An Integrated Framework for Staffing and Shift Scheduling in Hospitals

Over the years, one of the main concerns confronting hospital management is optimising the staffing and scheduling decisions. Consequences of inappropriate staffing can adversely impact on hospital performance, patient experience and staff satisfaction alike. A comprehensive review of literature (more than 1300 journal articles) is presented in a new taxonomy of three dimensions; problem contextualisation, solution approach, evaluation perspective and uncertainty. Utilising Operations Research methods, solutions can provide a positive contribution in underpinning staffing and scheduling decisions. However, there are still opportunities to integrate decision levels; incorporate practitioners view in solution architectures; consider staff behaviour impact, and offer comprehensive applied frameworks. Practitioners’ perspectives have been collated using an extensive exploratory study in Irish hospitals. A preliminary questionnaire has indicated the need of effective staffing and scheduling decisions before semi-structured interviews have taken place with twenty-five managers (fourteen Directors and eleven head nurses) across eleven major acute Irish hospitals (about 50% of healthcare service deliverers). Thematic analysis has produced five key themes; demand for care, staffing and scheduling issues, organisational aspects, management concern, and technology-enabled. In addition to other factors that can contribute to the problem such as coordination, environment complexity, understaffing, variability and lack of decision support. A multi-method approach including data analytics, modelling and simulation, machine learning, and optimisation has been employed in order to deliver adequate staffing and shift scheduling framework. A comprehensive portfolio of critical factors regarding patients, staff and hospitals are included in the decision. The framework was piloted in the Emergency Department of one of the leading and busiest university hospitals in Dublin (Tallaght Hospital). Solutions resulted from the framework (i.e. new shifts, staff workload balance, increased demands) have showed significant improvement in all key performance measures (e.g. patient waiting time, staff utilisation). Management team of the hospital endorsed the solution framework and are currently discussing enablers to implement the recommendation

Modeling Behaviour of Nurses in Clinical Medical Unit in University Hospital: Burnout Implications

High demand of healthcare services due to changes in population demography, technological and medical advancements, budget limitations have direct effect on medical staff and medical organizations in particularly hospitals. One of the major issues confronting the healthcare system is staff behavior when they get close to \u27burnout\u27 level. This study identifies factors affecting nurses\u27 behavior and its impact on patients experience time using system dynamics. A particular focus is given to nurses in one of the medical clinical units in one of the largest hospitals in Ireland. Armed with a comprehensive system dynamic model that revolves around the staff stresses, an examination of Skill-Mix, Work Intensity, and Time Per Activity is conducted to examine performance issues due to nurses\u27 fatigue and burnout. Results demonstrate serious consequences on patients\u27 experience time and service quality measures as a proportional result of the increased pressures on nurses in this unit

A multi-method scheduling framework for medical staff

Hospital planning teams are always concerned with optimizing staffing and scheduling decisions in order to improve hospital performance, patient experience, and staff satisfaction. A multi-method approach including data analytics, modeling and simulation, machine learning, and optimization is proposed to provide a framework for smart and applicable solutions for staffing and shift scheduling. Factors regarding patients, staff, and hospitals are considered in the decision. This framework is piloted using the Emergency Department(ED) of a leading university hospital in Dublin. The optimized base staffing patterns and shift schedules actively contributed to solving ED overcrowding problem and reduced the average waiting time for patients by 43% compared to the current waiting time of discharged patients. The reduction was achieved by optimizing the staffing level and then determining the shift schedule that minimized the understaffing and overstaffing of the personnel need to meet patient demand

A System Dynamics View of the Acute Bed Blockage Problem in the Irish Healthcare System

Global population ageing is creating immense pressures on hospitals and other healthcare services, compromising their abilities to meet the growing demand from elderly patients. Current demand–supply gaps result in prolonged waiting times in emergency departments (EDs), and several studies have focused on improving ED performance. However, the overcrowding in EDs generally stems from delayed patient flows to inpatient wards – which are congested with inpatients waiting for beds in post-acute facilities. This problem of bed blocking in acute hospitals causes substantial cost burdens on hospitals. This study presents a system dynamics methodology to model the dynamic flow of elderly patients in the Irish healthcare system aimed at gaining a better understanding of the dynamic complexity caused by the system\u27s various parameters. The model evaluates the stock and flow interventions that Irish healthcare executives have proposed to address the problem of delayed discharges, and ultimately reduce costs. The anticipated growth in the nation\u27s demography is also incorporated in the model. Policy makers can also use the model to identify the potential strategic risks that might arise from the unintended consequences of new policies designed to overcome the problem of the delayed discharge of elderly patients

Toward Inclusive Online Environments: Counterfactual-Inspired XAI for Detecting and Interpreting Hateful and Offensive Tweets

The prevalence of hate speech and offensive language on social media platforms such as Twitter has significant consequences, ranging from psychological harm to the polarization of societies. Consequently, social media companies have implemented content moderation measures to curb harmful or discriminatory language. However, a lack of consistency and transparency hinders their ability to achieve desired outcomes. This article evaluates various ML models, including an ensemble, Explainable Boosting Machine (EBM), and Linear Support Vector Classifier (SVC), on a public dataset of 24,792 tweets by T. Davidson, categorizing tweets into three classes: hate, offensive, and neither. The top-performing model achieves a weighted F1-Score of 0.90. Furthermore, this article interprets the output of the best-performing model using LIME and SHAP, elucidating how specific words and phrases within a tweet contextually impact its classification. This analysis helps to shed light on the linguistic aspects of hate and offense. Additionally, we employ LIME to present a suggestive counterfactual approach, proposing no-hate alternatives for a tweet to further explain the influence of word choices in context. Limitations of the study include the potential for biased results due to dataset imbalance, which future research may address by exploring more balanced datasets or leveraging additional features. Ultimately, through these explanations, this work aims to promote digital literacy and foster an inclusive online environment that encourages informed and responsible use of digital technologies

A hybrid system dynamics, discrete event simulation and data envelopment analysis to investigate boarding patients in acute hospitals

Timely access to health services has become increasingly difficult due to demographic change and aging people growth. These create new heterogeneous challenges for society and healthcare systems. Congestion at acute hospitals has reached unprecedented levels due to the unavailability of acute beds. As a consequence, patients in need of treatment endure prolonged waiting times as a decision whether to admit, transfer, or send them home is made. These long waiting times often result in boarding patients in different places in the hospital. This threatens patient safety and diminishes the service quality while increasing treatment costs. It is argued in the extant literature that improved communication and enhanced patient flow is often more effective than merely increasing hospital capacity. Achieving this effective coordination is challenged by the uncertainties in care demand, the availability of accurate information, the complexity of inter-hospital dynamics and decision times. A hybrid simulation approach is presented in this paper, which aims to offer hospital managers a chance at investigating the patient boarding problem. Integrating ‘System Dynamic’ and ‘Discrete Event Simulation’ enables the user to ease the complexity of patient flow at both macro and micro levels. ‘Design of Experiment’ and ‘Data Envelopment Analysis’ are integrated with the simulation in order to assess the operational impact of various management interventions efficiently. A detailed implementation of the approach is demonstrated on an emergency department (ED) and Acute Medical Unit (AMU) of a large Irish hospital, which serves over 50,000 patients annually. Results indicate that improving transfer rates between hospital units has a significant positive impact. It reduces the number of boarding patients and has the potential to increase access by up to 40% to the case study organization. However, poor communication and coordination, human factors, downstream capacity constraints, shared resources and services between units may affect this access. Furthermore, an increase in staff numbers is required to sustain the acceptable level of service delivery

Investigating Brexit Implications on the Irish Agri-Food Exports: A Simulation-Based Scenario Mapping Model

The Irish economy is highly dependent on the UK market with a total export value surpassing e 14 billion. Several reports have warned of severe bottlenecks at the Irish and British ports if new customs checks are reintroduced. A significant disruption is also expected to the traffic flow between Ireland and Britain because of the lack of proper checking infrastructure at some ports. This situation will cause devastating impact on the competitive advantage of various Irish exports to the UK market, particularly limited-shelf-life products. Hence, a simulation model has been developed to investigate three Brexit scenarios: 1) applying non-tariff barriers at ports, 2) replacing the UK Landbridge with direct routes to continental Europe, and 3) lack of checking infrastructure at the UK ports. The scenarios’ implications on the transportation time and shelf life of Irish Cheese exports to the UK are investigated, leading to one recommendable scenario

Insurance Reserve Prediction: Opportunities and Challenges

Predicting claims’ reserve is a critical challenge for insurers and has dramatic consequences on their managerial, financial and underwriting decisions. The insurers’ capital and their underwriting capacity of further business are impacted by inaccurate reserve estimates. Increasing premium rates and adjusting the underwriting policy decisions may balance the impact of unexpected claims, but will have a negative impact on their business opportunities. To address this, several papers focusing on the prediction of insurance reserve have been published in the literature. In this paper, we provide a comprehensive review of the research on the insurance reserve prediction techniques in economics and actuarial science literature as well as machine learning and computer science literature. Moreover, we classify these techniques into different approaches based on the prediction mechanism they use in estimation. For each approach, we survey reserve prediction methods, and then show the similarities and differences among them. In addition, the review is armed with a discussion on the challenges and the future opportunities

Management development in Small and Medium Sized Firms in The Republic of Ireland: An Investigation of Contingency Factors and Management Development Activities

Purpose The development of managerial skills is an important priority for small and medium sized firm globally yet we have few insights about the predictors and types of management development (MD) activities in SMEs. To date studies of MD have not sufficiently differentiated between small and medium sized firms. In this paper we investigate the impact of three sets of predictors (contextual, technology and innovation activities, behavioural and skill) on six dimensions of MD (formal internal development, formal internal with an external expert, formal external development, one-to-one MD activities, budget for MD and experiential focused MD). Design/methodology/approach Survey of 360 SMEs in Ireland involving 401 manager and owner managers in small and medium sized firms. Findings Our findings reveal that firm size is an important predictor of the six dimensions of MD investigated in this study. We also found that in terms of the different categories of predictors dimensions of the SME technological and innovation capacity explained differences between small and medium sized firm such as technologically improved product/service, changes existing products and services and process innovation. We also found the age of the firm, the existence of a clearly articulated business strategy and formal strategic planning approaches were significant. Research / practical / policy implications Overall our findings highlight significant differences between small and medium firms which have important research and policy implications. Management development is a government priority for supporting Irish SMEs. We address a fundamental problem providing insight into predictors of management development activities. Originality/value This is a large survey of SMEs in the Republic of Ireland. The findings have important theoretical and policy implications

SME Manager Skills and Practices Survey (2020)

Survey questions for assessing manager skills and practices in Small to Medium Enterprises under the Improving management development standards in SMEs in Ireland. A project funded by the European Union via the Directorate General for Structural Reform Support (DG REFORM) - No SRSS/C2019/05