초록

Most data-driven decision support tools do not include input from people. We study whether and how to incorporate physician input into such tools, in an empirical setting of predicting the surgery duration. Using data from a hospital, we evaluate and compare the performances of three families of models: models with physician forecasts, purely data-based models, and models that combine physician forecasts and data. We find that combined models perform the best, which suggests that physician forecasts have valuable information above and beyond what is captured by data. We also find that applying simple corrections to physician forecasts performs comparably well.

키워드

healthcare operations, operating room, predicting surgery duration, expert input, discretion

참고문헌(34)open

  1. [인터넷자료] American Society of Anesthesiologists / ASA Physical Status Classification System

  2. [학술지] Anand K. S. / 1997 / Information and Organization for Horizontal Multimarket Coordination / Management Science 43 (12) : 1609 ~ 1627

  3. [학술지] Bates D. W. / 2003 / Ten Commandments for Effective Clinical Decision Support: Making the Practice of Evidence-based Medicine a Reality / Journal of the American Medical Informatics Association 10 (6) : 523 ~ 530

  4. [보고서] Berner E. S. / 2009 / Clinical Decision Support Systems: State of the Art

  5. [학술지] Blattberg R. C. / 1990 / Database Models and Managerial Intuition : 50% Model + 50% Manager / Management Science 36 (8) : 887 ~ 899

  6. [학술지] Bowman E. H. / 1963 / Consistency and Optimality in Managerial Decision Making / Management Science 9 (2) : 310 ~ 321

  7. [학술지] Bunn D. / 1991 / Interaction of Judgemental and Statistical Forecasting Methods : Issues & Analysis / Management Science 37 (5) : 501 ~ 518

  8. [학술지] Cabana M. D. / 1999 / Why Don’t Physicians Follow Clinical Practice Guidelines? : A Framework for Improvement / Journal of the American Medical Association 282 (15) : 1458 ~ 1465

  9. [학술지] Cardoen B. / 2010 / Operating Room Planning and Scheduling : A Literature Review / European Journal of Operational Research 201 (3) : 921 ~ 932

  10. [학술지] Chong Y. Y. / 1986 / Econometric Evaluation of Linear Macro-economic Models / The Review of Economic Studies 53 (4) : 671 ~ 690

  11. [학술지] Dawes R. M. / 1989 / Clinical versus Actuarial Judgment / Science 243 (4899) : 1668 ~ 1674

  12. [학술지] Eijkemans M. J. / 2010 / Predicting the Unpredictable : A New Prediction Model for Operating Room Times Using Individual Characteristics and the Surgeon’s Estimate / The Journal of the American Society of Anesthesiologists 112 (1) : 41 ~ 49

  13. [학술지] Granger C. W. / 1984 / Improved Methods of Combining Forecasts / Journal of Forecasting 3 (2) : 197 ~ 204

  14. [학술대회] Hosseini N. / 2015 / Surgical Duration Estimation Via Data Mining and Predictive Modeling: A Case Study / AMIA Annual Symposium Proceedings 2015 : 640

  15. [학술지] Ibanez M. R. / 2018 / Discretionary Task Ordering : Queue Management in Radiological Services / Management Science 64 (9) : 4389 ~ 4407

  16. [학술지] Ibrahim R. / 2013 / Forecasting Call Center Arrivals : Fixedeffects, Mixed-effects, and Bivariate Models / Manufacturing & Service Operations Management 15 (1) : 72 ~ 85

  17. [학술지] Kahneman D. / 2009 / Conditions for Intuitive Expertise : A Failure to Disagree / American Psychologist 64 (6) : 515

  18. [학술지] Kim S. H. / 2015 / ICU Admission Control : An Empirical Study of Capacity Allocation and Its Implication for Patient Outcomes / Management Science 61 (1) : 19 ~ 38

  19. [학술지] Larsson A. / 2013 / The Accuracy of Surgery Time Estimations / Production Planning & Control 24 (10-11) : 891 ~ 902

  20. [학술지] Laskin D. M. / 2013 / Accuracy of Predicting the Duration of a Surgical Operation / Journal of Oral and Maxillofacial Surgery 71 (2) : 446 ~ 447

  21. [학술지] Macario A. / 2006 / Are Your Hospital Operating Rooms Efficient? A Scoring System with Eight Performance Indicators / Anesthesiology: The Journal of the American Society of Anesthesiologists 105 (2) : 237 ~ 240

  22. [학술지] Macario A. / 2010 / Is it Possible to Predict How Long a Surgery Will Last? / Medscape Anesthesiology 108 (3) : 681 ~ 685

  23. [학술지] McGlynn E. A. / 1997 / Six Challenges in Measuring the Quality of Health Care / Health Affairs 16 (3) : 7 ~ 21

  24. [단행본] Mincer, J. A. / 1969 / Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance / NBER : 3 ~ 46

  25. [학술지] Ozen A. / 2016 / Optimization and Simulation of Orthopedic Spine Surgery Cases at Mayo Clinic / Manufacturing & Service Operations Management 18 (1) : 157 ~ 175

  26. [학술지] Phillips R. / 2015 / The Effectiveness of Field Price Discretion : Empirical Evidence from Auto Lending / Management Science 61 (8) : 1741 ~ 1759

  27. [학술지] Stepaniak P. S. / 2009 / 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 / Anesthesia & Analgesia 109 (4) : 1232 ~ 1245

  28. [학술지] Strum D. P. / 2000 / Surgeon and Type of Anesthesia Predict Variability in Surgical Procedure Times / Anesthesiology : The Journal of the American Society of Anesthesiologists 92 (5) : 1454 ~ 1466

  29. [단행본] Theil H. / 1966 / Applied Economic Forecasting / Rand-McNally & Co

  30. [단행본] Timmermann A. / 2006 / Handbook of Economic Forecasting 1 / : 135 ~ 196

  31. [학술지] Travis E. / 2014 / Operating Theatre Time, Where Does It All Go? A Prospective Observational Study / BMJ 349 : g7182

  32. [학술지] Van Donselaar K. H. / 2010 / Ordering Behavior in Retail Stores and Implications for Automated Replenishment / Management Science 56 (5) : 766 ~ 784

  33. [학술지] Zhou J. / 1998 / 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 : The Journal of the American Society of Anesthesiologists 89 (5) : 1228 ~ 1232

  34. [학술대회] Zhou Z. / 2016 / Detecting Inaccurate Predictions of Pediatric Surgical Durations / 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) : 452 ~ 457