An Optimisation-based Framework for Complex Business Process: Healthcare Application

The Irish healthcare system is currently facing major pressures due to rising demand, caused by population growth, ageing and high expectations of service quality. This pressure on the Irish healthcare system creates a need for support from research institutions in dealing with decision areas such as resource allocation and performance measurement. While approaches such as modelling, simulation, multi-criteria decision analysis, performance management, and optimisation can – when applied skilfully – improve healthcare performance, they represent just one part of the solution. Accordingly, to achieve significant and sustainable performance, this research aims to develop a practical, yet effective, optimisation-based framework for managing complex processes in the healthcare domain. Through an extensive review of the literature on the aforementioned solution techniques, limitations of using each technique on its own are identified in order to define a practical integrated approach toward developing the proposed framework. During the framework validation phase, real-time strategies have to be optimised to solve Emergency Department performance issues in a major hospital. Results show a potential of significant reduction in patients average length of stay (i.e. 48% of average patient throughput time) whilst reducing the over-reliance on overstretched nursing resources, that resulted in an increase of staff utilisation between 7% and 10%. Given the high uncertainty in healthcare service demand, using the integrated framework allows decision makers to find optimal staff schedules that improve emergency department performance. The proposed optimum staff schedule reduces the average waiting time of patients by 57% and also contributes to reduce number of patients left without treatment to 8% instead of 17%. The developed framework has been implemented by the hospital partner with a high level of success

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

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

Family Views of End-of-Life Care in Acute and Community Hospitals

The Hospice friendly Hospitals Programme (HfH) commissioned this study to assess the quality of end-of-life care in acute and community hospitals from the perspectives of bereaved relatives. A major rationale for the study was to develop and test methodology to survey bereaved relatives' views of end-of-life care that covers the HfH Programme themes of Integrated Care, Communication, Patient Autonomy and Design & Dignity. Another driver was to inform the set-up of a Nationwide Audit of End-of-Life Care (McKeown et al., 2010).The overall aim of the study was to assess the quality of end-of-life care in two acute and two community hospitals from the perspectives of bereaved relatives. Study subobjectives were to conduct a literature review to ascertain important ethical and methodological issues; to describe a census of deaths across study sites; to field test a survey instrument aimed evaluating the impact of the Hospice friendly Hospitals (HfH) Programme; to collect data about HfH Programme themes; and to establish if there were any differences in the pattern of results between acute and community hospitals

A Machine Learning Framework for Length of Stay Minimization in Healthcare Emergency Department

The emergency departments (EDs) in most hospitals, especially in middle-and-low-income countries, need techniques for minimizing the waiting time of patients. The application and utilization of appropriate methods can enhance the number of patients treated, improve patients’ satisfaction, reduce healthcare costs, and lower morbidity and mortality rates which are often associated with poor healthcare facilities, overcrowding, and low availability of healthcare professionals.  Modeling the length of stay (LOS) of patients in healthcare systems is a challenge that must be addressed for sound decision-making regarding capacity planning and resource allocation. This paper presents a machine learning (ML) framework for predicting a patient’s LOS within the ED. A study of the services in the ED of a tertiary healthcare facility in Uyo, Nigeria was conducted to gain insights into its operational procedures and evaluate the impact of certain parameters on LOS. Then, a computer simulation of the system was performed in R programming language using data obtained from records in the hospital. Finally, the performance of four ML classifiers involved in patients’ LOS prediction: Classification and Regression Tree (CART), Random Forest (RF), K-Nearest Neighbour (K-NN), and Support Vector Machine (SVM), were evaluated and results indicate that SVM outperforms others with the highest coefficient of determination (R2) score of 0.986984 and least mean square error (MSE) value of 0.358594. The result demonstrates the capability of ML techniques to effectively assess the performance of healthcare systems and accurately predict patients’ LOS to mitigate the low physician-patient ratio and improve throughput

Patient Experience Informs Health Care Strategies in Irish Hospitals

Patients are central to health care facilities and institutions; therefore, a dire need arises to include feedback of their experience in the decision-making process. Patient experience is increasingly recognised as one of the three pillars of quality in healthcare alongside clinical effectiveness and patient safety. A comprehensive literature review (more than 2500 peer-reviewed articles) has identified five key frameworks for patient experience including: UK Picker Institute Principles and US H-CAHPS. The frameworks have enabled the identification of a potential range of patient experience dimensions and helped in grouping them into nine categories. However, there are still opportunities to address research gaps in developing a unified index to represent patient experience, and offering a practical framework to inform quality improvement strategies in hospitals. An extensive exploratory study is developed to complement the literature review. This study aims to confirm the importance of the identified nine dimensions from patients’ views, explore staff perceptions of patient experience, then compare patients’ views and staff’s perceptions. Semistructured interviews with 77 participants (26 senior staff members and 51patients) across three major acute Irish hospitals are conducted. Five important dimensions are highlighted from patients’ responses such as: staff communication and being treated with respect. While dimensions such as: continuity of care and involving family members are identified as less important. While staff in this study perceive dimensions such as quicker access to care and informing the patient with their status updates as more significant in shaping the patient experience. Both the exploratory study and literature review outcomes have contributed to the design of a patient experience questionnaire which examine dimensions that matter most to patient experience. The questionnaire is included as a component of a multi-method framework that integrated data analytics, simulation modelling, and optimisation. With an ultimate objective to improve patient experience, the proposed framework has been piloted in an Emergency Department of one of the leading and busiest university hospitals in Dublin. Fifty-eight patients responded to the questionnaire and their responses are analysed using a Partial Least Squares (PLS) model. PLS results have identified access to care as a negative predictor to patient experience. Improvement strategies such as increasing the internal capacity of the department are proposed by the management team to improve the Length of Stay (LOS) and provide better access to care. To examine and assess the impact of proposed strategies on LOS, a simulation model has complemented the solution framework. Results have showed that internal capacity of an ED has no direct impact on LOS and does not act as a performance constraint. However, other factors such as increasing downstream department’s capacity and the staffing levels can lead to a reduction in LOS (up to 25%)

The Impact of Work Environment on Successful Implementation of Lean Six Sigma in Emergency Department

Continuous improvement (CI) is an initiative to improve the performance of processes in alignment with the customer needs and organizational strategy. Lean Six Sigma (LSS) is one of the most successful CI techniques in redesigning and improving significant processes to improve quality and eliminate waste. The healthcare sector has benefited from applying LSS due to its complicated work practices that face many challenges including increased expenditures and difficulties related to individual or community access to appropriate care. In particular, Emergency Departments (ED) have an important unit within healthcare organizations due to their essential role in providing urgent medical care services to patients. The aim of this doctoral research study is to develop a theoretical model using grounded theory to investigate the factors for successful LSS implementation in ED including how ED work environment affects the reduction of patient length of stay, which is one of the biggest issues that ED face. Therefore, the main objectives of this research are to: (1) investigate trends in the research area using systematic literature review, (2) develop an Initial Conceptual Framework including identifying the relationships between the variables of LSS implementation, (3) use an expert study where a group of experts will provide additional evidence regarding LSS implementation, and (4) test the model using survey questionnaire that examines the behavior of the variables. This research will be documented as a manuscript-style dissertation including four peer-reviewed academic journal articles each summarizing the results from a phase of this research. The results of this research will provide a conceptual model to guide the implementation of LSS in ED bringing the potential benefits of this approach to a critical department in healthcare organizations. Further, this research will inform future research by investigating the work environment effects on application of LSS

Report on SHAFE policies, strategies and funding

The objective of Working Group (WG) 4 of the COST Action NET4Age-Friendly is to examine existing policies, advocacy, and funding opportunities and to build up relations with policy makers and funding organisations. Also, to synthesize and improve existing knowledge and models to develop from effective business and evaluation models, as well as to guarantee quality and education, proper dissemination and ensure the future of the Action. The Working Group further aims to enable capacity building to improve interdisciplinary participation, to promote knowledge exchange and to foster a cross-European interdisciplinary research capacity, to improve cooperation and co-creation with cross-sectors stakeholders and to introduce and educate students SHAFE implementation and sustainability (CB01, CB03, CB04, CB05). To enable the achievement of the objectives of Working Group 4, the Leader of the Working Group, the Chair and Vice-Chair, in close cooperation with the Science Communication Coordinator, developed a template (see annex 1) to map the current state of SHAFE policies, funding opportunities and networking in the COST member countries of the Action. On invitation, the Working Group lead received contributions from 37 countries, in a total of 85 Action members. The contributions provide an overview of the diversity of SHAFE policies and opportunities in Europe and beyond. These were not edited or revised and are a result of the main areas of expertise and knowledge of the contributors; thus, gaps in areas or content are possible and these shall be further explored in the following works and reports of this WG. But this preliminary mapping is of huge importance to proceed with the WG activities. In the following chapters, an introduction on the need of SHAFE policies is presented, followed by a summary of the main approaches to be pursued for the next period of work. The deliverable finishes with the opportunities of capacity building, networking and funding that will be relevant to undertake within the frame of Working Group 4 and the total COST Action. The total of country contributions is presented in the annex of this deliverable

Advances on Smart Cities and Smart Buildings

Modern cities are facing the challenge of combining competitiveness at the global city scale and sustainable urban development to become smart cities. A smart city is a high-tech, intensive and advanced city that connects people, information, and city elements using new technologies in order to create a sustainable, greener city; competitive and innovative commerce; and an increased quality of life. This Special Issue collects the recent advancements in smart cities and covers different topics and aspects