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There is a renewed interest among economists and policymakers in striking a perfect balance between the market, state, and the community for equitable and sustained development. Inclusive development is the need of the hour and healthcare cooperatives provide the perfect means to deliver that. The cooperatives can and should have an important role to play in this scenario. However, there is an important need to provide an enabling environment for the genesis and growth of Healthcare Cooperatives in India and to learn from the best models and practices across the world. The chapter discusses the effectiveness of healthcare cooperatives.

The objective of this study is to examine developments in the discursive practice of non-financial reporting in the public healthcare sector. In doing so, the authors investigate how the main reform foci of productivity and quality are represented, with a specific focus on the patient.

Drawing on critical discourse analysis (CDA), the authors conduct a longitudinal study (2007–2018) of healthcare reporting foci across the five administrative regions responsible for public hospitals in Denmark. The study analyses sixty annual reports and draws on contemporary reform documents over this period. CDA enables a micro-textual analysis, combined with macro-insights and discussions on social practice.

The findings show complex webs of presentation strategies, but in particular two changes occur during the period. First, the patient is centred throughout but the framing changes from productivity and waiting lists to quality and dialogue. Second, in the first years, the regions present themselves as actively highlighting financial and quality concerns, which changes to a passive and indirect form of presentation steered by indicators and patient legislation enforced by central government. This enhances passivity and distance in healthcare regional non-financial reporting where the regions seek to conform to such demands. Simultaneously, however, the authors find a tendency to highlight very different local initiatives, which shows an attempt to go beyond a pure automatic mode of reporting found in earlier studies.

Responding to the literature on both healthcare and financial reporting, this study identifies novel links between micro-level texts and macro-level social practices, enabling insights into the potentially intertwined impacts of public-sector reporting. The authors offer insights into the complexity of the construction of non-financial reporting in the public sector, which has a wider impact and different intentions than private-sector reporting.

Ensuring adequate access to healthcare services is a priority across European countries. The EU has developed performance indicators to compare access using self-reported unmet need. Cross-country comparisons require adjustment for factors outside the health systems' control. We address two research questions to improve the comparability of unmet need for medical and dental care across the EU and the comparability of socio-economic inequalities in unmet need across the EU. First, we explore the role of risk adjustment for demographic and socio-economic factors, which are outside health systems' control, for both overall unmet need and unmet need due to affordability, waiting lists and distance. Second, we compare differences in unmet need by socio-economic status, and investigate whether different forms of risk adjustment affect such comparison. We show that adjusting for age, gender and chronic conditions reduces dispersion of unmet need for medical care across the EU. Controlling for income further reduces the dispersion, mostly due to affordability. When comparing socio-economic inequalities across countries, risk adjustment for age, gender and chronic conditions play a limited role. Socio-economic inequalities by income and education vary by reason of unmet need: the income gradient, even controlling for education, is mostly due to affordability rather than waiting list or distance. For dental care, the main reason for unmet need is affordability. Risk adjustment for age, gender, chronic conditions and education plays a limited role. The income and education gradients are more pronounced for dental than medical care.

Nowadays, in many organizations, products are not delivered instantly. So, the customers should wait to receive their needed products, which will form a queueing-inventory model. Waiting a long time in the queue to receive products may cause dissatisfaction and churn of loyal customers, which can be a significant loss for organizations. Although many studies have been done on queueing-inventory models, more practical models in this area are needed, such as considering customer prioritization. Moreover, in many models, minimizing the total cost for the organization has been overlooked.

This paper will compare several machine learning (ML) algorithms to prioritize customers. Moreover, benefiting from the best ML algorithm, customers will be categorized into different classes based on their value and importance. Finally, a mathematical model will be developed to determine the allocation policy of on-hand products to each group of customers through multi-channel service retailing to minimize the organization’s total costs and increase the loyal customers' satisfaction level.

To investigate the application of the proposed method, a real-life case study on vaccine distribution at Imam Khomeini Hospital in Tehran has been addressed to ensure model validation. The proposed model’s accuracy was assessed as excellent based on the results generated by the ML algorithms, problem modeling and case study.

Prioritizing customers based on their value with the help of ML algorithms and optimizing the waiting queues to reduce customers' waiting time based on a mathematical model could lead to an increase in satisfaction levels among loyal customers and prevent their churn. This study’s uniqueness lies in its focus on determining the policy in which customers receive products based on their value in the queue, which is a relatively rare topic of research in queueing management systems. Additionally, the results obtained from the study provide strong validation for the model’s functionality.

Interventions that improve timely access to outpatient health services are essential in managing demand. This process evaluation aimed to describe the implementation, mechanism of impact and context of an intervention to reduce waiting for first appointments in an outpatient epilepsy clinic.

The UK Medical Research Council framework was used as the theoretical basis for a process evaluation alongside an intervention trial. The intervention, Specific Timely Appointments for Triage (STAT), is a data-driven approach that combines a one-off backlog reduction strategy with methods to balance supply and demand. A mixed methods process evaluation synthesised routinely collected quantitative and qualitative data, which were mapped to the domains of implementation, mechanisms of impact and contextual elements.

The principles of the STAT model were implemented as intended without adaptation. The STAT model reached all patients referred, including long waiters and was likely generalisable to other medical outpatient clinics. Mechanisms of impact were increased clinic capacity and elimination of unwanted variation. Contextual elements included the complexity of healthcare systems and the two-tier triage practice that contributes to prolonged waiting for patients classified as non-urgent.

This process evaluation shows how a data-driven strategy was applied in a medical outpatient setting to manage demand. Improving patient flow by reducing waiting in non-urgent, outpatient care is a complex problem. Understanding how and why interventions work is important for improved timeliness of care, and sustainability of public health services.

The primary objective of this paper is to show a systematic and methodological approach for the digitalization of critical clinical pathways (CPs) within the healthcare domain.

The methodology entails the integration of service design (SD) and action research (AR) methodologies, characterized by iterative phases that systematically alternate between action and reflective processes, fostering cycles of change and learning. Within this framework, stakeholders are engaged through semi-structured interviews, while the existing and envisioned processes are delineated and represented using BPMN 2.0. These methodological steps emphasize the development of an autonomous, patient-centric web application alongside the implementation of an adaptable and patient-oriented scheduling system. Also, business processes simulation is employed to measure key performance indicators of processes and test for potential improvements. This method is implemented in the context of the CP addressing transient loss of consciousness (TLOC), within a publicly funded hospital setting.

The methodology integrating SD and AR enables the detection of pivotal bottlenecks within diagnostic CPs and proposes optimal corrective measures to ensure uninterrupted patient care, all the while advancing the digitalization of diagnostic CP management. This study contributes to theoretical discussions by emphasizing the criticality of process optimization, the transformative potential of digitalization in healthcare and the paramount importance of user-centric design principles, and offers valuable insights into healthcare management implications.

The study’s relevance lies in its ability to enhance healthcare practices without necessitating disruptive and resource-intensive process overhauls. This pragmatic approach aligns with the imperative for healthcare organizations to improve their operations efficiently and cost-effectively, making the study’s findings relevant.