Patient No-Show Predictive Model Development using Multiple Data Sources for an Effective Overbooking Approach

Y. Huang; D.A. Hanauer

doi:10.4338/ACI-2014-04-RA-0026

Applied Clinical Informatics, Table of Contents

Appl Clin Inform 2014; 05(03): 836-860
DOI: 10.4338/ACI-2014-04-RA-0026

Research Article

Schattauer GmbH

Patient No-Show Predictive Model Development using Multiple Data Sources for an Effective Overbooking Approach

Authors

Y. Huang

¹New Mexico State University, Industrial Engineering, Las Cruces, New Mexico, United States
D.A. Hanauer

²Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI

Abstract

Summary

Background: Patient no-shows in outpatient delivery systems remain problematic. The negative impacts include underutilized medical resources, increased healthcare costs, decreased access to care, and reduced clinic efficiency and provider productivity.

Objective: To develop an evidence-based predictive model for patient no-shows, and thus improve overbooking approaches in outpatient settings to reduce the negative impact of no-shows.

Methods: Ten years of retrospective data were extracted from a scheduling system and an electronic health record system from a single general pediatrics clinic, consisting of 7,988 distinct patients and 104,799 visits along with variables regarding appointment characteristics, patient demographics, and insurance information. Descriptive statistics were used to explore the impact of variables on show or no-show status. Logistic regression was used to develop a no-show predictive model, which was then used to construct an algorithm to determine the no-show threshold that calculates a predicted show/no-show status. This approach aims to overbook an appointment where a scheduled patient is predicted to be a no-show. The approach was compared with two commonly-used overbooking approaches to demonstrate the effectiveness in terms of patient wait time, physician idle time, overtime and total cost.

Results: From the training dataset, the optimal error rate is 10.6% with a no-show threshold being 0.74. This threshold successfully predicts the validation dataset with an error rate of 13.9%. The proposed overbooking approach demonstrated a significant reduction of at least 6% on patient waiting, 27% on overtime, and 3% on total costs compared to other common flat-overbooking methods.

Conclusions: This paper demonstrates an alternative way to accommodate overbooking, accounting for the prediction of an individual patient’s show/no-show status. The predictive no-show model leads to a dynamic overbooking policy that could improve patient waiting, overtime, and total costs in a clinic day while maintaining a full scheduling capacity.

Citation: Huang Y, Hanauer D.A. Patient no-show predictive model development using multiple data sources for an effective overbooking approach. Appl Clin Inf 2014; 5: 836–860

http://dx.doi.org/10.4338/ACI-2014-04-RA-0026

Keywords

No-shows - overbooking - appointment scheduling - predictive models

Full Text

References

References
1 Deyo RA, Thomas SI. Dropouts and Broken Appointments: A Literature Review and Agenda for Future Research. Med Care 1980; 18 (11) 1146-1157.
2 Warden J. 4.5-million Outpatients Miss Appointments. BMJ. 1995; 310 6988 1158.
3 Hixon AL, Chapman RW, Nuovo J. Failure to Keep Clinic Appointments: Implications for Residency Education and Productivity. Fam Med. 1999; 31 (09) 627-30.
4 Casey RG, Quinlan MR, Flynn R, Grainger R, McDermott TE, Thornhill JA. Urology out-patient non-attenders: are we wasting our time?. Ir J Med Sci. 2007; 176 (04) 305-8.
5 Corfield L, Schizas A, Noorani A, Willians A. Non-attendance at the colorectal clinic: a prospective audit. Ann R Coll Surg Engl. 2008; 90 (05) 377-80.
6 Bean AG, Talaga J. Predicting Appointment Breaking. J Health Care Mark. 1995; 15 (01) 29-34.
7 Wiseman M, McBride M. Increasing the Attendance Rate for First Appointments at Child and Family Psychiatry Clinics: An opt-in System. Child and Adolescent Mental Health. 1998; 3 (02) 68-71.
8 Perron NJ, Dao MD, Kossovsky MP, Miserez V, Chuard C, Calmy A, Gaspoz JM. Reduction of missed appointments at an urban primary care clinic: a randomized controlled study. BMC Fam Pract. 2010; 11: 79.
9 Woods R. The Effectiveness of Reminder Phone Calls on Reducing No-Show Rates in Ambulatory Care. Nurs Econ. 2011; 29 (05) 278-82.
10 Hogan AM, McCormack O, Traynor O, Winter DC. Potential impact of text message reminders on non-attendance at outpatient clinics. Ir J Med Sci. 2008; 177 (04) 355-58.
11 Foley J, O’Neill M. Use of Mobile Telephone Short Message Service (SMS) as a Reminder: the Effect on Patient Attendance. Eur Arch Paediatr Dent. 2009; 10 (01) 15-8.
12 Woodcock EW. Cancellations: Don’t let them erode your bottom line. Dermatology Times 2006; 27 (08) 82-3.
13 Van Dieren Q, Rijckmans M, Mathijssen J, Lobbestael J, Arntz AR. Reducing no-show Behavior at a Community Mental Health Center. J Community Psychol. 2013; 41 (07) 844-50.
14 Cayirli T, Yang KK, Quek SA. A Universal Appointment Rule in the Presence of No-Shows and Walk-Ins. Prod Oper Manag. 2012; 21 (04) 682-97.
15 Al-Aomar R, Awad M. Dynamic process modeling of patients’ no-show rates and overbooking strategies in healthcare clinics. Int J Eng Manage Econ. 2012; 3 1/2 3-21.
16 Erdogan SA, Denton B. Dynamic Appointment Scheduling of a Stochastic Server with Uncertain Demand. J Comput. 2013; 25 (01) 116-32.
17 Chakraborty S, Muthuraman K, Lawley M. Sequential clinical scheduling with patient no-show: The impact of pre-defined slot structures. Socio Econ Plan Sci. 2013; 47 (03) 205-19.
18 Zacharias C, Pinedo M. Appointment Scheduling with No-Shows and Overbooking. Prod Oper Manag. 2014; 23 (05) 788-801.
19 Tang J, Yan C, Cao P. Appointment scheduling algorithm considering routine and urgent patients. Expert Syst Appl. 2014; 41 (10) 4529-41.
20 O’Hare CD, Corlett J. The outcomes of open access scheduling. Fam Pract Manag. 2004; 11 (02) 35-8.
21 Bundy DG. Open access in primary care: results of a North Carolina pilot project. Pediatrics. 2005; 116 (01) 82-8.
22 O’Connor ME, Matthews BS, Gao D. Effect of Open Access Scheduling on Missed Appointments, Immunizations, and Continuity of Care for Infant Well-Child Care Visits. Arch Pediatr Adolesc Med. 2006; 160 (09) 889-93.
23 Robinson L, Chen R. A Comparison of Traditional and Open-Access Policies for Appointment Scheduling. Manuf Serv Oper Manag. 2010; 12 (02) 330-46.
24 Liu N, Ziya S, Kulkarni VG. Dynamic Scheduling of Outpatient Appointments under Patient No-Shows and Cancellations. Manuf Serv Oper Manag. 2010; 12 (02) 347-64.
25 Phan K, Brown S. Decreased continuity in a residency clinic: a consequence of open access scheduling. Fam Med. 2009; 41 (01) 46-50.
26 Mehrotra A, Keehl-Markowitz L, Ayanian JZ. Implementing Open-Access Scheduling of Visits in Primary Care Practices: A Cautionary Tale. Ann Intern Med. 2008; 148 (12) 915-22.
27 Patrick J. A Markov decision model for determining optimal outpatient scheduling. Health Care Manag Sci. 2012; 15 (02) 91-102.
28 Lee S, Min D, Ryu J, Yih Y. A simulation study of appointment scheduling in outpatient clinics: Open access and overbooking. Simulation. 2013; 89 (12) 1459-73.
29 Huang Y, Zuniga P. Dynamic Overbooking Scheduling System to Improve Patient Access. J Oper Res Soc. 2012; 63 (06) 810-820.
30 Kim S, Giachetti RE. A Stochastic Mathematical Appointment Overbooking Model for Healthcare Providers to Improve Profits. IEEE T Syst Man Cyb. 2006; 36 (06) 1211-9.
31 LaGanga LR, Lawrence SR. Clinic Overbooking to Improve Patient Access and Increase Provider Productivity. Decision Sci. 2007; 38 (02) 251-76.
32 Muthuraman K, Lawley M. A stochastic overbooking model for outpatient clinical scheduling with no-shows. IIE Trans. 2008; 40 (09) 820-37.
33 LaGanga LR, Lawrence SR. Appointment Overbooking in Health Care Clinics to Improve Patient Service and Clinic Performance. Prod Oper Manag. 2012; 21 (05) 874-88.
34 Lawrence RD, Hong SJ, 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; 397-406.
35 Shonick W, Klein BW. An approach to reducing the adverse effects of broken appointments in primary care systems: development of a decision rule based on estimated conditional probabilities. Med Care. 1977; 15 (05) 419-29.
36 Kros J, Dellana S, West D. Overbooking Increases Patient Access at East Carolina University’s Student Health Services Clinic. Interfaces. 2009; 39 (03) 271-87.
37 Chakraborty S, Muthuraman K, Lawley M. Sequential clinical scheduling with patient no-shows and general service time distributions. IIE Trans. 2010; 42 (05) 354-66.
38 Zeng B, Turkcan A, Lin J, Lawley M. Clinic scheduling models with overbooking for patients with heterogeneous no-show probabilities. Ann Oper Res. 2010; 178 (01) 121-44.
39 Kopach R, DeLaurentis P, Lawley M, Muthuraman K, Ozsen L, Rardin R, Wan H, Intrevado P, Qu X, Willis D. Effects of clinical characteristics on successful open access scheduling. Health Care Manage Sci. 2007; 10 (02) 111-24.
40 Kaplan-Lewis E, Percac-Lima S. No-Show to Primary Care Appointments: Why Patients Do Not Come. J Prim Care Commun Hlth. 2013; 4 (04) 251-5.
41 Feitsma WN, Popping R, Jansen DE. No-Show at a Forensic Psychiatric Outpatietn Clinic: Risk Factors and Reasons. Int J Offender Ther Comp Criminol. 2012; 56 (01) 96-112.
42 Sharp DJ. Non-attendance at general practices and outpatient clinics: local systems are needed to address local problems. BMJ. 2001; 323 7321 1081-2.
43 Turner BJ, Weiner M, Yang C, TenHave T. Predicting Adherence to Colonoscopy or Flexible Sigmoidoscopy on the Basis of Physician Appointment–Keeping Behavior. Ann Intern Med. 2004; 140 (07) 528-32.
44 McCarthy K, McGee HM, O’Boyle CA. Outpatient clinic waiting times and non-attendance as indicators of quality. Psychol Healt Med. 2000; 5 (03) 287-93.
45 Gallucci G, Swartz W, Hackerman F. Brief Reports: Impact of the Wait for an Initial Appointment on the Rate of Kept Appointments at a Mental Health Center. Psychiatr Serv 2005; 56 (03) 344-46.
46 Grunebaum M, Luber P, Callahan M, Leon AC, Olfson M, Portera L. Predictors of missed appointments for psychiatric consultations in a primary care clinic. Psychiatr Serv. 1996; 47 (08) 848-52.
47 Glowacka KJ, Henry RM, May JH. A hybrid data mining/simulation approach for modelling outpatient no-shows in clinic scheduling. J Oper Res Soc. 2009; 60 (08) 1056-68.
48 Lotfi V, Torres E. Improving an outpatient clinic utilization using decision analysis based patient scheduling. Socio Econ Plan Sci. 2014; 48 (02) 115-26.
49 Alaeddini A, Yang K, Reddy C, Yu S. A probabilistic model for predicting the probability of no-show in hospital appointments. Health Care Manag Sci. 2011; 14 (02) 146-57.
50 Daggy J, Lawley M. Using no-show modeling to improve clinical performance. Health Infor J 2010; 16 (04) 246-59.
51 Huang Y, Hancock WM, Herrin GD. An alternative outpatient scheduling system: Improving the out-patient experience. IIE Trans Hlthc Syst Eng. 2012; 2 (02) 97-111.
52 Ho C, Lau H. Minimizing Total Cost in Scheduling Outpatient Appointments. Manage Sci. 1992; 38 (12) 1750-63.
53 Vissers J. Selecting a Suitable Appointment System in an Outpatient Setting. Med Care. 1979; 17 (12) 1207-20.
54 Klassen KJ, Rohleder TR. Outpatient appointment scheduling with urgent clients in a dynamic, multi-period environment. Int J Serv Ind Manag. 2004; 15 (02) 167-86.
55 Yang KK, Lau ML, Quek SA. A New Appointment Rule for a Single-Server, Multiple-Customer Service System. Nav Res Log. 1998; 45 (03) 313-26.
56 Cayirli T, Veral E. Outpatient Scheduling in Health Care: A Review of Literature. Prod Oper Manag. 2003; 12 (04) 519-49.