Optimising the queuing system for an ear, nose and throat outpatient clinic

A Decision-Making tool based on historical data for service time prediction in outpatient scheduling

BACKGROUND

Appointment scheduling in outpatient settings typically uses simple classification rules to assign patients to long or short appointment slots, based on the anticipated duration of the patient-physician consultation, i.e., the service time. For example, new patients are assigned longer appointment slots, and return patients are assigned shorter slots. While these rules are convenient, they fail to account for the significant variability in service time of outpatient visits.

METHODS

We present a data-mining approach that allows practices to predict service time based on patient characteristics and several other clinical attributes. This approach provides a decision-support tool that helps practices determine the length of time to allocate to a patient's appointment. Specifically, we use a neural network to accurately estimate service time for each patient based on his/her characteristics. The neural network is trained using eight years of real appointment data (2010 to 2018) from a local outpatient clinic. We compare the performance of the neural network predictions against commonly used classification rules, using a randomly sampled test dataset and a statistical test.

RESULTS

Our results suggest that outpatient practices can refine their current practices by adopting a data-driven approach to determining slot lengths for appointments. The average absolute difference and the standard deviation of differences between the neural network predictions and the actual service times in practice (case study) are 5.7 min and 4.0 min, respectively. These two measures are significantly lower than the same comparison with the common classification rule (new patient versus return patient) at the clinic; i.e. average time and standard deviations are 14.3 min and 8.2 min, respectively.

CONCLUSION

Neural network modeling can capture the effect of processes in a medical facility and create individualized predictions of patient service time with more accuracy.

Patients, primary care, and policy: Agent-based simulation modeling for health care decision support

Primary care systems are a cornerstone of universally accessible health care. The planning, analysis, and adaptation of primary care systems is a highly non-trivial problem due to the systems’ inherent complexity, unforeseen future events, and scarcity of data. To support the search for solutions, this paper introduces the hybrid agent-based simulation model SiM-Care. SiM-Care models and tracks the micro-interactions of patients and primary care physicians on an individual level. At the same time, it models the progression of time via the discrete-event paradigm. Thereby, it enables modelers to analyze multiple key indicators such as patient waiting times and physician utilization to assess and compare primary care systems. Moreover, SiM-Care can evaluate changes in the infrastructure, patient behavior, and service design. To showcase SiM-Care and its validation through expert input and empirical data, we present a case study for a primary care system in Germany. Specifically, we study the immanent implications of demographic change on rural primary care and investigate the effects of an aging population and a decrease in the number of physicians, as well as their combined effects.

Enhancing outpatient appointment scheduling system performance when patient no-show percent and lateness rates are high

Purpose High lateness and no-show percentages pose great challenges on the patient scheduling process. Usually this is addressed by optimizing the time between patients in the scheduling process and the percent of extra patients scheduled to account for absent patients. However, since the patient no-show and lateness is highly stochastic we might end up with many patients showing up on time which leads to crowded clinics and high waiting times. The clinic might end up as well with low utilization of the doctor time. The purpose of this paper is to study the effect of scheduled overload percentages and the patient interval on the waiting time, overtime, and the utilization. Design/methodology/approach Actual data collection and statistical modeling are used to model the distribution for common dentist procedures. Simulation and validation are used to model the treatment process. Then algorithm development is used to model and generate the patient arrival process. The simulation is run for various values of basic interval scheduled time between arrivals for the patients. Further, 3D graphical illustration for the objectives is prepared for the analysis. Findings This work initially reports on the statistical distribution for the common procedures in dentist clinics. This can be used for developing a scheduling system and for validating the scheduling algorithms developed. This work also suggest a model for generating patient arrivals in simulation. It was found that the overtime increases excessively when coupling both high basic interval and high overloading percentage. It was also found that: to obtain low overtime we must reduce the basic interval. Waiting time increases when reducing the basic scheduled appointment interval and increase the scheduled overload percentage. Also doctors' utilization is increased when the basic interval is reduced. Research limitations/implications This work was done at a local clinic and this might limit the value of the modeled procedure times. Practical implications This work presents a statistical model for the various procedures and a detailed technique to model the operations of the clinics and the patient arrival time which might assist researches and developers in developing their own model. This work presents a procedure for troubleshooting scheduling problems in outpatient clinics. For example, a clinic suffering from high patient waiting time is directly instructed to slightly increase their basic scheduled interval between patients or slightly reduce the overloading percentage. Social implications This work is targeting an extremely important constituent of the health-care system which is the outpatient clinics. It is also targeting multiple objectives namely waiting times, utilization overtime, which in turn is related to the economics and doctor utilization. Originality/value This work presents a detailed modeling procedure for the outpatient clinics under high lateness and no-show and addresses the modeling procedure for the patient arrivals. This 3D graphical charting for the objectives includes a study of the multiple objectives that are of high concern to outpatient clinic scheduling interested parties in one paper.

Patient- and Visit-Level Variables Associated With Late Arrival to Pediatric Clinic Appointments

We conducted a cross-sectional study to evaluate timeliness of patient arrival at a pediatric multispecialty clinic. Bivariate and ordered logistic regression analyses were conducted to determine the odds of late arrival by specified patient- and visit-level characteristics. A total of 64 856 visits were available for analysis, of which 6513 (10.0%) were late arrivals. The odds of late arrival were higher for patients who spoke English (odds ratio [OR] = 1.34, P < .001) compared with those who spoke Spanish, had Medicaid (OR = 1.54, P < .001) or no insurance (OR = 1.49, P < .001) compared with those with insurance other than Medicaid, and were late to their previous visit (OR = 2.46, P < .001). Visit-level variables associated with late arrival included appointment time earlier in the day (i.e. 8-10 am, OR = 2.77, P < .001 compared with 4-6 pm), earlier in the week (i.e. on Mondays, OR = 1.21, P < .001 compared with Wednesdays), and for certain subspecialty clinics ( P < .001). Numerous variables are significantly associated with late arrival for pediatric clinic appointments.

Optimizing appointment template and number of staff of an OB/GYN clinic--micro and macro simulation analyses

Background

The Department of Obstetrics and Gynecology (OB/GYN) at the University of Arkansas for Medical Sciences (UAMS) tested various, new system-restructuring ideas such as varying number of different types of nurses to reduce patient wait times for its outpatient clinic, often with little or no effect on waiting time. Witnessing little progress despite these time-intensive interventions, we sought an alternative way to intervene the clinic without affecting the normal clinic operations.

Aim

The aim is to identify the optimal (1) time duration between appointments and (2) number of nurses to reduce wait time of patients in the clinic.

Methods

We developed a discrete-event computer simulation model for the OB/GYN clinic. By using the patient tracker (PT) data, appropriate probability distributions of service times of staff were fitted to model different variability in staff service times. These distributions were used to fine-tune the simulation model. We then validated the model by comparing the simulated wait times with the actual wait times calculated from the PT data. The validated model was then used to carry out “what-if” analyses.

Results

The best scenario yielded 16 min between morning appointments, 19 min between afternoon appointments, and addition of one medical assistant. Besides removing all peak wait times and bottlenecks around noon and late in the afternoon, the best scenario yielded 39.84 % (p<.001), 30.31 % (p<.001), and 15.12 % (p<.001) improvement in patients’ average wait times for providers in the exam rooms, average total wait time at various locations and average total spent time in the clinic, respectively. This is achieved without any compromise in the utilization of the staff and in serving all patients by 5pm.

Conclusions

A discrete-event simulation model is developed, validated, and used to carry out “what-if” scenarios to identify the optimal time between appointments and number of nurses. Using the model, we achieved a significant improvement in wait time of patients in the clinic, which the clinic management initially had difficulty achieving through manual interventions. The model provides a tool for the clinic management to test new ideas to improve the performance of other UAMS OB/GYN clinics.

Designing appointment scheduling systems for ambulatory care services

The current climate in the health care industry demands efficiency and patient satisfaction in medical care delivery. These two demands intersect in scheduling of ambulatory care visits. This paper uses patient and doctor-related measures to assess ambulatory care performance and investigates the interactions among appointment system elements and patient panel characteristics. Analysis methodology involves simulation modeling of clinic sessions where empirical data forms the basis of model design and assumptions. Results indicate that patient sequencing has a greater effect on ambulatory care performance than the choice of an appointment rule, and that panel characteristics such as walk-ins, no-shows, punctuality and overall session volume, influence the effectiveness of appointment systems.