Optimization of scheduling patient appointments in clinics using a novel modelling technique of patient arrival

A Next Available Appointment (NAA) Tool to Better Manage Patient Delay Risk and Patient Scheduling Expectations in Specialist Clinics

Every year there are approximately 3 million new outpatient specialist clinic appointments at local hospital networks in Victoria, Australia. Growing daily demand for these services leads to high-volume waiting lists and therefore long appointment delays for patients. This phenomenon emphasises the importance of providing analytics and tools to assist with waiting list management in outpatient specialist clinics. In this paper, we developed a novel Next Available Appointment (NAA) tool, to assist clinicians to manage delayed-appointment risk and improve the patient experience by aligning the expected and actual day of the appointment. The NAA uses simulation to determine the earliest available week for a patient appointment on or after the timeframe requested by the clinician, considering the current waiting list and future planned clinician availability. It was validated using 3 years of historical waiting list information across several scenarios chosen to capture operational diversity. As a practical example, a scenario chosen for implementation within the clinic's operational setting achieved a simulated reduction in overdue appointments from 41% to 25% (i.e., a reduction of 47,000 overdue appointments over 3 years). We also provided early details on the implementation of the tool currently underway.

Patient Unpunctuality's Effect on Appointment Scheduling: A Scenario-Based Analysis

This study examined patient unpunctuality's effect on patient appointment scheduling in the ultrasound department of a hospital. The study created a simulation system incorporating the formulated F3 distribution to describe patient unpunctuality. After the simulation model passed verification and validation processes, what-if scenarios were conducted under two policies: The preempt policy and the wait policy. A comparison of the total cost of each policy showed that the preempt policy performed better than the wait policy in the presence of unpunctuality. The study used sensitivity analyses to identify the different effects of patient unpunctuality on the system. The weights of the cost coefficient of both radiological technician's idle time and patient waiting time must be equal in order to achieve a lower cost. The patient's inter-arrival time must be close to the average total time in the system to achieve lower costs. Moreover, utilization decreases as the patient's inter-arrival increases. Therefore, the patient's inter-arrival time should be higher than, but close to, the service time to ensure less radiological technician's idle time and patient waiting time.

Application of job shop scheduling approach in green patient flow optimization using a hybrid swarm intelligence

With the increasing demand for hospital services amidst the COVID-19 pandemic, allocation of limited public resources and management of healthcare services are of paramount importance. In the field of patient flow scheduling, previous research primarily focused on classical-based objective functions, while ignoring environmental-based objective functions. This study presents a flexible job shop scheduling problem to optimize patient flow and, thereby, minimize the total carbon footprint, as the sustainability-based objective function. Since flexible job shop scheduling is an NP-hard problem, a metaheuristic optimization algorithm, called Chaotic Salp Swarm Algorithm Enhanced with Opposition-Based Learning and Sine Cosine (CSSAOS), was developed. The proposed algorithm integrates the Salp Swarm Algorithm (SSA) with chaotic maps to update the position of followers, the sine cosine algorithm to update the leader position, and opposition-based learning for a better exploration of the search space. generating more accurate solutions. The proposed method was successfully applied in a real-world case study and demonstrated better performance than other well-known metaheuristic algorithms, including differential evolution, genetic algorithm, grasshopper optimization algorithm, SSA based on opposition-based learning, quantum evolutionary SSA, and whale optimization algorithm. In addition, it was found that the proposed method is scalable to different sizes and complexities.

Using Simulation Optimization to Solve Patient Appointment Scheduling and Examination Room Assignment Problems for Patients Undergoing Ultrasound Examination

This study investigates patient appointment scheduling and examination room assignment problems involving patients who undergo ultrasound examination with considerations of multiple examination rooms, multiple types of patients, multiple body parts to be examined, and special restrictions. Following are the recommended time intervals based on the findings of three scenarios in this study: In Scenario 1, the time interval recommended for patients’ arrival at the radiology department on the day of the examination is 18 min. In Scenario 2, it is best to assign patients to examination rooms based on weighted cumulative examination points. In Scenario 3, we recommend that three outpatients come to the radiology department every 18 min to undergo ultrasound examinations; the number of inpatients and emergency patients arriving for ultrasound examination is consistent with the original time interval distribution. Simulation optimization may provide solutions to the problems of appointment scheduling and examination room assignment problems to balance the workload of radiological technologists, maintain high equipment utilization rates, and reduce waiting times for patients undergoing ultrasound examination.

A universal deep learning approach for modeling the flow of patients under different severities

BACKGROUND AND OBJECTIVE

The Accident and Emergency Department (A&ED) is the frontline for providing emergency care in hospitals. Unfortunately, relative A&ED resources have failed to keep up with continuously increasing demand in recent years, which leads to overcrowding in A&ED. Knowing the fluctuation of patient arrival volume in advance is a significant premise to relieve this pressure. Based on this motivation, the objective of this study is to explore an integrated framework with high accuracy for predicting A&ED patient flow under different triage levels, by combining a novel feature selection process with deep neural networks.

METHODS

Administrative data is collected from an actual A&ED and categorized into five groups based on different triage levels. A genetic algorithm (GA)-based feature selection algorithm is improved and implemented as a pre-processing step for this time-series prediction problem, in order to explore key features affecting patient flow. In our improved GA, a fitness-based crossover is proposed to maintain the joint information of multiple features during iterative process, instead of traditional point-based crossover. Deep neural networks (DNN) is employed as the prediction model to utilize their universal adaptability and high flexibility. In the model-training process, the learning algorithm is well-configured based on a parallel stochastic gradient descent algorithm. Two effective regularization strategies are integrated in one DNN framework to avoid overfitting. All introduced hyper-parameters are optimized efficiently by grid-search in one pass.

RESULTS

As for feature selection, our improved GA-based feature selection algorithm has outperformed a typical GA and four state-of-the-art feature selection algorithms (mRMR, SAFS, VIFR, and CFR). As for the prediction accuracy of proposed integrated framework, compared with other frequently used statistical models (GLM, seasonal-ARIMA, ARIMAX, and ANN) and modern machine models (SVM-RBF, SVM-linear, RF, and R-LASSO), the proposed integrated "DNN-I-GA" framework achieves higher prediction accuracy on both MAPE and RMSE metrics in pairwise comparisons.

CONCLUSIONS

The contribution of our study is two-fold. Theoretically, the traditional GA-based feature selection process is improved to have less hyper-parameters and higher efficiency, and the joint information of multiple features is maintained by fitness-based crossover operator. The universal property of DNN is further enhanced by merging different regularization strategies. Practically, features selected by our improved GA can be used to acquire an underlying relationship between patient flows and input features. Predictive values are significant indicators of patients' demand and can be used by A&ED managers to make resource planning and allocation. High accuracy achieved by the present framework in different cases enhances the reliability of downstream decision makings.

Alternative Outpatient Chemotherapy Scheduling Method to Improve Patient Service Quality and Nurse Satisfaction

INTRODUCTION

Optimal scheduling and calendar management in an outpatient chemotherapy unit is a complex process that is driven by a need to focus on safety while accommodating a high degree of variability. Primary constraints are infusion times, staffing resources, chair availability, and unit hours.

METHODS

We undertook a process to analyze our existing management models across multiple practice settings in our health care system, then developed a model to optimize safety and efficiency. The model was tested in one of the community chemotherapy units. We assessed staffing violations as measured by nurse-to-patient ratios throughout the workday and at key points during treatment. Staffing violations were tracked before and after the implementation of the new model.

RESULTS

The new model reduced staffing violations by nearly 50% and required fewer chairs to treat the same number of patients for the selected clinic day. Actual implementation results indicated that the new model leveled the distribution of patients across the workday with an 18% reduction in maximum chair utilization and a 27% reduction in staffing violations. Subsequently, a positive impact on peak pharmacy workload reduced delays by as much as 35 minutes. Nursing staff satisfaction with the new model was positive.

CONCLUSION

We conclude that the proposed optimization approach with regard to nursing resource assignment and workload balance throughout a day effectively improves patient service quality and staff satisfaction.

The Development of Patient Scheduling Groups for an Effective Appointment System

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.

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

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 re-classification 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.

Scheduling prioritized patients in emergency department laboratories

This research focuses on scheduling patients in emergency department laboratories according to the priority of patients' treatments, determined by the triage factor. The objective is to minimize the total waiting time of patients in the emergency department laboratories with emphasis on patients with severe conditions. The problem is formulated as a flexible open shop scheduling problem and a mixed integer linear programming model is proposed. A genetic algorithm (GA) is developed for solving the problem. Then, the response surface methodology is applied for tuning the GA parameters. The algorithm is tested on a set of real data from an emergency department. Simulation results show that the proposed algorithm can significantly improve the efficiency of the emergency department by reducing the total waiting time of prioritized patients.

Patient punctuality and clinic performance: observations from an academic-based private practice pain centre: a prospective quality improvement study

OBJECTIVES

The aim of this study was to examine the effects of an intervention to alter patient unpunctuality. The major hypothesis was that the intervention will change the distribution of patient unpunctuality by decreasing patient tardiness and increasing patient earliness.

DESIGN

Prospective Quality Improvement.

SETTING

Specialty Pain Clinic in suburban Baltimore, Maryland, USA.

PARTICIPANTS

The patient population ranged in age from 18 to 93 years. All patients presenting to the clinic during the study period were included in the study. The average monthly volume was 86.2 (SD=13) patients. A total of 1500 patient visits were included in this study.

INTERVENTIONS

We tracked appointment times and patient arrival times at an ambulatory pain clinic. An intervention was made in which patients were informed that tardy patients would not be seen and would be rescheduled. This policy was enforced over a 12-month period.

PRIMARY AND SECONDARY OUTCOME MEASURES

The distribution of patient unpunctuality was developed preintervention and at 12 months after implementation. Distribution parameters were used as inputs to a discrete event simulation to determine effects of the change in patient unpunctuality on clinic delay.

RESULTS

Data regarding patient unpunctuality were gathered by direct observation before and after implementation of the intervention. The mean unpunctuality changed from -20.5 min (110 observations, SD=1.7) preintervention to -23.2 (169, 1.2) at 1 month after the intervention, -23.8 min (69, 1.8) at 6 months and -25.0 min (71, 1.2) after 1 year. The unpunctuality 12 months after initiation of the intervention was significantly different from that prior to the intervention (p<0.05).

CONCLUSIONS

Physicians and staff are able to alter patient arrival patterns to reduce patient unpunctuality. Reducing tardiness improves some measures of clinic performance, but may not always improve waiting times. Accommodating early arriving patients does serve to improve clinic performance.

Improving waiting time and operational clinic flow in a tertiary diabetes center

The Singapore General Hospital Diabetes Centre (DBC) is a multidisciplinary specialist outpatient clinic which aims to provide an integrated one-stop service for diabetes. As with many tertiary academic centre clinics, DBC encounters an expanding patient load, greater patient expectations and increasingly complicated patients who require services from a multitude of health providers. Such rising demands amidst limited resources cause inefficiencies and long waiting times to consultation. This result in low patient satisfaction and an unpleasant clinic experience. A multidisciplinary team was formed to reduce the waiting time at DBC and improve communication and work processes of staff.

Addressing wait-times is complicated as multiple stakeholders and operational processes are involved and interlinked. By systematically breaking down processes and identifying problem areas, targeted changes were implemented. This included a revised model of appointment scheduling, a patient reminder system, more effective communication sheets and work reassignments.

The primary aim of this project was to improve the patient turn-around time (duration a patient spends at the centre for a visit). There was no documented improvement in turn-around time after project implementation (108.23 minutes versus 106.6 minutes) but other secondary aims were achieved. These included an increase in the percentage of patients seen by the doctor within 60 minutes from 80% to 84%, a reduction in wait-time for payment and reappointment at the cashier by 36.6% and a reduction in non-attendances of new cases to the clinic from 30.2% to 21.3%. Staff satisfaction and communication were greatly improved. To aid sustainability, personalized reports of individual doctor's waiting times and workload were produced quarterly and tracked.

As this is a first step quality improvement project, efforts to track, examine and further improve turn-around times are on-going. Future initiatives are directed at time-efficient appointment scheduling between care providers for same day appointments, a reactive SMS system for reminders and reappointments and optimization of processes and manpower allocation for clinics.