Parameters , 1967 .

[2]  E. S. Pearson,et al.  Tests for departure from normality: Comparison of powers , 1977 .

[3]  D. Dannenbring.  Procedures for Estimating Optimal Solution Values for Large Combinatorial Problems , 1977 .

[4]  Linus Schrage,et al.  A guide to simulation , 1983 .

[5]  Paul Bratley,et al.  A guide to simulation , 1983 .

[6]  F. D. Moore.  Surgical Streams in the Flow of Health Care Financing: The Role of Surgery in National Expenditures; What Costs Are Controllable , 1985, Annals of surgery.

[7]  Paul Bratley,et al.  A guide to simulation (2nd ed.) , 1986 .

[8]  T. Wickizer.  Effect of hospital utilization review on medical expenditures in selected diagnostic areas: an exploratory study. , 1991, American journal of public health.

[9]  P. Sen,et al.  Order statistics and inference : estimation methods , 1992 .

[10]  Krishnamurty Muralidhar,et al.  A Simple Minimum‐Bias Percentile Estimator of the Location Parameter for the Gamma, Weibull, and Log‐Normal Distributions , 1992 .

[11]  D A Redelmeier,et al.  Hospital expenditures in the United States and Canada. , 1993, The New England journal of medicine.

[12]  Uday S. Karmarkar,et al.  A robust forecasting technique for inventory and leadtime management , 1994 .

[13]  A. Epstein.  US teaching hospitals in the evolving health care system. , 1995, JAMA.

[14]  N. L. Johnson,et al.  Continuous Univariate Distributions. , 1995 .

[15]  Narayanaswamy Balakrishnan,et al.  CRC Handbook of Tables for the Use of Order Statistics in Estimation , 1996 .

[16]  X H Zhou.  Estimation of the log-normal mean. , 1998, Statistics in medicine.

[17]  F. Dexter,et al.  Method to Assist in the Scheduling of Add‐on Surgical Cases‐Upper Prediction Bounds for Surgical Case Durations Based on the Log‐normal Distribution , 1998, Anesthesiology.

[18]  F. Dexter,et al.  Estimating the duration of a case when the surgeon has not recently scheduled the procedure at the surgical suite. , 1999, Anesthesia and Analgesia.

[19]  Luis G. Vargas,et al.  Surgical subspecialty block utilization and capacity planning: a minimal cost analysis model. , 1999, Anesthesiology.

[20]  C. Batson,et al.  Improving on‐time performance in health care organizations: a case study , 1999, Health Care Management Science.

[21]  F. Dexter,et al.  Relying solely on historical surgical times to estimate accurately future surgical times is unlikely to reduce the average length of time cases finish late. , 1999, Journal of clinical anesthesia.

[22]  F. Dexter,et al.  Which algorithm for scheduling add-on elective cases maximizes operating room utilization? Use of bin packing algorithms and fuzzy constraints in operating room management. , 1999, Anesthesiology.

[23]  F. Dexter,et al.  An operating room scheduling strategy to maximize the use of operating room block time: computer simulation of patient scheduling and survey of patients' preferences for surgical waiting time. , 1999, Anesthesia and analgesia.

[24]  E. Litvak,et al.  Cost and quality under managed care: irreconcilable differences? , 2000, The American journal of managed care.

[25]  Franklin Dexter,et al.  Sequencing cases in the operating room: predicting whether one surgical case will last longer than another. , 2000 .

[26]  Luis G. Vargas,et al.  Surgeon and Type of Anesthesia Predict Variability in Surgical Procedure Times , 2000, Anesthesiology.

[27]  Luis G. Vargas,et al.  Fitting the Lognormal Distribution to Surgical Procedure Times , 2000, Decis. Sci..

[28]  Luis G. Vargas,et al.  Modeling the Uncertainty of Surgical Procedure Times: Comparison of Log-normal and Normal Models , 2000, Anesthesiology.

[29]  P. Michelon,et al.  A mathematical programming approach for scheduling physicians in the emergency room , 2000, Health care management science.

[30]  M. Carter,et al.  Scheduling Emergency Room Physicians , 2001, Health care management science.

[31]  R. Epstein,et al.  A Statistical Analysis of Weekday Operating Room Anesthesia Group Staffing Costs at Nine Independently Managed Surgical Suites , 2001, Anesthesia and analgesia.

[32]  John T. Blake,et al.  Operating Room Managers’ Use of Integer Programming for Assigning Block Time to Surgical Groups: A Case Study , 2002, Anesthesia and analgesia.

[33]  Franklin Dexter,et al.  Changing Allocations of Operating Room Time From a System Based on Historical Utilization to One Where the Aim is to Schedule as Many Surgical Cases as Possible , 2002, Anesthesia and analgesia.

[34]  John T. Blake,et al.  Mount Sinai Hospital Uses Integer Programming to Allocate Operating Room Time , 2002, Interfaces.

[35]  M. Pagano,et al.  Variability in Surgical Caseload and Access to Intensive Care Services , 2003, Anesthesiology.

[36]  F. Dexter,et al.  How to release allocated operating room time to increase efficiency: predicting which surgical service will have the most underutilized operating room time. , 2003 .