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The healthcare sector in India is witnessing phenomenal growth, such that by the year 2022, it will be a market worth trillions of INR. Increase in income levels, awareness regarding personal health, the occurrence of lifestyle diseases, better insurance policies, low-cost healthcare services, and the emergence of newer technologies like telemedicine are driving this sector to new heights. Abundant quantities of healthcare data are being accumulated each day, which is difficult to analyze using traditional statistical and analytical tools, calling for the application of Big Data Analytics in the healthcare sector. Through provision of evidence-based decision-making and actions across healthcare networks, Big Data Analytics equips the sector with the ability to analyze a wide variety of data. Big Data Analytics includes both predictive and descriptive analytics. At present, about half of the healthcare organizations have adopted an analytical approach to decision-making, while a quarter of these firms are experienced in its application. This implies the lack of understanding prevalent in healthcare sector toward the value and the managerial, economic, and strategic impact of Big Data Analytics. In this context, this chapter on “Predictive Analytics in Healthcare” discusses sources, areas of application, possible future areas, advantages and limitations of the application of predictive Big Data Analytics in healthcare.

This paper proposes the adoption of Industry 4.0 (I4) technologies and lean techniques for improving operational performance in the healthcare sector.

The research adopted a systematic literature review and feedback of healthcare professionals to identify the inefficiencies in the current healthcare system. A questionnaire was used to get feedback from the patients and the hospital staff about the current practices and issues, and the expected impact of technology on existing practices. Data were analysed using descriptive statistics, correlation analysis and multiple regression analysis.

The results indicate that I4 technologies lead to the improvement of the operational performance, and the perceptions about I4 technologies are made through the pre-medical diagnosis. However, a weak correlation between lean practices and healthcare operational performance compared to that of I4 technologies and operational performance indicate that lean practices are not fully implemented in the Sri Lankan healthcare sector to their full potential.

This study is limited to two government hospitals, with insights from only the doctors and nurses in Sri Lanka. Furthermore, the study is limited to only selected aspects of I4 technologies (big data, cloud computing and IoT) and lean concepts (value stream mapping and 5S). Therefore, recommendations on the adoption of I4 technologies in the healthcare sector need to be made within the scope of the study investigation.

The implementation of I4 technologies needs careful consideration of process improvement as part of the overall plan for achieving the maximum benefits of technology adoption.

The findings of the research can be used as a benchmark/guide for other hospitals to explore the adoption of I4 technologies, and how process improvement from lean concepts could influence the overall operational performance.

The purpose of this paper is to describe the application of soft systems methodology (SSM) to address the problematic situation of low opt-in rates for Precision Health-Care (PHC).

The design logic is that when trust is enhanced and compliance is better assured, participants such as patients and their doctors would be more likely to share their medical data and diagnosis for the purpose of precision modeling.

The authors present the findings of an empirical study that confronts the design challenge of increasing participant opt-in to a PHC repository of Electronic Medical Records and genetic sequencing. Guided by SSM, the authors formulate design rules for the establishment of a trust-less platform for PHC which incorporates key principles of transparency, traceability and immutability.

The SSM approach has been criticized for its lack of “rigour” and “replicability”. This is a fallacy in understanding its purpose – theory exploration rather than theory confirmation. Moreover, it is unlikely that quantitative modeling yields any clearer an understanding of complex, socio-technical systems.

The application of Blockchain, a platform for distributed ledgers, and associated technologies present a feasible approach for resolving the problematic situation of low opt-in rates.

A consequence of low participation is the weak recall and precision of descriptive, predictive and prescriptive analytic models. Factors such as cyber-crime, data violation and the potential for misuse of genetic and medical records have led to a lack of trust from key stakeholders – accessors, participants, miners and regulators – to varying degrees.

The application of Blockchain as a trust-enabling platform in the domain of an emerging eco-system such as precision health is novel and pioneering.

Over recent years, few industries have seen such dramatic changes as the healthcare industry. The potential connectivity of digital technologies is completely transforming the healthcare ecosystem. This has resulted in companies increasingly investing in digital transformations to exploit data across channels, operations and patient outreach, by building on a practice approach and actor-network theory and being informed by service-dominant logic, this study aims to contribute by advancing the agential role of third-party actors to prompt innovation and shape service ecosystems.

This research is grounded in an epistemological contextualism. To gain situated knowledge and address the role of context in knowledge, understanding and meaning the authors adopted a qualitative methodology to study actors in their different contexts. The empirical research was based on case theory. The authors also took guidance from practice scholars about how to investigate actors’ practices. The unit of analysis moves from dyadic relationships to focus on practices across different networks of actors.

This study expands on the conceptualization of triad as proposed by Siltaloppi and Vargo (2017) by moving from the form of triadic relationships – brokerage, mediation and coalition – to the agency of e-health third-parties; and their practices to innovate in the healthcare ecosystem. This study focuses on the actors and the performativity of actions and grounding the conceptual view on an empirical base.

Third-party actors bring about innovative ways of doing business in the healthcare ecosystem. Their actions challenge the status quo and run counter to long-time practices. Third-parties support the complex set of interconnections between different healthcare actors for the provision of new service co-creation opportunities. Considering how these e-health third-parties performs has implications for health managers, patients and other actors.

This study focuses on the actors and the performativity of actions and grounding the conceptual view on an empirical base. The agency of third-party actors is their ability to act among others and to connect multiple social and material structures to boost innovation. They prompt innovation and shape service ecosystems by brokering, mediating and coalescing among a great variety of resources, practices and institutions.

Customers today expect businesses to cater to their individual needs by tailoring the products they purchase to their own preferences. The term “Industry 5.0” refers to a new wave of manufacturing that aims to meet each customer's unique demands. Even while Industry 4.0 allowed for mass customization, that wasn't good enough before, customers today demand individualized products at scale, and Industry 5.0 is driving the transition from mass customization to mass personalization to meet these demands. It caters to the individual needs of each consumer by meeting their demands. More specialized components for use in medicine are made possible by the widespread customization made possible by Industry 5.0. These individualized parts are included into the medical care of the patient to meet their specific needs and preferences. In the current medical revolution, an enabling technology of Industry 5.0 can produce medical implants, artificial organs, bodily fluids, and transplants with pinpoint accuracy. With the advent of AI-enabled sensors, we now live in a world where data can be swiftly analyzed. Machines may be programmed to make complex choices on the fly. In the medical field, these innovations allow for exact measurement and monitoring of human body variables according to the individual's needs. They aid in monitoring the body's response to training for peak performance. It allows for the digital dissemination of accurate healthcare data networks. In order to collect and exchange relevant patient data, every equipment is online.

The purpose of this paper is to describe a recent experiment in research coproduction in an evaluation of service planning at a London Mental Health NHS Trust. The paper aims to consider whether members of the research team who have themselves been users of mental health services are able to contribute to the research process as “experts by experience”, or if their experiential knowledge is “colonized” within the academic research team.

A qualitative, comparative case study approach was adopted, using structured observations and semi‐structured interviews. Researchers' reflective accounts and a reflective focus group were employed to explore the process of coproduction.

The paper concludes that, far from “colonising” expertise by experience, the experiment builds local capacity in research coproduction and usefully informs a service planning process that reflects the priorities and concerns of a range of stakeholders.

The paper describes a small, local experiment in research coproduction and so findings are limited in their scope. However, the study demonstrates an effective methodological approach to evaluating, empirically, the impact of coproduction on the health services research (HSR) process.

The paper demonstrates the potential for repeated exercises in coproduction to build capacity in collaborative approaches to both HSR and service planning.

The involvement of experts by experience is increasingly a policy requirement in the domains of both health service planning and HSR in the UK. There are very few empirical studies that evaluate the impact of that coproduction.

Purpose of this paper is to identify capabilities required for healthcare service providers to provide mass personalized services and to provide directions to conduct empirical studies to understand the phenomenon of mass personalization in Indian healthcare industry.

The research is conducted by undertaking a literature review followed by field visits and interviews conducted at a leading healthcare service provider in India.

There is limited empirical research on understanding the mass personalization capabilities in healthcare setting and empirical validation of the sand cone model and theory of competitive progression in the context of healthcare. Our interviews and field visits to Narayana Hrudayalaya (NH) indicate that it is attempting to provide personalized services to a large number and variety of patients. By doing so, it is expected to improve on both resource and flow efficiencies at the same time and hence break the trade‐offs between those. Literature review coupled with field visits and interviews help us in identifying key research questions related to mass personalization of healthcare in Indian healthcare industry.

This study is one of the first attempts to understand this trade‐off between resource and flow efficiency in the context of the Indian healthcare industry and to identify areas for future research. The unique characteristics of the Indian healthcare industry provide the ideal research setting to study this phenomenon. Literature review and our exploratory study have helped us in providing directions for future research in this unexplored area.

This paper considers the question: what would happen if healthcare providers, like their counterparts in the hospitality industry, adopted the principles of customer experience management (CEM) in order to facilitate a more holistic and personalized patient experience? It proposes an alternative vision of the patient experience by adding to an emerging hospitality–healthcare literature base, this time focusing upon CEM. A hospitality-oriented patient experience (HOPE) framework is introduced, designed to enhance the patient experience across all the touchpoints of the healthcare journey.

This is a conceptual paper that draws upon three distinct literatures: hospitality literature; healthcare literature; and CEM literature. It utilizes this literature to develop a framework, the HOPE framework, designed to offer an alternative lens to understanding the patient experience. The paper utilizes descriptions of three unique patient experiences, one linked to chronic pain, a second to gastro issues and a third to orthopedic issues, to illustrate how adopting the principles of hospitality management, within a healthcare context, could promote an enhanced patient experience.

The main theoretical contribution is the development of the HOPE framework that brings together research on CEM with research on cocreative customer practices in health care. By selecting and connecting key ingredients of two separate research streams, this vision and paradigm provide an alternative lens into ways of addressing the key challenges in the implementation of person-centered care in healthcare services. The HOPE framework offers an actionable roadmap for healthcare organizations to realize greater understanding and to operationalize new ways of improving the patient experience.

This paper applies the principles of hospitality and CEM to the domain of health care. In so doing it adds value to a hospitality literature primarily focused upon extensive employee–customer relationships. To a healthcare literature seeking to more fully understand a person-centered care model typically delivered by a care team consisting of professionals and family/friends. And to a CEM literature in hospitality, which seeks to facilitate favorable employee–customer interactions. Connecting these separate literature streams enables an original conceptual framework, a HOPE framework, to be introduced.

The article discusses how care programmes and integrated care pathways can be linked, finding ways to improve healthcare process professional and logistical quality from a supply chain and a network point‐of‐view.

The authors argue that owing to cost containment goals and increasing healthcare demand, healthcare services systems are challenged to improve service quality, whilst at the same time finding ways to improve delivery processes. It explores if the combination of two instruments, care programmes and integrated care pathways, can meet both goals. This combination is illustrated by an example from the Institute of Mental Health Care Eindhoven en de Kempen.

Analysis suggests that care programmes can be combined with integrated care pathways, leading to a situation where both quality and process improvement can be reached. These instruments are complementary.

The article is largely conceptual; ideas are presented to stimulate thinking rather than to prove an argument.

Combining care programmes and integrated care pathways has implications for the way we think about and organise healthcare processes.

There have been few publications on instruments combining both a network and a supply chain approach to describe and understand healthcare processes.