Patient Appointment Scheduling Research Articles

CareConnect: Improving Patient Appointment Scheduling using Markov Decision Process with K-Means Algorithms

Abstract: In the healthcare sector, the number of patients has increased by a significant amount in the last decade which has resulted in overcrowding of hospitals and clinics. Using machine learning (ML) in this domain has proven very useful for both doctors and patients. This research mainly focuses on designing an algorithm for appointment scheduling for doctor’s which helps them prioritize patients based on their medical conditions and history which will give them an idea of which patient needs attention on a priority basis. The study implements various machine learning algorithms such as K-means clustering, which clusters patients into three classes of priority, Markov’s Decision Process (MDP) is used to define the problem and Q-Learning is used to solve it. Using these techniques the research aims to develop a system which automatically books an appointment with a doctor based on inputs given to certain questions related to their medical conditions.

HOSPITAL MANAGEMENT SYSTEM BASED ON WEB

Hospital Management Systems (HMS) represent a key advancement in healthcare technology designed to revolutionize operational efficiency, raise standards of patient care and ensure compliance with regulatory protocols. This paper explores the multifaceted functions of HMS, highlighting its role in automating administrative tasks, supporting real-time access to patient data, and facilitating seamless communication between healthcare professionals. Drawing on current research and practical implementations, we delve into the transformative potential of HMS, particularly in the context of recent innovations in telemedicine, data analytics, and remote monitoring systems. In our research, we present SIMRS, a robust hospital management information system developed using the waterfall method, strategically focused on the systematic improvement of hospital operations. By automating key processes such as patient registration, appointment scheduling, payment and inventory management, SIMRS optimizes hospital workflows and ultimately improves patient care delivery. In addition, we examine the empirical evidence that shows the significant impact of management systems and leadership traits on the quality of care in hospital settings, and highlight the indispensable role of effective leadership and management frameworks. Next, we discuss the technical foundations of HMS and highlight the integration of various programming languages and database management systems such as HTML, CSS, JavaScript, PHP, MySQL, and MongoDB. This cross-disciplinary approach underscores the synergy between technological innovation and healthcare management, paving the way for a more efficient patient- centered healthcare ecosystem. In summary, this research illuminates the transformative potential of HMS to revolutionize healthcare operations, improve patient outcomes and optimize resource allocation, advocating for its widespread adoption to shape the future of healthcare delivery. Key Words: JavaScript, MySql, HMS, Electronic Health Records, Telehealth

Smart Health Care System

System is a Java-based web application designed to streamline hospital operations. Its user- friendly interface simplifies administrative complexities, focusing on optimizing patient management, appointment scheduling, and record-keeping. This system enhances coordination among medical staff, ensuring smooth patient care and better outcomes. By automating tasks and organizing crucial information, hospitals can operate more efficiently, delivering faster and more accurate care. This paper introduces a web-based platform facilitating the transition from paper to electronic records, particularly emphasizing the elimination of paper prescriptions through E-Medical Management. This approach enhances patient management efficiency, doctor scheduling, and provides universal access to patient data within the hospital premises.

Artificial Intelligence for Patient Flow

Why Is This an issue? Inefficient patient flow contributes to the overcrowding of health care settings and negative clinical outcomes and patient experiences downstream. Patient flow management aims to achieve seamless patient movement through the health care system and between acute and long-term settings, ensuring timely access to quality care. What Is the Technology? Artificial intelligence (AI)-based patient flow management tools are interventions designed to forecast and monitor patient movement from admission to discharge as they progress through different care settings. AI-driven tools can leverage big data and digital information systems (e.g., electronic health records) to facilitate effective patient flow. AI-based patient appointment scheduling tools, which can help improve patient flow, are created to automate appointment scheduling and optimize it by minimizing wait times and matching the demand for health services and hospital capacity. What Is the Potential Impact? AI tools for patient flow management can support volume forecasting of patients with various conditions, especially those experiencing chronic conditions that require different types of treatment or care in different settings over a long period of time. These AI tools can predict admissions, patient movement from the emergency department to inpatient beds, discharge, and transfers to different health care settings. Evidence for their effectiveness in patients with emergency admissions and those transferred to tertiary and quaternary care, as well as inpatients from the general, cardiology, and mental health departments, was reported. In addition, evidence suggested that AI tools can optimize appointment scheduling in general outpatient settings and operating rooms. In health care systems in Canada, AI tools are being used or investigated to enhance patient flow by predicting emergency admissions, transfers to alternate levels of care, and general inpatient discharges, as well as optimizing capacity planning for patients receiving oncology care. AI appointment scheduling tools are currently being used in some oncology care settings and operating rooms across Canada. The implementation of AI systems generally requires an upfront investment of time and other resources in addition to the financial cost of the system itself for set-up, integration, and staff training. The goal of these systems is to improve efficiency and save money, time, and human resources in the long run. What Else Do We Need to Know? Patient privacy and data security issues are concerns regarding widespread implementation of AI tools trained on electronic health records systems and patient datasets. AI algorithms trained on datasets lacking adequate representation of all relevant patients may not predict their flow accurately. Training datasets with sufficient data from all relevant patient groups can ensure the inputs and outputs of the algorithms accurately reflect patient care needs and mitigate potential bias. To accurately predict the care needs of local patients, AI algorithms, once deployed, should be retrained on site-specific datasets containing data for the patient populations that are representative of the hospital or health system in which they are being used. Not all institutions have the hardware or computing power available to adequately or efficiently process the large amounts of data that are required by these AI systems, or the infrastructure needed to use big data (e.g., from electronic health records), and may require additional resources for implementation, as well as for ongoing maintenance and updating of the systems.

Revolutionizing Hospital Operations: A Comprehensive Analysis of Hospital Management System Implementation

Hospital Management System (HMS) technology will transform hospital operations. This summary summarizes HMS implementation's main components, goals, advantages, and drawbacks. Patient registration, appointment scheduling, EMR, billing, inventory management, and analytics are integrated into the HMS. Its main goal is to improve hospital efficiency, automate operations, and centralize information management. Implementing an HMS improves patient care by providing rapid access to precise information, operational efficiency, financial procedures, and data accuracy. The challenges include comprehensive staff training, strong data security, and smooth healthcare system connection. Healthcare organizations are developing digitally, making the hospital administration system essential for patient outcomes and hospital administration

“Patient Care System

Patients can register, enter their information into the system, and schedule medical visits through our project's patient care system. Every patient can receive a unique ID from our program, which also automatically saves all of the patient and staff information. Using the ID, the user can look up a doctor's availability and a patient's data. You can log in to the patient care system with your username and password. An administrator or a receptionist can access it. The database can only contain data that they add. It is simple to retrieve the data. It has an extremely user-friendly interface. The patient care system also has features that allow for easy communication between patients and healthcare providers, such as secure messaging and appointment reminders. Additionally, the system can generate reports on patient demographics, appointment scheduling, and medical history, which can be useful for tracking patient progress and improving overall patient care. Overall, our project's patient care system streamlines the healthcare process and enhances the patient experience. Keyword: Admin Module, Doctor module, health care services, user module.

A Survey Paper on Integrated Hospital Management System

Hospital Management Systems (HMS) are essential tools in modern healthcare, focusing on efficiency and streamlined operations. Key functionalities include patient registration, appointment scheduling, and doctor availability search. Accessible to administrators, doctors, and patients, HMS offers a customizable, user-friendly interface while emphasizing data protection and fast processing. Ongoing advancements promise to enhance the patient experience and diagnostic procedures, solidifying HMS's role in patient centric healthcare delivery.

Developing a prototype system of computer-aided appointment scheduling: A radiology department case study.

Scheduling patient appointments in hospitals is complicated due to various types of patient examinations, different departments and physicians accessed, and different body parts affected. This study focuses on the radiology scheduling problem, which involves multiple radiological technologists in multiple examination rooms, and then proposes a prototype system of computer-aided appointment scheduling based on information such as the examining radiological technologists, examination departments, the patient's body parts being examined, the patient's gender, and the patient's age. The system incorporated a stepwise multiple regression analysis (SMRA) model to predict the number of examination images and then used the K-Means clustering with a decision tree classification model to classify the patient's examination time within an appropriate time interval. The constructed prototype creates a feasible patient appointment schedule by classifying patient examination times into different categories for different patients according to the four types of body parts, eight hospital departments, and 10 radiological technologists. The proposed patient appointment scheduling system can schedule appointment times for different types of patients according to the type of visit, thereby addressing the challenges associated with diversity and uncertainty in radiological examination services. It can also improve the quality of medical treatment.

Process improvement of a paediatric feeding clinic

PurposeThe purpose of this study is to identify opportunities to improve processes within a paediatric feeding clinic to enhance timely patient access to healthcare through effective and efficient resource use. Design/methodology/approachThe study involved three interrelated methods. First, de-identified feeding clinic data, collected over seven years, were analysed to understand patient appointments. Second, clinician workshops and the swim lane method were used to map feeding clinic processes. Third, root cause analysis was conducted to identify bottlenecks and identify improvement opportunities. FindingsThe results revealed three, poorly connected sub-processes within the feeding clinic – namely: the patient triaging and appointment scheduling or allocation process; the clinic reporting process; and the cancellation and rescheduling process. These sub-processes were poorly connected because of inadequate resources, few standardised processes, and limited coordination between the different processes. Consequently, patient appointments were typically delayed, and patient reports were not always completed in a timely manner. Processes within the paediatric feeding clinic could therefore be improved by using digital tools, patient portals and telehealth, online interventions, an automated appointment confirmation system, and/or an automated transcription of each appointment. Originality/valueThis is the first published study to apply business process management to a paediatric feeding clinic. By using three methods to clarify opportunities to improve clinic processes, it highlights the potential value of health information technology in this context. This evidence will enable health service managers to ensure that children with feeding difficulties have timely access to appropriate care.

Smartwatches in healthcare medicine: assistance and monitoring; a scoping review

Smartwatches have become increasingly popular in recent times because of their capacity to track different health indicators, including heart rate, patterns of sleep, and physical movements. This scoping review aims to explore the utilisation of smartwatches within the healthcare sector. According to Arksey and O'Malley's methodology, an organised search was performed in PubMed/Medline, Scopus, Embase, Web of Science, ERIC and Google Scholar. In our search strategy, 761 articles were returned. The exclusion/inclusion criteria were applied. Finally, 35 articles were selected for extracting data. These included six studies on stress monitoring, six on movement disorders, three on sleep tracking, three on blood pressure, two on heart disease, six on covid pandemic, three on safety and six on validation. The use of smartwatches has been found to be effective in diagnosing the symptoms of various diseases. In particular, smartwatches have shown promise in detecting heart diseases, movement disorders, and even early signs of COVID-19. Nevertheless, it should be emphasised that there is an ongoing discussion concerning the reliability of smartwatch diagnoses within healthcare systems. Despite the potential advantages offered by utilising smartwatches for disease detection, it is imperative to approach their data interpretation with prudence. The discrepancies in detection between smartwatches and their algorithms have important implications for healthcare use. The accuracy and reliability of the algorithms used are crucial, as well as high accuracy in detecting changes in health status by the smartwatches themselves. This calls for the development of medical watches and the creation of AI-hospital assistants. These assistants will be designed to help with patient monitoring, appointment scheduling, and medication management tasks. They can educate patients and answer common questions, freeing healthcare providers to focus on more complex tasks.

The Transformative Power of Artificial Intelligence in Dentistry

Artificial Intelligence (AI) has emerged as a disruptive force in the field of dentistry, reshaping traditional practices and revolutionizing patient care. This abstract explores the transformative power of AI in dentistry, highlighting its potential to enhance diagnostics, treatment planning, and patient outcomes. AI-driven algorithms, powered by deep learning and machine learning techniques, have shown remarkable capabilities in the analysis of dental images, such as radiographs and intraoral scans, leading to more accurate and efficient disease detection. Moreover, AI systems have enabled personalized treatment planning by considering individual patient characteristics and historical data, leading to optimized treatment regimens. This abstract also delves into the role of AI in improving patient engagement and telemedicine services, making dental care more accessible and convenient. Furthermore, the integration of AI in dental practice management has streamlined administrative tasks, allowing dental professionals to focus more on patient care. AI-driven chatbots and virtual assistants have enhanced patient communication and appointment scheduling. However, the adoption of AI in dentistry is not without its challenges, including concerns about data privacy, ethical considerations, and the need for continuous training and education for dental professionals. In conclusion, the transformative power of AI in dentistry is undeniable, offering the potential to improve diagnostics, treatment planning, patient engagement, and practice management. Embracing AI technologies in a responsible and ethical manner has the potential to elevate the quality of dental care, making it more efficient, accurate, and patient-centric. This abstract underscores the need for continued research, regulation, and collaboration among dental professionals, technologists, and policymakers to unlock the full potential of AI in dentistry while addressing its associated challenges.

A New Scenario-Based Simulation Model for Cost Management of Healthcare Services through Improving the Efficiency of the Health Centers

There is a need for an appropriate decision-making approach and a precise costing method in order to make appropriate decisions that result in managing the resource utilization, improving the efficiency of healthcare systems and processes, increasing the patient consent, and reducing the costs of healthcare services. Therefore, this study is to develop a new method for estimating and reducing the services’ costs by assessing the scenarios intended for the improvement of departments using time-driven activity-based costing (TDABC) and simulation model with conditions of uncertainty. To investigate the application of this method, the lithotripsy department of a kidney center has been studied and attempted to investigate the effects of different scenarios of scheduling the technicians’ presence and scheduling patients’ appointments, alongside the effects of controlling the factors of patient cancellation in different stages of the treatment process on lowering the healthcare costs and waiting time and on improving the hospital efficiency. The results showed that significant improvements in costs, waiting time, human resource utilization, and overall system efficiency were emerged. The impact of patient appointment scheduling scenarios on the abovementioned indexes was found to be far more than other scenarios. Furthermore, the results from the costs determined indicate a difference of 0.60, 0.61, 0.67, 0.67, and 0.63 percent between these costs and the costs determined by the traditional approaches. Such differences among the costs determined by these two approaches mainly occur due to different methods of allocating indirect costs and existence of uncertainty in the costs and time of therapeutic activities.

I Asked a ChatGPT to Write an Editorial About How We Can Incorporate Chatbots Into Neurosurgical Research and Patient Care….

I Asked a ChatGPT to Write an Editorial About How We Can Incorporate Chatbots Into Neurosurgical Research and Patient Care….

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.

Multi-Objective Patient Appointment Scheduling Framework (MO-PASS): a data-table input simulation–optimization approach

Appointment scheduling is one of the critical factors for improving patient satisfaction with healthcare services. A practical and robust appointment scheduling solution allows clinics to efficiently utilize medical devices, equipment, and other resources. This study introduces a Multi-Objective Patient Appointment Scheduling (MO-PASS) framework to enhance clinic operations and quality of care. The proposed framework integrates three modules: (1) Optimization (using MATLAB), (2) Data-Exchange (MS Excel), and (3) Simulation (Simio). To implement MO-PASS, the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is coded in MATLAB, and a Simio API is developed, which exchanges simulated scenarios with MOPSO via Excel. The efficiency of the proposed framework is evaluated in a breast cancer clinic with multiple physicians and patient types. Two objective functions are defined for evaluating the solutions of the AS problem: (1) minimizing the total service time and (2) maximizing the number of (admitted) patients with zero overtime. Finally, the performance of MO-PASS is tested against three heuristic approaches with respect to objective functions. The computational experiment results show that the proposed MO-PASS outperforms the existing heuristic benchmarks. Also, the framework is accompanied by all the necessary details to make it practical and easy to implement.

The utilization of patients’ information to improve the performance of radiotherapy centers: A data-driven approach

The high demand for radiotherapy services, combined with the limited capacity of available resources, patient unpunctuality, and series of appointments, makes Patient Appointment Scheduling (PAS) in radiotherapy centers very challenging. Although most centers use a First-Come-First-Serve (FCFS) policy for appointment scheduling, this approach does not consider patients’ behaviors, and consequently, it performs poorly. This type of inappropriate scheduling usually leads to inefficiency at the center and/or patient dissatisfaction. This study provides a data-driven approach to patient appointment scheduling to deal with the challenges mentioned above, and it considers patients’ histories of unpunctuality, including the amount of time they are usually late and whether they will miss the appointment. This study first employs data-mining techniques to predict patients’ behaviors and then incorporates them into PAS. In addition, it presents a novel double-stage prioritization method that considers both patients’ gradual health improvement during the treatment process and any treatment prolongation that occurs. These predictions and priorities are then utilized in the developed Mixed Integer Linear Programming (MILP) model to determine the optimal sequence of patients for treatment. The developed model also considers no-show patients and rearranges their makeup session(s) to meet their service requirements. Lastly, the proposed approach is applied to two business configurations (i.e., single-server and multi-server radiotherapy centers) to highlight its advantages and demonstrate its performance against the current policy. The results reveal that employing the developed model improves the center’s total cost by up to 30%.

Mental Health Intent Recognition for Arabic-Speaking Patients Using the Mini International Neuropsychiatric Interview (MINI) and BERT Model

For many years, mental health has been hidden behind a veil of shame and prejudice. In 2017, studies claimed that 10.7% of the global population suffered from mental health disorders. Recently, people started seeking relaxing treatment through technology, which enhanced and expanded mental health care, especially during the COVID-19 pandemic, where the use of mental health forums, websites, and applications has increased by 95%. However, these solutions still have many limits, as existing mental health technologies are not meant for everyone. In this work, an up-to-date literature review on state-of-the-art of mental health and healthcare solutions is provided. Then, we focus on Arab-speaking patients and propose an intelligent tool for mental health intent recognition. The proposed system uses the concepts of intent recognition to make mental health diagnoses based on a bidirectional encoder representations from transformers (BERT) model and the International Neuropsychiatric Interview (MINI). Experiments are conducted using a dataset collected at the Military Hospital of Tunis in Tunisia. Results show excellent performance of the proposed system (the accuracy is over 92%, the precision, recall, and F1 scores are over 94%) in mental health patient diagnosis for five aspects (depression, suicidality, panic disorder, social phobia, and adjustment disorder). In addition, the tool was tested and evaluated by medical staff at the Military Hospital of Tunis, who found it very interesting to help decision-making and prioritizing patient appointment scheduling, especially with a high number of treated patients every day.

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.

Managing appointments of outpatients considering the presence of emergency patients: the combination of the analytical and data-driven approach

Patient appointments are an effective method to reduce patient waiting time. However, not all patients can make an appointment before receiving medical services. In this paper, we focus on the patient appointment scheduling problem in the presence of emergency patients. We formulate the problem as a stochastic programming (SP) model to reduce the patient waiting time and increase server utilisation. Considering the service system as a time-varying queuing system with dual-class patients, we propose two methods to evaluate the patients waiting times and the server utilisation for a given patient appointment schedule. The uniformisation method can ‘exactly’ evaluate the performance metrics with a high computation cost, while the trained machine learning models can approximate the metrics with high computing speed. Based on the proposed evaluation methods, we design a simulated annealing algorithm to solve the SP model. Numerical experiments show that the schedule computed by our heuristic algorithm can effectively improve the real-life patient appointment schedule.

Efficient Prediction of Missed Clinical Appointment Using Machine Learning.

Public health and its related facilities are crucial for thriving cities and societies. The optimum utilization of health resources saves money and time, but above all, it saves precious lives. It has become even more evident in the present as the pandemic has overstretched the existing medical resources. Specific to patient appointment scheduling, the casual attitude of missing medical appointments (no-show-ups) may cause severe damage to a patient's health. In this paper, with the help of machine learning, we analyze six million plus patient appointment records to predict a patient's behaviors/characteristics by using ten different machine learning algorithms. For this purpose, we first extracted meaningful features from raw data using data cleaning. We applied Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling Method (Adasyn), and random undersampling (RUS) to balance our data. After balancing, we applied ten different machine learning algorithms, namely, random forest classifier, decision tree, logistic regression, XG Boost, gradient boosting, Adaboost Classifier, Naive Bayes, stochastic gradient descent, multilayer perceptron, and Support Vector Machine. We analyzed these results with the help of six different metrics, i.e., recall, accuracy, precision, F1-score, area under the curve, and mean square error. Our study has achieved 94% recall, 86% accuracy, 83% precision, 87% F1-score, 92% area under the curve, and 0.106 minimum mean square error. Effectiveness of presented data cleaning and feature selection is confirmed by better results in all training algorithms. Notably, recall is greater than 75%, accuracy is greater than 73%, F1-score is more significant than 75%, MSE is lesser than 0.26, and AUC is greater than 74%. The research shows that instead of individual features, combining different features helps make better predictions of a patient's appointment status.