A hybrid data mining/simulation approach for modelling outpatient no-shows in clinic scheduling

Journal of the Operational Research Society

Volumn 60, Issue 8, 2009, Pages 1056-1068

  Glowacka, K J ^a   Henry, R M ^b   May, J H ^c  

^a MCGILL UNIVERSITY   (Canada)

^b CLEMSON UNIVERSITY   (United States)

^c UNIVERSITY OF PITTSBURGH   (United States)

Author keywords

Association rules; Data mining; Healthcare; Outpatient scheduling; Simulation

Indexed keywords

ASSOCIATION RULES; HEALTH CARE; SCHEDULING;

ASSOCIATION RULE MININGS (ARM); OPERATIONAL RESEARCH; OPTIMIZATION METHOD; OUTPATIENT CLINIC; OUTPATIENT SCHEDULING; PERFORMANCE MEASURE; SIMULATION; SIMULATION TECHNIQUE;

DATA MINING;

EID: 68149099919     PISSN: 01605682     EISSN: 14769360     Source Type: Journal    
DOI: 10.1057/jors.2008.177     Document Type: Article

References (44)

1. Agrawal R, Imielinski T and Swami A (1993). Mining association rules between sets of items in large databases. In: Buneman P and Jojodia S (eds). Proceedings of the ACM SIGMOD Conference. Washington, DC., pp 1-10.
2. Apte C and Weiss SM (1997). Data mining with decision trees and decision rules. Future generation computer systems. Comput Syst 13: 197-210.
3. Ashton R, Hague L, Brandreth M, Worthington D and Cropper S (2005). A simulation-based study of a NHS Walk-in Centre. J Opl Res Soc 56: 153-161.
4. Au WH and Chan KC (2003). Mining fuzzy association rules in a bank account data-base. IEEE Trans Fuzzy Syst 11(2): 238-248.
5. Bailey N (1952). A study of queues and appointment systems in hospital outpatient departments with special references to waiting times. J R Stat Soc 3: 137-145.
6. Bean AG and Talaga J (1995). Predicting appointment breaking. J Health Care Mark 15(1): 29-34.
7. Cabena P, Hadjnian P, Stadler R, Verhees J and Zanasi A (1997). Discovering Data Mining: From Concept to Implementation. Prentice-Hall: Englewood Cliffs, NJ.
8. Campbell JD, Chex RA, Queen T, Barcelo A and Patron E (2000). The no-show rate in a high risk obstetric clinic. J Women's Health & Gender-based Medicine 9: 891-895.
9. Cashman SB (2004). Patient health status and appointment keeping in an urban community health center. J Health Care Poor Underserved 15(3): 474-488.
10. Cayirli T and Veral E (2003). Outpatient scheduling in health care: A review of the literature. Prod Opns Mngt 12(4): 519-549.
11. Cayirli T, Veral E and Rosen H (2008). Assessment of patient classification in appointment system design. Prod Opns Mngt 17(3):338-353.
12. Ceglowski R, Churilov L and Wasserthiel J (2007). Combining data mining and discrete event simulation for a value-added view of a hospital emergency department. J Opl Res Soc 58: 246-254.
13. Chen G, Liu H, Yu L, Wei Q and Zhang X (2006). A new approach to classification based on association rule mining. Dec Sup Syst 42: 674-689.
14. Cote MJ (1999). Patient flow and resource utilization in an outpatient clinic. Socio-Econ Plan Sci 33: 231-245.
15. Cox TF, Birchall JF and Wong H (1985). Optimizing the queuing system for an ear, nose, and throat outpatient clinic. J Appl Stat 12: 113-126.
16. Dove HG and Schneider KC (1981). The usefulness of patients' individual characteristics in predicting no-shows in outpatient clinics. Med Care 19(7): 734-740.
17. Eldabi T, Paul RJ and Young T (2007). Simulation modelling in healthcare: Reviewing legacies and investigating futures. J Opl Res Soc 58: 262-270.
18. Fry J (1964). Appointments in general practice. Opl Res Q 15(3): 233-237.
19. Garuda SR, Javalgi RG and Talluri VS (1998). Tackling no-show behavior: A market driven approach. Health Mark Q 15(4): 25-44.
20. Glover F, Kelly JP and Laguna M (2002). The OptQuest Callable Library User'S Documentation. Optimization Technologies, Inc.: Boulder, CO.
21. Goldman L, Freidin R, Cook EF, Eigner J and Grich P (1982). A multivariate approach to the prediction of no-show behavior in a primary care center. Arch Intern Med 142: 563-567.
22. Gregg AC, DeHaven M, Meires J, Kane A and Gullison G (2001). Perspectives on adherence to recommended health behavior among low-income patients. Health Promot Pract 2(2): 162-171.
23. Guo M, Wagner M and West C (2004). Outpatient scheduling - A simulation approach. In: Ingalls RG, Rossetti MD, Smith JS and Peters BA (eds). Proceedings of the Winter Simulation Conference 1981-1987.
24. Gupta D and Denton B (2008). Appointment scheduling in health care: Challenges and opportunities. IIE Trans 40(9): 800-819.
25. Ho C and Lau H (1992). Minimizing total cost in scheduling outpatient appointments. Mngt Sci 38(2): 1750-1764.
26. Huang X (1994). Patient attitudes towards waiting in out-patient queues and its application. Health Serv Manage Res 7: 2-8.
27. Huang X, Li J, Su H, Watts G and Chen H (2007). Large-scale regulatory network analysis from microarray data: modified Bayesian network learning and association rule mining. Dec Sup Syst 43: 1207-1255.
28. Isken MW and Rajagopalan B (2002). Data mining to support simulation modelingofpatient flowinhospitals. J Med Syst 26(2): 179-197.
29. Jukic N and Nestorov S (2006). Comprehensive data warehouse exploration with qualified association-rule mining. Dec Sup Syst 42: 859-878.
30. Jun JB, Jacobson SH and Swisher JR (1999). Application of discreteevent simulation in health care clinics: A survey. J Opl Res Soc 50: 109-123.
31. Klassen KJ and Rohleder TR (1996). Scheduling outpatient appointments in a dynamic environment. J Opns Mngt 14(2):83-101.
32. Klein R, Dittus RS, Roberts SD and Wilson JR (1993). Simulation modeling and health-care decision making. Med Decis Making 13: 347-354.
33. LaGanga LR and Lawrence SR (2007). Clinic overbooking to improve patient access and increase provider productivity. Dec Sci 38: 251-276.
34. Lindley DV (1952). The theory of queues with a single server. Proc Camb Phil Soc 48: 277-289.
35. Lowery JC (1996). Introduction to simulation in health care. In: Charnes JM, Morrice DM, Brunner DT and Swain JJ (eds). Proceedings of the 1996 Winter Simulation Conference. IEEE: Coronado, CA, pp 78-84.
36. Mahachek AR (1992). An introduction to patient flow simulation for health-care managers. J Soc Health Syst 3: 73-81.
37. Nadkarni MM and Philbrick JT (2005). Free clinics: A national study. Am J Med Sci 330(1): 25-31.
38. Patrick J and Puterman ML (2007). Improving resource utilization for diagnostic services through flexible inpatient scheduling: A method for improving resource utilization. J Opl Res Soc 58: 235-245.
39. Sharp DJ and Hamilton W (2001). Non-attendance at general practices and outpatient clinics: local systems are needed to address local problems. BMJ 323(7321): 1081-1082.
40. Shonick W and Klein BW (1977). An approach to reducing the adverse effects of broken appointments in primary care systems. Med Care 15(5): 419-429.
41. Smith-Daniels VL, Schweikhart SB and Smith-Daniels DE (1988). Capacity management in health care services: Review and future research directions. Dec Sci 19(3): 889-918.
42. Smyth P and Goodman RM (1992). An information theoretic approach to rule induction from databases. IEEE Trans Knowledge Data Eng 4(4): 301-316.
43. Vasilakis C, Sobolev BG, Kuramoto L and Levy AR (2007). A simulation study of scheduling clinic appointments in surgical care: Individual surgeon versus pooled lists. J Opl Res Soc 58: 202-211.
44. Wolff R and Schuster A (2004). Association rule mining in peer-to-peer systems. IEEE Trans Syst Man Cyber Part B 34(6): 2426-2438.