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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

Y. Huang
1   New Mexico State University, Industrial Engineering, Las Cruces, New Mexico, United States
,
D.A. Hanauer
2   Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI
› Author Affiliations
Further Information

Publication History

received: 03 April 2014

accepted: 07 September 2014

Publication Date:
19 December 2017 (online)

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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
 

  • 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.
    CrossrefPubMedGoogle Scholar
  • 2 Warden J. 4.5-million Outpatients Miss Appointments. BMJ. 1995; 310 6988 1158.
    PubMedGoogle Scholar
  • 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.
    PubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 6 Bean AG, Talaga J. Predicting Appointment Breaking. J Health Care Mark. 1995; 15 (01) 29-34.
    PubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 9 Woods R. The Effectiveness of Reminder Phone Calls on Reducing No-Show Rates in Ambulatory Care. Nurs Econ. 2011; 29 (05) 278-82.
    PubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 12 Woodcock EW. Cancellations: Don’t let them erode your bottom line. Dermatology Times 2006; 27 (08) 82-3.
    PubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    PubMedGoogle Scholar
  • 16 Erdogan SA, Denton B. Dynamic Appointment Scheduling of a Stochastic Server with Uncertain Demand. J Comput. 2013; 25 (01) 116-32.
    PubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 18 Zacharias C, Pinedo M. Appointment Scheduling with No-Shows and Overbooking. Prod Oper Manag. 2014; 23 (05) 788-801.
    CrossrefPubMedGoogle Scholar
  • 19 Tang J, Yan C, Cao P. Appointment scheduling algorithm considering routine and urgent patients. Expert Syst Appl. 2014; 41 (10) 4529-41.
    CrossrefPubMedGoogle Scholar
  • 20 O’Hare CD, Corlett J. The outcomes of open access scheduling. Fam Pract Manag. 2004; 11 (02) 35-8.
    PubMedGoogle Scholar
  • 21 Bundy DG. Open access in primary care: results of a North Carolina pilot project. Pediatrics. 2005; 116 (01) 82-8.
    CrossrefPubMedGoogle Scholar
  • 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.
    PubMedGoogle Scholar
  • 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.
    PubMedGoogle Scholar
  • 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.
    PubMedGoogle Scholar
  • 25 Phan K, Brown S. Decreased continuity in a residency clinic: a consequence of open access scheduling. Fam Med. 2009; 41 (01) 46-50.
    PubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 27 Patrick J. A Markov decision model for determining optimal outpatient scheduling. Health Care Manag Sci. 2012; 15 (02) 91-102.
    CrossrefPubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 29 Huang Y, Zuniga P. Dynamic Overbooking Scheduling System to Improve Patient Access. J Oper Res Soc. 2012; 63 (06) 810-820.
    CrossrefPubMedGoogle Scholar
  • 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.
    PubMedGoogle Scholar
  • 31 LaGanga LR, Lawrence SR. Clinic Overbooking to Improve Patient Access and Increase Provider Productivity. Decision Sci. 2007; 38 (02) 251-76.
    CrossrefPubMedGoogle Scholar
  • 32 Muthuraman K, Lawley M. A stochastic overbooking model for outpatient clinical scheduling with no-shows. IIE Trans. 2008; 40 (09) 820-37.
    CrossrefPubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    PubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    PubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    PubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 50 Daggy J, Lawley M. Using no-show modeling to improve clinical performance. Health Infor J 2010; 16 (04) 246-59.
    PubMedGoogle Scholar
  • 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.
    PubMedGoogle Scholar
  • 52 Ho C, Lau H. Minimizing Total Cost in Scheduling Outpatient Appointments. Manage Sci. 1992; 38 (12) 1750-63.
    CrossrefPubMedGoogle Scholar
  • 53 Vissers J. Selecting a Suitable Appointment System in an Outpatient Setting. Med Care. 1979; 17 (12) 1207-20.
    CrossrefPubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 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.
    CrossrefPubMedGoogle Scholar
  • 56 Cayirli T, Veral E. Outpatient Scheduling in Health Care: A Review of Literature. Prod Oper Manag. 2003; 12 (04) 519-49.
    PubMedGoogle Scholar