Staff Scheduling Research Articles

An MILP model for workload fairness and incompatibility in seafaring staff scheduling problem

An MILP model for workload fairness and incompatibility in seafaring staff scheduling problem

7902 Improvement In Follow-Up Of Post-Hospital Discharge Patients After Targeted Approach

Abstract Disclosure: S. Ward: None. Q. Aziz: None. A.L. Champion: None. K.E. Izuora: None. Improvement in Follow-up of Post-hospital Discharge Patients after Targeted Approach Introduction: Outpatient follow up after discharge from the hospital is a well-established factor affecting health outcomes in patients with chronic medical conditions. A prior QI project at our institution utilizing a Plan-Do-Study-Act (PDSA) cycle showed that overall follow up rate was 24% in patient expected to follow up in our outpatient clinic. The aim of this QI project was to identify and implement a targeted approach to improve outpatient follow up at our clinic. Methods: This study was a second round of a PDSA cycle with the goal of improving outpatient follow up. Analysis of prior data identified 54 patients referred for outpatient follow up in our endocrinology clinic after hospital discharge (non-targeted approach). Of these 54 only 13 patients had established follow up care (24%). We looked closer at the 39 patients who did not follow up to identify areas we could act on to improve follow up; 38% of these patients did not follow due to one of the following: did not know they needed to follow up, lack of knowledge of how to contact office, unable to reach office by phone. To overcome these obstacles, we developed a targeted approach for all patients seen in the hospital who were expected to follow up in the outpatient setting. This approach included the following: clinic business card with contact information given to patient, follow up instructions included in discharge instructions including how and when to contact office, message sent from physician to outpatient clinic staff requesting staff contact patient and schedule follow up appointment. This targeted approach was implemented for all patients requiring outpatient follow up seen by the UNLV Endocrinology consult service from August 1, 2023 to September 30, 2023. Results: During the two months of recruitment we identified 34 patients who needed outpatient follow up. All 34 of these patients had the targeted approach implemented. Of the 34 patients 12 have successfully followed up and one patient has a future appointment scheduled. This is a follow up rate of 35%, an increase of 11% compared with the prior QI data that did not use a targeted approach. If all future appointments are kept the follow up rate will be 38%. Five patients who had scheduled appointments did not show up to their appointment and are not counted in the successful follow group . Conclusions: Our targeted approach showed an improvement in outpatient follow up from 24% to 35% compared to a non-targeted approach. Five patients that scheduled appointments did not keep those appointments. Future steps would include evaluating reasons for no-show and identifying approaches to improve our no-show rate. . Presentation: 6/2/2024

Enhancing Patient Safety Through an Integrated Internet of Things Patient Care System: Large Quasi-Experimental Study on Fall Prevention.

The challenge of preventing in-patient falls remains one of the most critical concerns in health care. This study aims to investigate the effect of an integrated Internet of Things (IoT) smart patient care system on fall prevention. A quasi-experimental study design is used. The smart patient care system is an integrated IoT system combining a motion-sensing mattress for bed-exit detection, specifying different types of patient calls, integrating a health care staff scheduling system, and allowing health care staff to receive and respond to alarms via mobile devices. Unadjusted and adjusted logistic regression models were used to investigate the relationship between the use of the IoT system and bedside falls compared with a traditional patient care system. In total, 1300 patients were recruited from a medical center in Taiwan. The IoT patient care system detected an average of 13.5 potential falls per day without any false alarms, whereas the traditional system issued about 11 bed-exit alarms daily, with approximately 4 being false, effectively identifying 7 potential falls. The bedside fall incidence during hospitalization was 1.2% (n=8) in the traditional patient care system ward and 0.1% (n=1) in the smart ward. We found that the likelihood of bedside falls in wards with the IoT system was reduced by 88% (odds ratio 0.12, 95% CI 0.01-0.97; P=.047). The integrated IoT smart patient care system might prevent falls by assisting health care staff with efficient and resilient responses to bed-exit detection. Future product development and research are recommended to introduce IoT into patient care systems combining bed-exit alerts to prevent inpatient falls and address challenges in patient safety.

Ethical challenges of the implementation of artificial intelligence in nursing care

The advancement of digitalization and technology has facilitated the utilization of artificial intelligence (AI) to enhance the delivery of healthcare and nursing services. AI refers to systems capable of exhibiting intelligent behavior, allowing them to analyze their surroundings and execute a range of tasks independently to accomplish predetermined objectives [1]. AI has been utilized extensively in the field of nursing for an extended period. Despite not being commonly recognized as AI, there has been a significant surge of interest in its application within the healthcare sector. This heightened interest has sparked new discussions regarding the intersection of AI and nursing, prompting further exploration and analysis of their relationship [2]. Over the past few years, there has been a growing focus on research highlighting the potential for the advancement of AI in the field of nursing care. AI can enhance and automate planning and scheduling processes, thereby increasing the effectiveness of human approaches. This includes tasks like generating nursing staff schedules and making care-related scheduling decisions, as well as providing support to nurses in remotely conducted tasks during direct patient interactions [3]. Nurses can enhance their ability to deliver comprehensive care by utilizing AI to consolidate various forms of interconnected data, including environmental, genomic, health, social, and demographic information [4].

How are US hospitals adopting artificial intelligence? Early evidence from 2022.

US hospitals are rapidly adopting artificial intelligence (AI), but there is a lack of knowledge about AI-adopting hospitals' characteristics, trends, and spread. This study aims to fill this gap by analyzing the 2022 American Hospital Association (AHA) data. The novel Hospital AI Adoption Model (HAIAM) is developed to categorize hospitals based on their AI adoption characteristics in the fields of (1) predicting patient demand, (2) optimizing workflow, (3) automating routine tasks, (4) staff scheduling, and (5) predicting staffing needs. Nearly one-fifth of US hospitals (1107 or 18.70%) have adopted some form of AI by 2022. The HAIAM shows that only 3.82% of hospitals are high adopters, followed by 6.22% moderate and 8.67% low adopters. Artificial intelligence adoption rates are highest in optimizing workflow (12.91%), while staff scheduling (9.53%) has the lowest growth rate. Hospitals with large bed sizes and outpatient surgical departments, private not-for-profit ownership, teaching status, and part of health systems are more likely to adopt different forms of AI. New Jersey (48.94%) is the leading hospital AI-adopting state, whereas New Mexico (0%) is the most lagging. These data can help policymakers better understand variations in AI adoption by hospitals and inform potential policy responses.

A multi-channel queue model to optimize service level and staff availability in bank industry

Staff scheduling in service organizations like banks, stores, call centers, and emergency centers is critical due to direct customer interaction and uncertain service demand. This research presents a multi-objective model for scheduling bank staff, focusing on uncertain customer arrival and service rates. The model aims to optimize customer service efficiency and maximize staff satisfaction through three objective functions: minimizing the customer waiting queue length (using an M/M/C system), minimizing the number of assigned employees, and maximizing employee satisfaction by considering preferred working times. By simulating Poisson distribution for client arrival and service times, we predicted the bank's queue system performance and optimized staffing levels using the proposed model. Tested with real data from Agribank in Iran, the results showed an 8% reduction in customer waiting times and a 53% increase in employee satisfaction, demonstrating significant improvements in service efficiency and workplace morale. These percentages highlight the model's ability to effectively balance operational efficiency and employee well-being, facilitated by its transparent work pattern structure. Given the NP-hard nature of the model, we employed a meta-heuristic approach (NSGA-II) and GAMS with the ε-constraint method to solve it. Comparative results indicated that NSGA-II outperformed GAMS in both solution quality and computational time

Poisson-stopped sum Lévy-type processes with application to stochastic modeling of hospital arrivals

Patient arrivals at a hospital typically occur in clusters and the arrivals count data exhibits over-dispersion. To model these characteristics, the Lévy-type processes based on Poisson-stopped sum distributions are proposed as alternatives to the non-homogeneous Poisson process (NHPP), a popular model for hospital arrivals. The arrival rate for the NHPP is a deterministic function of time that does not account for arrivals in clusters while the genesis of Poisson-stopped sum distributions arises from modeling event clusters. Data on daily scheduled patient arrivals over 15-minute intervals collected from a Malaysian public hospital were used to illustrate the application of the Levy-type processes in healthcare management. It is shown that the proposed Lévy-type negative binomial and Lévy-type Thomas processes fit the data better than the NHPP. In addition, the Levy-type processes are computationally simpler and hence, it is envisaged that they will be potentially useful for implementation in staff scheduling and resource allocations.

Café Management System

The Café Management System (CMS) project is designed to revolutionize the traditional café operations by integrating digital solutions to streamline management processes and enhance customer experiences. This thesis explores the development and implementation of a comprehensive CMS, encompassing an Admin Panel, Staff Panel, and Customer Panel. The Admin Panel serves as the central hub for managing the entire system. It includes functionalities such as Staff Management, Product Management, and Customer Management. Admins can efficiently oversee staff schedules, update menu items, and manage customer profiles, ensuring smooth operations. The Staff Panel empowers café employees with tools to improve workflow efficiency. Staff members can take orders, process payments, and generate order reports seamlessly. This panel facilitates better communication and coordination among staff members, resulting in improved service quality. The Customer Panel offers patrons a user- friendly interface to interact with the café digitally. Customers can place orders, make payments, and view order history conveniently. Additionally, they can provide feedback and suggestions, fostering a collaborative relationship between the café and its clientele. Key features of the CMS include order management functionalities such as Place Food Orders and Cancel Orders, facilitating a hassle-free ordering experience for customers. The system also supports secure payment processing and generates detailed receipts for transactions, ensuring transparency and accountability. Furthermore,the CMS provides robust reporting capabilities, allowing administrators to access Order and Payment Reports, as well as view Total Sales data. These insights enable informed decision-making and strategic planning to optimize café performance. Key Words: Payment report, update menu items, food order, cancle order, user friendly interface, Total sales data,Admin panel Services,managing the entire system, staff panel, customer panel, Admin panel,

Project Staff Scheduling and Performance of Road Construction Projects in Nairobi Metropolitan, Kenya

Project Staff Scheduling and Performance of Road Construction Projects in Nairobi Metropolitan, Kenya

A Journey into Restaurant Ownership by Lean Management Practices

"A Journey into Restaurant Ownership, Management, and Customer Satisfaction" delves into the multifaceted world of running a successful restaurant, emphasizing the critical role of customer satisfaction. This project explores the entire lifecycle of restaurant ownership and management, from conceptualization to daily operations, with a keen focus on creating exceptional dining experiences. The project begins by examining the process of conceptualizing and planning a restaurant, including choosing a concept, location, and target market. It then delves into the intricacies of menu development, pricing strategies, and sourcing ingredients to ensure quality and appeal. Operational management is a key focus, covering staff recruitment, training, and scheduling, as well as inventory management and maintaining high standards of cleanliness and safety. Marketing and promotion strategies are also explored, with an emphasis on digital marketing, social media engagement, and customer relationship management. Customer satisfaction is at the core of this project, with an analysis of factors that influence customer perceptions, such as food quality, service efficiency, ambiance, and value for money. Strategies for measuring and improving customer satisfaction are discussed, including feedback mechanisms and service recovery techniques. practical recommendations for aspiring and current restaurant owners and managers. It underscores the importance of customer satisfaction as a driver of success in the competitive restaurant.

RESTAURANT MANAGEMENT SYSTEM

Abstract— The restaurant industry is a dynamic and competitive field that requires efficient management to ensure success. This research paper introduces a Restaurant Management System (RMS) designed to streamline various aspects of restaurant operations, including order processing, inventory management, staff scheduling, and customer engagement. The system leverages modern technologies to enhance efficiency, reduce costs, and improve overall customer satisfaction. The primary objectives of the IRMS are to automate routine tasks, optimize resource utilization, and provide real-time insights for data-driven decision-making. The system integrates seamlessly with existing restaurant infrastructure, offering a user-friendly interface for both restaurant staff and management. The system is implemented to reduce the manual work and enhances the accuracy of work in a restaurant. This system manages and maintains the record of customers and their order online. This Android App has been made in a user-friendly interface. So that Customer can add and delete the food items easily. The menu card of the restaurant consists of various food varieties available in the restaurant. Through the place ordering menu, the customer can simply click on and order the food. The billing system prepares the bill according to the food. This system entirely reduces the unnecessary time and man power. This system increases quality and speed of service. This system also increases attraction of place for large range of customers Index Terms – Restaurant Management System, Integrated System, Order Processing, Point of Sale (POS), Inventory Management, Performance Monitoring, User Interface.

Implementation of hematology oncology lecture series for nursing staff.

e21005 Background: Westchester Medical Center has an oncology unit that provides care for hundreds of patients annually. Inpatient cancer patients often have especially complex medical treatment plans that require interdisciplinary collaboration and specialized knowledge of oncologic conditions. There is a need to implement formal teaching tailored to nursing staff devoted to topics in hematology and oncology. Our goal was to develop a monthly lecture series for nursing that deepened their understanding of cancer treatments, enabling them to assess and monitor patients with greater expertise. Methods: Topics were chosen based on relevance and are listed in the table. Speakers included full-time faculty, fellows and residents, pharmacists, and medical students. Ten lectures were completed and scheduled approximately once monthly in the mornings just prior to sign out for nursing shift change. Nursing staff were asked to complete a set of questions prior to each lecture (Pre-Test) They were given the same set of questions after each lecture (Post-Test) to determine whether there was an improvement in understanding of the topic. Informational handouts were distributed at the end of each lecture to provide participants with a summary of key points. A survey administered at the conclusion of the lecture series was collected to assess attitudes towards the lectures. Results: Overall test scores improved after each lecture was given, as demonstrated in the table: Pre and Post Lecture Test Scores. Additionally, feedback regarding the lectures was positive. 100% agreed that they gained a better understanding of topics related to hematology oncology and 92% agreed that participation in these lectures has improved their ability to function collaboratively on interprofessional healthcare teams. Conclusions: A didactic lecture series is a valuable component of hematology oncology education for nursing staff and will improve quality of care of the oncology service. Some limitations of this lecture series concerned nursing staff scheduling conflicts and their availability for lecture attendance. Possible interventions in the future may include setting aside time dedicated for monthly lectures and scheduling lectures for afternoons to avoid conflicts with shift changes and morning rounds. Nevertheless, our findings highlight the value of continued education in empowering nursing staff to deliver quality care to hematology oncology patients.[Table: see text]

Multi-objective robust optimization of staff scheduling for emergency under stochastic demand

Large-scale centralized emergency rescue staff scheduling under emergencies has always been a challenge. With a specific focus on nucleic acid testing amid the COVID-19 pandemic, this study introduces a model that considers dynamic changes in staff supply at rescue points, the varying demand at sampling points over time, and their impact on the spread of the epidemic. We quantified the emergency weight of sampling points by the demand and the regional risk degree and adopted robust optimization with base constraint. With the objectives of minimum comprehensive service distance, maximum weighted value of demand satisfaction rate, and minimum loss caused by unmet demand, a multi-stage, multi-medical rescue point medical staff scheduling model for nucleic acid sampling points with the influence caused by uncertain demand disturbance was built, and an improved NSGA-II-HC algorithm was designed to solve the optimization problem under the influence of demand disturbances. The testing results proved that the NSGA-II-HC algorithm can tackle the issue of insufficient uniformity and diversity in the solution set of NSGA-II while Multi-Objective Particle Swarm Optimization (MOPSO) failed to do so. Taking the epidemic data of Guangzhou city in May 2021 as a case study, our model and algorithm are verified to be feasible. We offer a scheme selection strategy according to the loss degree of two conflicting objectives, the comprehensive service distance and shortage loss. The results suggest that, compared with the demand deterministic model, the decision mechanism under the robust model can reduce the deviation from the optimization objective due to the demand disruption.

ENHANCING MEDICAL OFFICER SCHEDULING IN HEALTHCARE ORGANIZATIONS: A COMPREHENSIVE INVESTIGATION OF GENETIC AND GOOGLE OR TOOLS ALGORITHMS FOR MULTI-PROJECT RESOURCE-CONSTRAINED OPTIMIZATION

In healthcare organizations, medical staff scheduling is vital to achieving optimal patient care, ensuring the well-being of medical officers, and the efficiency of operations. This research aims to address the challenges of optimizing the scheduling of limited resources for multiple projects for medical staff, through a comparative analysis of Google OR tools and genetic algorithms. We evaluate the performance of these tools in various scenarios, taking into account factors such as overtime, work balance, and scheduling efficiency. This comparative analysis reveals the strengths and weaknesses of each approach, facilitating the development of improved medical staff scheduling solutions. Additionally, we offer algorithmic optimizations tailored to meet the requirements of specific healthcare settings, which contribute to enhancing the adaptability and effectiveness of scheduling tools. The research findings provide valuable insights to guide decision-making in healthcare institutions, ultimately aiming to enhance the quality of care provided by medical officers and improve the overall efficiency of the healthcare system. In conclusion, the results show that the modified Google OR algorithm significantly outperforms the Google OR tools and the regular genetic algorithm in performance.

‘You make the call’: Improving radiology staff scheduling with AI-generated self-rostering in a medical imaging department

IntroductionNew Zealand's shortage of medical imaging technicians has intensified due to factors like illness, the pandemic, and an ageing workforce. Addressing staff retention issues requires attention to intrinsic factors like workplace satisfaction and work-life balance. Self-rostering has proven effective in healthcare by enhancing work-life balance, job satisfaction, and retention, but it has not been implemented widely in radiology. This study aimed to explore the perceptions, benefits, and challenges of implementing AI-generated self-rostering in a radiology department through simulated trials. MethodsThis study simulated an AI-generated roster in a regional New Zealand radiology department, engaging 23 staff members. A mixed-methods approach included surveys and discussion groups. Community-based participatory action methodology guided discussion groups and informed modifications. ResultsThe AI-generated self-rostering method demonstrated success by meeting a high percentage of shift preferences while fulfilling service demands. Participants perceived potential benefits in work-life balance and autonomy, though uncertainties persisted regarding implementation and fairness. Despite staff reservations, we found that an AI-generated self-rostering system may be fairer than manual self-rostering, while saving radiology staff time and cost. ConclusionAI-generated self-rostering offers an innovative solution to an old problem. This self-rostering system provides a fair way for staff to have a say in the shifts they do, which increases feelings of work-life balance and autonomy. In this simulation, AI-generated self-rostering was well received, and most staff were receptive to moving to pilot the programme. Implications for practiceSelf-rostering could be a potential solution to staff retention issues in radiology; we recommend a pilot study is implemented. When switching to self-rostering, departments should consider implementing one-on-one support systems to assist staff with entering preferences. Education is essential to encourage staff understanding and cooperation.

A qualitative evaluation of the pathway for eating disorders and autism developed from clinical experience (PEACE): clinicians' perspective.

The Pathway for Eating disorders and Autism developed from Clinical Experience (PEACE pathway) is a clinical pathway of adapted treatment for individuals with eating disorders and autism in the UK. This study aims to investigate multidisciplinary clinicians' views of the strengths and challenges of PEACE pathway adaptations, while identifying areas where further improvement is needed. Semi-structured interviews were conducted with 16 clinicians who worked on the PEACE pathway. Themes relevant to the benefits, challenges and areas of improvement were identified, and a thematic map was produced. PEACE Pathway brought clinical benefits such as improved understanding of patients' perspective, improved flexibility and individualisation in clinicians' approach, increased patient engagement, and provision of resources that are helpful to all patients with or without autism. Benefits to the service included increase in autism awareness, clinicians' confidence, and team collaboration. Challenges were also identified, including difficulties in incorporating autism adaptations into existing treatment protocol, implementing PEACE at different levels of care, staff schedule conflicts, and increased pressure to meet patients' needs. Overall, there is a need for systemic improvement in aftercare and community support for autism, more suitable autism screening tool, and more structured guidelines for making adaptations. PEACE Pathway has brought clinical and service benefits, while also bringing practical challenges rooted in the difficulty in distinguishing between autism and eating disorder in comorbid population. Future areas of improvement are highlighted for PEACE resources as well as in the national support system for autistic individuals.

AN APPLICATION OF GRAPH COLORING: NURSE SCHEDULING PROBLEM

The Nurse Rostering Problem (NRP), also known as the Nurse Scheduling Problem (NSP), represents a challenging scheduling issue that significantly impacts hospital staff worldwide on a daily basis. Nursing staff scheduling is one of the most important but complex problem for the hospital management. NSP is a critical subclass of scheduling problems known for their complexity. This paper introduces an efficient approach to solving the Nurse Scheduling Problem (NSP), aiming to meet the needs of nurses, patients, and hospital management. It requires critical thinking and good decision-making skills to develop a roster that effectively balances patient care, employee and other needs in the organization. To reduce the problem's complexity, graph coloring techniques have been utilized.

Online Hotel Management System

The Online Hotel Management System is a web-based application designed with React.js for the frontend and Firebase Firestore as the backend database. Utilizing React.js's component-based architecture, the system ensures a responsive and modular user interface, enhancing user experience through efficient state management and virtual DOM rendering. Firebase Firestore, as the backend database, offers real-time synchronization and scalable storage, enabling seamless data management for hotel-related information such as reservations, room availability, and staff management. This system streamlines various hotel operations, including reservation management, guest check-in/check-out processes, billing, and staff scheduling, all accessible through an intuitive and user-friendly interface. By automating processes and reducing manual effort, the system improves overall efficiency in hotel management, catering to both administrators and guests. Through the integration of React.js and Firebase Firestore, the Online Hotel Management System provides a modern and robust solution for hotel administrators to effectively manage their operations and enhance the guest experience. Keywords – Web application, frontend , database, web server.

Optimization of vehicle charging and dynamic relocation in free-floating electric carsharing systems with advanced reservations

The electric free-floating carsharing (FFCS) system is an emerging type of shared mobility and is widely adopted in urban areas due to its eco-friendliness, effectiveness, and flexibility. However, as most trips in FFCS are uni-directional, the distributions of vehicles in FFCS are usually spatially uneven. The operators of FFCS must relocate cars during the operation period to maintain the availability of cars to the users and improve the profitability of the system for dealing with imbalanced vehicle distribution caused by uneven demand. To optimize the relocation operations for FFCS operators, this study formulates the problem as a mixed-integer linear programming (MILP) model, which simultaneously addresses dynamic relocation, battery charging of electric vehicles, and scheduling of relocation staff in an FFCS with advanced reservation. A novel, efficient algorithm using a new structure of relocation tuples (RTs) is proposed to solve the problem that facilitates the search for feasible solutions using an RT exchange-based local search algorithm. Acceleration techniques are introduced to strengthen the solution search in large-scale scenarios. Numerical examples are used to evaluate the performance of the solution algorithm and validate the benefit of the proposed methodology. This shows that the methodology could be used by operators for decision-making at different levels.

A multi-skilled staff scheduling and team configuration optimisation model for artificial intelligence project portfolio considering competence development and innovation-driven

This paper emphasises the pivotal role of enhancing research and development (R&D) staff competence and boosting team innovation efficiency in configuring R&D project teams for artificial intelligence (AI) product development. Diverging from traditional studies primarily focused on time, quality, and cost objectives, this study proposes prioritising the skill increments of staff and team diversity, aligning with the overarching goals of the R&D cycle. Targeting multi-skilled R&D personnel for scheduling and configuration, a three-objective tradeoff optimisation model is established. The nonlinear mixed-integer constrained programming model incorporates a learning effect for calculating employee skill value and employs a heterogeneity efficiency formula for assessing team diversity, particularly emphasising the diversity of R&D personnel research backgrounds. An algorithm based on Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is designed to obtain the approximate Pareto optimal solution. Furthermore, it compares the algorithm with the multi-objective particle swarm optimisation (MOPSO), explores practical decision-making methods for Pareto solutions, and conducts sensitivity analyses on the learning rate and diversity level. Our research is relevant to enterprises seeking to enhance R&D capabilities with a certain degree of homogeneity among R&D employees. This paper exemplifies and validates the proposed model and solution approach using a new AI product from a healthcare company.