Appl Clin Inform 2015; 06(02): 271-287
DOI: 10.4338/ACI-2014-10-RA-0094
Research Article
Schattauer GmbH

Appointment Template Redesign in a Women’s Health Clinic Using Clinical Constraints to Improve Service Quality and Efficiency

Y. Huang
1   Department of Industrial Engineering, New Mexico State University, Las Cruces, NM, USA
,
S. Verduzco
1   Department of Industrial Engineering, New Mexico State University, Las Cruces, NM, USA
› Author Affiliations
Further Information

Correspondence to:

Yu-Li Huang, Ph.D.
Department of Industrial Engineering
MSC 4230, New Mexico State University
P.O. Box 30001
Las Cruces, NM 88003 USA

Publication History

received: 23 October 2014

accepted: 01 March 2015

Publication Date:
19 December 2017 (online)

 

Summary

Background: Patient wait time is a critical element of access to care that has long been recognized as a major problem in modern outpatient health care delivery systems. It impacts patient and medical staff productivity, stress, quality and efficiency of medical care, as well as health-care cost and availability.

Objectives: This study was conducted in a Women’s Health Clinic. The objective was to improve clinic service quality by redesigning patient appointment template using the clinical constraints.

Methods: The proposed scheduling template consisted of two key elements: the redesign of appointment types and the determination of the length of time slots using defined constraints. The reclassification technique was used for the redesign of appointment visit types to capture service variation for scheduling purposes. Then, the appointment length was determined by incorporating clinic constraints or goals, such as patient wait time, physician idle time, overtime, finish time, lunch hours, when the last appointment was scheduled, and the desired number of appointment slots, to converge the optimal length of appointment slots for each visit type.

Results: The redesigned template was implemented and the results indicated a 73% reduction in average patient waiting from the reported 40 to 11 minutes. The patient no-show rate was reduced by 4% from 24% to 20%. The morning section on average finished about 11:50 am. The clinic day was finished around 4:45 pm. Provider average idle time was estimated to be about 5 minutes, which can be used for charting/documenting patients.

Conclusions: This study provided an alternative method of redesigning appointment scheduling templates using only the clinical constraints rather than the traditional way that required an objective function. This paper also documented the employed methods step by step in a real clinic setting. The implementation results concluded a significant improvement on patient wait time and no-show rate.

Citation: Huang Y, Verduzco S. Appointment Template Redesign in a Women’s Health Clinic Using Clinical Constraints to Improve Service Quality and Efficiency. Appl Clin Inf 2015; 6: 271–287

http://dx.doi.org/10.4338/ACI-2014-10-RA-0094


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Conflicts of interest statement

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

  • References

  • 1 McCarthy K, McGee HM, O’Boyle CA, McCarthy K. Outpatient clinic waiting times and non-attendance as indicators of quality. Psychol Health Med 2000; 5 (03) 287-293.
  • 2 DiMino J, Blau G. The relationship between wait time after triage and show rate for intake in a nonurgent student population. J College Stud Psychother 2012; 26 (03) 241-247.
  • 3 Cayirli T, Veral E, Rosen H. Assessment of Patient Classification in Appointment System Design. Prod Oper Manag. 2008; 17 (03) 338-53.
  • 4 Wijewickrama A, Takakuwa S. Designing Outpatient Appointment Systems with Patient Characteristics: a Case Study. Int J of Healthcare Technology and Management 2012; 13 1/2/3 157-169.
  • 5 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.
  • 6 Huang Y, Marcak J. Radiology scheduling with consideration of patient characteristics to improve patient access to care and medical resource utilization. Health Systems 2013; 2 (02) 93-102.
  • 7 Erdogan SA, Denton B. Dynamic Appointment Scheduling of a Stochastic Server with Uncertain Demand. J Comput. 2013; 25 (01) 116-32.
  • 8 Schutz HJ, Kolisch R. Capacity allocation for demand of different customer-product-combinations with cancellations, no-shows, and overbooking when there is a sequential delivery of service. Ann Oper Res 2013; 206 (01) 401-423.
  • 9 Hahn-Goldberg S, Carter MW, Beck JC, Trudeau M, Sousa P, Beattie K. Dynamic optimization of chemo-therapy outpatient scheduling with uncertainty. Health Care Manage Sci DOI 10. 1007/s10729–014–9268-0.
  • 10 Tang J, Yan C, Cao P. Appointment scheduling algorithm considering routine and urgent patients. Expert Syst Appl 2014; 41 (10) 4529-4541.
  • 11 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.
  • 12 Chen RR, Robinson LW. Sequencing and Scheduling Appointments with Potential Call-In Patients. Prod Oper Manag 2014; 23 (09) 1522-1538.
  • 13 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.
  • 14 Klassen KJ, Yoogalingam R. Appointment system design with interruptions and physician lateness. Int J Oper Prod Man 2013; 33 (04) 394-414.
  • 15 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.
  • 16 Huang Y. Ancillary service impact on outpatient scheduling. Int J Health Care Qual Assur 2013; 26 (08) 746-759.
  • 17 Huang Y, Zuniga P. Dynamic overbooking scheduling system to improve patient access. J Oper Res Soc 2012; 63 (06) 810-820.
  • 18 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.
  • 19 Zeng B, Zhao H, Lawley M. The impact of overbooking on primary care patient no-show. IIE Trans Healthc Syst Eng 2013; 3 (03) 147-170.
  • 20 Zacharias C, Pinedo M. Appointment Scheduling with No-Shows and Overbooking. Prod Oper Manag 2014; 23 (05) 788-801.
  • 21 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.
  • 22 Huang Y, Hanauer DA. Patient No-Show Predictive Model Development using Multiple Data Sources for an Effective Overbooking Approach. Appl Clin Inform 2014; 5 (03) 836-860.
  • 23 Samorani M, LaGanga LR. Outpatient appointment scheduling given individual day-dependent no-show predictions. Eur J Oper Res 2015; 240 (01) 245-257.
  • 24 Kim S, Giachetti RE. A Stochastic Mathematical Appointment Overbooking Model for Healthcare Providers to Improve Profits. IEEE Trans Syst Man Cybern 2006; 36 (06) 1211-1219.
  • 25 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.
  • 26 Vanden Bosch PM, Dietz DC. Minimizing expected waiting in a medical appointment system. IIE Trans 2000; 32 (09) 841-848.
  • 27 Harper PR, Gamlin HM. Reduced outpatient waiting times with improved appointment scheduling: a simulation modeling approach. OR Spectrum 2003; 25 (02) 207-222.
  • 28 Huang Y, Hancock WM, Herrin GD. An alternative outpatient scheduling system: Improving the out-patient experience. IIE Trans Healthc Syst Eng 2012; 2 (02) 97-111.
  • 29 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; 3 (04) 263-279.
  • 30 Kuiper A, Kemper B, Mandjes M. A Computational approach to optimized appointment scheduling. Queueing Systems. DOI 10.1007/s11134–014–9398-6.
  • 31 Keller TF, Laughhunn DJ. An Application of Queuing Theory to a Congestion Problem in an Outpatient Clinic. Decision Sci 1973; 4 (03) 379-394.
  • 32 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.
  • 33 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 Pediat Adol Med 2006; 160 (09) 889-893.
  • 34 Bundy DG, Randolph GD, Murray M, Anderson J, Margolis PA. Open Access in Primary Care: Results of a North Carolina. Pilot Project Pediatrics 2005; 116 (Suppl. 01) 82-87.
  • 35 Forjuoh SN, Averitt WM, Cauthen DB, Couchman GR, Symm B, Mitchell M. Open-Access Appoinment Scheduling in Family Practice: Comparison of a Demand Prediction Grid With Actual Appointments. J Am Board Fam Pract 2001; 14 (04) 259-265.
  • 36 Peng Y, Qu X, Shi J. A hybrid simulation and genetic algorithm approach to determine the optimal scheduling templates for open access clinics admitting walk-in patients. Comput Ind Eng 2014; 72: 282-296.
  • 37 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) 1459-1473.
  • 38 Klassen KJ, Rohleder TR. Scheduling outpatient appointments in a dynamic environment. J Oper Manag 1996; 14 (02) 83-101.
  • 39 Cayirli T, Veral E, Rosen H. Designing appointment scheduling systems for ambulatory care services. Health Care Manage Sci 2006; 9 (01) 47-58.

Correspondence to:

Yu-Li Huang, Ph.D.
Department of Industrial Engineering
MSC 4230, New Mexico State University
P.O. Box 30001
Las Cruces, NM 88003 USA

  • References

  • 1 McCarthy K, McGee HM, O’Boyle CA, McCarthy K. Outpatient clinic waiting times and non-attendance as indicators of quality. Psychol Health Med 2000; 5 (03) 287-293.
  • 2 DiMino J, Blau G. The relationship between wait time after triage and show rate for intake in a nonurgent student population. J College Stud Psychother 2012; 26 (03) 241-247.
  • 3 Cayirli T, Veral E, Rosen H. Assessment of Patient Classification in Appointment System Design. Prod Oper Manag. 2008; 17 (03) 338-53.
  • 4 Wijewickrama A, Takakuwa S. Designing Outpatient Appointment Systems with Patient Characteristics: a Case Study. Int J of Healthcare Technology and Management 2012; 13 1/2/3 157-169.
  • 5 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.
  • 6 Huang Y, Marcak J. Radiology scheduling with consideration of patient characteristics to improve patient access to care and medical resource utilization. Health Systems 2013; 2 (02) 93-102.
  • 7 Erdogan SA, Denton B. Dynamic Appointment Scheduling of a Stochastic Server with Uncertain Demand. J Comput. 2013; 25 (01) 116-32.
  • 8 Schutz HJ, Kolisch R. Capacity allocation for demand of different customer-product-combinations with cancellations, no-shows, and overbooking when there is a sequential delivery of service. Ann Oper Res 2013; 206 (01) 401-423.
  • 9 Hahn-Goldberg S, Carter MW, Beck JC, Trudeau M, Sousa P, Beattie K. Dynamic optimization of chemo-therapy outpatient scheduling with uncertainty. Health Care Manage Sci DOI 10. 1007/s10729–014–9268-0.
  • 10 Tang J, Yan C, Cao P. Appointment scheduling algorithm considering routine and urgent patients. Expert Syst Appl 2014; 41 (10) 4529-4541.
  • 11 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.
  • 12 Chen RR, Robinson LW. Sequencing and Scheduling Appointments with Potential Call-In Patients. Prod Oper Manag 2014; 23 (09) 1522-1538.
  • 13 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.
  • 14 Klassen KJ, Yoogalingam R. Appointment system design with interruptions and physician lateness. Int J Oper Prod Man 2013; 33 (04) 394-414.
  • 15 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.
  • 16 Huang Y. Ancillary service impact on outpatient scheduling. Int J Health Care Qual Assur 2013; 26 (08) 746-759.
  • 17 Huang Y, Zuniga P. Dynamic overbooking scheduling system to improve patient access. J Oper Res Soc 2012; 63 (06) 810-820.
  • 18 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.
  • 19 Zeng B, Zhao H, Lawley M. The impact of overbooking on primary care patient no-show. IIE Trans Healthc Syst Eng 2013; 3 (03) 147-170.
  • 20 Zacharias C, Pinedo M. Appointment Scheduling with No-Shows and Overbooking. Prod Oper Manag 2014; 23 (05) 788-801.
  • 21 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.
  • 22 Huang Y, Hanauer DA. Patient No-Show Predictive Model Development using Multiple Data Sources for an Effective Overbooking Approach. Appl Clin Inform 2014; 5 (03) 836-860.
  • 23 Samorani M, LaGanga LR. Outpatient appointment scheduling given individual day-dependent no-show predictions. Eur J Oper Res 2015; 240 (01) 245-257.
  • 24 Kim S, Giachetti RE. A Stochastic Mathematical Appointment Overbooking Model for Healthcare Providers to Improve Profits. IEEE Trans Syst Man Cybern 2006; 36 (06) 1211-1219.
  • 25 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.
  • 26 Vanden Bosch PM, Dietz DC. Minimizing expected waiting in a medical appointment system. IIE Trans 2000; 32 (09) 841-848.
  • 27 Harper PR, Gamlin HM. Reduced outpatient waiting times with improved appointment scheduling: a simulation modeling approach. OR Spectrum 2003; 25 (02) 207-222.
  • 28 Huang Y, Hancock WM, Herrin GD. An alternative outpatient scheduling system: Improving the out-patient experience. IIE Trans Healthc Syst Eng 2012; 2 (02) 97-111.
  • 29 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; 3 (04) 263-279.
  • 30 Kuiper A, Kemper B, Mandjes M. A Computational approach to optimized appointment scheduling. Queueing Systems. DOI 10.1007/s11134–014–9398-6.
  • 31 Keller TF, Laughhunn DJ. An Application of Queuing Theory to a Congestion Problem in an Outpatient Clinic. Decision Sci 1973; 4 (03) 379-394.
  • 32 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.
  • 33 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 Pediat Adol Med 2006; 160 (09) 889-893.
  • 34 Bundy DG, Randolph GD, Murray M, Anderson J, Margolis PA. Open Access in Primary Care: Results of a North Carolina. Pilot Project Pediatrics 2005; 116 (Suppl. 01) 82-87.
  • 35 Forjuoh SN, Averitt WM, Cauthen DB, Couchman GR, Symm B, Mitchell M. Open-Access Appoinment Scheduling in Family Practice: Comparison of a Demand Prediction Grid With Actual Appointments. J Am Board Fam Pract 2001; 14 (04) 259-265.
  • 36 Peng Y, Qu X, Shi J. A hybrid simulation and genetic algorithm approach to determine the optimal scheduling templates for open access clinics admitting walk-in patients. Comput Ind Eng 2014; 72: 282-296.
  • 37 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) 1459-1473.
  • 38 Klassen KJ, Rohleder TR. Scheduling outpatient appointments in a dynamic environment. J Oper Manag 1996; 14 (02) 83-101.
  • 39 Cayirli T, Veral E, Rosen H. Designing appointment scheduling systems for ambulatory care services. Health Care Manage Sci 2006; 9 (01) 47-58.