Predictive analytics model for healthcare planning and scheduling. (English) Zbl 1346.90350
Summary: Patients who fail to attend their appointments complicate appointment scheduling systems. The accurate prediction of no-shows may assist a clinic in developing operational mitigation strategies, such as overbooking appointment slots or special management of patients who are predicted as being highly likely to not attend. We present a new model for predicting no-show behavior based solely on the binary representation of a patient’s historical attendance history. Our model is a parsimonious, pure predictive analytics technique, which combines regression-like modeling and functional approximation, using the sum of exponential functions, to produce probability estimates. It estimates parameters that can give insight into the way in which past behavior affects future behavior, and is important for clinic planning and scheduling decisions to improve patient service. Additionally, our choice of exponential functions for modeling leads to tractable analysis that is proved to produce optimal and unique solutions. We illustrate our approach using data from patients’ attendance and non-attendance at Veteran Health Administration (VHA) outpatient clinics.
MSC:
| 90B35 | Deterministic scheduling theory in operations research |
| 90B90 | Case-oriented studies in operations research |
Keywords:
analytics; OR in health services; predictive modeling; outpatient appointments; no-show modelingReferences:
| [1] | Bean, A.; Talaga, J., Predicting appointment breaking, Journal of Health Care Marketing, 15, 1, 29 (1995) |
| [2] | Berchtold, A.; Raftery, A. E., The mixture transition distribution model for high-order Markov chains and non-Gaussian time series, Statistical Science, 17, 3, 328-356 (2002) · Zbl 1013.62088 |
| [4] | Berg, B. P.; Denton, B. T.; Erdogan, S. A.; Rohleder, T.; Huschka, T., Optimal booking and scheduling in outpatient procedure centers, Computers & Operations Research, 50, 24-37 (2014) · Zbl 1348.90341 |
| [5] | Beylkin, G.; Monzón, L., On approximation of functions by exponential sums, Applied and Computational Harmonic Analysis, 19, 1, 17-48 (2005) · Zbl 1075.65022 |
| [6] | Beylkin, G.; Monzón, L., Approximation by exponential sums revisited, Applied and Computational Harmonic Analysis, 28, 2, 131-149 (2010) · Zbl 1189.65035 |
| [7] | Box, G. E.; Jenkins, G. M.; Reinsel, G. C., Time series analysis: Forecasting and control, 734 (2011), John Wiley & Sons |
| [8] | Brier, G. W., Verification of forecasts expressed in terms of probability, Monthly Weather Review, 78, 1, 1-3 (1950) |
| [9] | Burnham, K. P.; Anderson, D. R., Model selection and multimodel inference: A practical information-theoretic approach (2002), Springer · Zbl 1005.62007 |
| [10] | Cayirli, T.; Veral, E., Outpatient scheduling in health care: A review of literature, Production and Operations Management, 12, 4, 519-549 (2003) |
| [11] | Chiu, T.; Fang, D.; Chen, J.; Wang, Y.; Jeris, C., A robust and scalable clustering algorithm for mixed type attributes in large database environment, (Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining (2001)) |
| [12] | Cosgrove, M., Defaulters in general practice: Reasons for default and patterns of attendance, The British Journal of General Practice, 40, 331, 50 (1990) |
| [13] | Cox, D. R.; Gudmundsson, G.; Lindgren, G.; Bondesson, L.; Harsaae, E.; Laake, P.; Juselius, K.; Lauritzen, S. L., Statistical analysis of time series: Some recent developments [with discussion and reply], Scandinavian Journal of Statistics, 8, 2, 93-115 (1981) · Zbl 0468.62079 |
| [14] | Daggy, J.; Lawley, M.; Willis, D.; Thayer, D.; Suelzer, C.; DeLaurentis, P. C.; Turkcan, A.; Chakraborty, S.; Sands, L., Using no-show modeling to improve clinic performance, Health Informatics Journal, 16, 4, 246-259 (2010) |
| [15] | DeLong, E. R.; DeLong, D. M.; Clarke-Pearson, D. L., Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach, Biometrics, 44, 3, 837-845 (1988) · Zbl 0715.62207 |
| [16] | Fader, P. S.; Hardie, B. G.S.; Shang, J., Customer-base analysis in a discrete-time noncontractual setting, Marketing Science, 29, 6, 1086-1108 (2010) |
| [17] | Garuda, S. R.; Javalgi, R. G.; Talluri, V. S., Tackling no-show behavior: A market-driven approach, Health Marketing Quarterly, 15, 4, 25-44 (1998) |
| [18] | Goffman, R.; Harris, S. L.; May, E. K.; May, J. H.; Tjader, Y. C.; Vargas, D. L., Modeling patient no-show history and predicting future outpatient appointment behavior (2016), Joseph M. Katz Graduate School of Business, University of Pittsburgh, Working Paper |
| [19] | Glowacka, K. J.; Henry, R. M.; May, J. H., A hybrid data mining/simulation approach for modelling outpatient no-shows in clinic scheduling, Journal of the Operational Research Society, 60, 8, 1056-1068 (2009) · Zbl 1168.90440 |
| [20] | Griva, I.; Nash, S. G.; Sofer, A., Linear and nonlinear optimization (2009), Siam · Zbl 1159.90002 |
| [21] | Huang, Y.; Hanauer, D., Patient no-show predictive model development using multiple data sources for an effective overbooking approach, Applied Clinical Informatics, 5, 3, 836-860 (2014) |
| [22] | LaGanga, L. R.; Lawrence, S. R., Clinic overbooking to improve patient access and increase provider productivity, Decision Sciences, 38, 2, 251-276 (2007) |
| [23] | LaGanga, L. R.; Lawrence, S. R., Appointment overbooking in health care clinics to improve patient service and clinic performance, Production and Operations Management, 21, 5, 874-888 (2012) |
| [24] | Lawrence, R. D.; Hong, S. J.; Cherrier, J., Passenger-based predictive modeling of airline no-show rates, (Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining (2003)) |
| [25] | Li, W., Time series models based on generalized linear models: Some further results, Biometrics, 50, 2, 506-511 (1994) · Zbl 0825.62606 |
| [26] | Liu, N.; Ziya, S.; Kulkarni, V. G., Dynamic scheduling of outpatient appointments under patient no-shows and cancellations, Manufacturing & Service Operations Management, 12, 2, 347-364 (2010) |
| [27] | Mehran, F., Longitudinal analysis of employment and unemployment based on matched rotation samples, Labour, 3, 1, 3-20 (1989) |
| [28] | Pereyra, V.; Scherer, G., Exponential data fitting and its applications, 1-26 (2010), Bentham Science Publishers · Zbl 1469.65009 |
| [29] | Prinzie, A.; Van den Poel, D., Investigating purchasing-sequence patterns for financial services using Markov, MTD and MTDg models, European Journal of Operational Research, 170, 3, 710-734 (2006) · Zbl 1091.90527 |
| [30] | Raftery, A. E., A model for high-order Markov chains, Journal of the Royal Statistical Society. Series B (Methodological), 47, 3, 528-539 (1985) · Zbl 0593.62091 |
| [31] | Rust, C. T.; Gallups, N. H.; Clark, W. S.; Jones, D. S.; Wilcox, W. D., Patient appointment failures in pediatric resident continuity clinics, Archives of Pediatrics & Adolescent Medicine, 149, 6, 693 (1995) |
| [32] | Startz, R., Binomial autoregressive moving average models with an application to US recessions, Journal of Business and Economic Statistics, 26, 1, 1-8 (2008) |
| [33] | Vasilakis, C.; Marshall, A. H., Modelling nationwide hospital length of stay: Opening the black box, Journal of the Operational Research Society, 56, 7, 862-869 (2005) · Zbl 1084.90512 |
| [34] | Xie, H.; Chaussalet, T. J.; Millard, P. H., A continuous time Markov model for the length of stay of elderly people in institutional long‐term care, Journal of the Royal Statistical Society: Series A (Statistics in Society), 168, 1, 51-61 (2005) · Zbl 1100.62105 |
| [35] | Zacharias, C.; Pinedo, M., Appointment scheduling with no‐shows and overbooking, Production and Operations Management, 23, 5, 788-801 (2014) |
| [36] | Zeger, S. L.; Qaqish, B., Markov regression models for time series: A quasi-likelihood approach, Biometrics, 44, 4, 1019-1031 (1988) · Zbl 0715.62166 |
| [37] | Zeng, B.; Turkcan, A.; Lin, J.; Lawley, M., Clinic scheduling models with overbooking for patients with heterogeneous no-show probabilities, Annals of Operations Research, 178, 1, 121-144 (2010) · Zbl 1197.90245 |
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