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.

Mr. Dr. Health-Assistant Chatbot

Research says 60% of visits to a doctor are for simple small-scale diseases, 80% of which can be diagnosed at home using simple check-up. These diseases mostly include common cold and cough, headache, abdominal pains etc. Whereas, chat-bots in healthcare are highly in demand, which functioning can offer various services from symptom checking and appointment scheduling. Therefore, the purpose of the research aims to design, develop and evaluate a health-assistant Chat-bot Application entitled “MR.Dr.” that helps users to ask any personal query related to healthcare without physically available to the hospital. MR.Dr. is evaluated in term of usability. 30 respondents attended the survey of usability evaluation. In the system usability scale MR.Dr. achieved 87.6 % rating which means Grade A (excellent). User's feedback level was pretty satisfying where 24/7 service is the highest one.

Indicators for dental appointment scheduling in primary health care: a national cross-sectional study

Background Integrated dental services within the Health System, particularly at primary health care, are crucial to reverse the current impact of oral diseases, which are among the most prevalent diseases worldwide. However, the use of dental services is determined by complex phenomena related to the individual, the environment and practices in which care is offered. Therefore, factors associated with dental appointments scheduling can affect positively or negatively the use of dental services. The aim of the present study was to evaluate the indicators for dental appointment scheduling in Primary Health Care (PHC). Methods The present is a cross-sectional analytical study that used data from the external assessment of the third cycle of the National Program for Improving Access and Quality in Primary Care (PMAQ-AB), carried out between 2017 and 2018, in Brazil. The final sample consisted of 85,231 patients and 22,475 Oral Health teams (OHTs). The outcome variable was the fact that the user sought for a dental appointment at the Primary Health Care Unit. A multilevel analysis was carried out to verify the association between individual variables (related to users) and contextual variables (related to the OHTs) in relation to the outcome. Results Only 58.1% of the users interviewed at these Primary Health Care Units seek the available dental care. The variables with the greatest effect on the outcome were the patient’s age up to 42 years old (OR = 2.03, 95% CI: 1.96–2.10), at individual level, and ‘oral health teams that assisted no more than a single family health team (FHT)’ (OR = 1.29, 95% CI: 1.23–1.36) at contextual level. Other variables were also associated with the outcome, but with a smaller effect size. Conclusion In conclusion, users’ age and work process of OHT were indicators for dental appointment scheduling. Our results suggest that when OHT put the National Oral Health Policy guidelines into practice, by assisting only one FHT, the chance for PHC users seeking dental appointments is higher than OHTs that assist more than one FHT. Regarding age, patients aged up to 42 years are more likely to seek an appointment with a dentist.

Assimilation of Medical Appointment Scheduling Systems and Their Impact on the Accessibility of Primary Care: Mixed Methods Study

Background The COVID-19 pandemic has prompted the adoption of digital health technologies to maximize the accessibility of medical care in primary care settings. Medical appointment scheduling (MAS) systems are among the most essential technologies. Prior studies on MAS systems have taken either a user-oriented perspective, focusing on perceived outcomes such as patient satisfaction, or a technical perspective, focusing on optimizing medical scheduling algorithms. Less attention has been given to the extent to which family medicine practices have assimilated these systems into their daily operations and achieved impacts. Objective This study aimed to fill this gap and provide answers to the following questions: (1) to what extent have primary care practices assimilated MAS systems into their daily operations? (2) what are the impacts of assimilating MAS systems on the accessibility and availability of primary care? and (3) what are the organizational and managerial factors associated with greater assimilation of MAS systems in family medicine clinics? Methods A survey study targeting all family medicine clinics in Quebec, Canada, was conducted. The questionnaire was addressed to the individual responsible for managing medical schedules and appointments at these clinics. Following basic descriptive statistics, component-based structural equation modeling was used to empirically explore the causal paths implied in the conceptual framework. A cluster analysis was also performed to complement the causal analysis. As a final step, 6 experts in MAS systems were interviewed. Qualitative data were then coded and extracted using standard content analysis methods. Results A total of 70 valid questionnaires were collected and analyzed. A large majority of the surveyed clinics had implemented MAS systems, with an average use of 1 or 2 functionalities, mainly “automated appointment confirmation and reminders” and “online appointment confirmation, modification, or cancellation by the patient.” More extensive use of MAS systems appears to contribute to improved availability of medical care in these clinics, notwithstanding the effect of their application of advanced access principles. Also, greater integration of MAS systems into the clinic’s electronic medical record system led to more extensive use. Our study further indicated that smaller clinics were less likely to undertake such integration and therefore showed less availability of medical care for their patients. Finally, our findings indicated that those clinics that showed a greater adoption rate and that used the provincial MAS system tended to be the highest-performing ones in terms of accessibility and availability of care. Conclusions The main contribution of this study lies in the empirical demonstration that greater integration and assimilation of MAS systems in family medicine clinics lead to greater accessibility and availability of care for their patients and the general population. Valuable insight has also been provided on how to identify the clinics that would benefit most from such digital health solutions.

Optimization of an appointment scheduling problem for healthcare systems based on the quality of fairness service using whale optimization algorithm and NSGA-II

Effective appointment scheduling (EAS) is essential for the quality and patient satisfaction in hospital management. Healthcare schedulers typically refer patients to a suitable period of service before the admission call closes. The appointment date can no longer be adjusted. This research presents the whale optimization algorithm (WOA) based on the Pareto archive and NSGA-II algorithm to solve the appointment scheduling model by considering the simulation approach. Based on these two algorithms, this paper has addressed the multi-criteria method in appointment scheduling. This paper computes WOA and NSGA with various hypotheses to meet the analysis and different factors related to patients in the hospital. In the last part of the model, this paper has analyzed NSGA and WOA with three cases. Fairness policy first come first serve (FCFS) considers the most priority factor to obtain from figure to strategies optimized solution for best satisfaction results. In the proposed NSGA, the FCFS approach and the WOA approach are contrasted. Numerical results indicate that both the FCFS and WOA approaches outperform the strategy optimized by the proposed algorithm.

Appointment scheduling system in multi doctor/multi services environment

Appointment scheduling systems are used by health care providers to manage access to their services. In this paper an algorithm and a web application for automatic appointment scheduling is presented. Both are implemented using the concept of booking appointments for patients for a specific service offered by each doctor. The purpose of the application is to make signing up for a specific service easier for patients and to improve health tourism in Croatia by maximizing doctor’s efficiency and minimize patient waiting time. Medical providers are added to the system, they add the services which they provide, and each service offered has its own duration time. Users register, search for services matching their parameters, and schedule an appointment for the requested service. Available appointments are generated using the presented algorithm, which is the main part of this paper. The algorithm searches the database and returns possible appointments. If patient has more than one appointment, possible appointments time can be before the existing appointment, between two appointments, or at the end of the last appointment. Thus, web application enables the patient to reserve desirable appointment time.

Overbooked and Overlooked: Machine Learning and Racial Bias in Medical Appointment Scheduling

Problem definition: Machine learning is often employed in appointment scheduling to identify the patients with the greatest no-show risk, so as to schedule them into or right after overbooked slots. That scheduling decision maximizes the clinic performance, as measured by a weighted sum of all patients’ waiting time and the provider’s overtime and idle time. However, if a racial group is characterized by a higher no-show risk, then the patients belonging to that racial group will be scheduled into or right after overbooked slots disproportionately to the general population. Academic/Practical Relevance: That scheduling decision is problematic because patients scheduled in those slots tend to have a worse service experience than the other patients, as measured by the time they spend in the waiting room. Thus, the challenge becomes minimizing the schedule cost while avoiding racial disparities. Methodology: Motivated by the real-world case of a large specialty clinic whose black patients have a higher no-show probability than non-black patients, we analytically study racial disparity in this context. Then, we propose new objective functions that minimize both schedule cost and racial disparity and that can be readily adopted by researchers and practitioners. We develop a race-aware objective, which instead of minimizing the waiting times of all patients, minimizes the waiting times of the racial group expected to wait the longest. We also develop race-unaware methodologies that do not consider race explicitly. We validate our findings both on simulated and real-world data. Results: We demonstrate that state-of-the-art scheduling systems cause the black patients in our data set to wait about 30% longer than nonblack patients. Our race-aware methodology achieves both goals of eliminating racial disparity and obtaining a similar schedule cost as that obtained by the state-of-the-art scheduling method, whereas the race-unaware methodologies fail to obtain both efficiency and fairness. Managerial implications: Our work uncovers that the traditional objective of minimizing schedule cost may lead to unintended racial disparities. Both efficiency and fairness can be achieved by adopting a race-aware objective.