The Development of Patient Scheduling Groups for an Effective Appointment System

Summary

Background

Patient access to care and long wait times has been identified as major problems in outpatient delivery systems. These aspects impact medical staff productivity, service quality, clinic efficiency, and health-care cost.

Objectives

This study proposed to redesign existing patient types into scheduling groups so that the total cost of clinic flow and scheduling flexibility was minimized. The optimal scheduling group aimed to improve clinic efficiency and accessibility.

Methods

The proposed approach used the simulation optimization technique and was demonstrated in a Primary Care physician clinic. Patient type included, emergency/urgent care (ER/UC), follow-up (FU), new patient (NP), office visit (OV), physical exam (PE), and well child care (WCC). One scheduling group was designed for this physician. The approach steps were to collect physician treatment time data for each patient type, form the possible scheduling groups, simulate daily clinic flow and patient appointment requests, calculate costs of clinic flow as well as appointment flexibility, and find the scheduling group that minimized the total cost.

Results

The cost of clinic flow was minimized at the scheduling group of four, an 8.3% reduction from the group of one. The four groups were: 1. WCC, 2. OV, 3. FU and ER/UC, and 4. PE and NP. The cost of flexibility was always minimized at the group of one. The total cost was minimized at the group of two. WCC was considered separate and the others were grouped together. The total cost reduction was 1.3% from the group of one.

Conclusions

This study provided an alternative method of redesigning patient scheduling groups to address the impact on both clinic flow and appointment accessibility. Balance between them ensured the feasibility to the recognized issues of patient service and access to care. The robustness of the proposed method on the changes of clinic conditions was also discussed.

 

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Keywords

Appointment scheduling - scheduling group - scheduling efficiency - scheduling flexibility

 

 

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Conflicts of interest statement

The authors declare that they have no conflicts of interest in the research.

• References

• 1 Lian J, Distefano K, Shields SD, Heinichen C, Giampietri M, Wang L. Clinical appointment process: improvement through schedule defragmentation. IEEE Eng Med Biol Mag 2010; 29 (02) 127-134.
• 2 Huang Y, Zuniga P. Dynamic overbooking scheduling system to improve patient access. J Oper Res Soc 2012; 63 (06) 810-820.
• 3 Keehl-Markowitz L, Ayanian JZ, Mehrotra A. Implementing Open-Access Scheduling of Visits in Primary Care Practices: A Cautionary Tale. Ann Intern Med 2008; 148 (12) 915-922.
• 4 Vanden PMBosch, Dietz DC. Minimizing expected waiting in a medical appointment system. IIE Trans 2000; 32 (09) 841-848.
• 5 Huang Y. Ancillary service impact on outpatient scheduling. Int J Health Care Qual Assur 2013; 26 (08) 746-759.
• 6 Millhiser WP, Veral EA. Designing appointment system templates with operational performance targets. IIE Trans Healthc Syst Eng 2015; 05 (03) 125-146.
• 7 LaGanga LR, Lawrence SR. Clinic Overbooking to Improve Patient Access and Increase Provider Productivity. Dec Sci 2007; 38 (02) 251-276.
• 8 Huang Y, Hancock WM, Herrin GD. An alternative outpatient scheduling system: Improving the outpatient experience. IIE Trans Healthc Syst Eng 2012; 02 (02) 97-111.
• 9 Swisher JR, Jacobson SH, Jun JB, Balci O. Modeling and Analyzing a Physician Clinic Environment Using Discrete-Event (Visual) Simulation. Comput Oper Res 2001; 28 (02) 105-125.
• 10 Sweeney DR. Your office: a lot of things will have to change. Med Econ 1996; 73 (07) 97-106.
• 11 Blumenthal D. Performance improvement in health care – seizing the moment. N Eng J Med 2012; 366 (21) 1953-1955.
• 12 Tai G, Williams P. Optimization of scheduling patient appointments in clinics using a novel modeling technique of patient arrival. Comput Meth Prog Bio 2011; 108 (02) 467-476.
• 13 Klassen KJ, Yoogalingam R. Appointment system design with interruptions and physician lateness. Int J Oper Prod Man 2013; 33 (04) 394-414.
• 14 Zeng B, Zhao H, Lawley M. The impact of overbooking on primary care patient no-show. IIE Trans Healthc Syst Eng 2013; 03 (03) 147-170.
• 15 Vanden PMBosch, Dietz DC. Minimizing expected waiting in a medical appointment system. IIE Trans 2000; 32 (09) 841-848.
• 16 Harper PR, Gamlin HM. Reduced outpatient waiting times with improved appointment scheduling: a simulation modeling approach. OR Spectrum 2003; 25 (02) 207-222.
• 17 Cayirli T, Veral E, Rosen H. Assessment of Patient Classification in Appointment System Design. Prod Oper Manag 2008; 17 (03) 338-353.
• 18 Tang J, Yan C, Cao P. Appointment scheduling algorithm considering routine and urgent patients. Expert Syst Appl 2014; 41 (10) 4529-4541.
• 19 Huang Y, Zuniga P, Marcak J. A cost-effective urgent care policy to improve patient access in a dynamic scheduled clinic setting. J Oper Res Soc 2014; 65 (05) 763-776.
• 20 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) 682-697.
• 21 OH H, Muriel A, Balasubramanian H, Atkinson K, Ptaszkiewicz T. Guidelines for scheduling in primary care under different patient types and stochastic nurse and provider service times. IIE Trans Healthc Syst Eng 2013; 03 (04) 263-279.
• 22 Kuiper A, Kemper B, Mandjes M. A Computational approach to optimized appointment scheduling. Queueing Systems 2015; 79 (01) 5-36.
• 23 LaGanga LR, Lawrence SR. Appointment Overbooking in Health care Clinics to Improve Patient Service and Clinic Performance. Prod Oper Manag 2012; 21 (05) 874-888.
• 24 Zacharias C, Pinedo M. Appointment Scheduling with No-Shows and Overbooking. Prod Oper Manag 2014; 23 (05) 788-801.
• 25 Tsai PJ, Teng G. A stochastic appointment scheduling system on multiple resources with dynamic call-in sequence and patient no-shows for an outpatient clinic. Eur J Oper Res 2014; 239 (02) 427-436.
• 26 Huang Y, Hanauer DA. Patient No-Show Predictive Model Development using Multiple Data Sources for an Effective Overbooking Approach. Appl Clin Inform 2014; 05 (03) 836-860.
• 27 Samorani M, LaGanga LR. Outpatient appointment scheduling given individual day-dependent no-show predictions. Eur J Oper Res 2015; 240 (01) 245-257.
• 28 Lin J, Muthuraman K, Lawley M. Optimal and approximate algorithms for sequential clinical scheduling with no-shows. IIE Trans Healthc Syst Eng 2011; 01 (01) 20-36.
• 29 Berg BP, Denton BT, Erdogan SA, Rohleder T, Huschka T. Optimal booking and scheduling in outpatient procedure centers. Comput Oper Res 2014; 50: 24-37.
• 30 Huang Y, Marcak J. Grid Patient Appointment Template Design to Improve Scheduling Effectiveness. J Healthc Eng 2015; 06 (02) 239-258.
• 31 Huang Y, Kammerdiner A. Reduction of service time variation in patient visit groups using decision tree method for an effective scheduling. Int. J. of Healthcare Technology and Management 2013; 14 1/2 3-21.
• 32 Huang Y, Verduzco S. Appointment Template Redesign in a Women’s Health Clinic Using Clinical Constraints to Improve Service Quality and Efficiency. Appl Clin Inform 2015; 06 (02) 271-287.
• 33 Huang Y, Marcak J. Radiology scheduling with consideration of patient characteristics to improve patient access to care and medical resource utilization. Health Systems 2013; 02 (02) 93-102.
• 34 Wijewickrama A, Takakuwa S. Designing Outpatient Appointment Systems with Patient Characteristics: a Case Study. Int. J. of Healthcare Technology and Management 2012; 13 1/2/3 157-69.
• 35 Carson Y, Maria A. Simulation Optimization: Methods and Applications. Proceedings of the 1997 Winter Simulation Conference.
• 36 Yang KK, Lau ML, Quek SA. A New Appointment Rule for a Single-Server, Multiple-Customer Service System. Nav Res Log 1998; 45 (03) 313-326.
• 37 Cayirli T, Veral E. Outpatient Scheduling in Health Care: A Review of Literature. Prod Oper Manag 2003; 12 (04) 519-549.
• 38 Kros J, Dellana S, West D. Overbooking Increases Patient Access at East Carolina University’s Student Health Services Clinic. Interfaces 2009; 39 (03) 271-287.