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Consumer confusion is an emerging phenomenon of interest that significantly drives choice behaviour. Considering the dearth of scholarly focus on confusion faced by consumers in a healthcare setting, this paper aims to conceptualize and validate a patient confusion model consisting of its drivers and outcomes.

Drawing upon adaptive decision-making framework and consumer confusion literature, patient confusion model has been developed. Empirical data of 310 patients from three private sector hospitals in India was collected through pen and paper survey administration. The hypothesized patient confusion model was tested using partial least squares structural equation modelling (PLS-SEM) to derive confirmatory results.

The results confirm the role of decision-making variables such as information overload, information similarity, information ambiguity, information asymmetry, patient involvement and physician-patient communication in the occurrence of patient confusion. A significant impact of confusion on switching intention was also confirmed, providing insights for healthcare managers.

The effect of confusion on switching intention of consumers found through the present study holds significant implications from a healthcare management standpoint. Dissemination of credible information, improved communication between doctors and patients and creation of organized channels of health information provision also represent some of the notable implications for healthcare managers to mitigate patient confusion.

This study presents an empirically validated model of patient confusion creating a research agenda for theory development in this emerging area. Consumer confusion represents a core consumer behaviour problem that is of utmost significance in the healthcare sector. This paper is one of the first and early attempts to address this research problem.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.

Business intelligence (BI) systems and tools are deemed to be a transformative source with the potential to contribute to reshaping the way different healthcare organizations’ (HCOs) services are offered and managed. However, this emerging field of research still appears underdeveloped and fragmented. Hence, this paper aims to reconciling, analyzing and synthesizing different strands of managerial-oriented literature on BI in HCOs and to enhance both theoretical and applied future contributions.

A literature-based framework was developed to establish and guide a three-stage state-of-the-art systematic literature review (SLR). The SLR was undertaken adopting a hybrid methodology that combines a bibliometric and a content analysis.

In total, 34 peer-review articles were included. Results revealed significant heterogeneity in theoretical basis and methodological strategies. Nonetheless, the knowledge structure of this research’s stream seems to be primarily composed of five clusters of interconnected topics: (1) decision-making, relevant capabilities and value creation; (2) user satisfaction and quality; (3) process management, organizational change and financial effectiveness; (4) decision-support information, dashboard and key performance indicators; and (5) performance management and organizational effectiveness.

To the authors’ knowledge, this is the first SLR providing a business and management-related state-of-the-art on the topic. Besides, the paper offers an original framework disentangling future research directions from each emerged cluster into issues pertaining to BI implementation, utilization and impact in HCOs. The paper also discusses the need of future contributions to explore possible integrations of BI with emerging data-driven technologies (e.g. artificial intelligence) in HCOs, as the role of BI in addressing sustainability challenges.

The resource crisis in healthcare can be alleviated by engaging external stakeholders and resources in healthcare delivery. The authors use value and open innovation concepts to understand what motivates the stakeholders to join the healthcare innovation ecosystem and what value such an ecosystem brings to healthcare.

A systematic literature review following the PRISMA framework method was applied to reach the research objective. Out of a total of 509 identified articles published till 2021, 25 were selected as relevant for this review.

Six categories of actors were identified, including innovation intermediaries, which were so far neglected in the healthcare innovation literature. Furthermore, patients, healthcare providers, innovation suppliers, investors and influencers were described. The authors also distinguished internal and external stakeholders. The authors show why and how open innovation projects contribute to involving external stakeholders and resources in healthcare delivery by contributing to patient autonomy, relationship building, knowledge transfer, improving collaborative mindset and culture, advancing know-how and bringing additional finances.

This article is the first one to systematically describe the value of open innovation in healthcare. The authors challenge the positivist approach in value presented by value-based healthcare. The authors show how openness contributes to addressing the resource crisis by involving new stakeholders and resources in the care delivery process.

Patient-centric exchanges, a major type of Health Information Exchange (HIE), empower patients to aggregate and manage their health information. This exchange model helps patients access, modify and share their medical information with multiple healthcare organizations. Although existing studies examine patient engagement, more research is required to investigate patients' attitudes and willingness to play an active role in patient-centered information exchange. The study's main objective is to develop a model based on the belief-attitude-intention paradigm to empirically examine the effects of patients' attitudes toward engagement in care on their willingness to participate in patient-centric HIE.

The authors conducted an online survey study to identify the antecedents and consequences of patients' attitudes toward engagement in care. To empirically test the research model, the authors collected data from a national sample (n = 357) of individuals in the United States. The data were analyzed using structural equation modeling (SEM).

The proposed model categorizes the antecedents to patients' attitudes toward engagement in patient-related and healthcare system factors. The results show that patient-related factors (perceived health literacy and perceived coping ability) and health system factors (perceived experience with the healthcare organization and perceived patient-provider interaction) significantly shape patient attitude toward care management engagement. The results indicate that patients' attitudes toward engaging in their healthcare significantly contribute to their willingness to participate in medical information sharing through patient-centric HIE initiatives. Moreover, the authors’ findings also demonstrate that the link between patient engagement and willingness to participate in HIE is stronger for individuals who perceive lower levels of privacy and security concerns.

The authors validate the proposed model explaining patients' perceptions about their characteristics and the healthcare system significantly influence their attitude toward engaging in their care. This study also suggests that patients' favorable attitude toward engagement can bring patient-centric HIE efforts onto a path to success. The authors’ research attempts to shed light on the importance of patients' roles in adopting patient-centric HIE initiatives. Theoretical and practical contributions of this study are noticeable since they could result in a deeper understanding of the concept of patient engagement and how it may affect healthcare services in an evolving digital world. The authors’ findings can help healthcare organizations provide public citizen-centric services by introducing user-oriented approaches in healthcare delivery systems.

Disinformation on social media is a serious issue. This study examines the effects of disinformation on COVID-19 vaccination decision-making to understand how social media users make healthcare decisions when disinformation is presented in their social media feeds. It examines trust in post owners as a moderator on the relationship between information types (i.e. disinformation and factual information) and vaccination decision-making.

This study conducts a scenario-based web survey experiment to collect extensive survey data from social media users.

This study reveals that information types differently affect social media users' COVID-19 vaccination decision-making and finds a moderating effect of trust in post owners on the relationship between information types and vaccination decision-making. For those who have a high degree of trust in post owners, the effect of information types on vaccination decision-making becomes large. In contrast, information types do not affect the decision-making of those who have a very low degree of trust in post owners. Besides, identification and compliance are found to affect trust in post owners.

This study contributes to the literature on online disinformation and individual healthcare decision-making by demonstrating the effect of disinformation on vaccination decision-making and providing empirical evidence on how trust in post owners impacts the effects of information types on vaccination decision-making. This study focuses on trust in post owners, unlike prior studies that focus on trust in information or social media platforms.

In this viewpoint article, the authors recognize the increased focus in health systems on co-design for innovation and change. This article explores the role of leaders and mangers in developing and enhancing a culture of trust in their organizations to enable co-design, with the potential to drive innovation and change in healthcare.

Using social science analyses, the authors argue that current co-design literature has limited focus on interactions between senior leaders and managers, and healthcare staff and service users in supporting co-designed innovation and change. The authors draw on social and health science studies of trust to highlight how the value-based co-design process needs to be supported and enhanced. We outline what co-design innovation and change involve in a health system, conceptualize trust and reflect on its importance within the health system, and finally note the role of senior leaders and managers in supporting trust and responsiveness for co-designed innovation and change.

Healthcare needs leaders and managers to embrace co-design that drives innovation now and in the future through people – leading to better healthcare for society at large. As authors we argue that it is now the time to shift our focus on the role of senior managers and leaders to embed co-design into health and social care structures, through creating and nurturing a culture of trust.

Building public trust in the health system and interpersonal trust within the health system is an ongoing process that relies upon personal behavior of managers and senior leaders, organizational practices within the system, as well as political processes that underpin these practices. By implementing managerial, leadership and individual practices on all levels, senior managers and leaders provide a mechanism to increase both trust and responsiveness for co-design that supports innovation and change in the health system.

The effect of vicarious learning during clinical or medical internships on graduates' adaptive career behaviours has attracted scant attention from healthcare researchers, particularly, in the developing world context. Drawing upon the social cognitive career theory model of career self-management (SCCT-CSM), the current study examines how vicarious learning influences the clinical graduates' adaptive career behaviours (i.e. career exploration and decision-making) via career exploration and decision-making self-efficacy (CEDSE) and career intention.

Data were collected from 293 nursing graduates undertaking clinical internships in 25 hospitals across Nigeria who willingly participated in this study as they were also assured of confidentiality at two-waves. The proposed hypotheses were tested using a path analysis.

The findings showed that vicarious learning during clinical internship had a direct effect on career exploration, decision-making and career decision self-efficacy among graduate trainees. Also, the findings revealed that the effects of vicarious learning on the graduates' career exploration and career decision-making were significantly mediated by career decision self-efficacy and career intentions.

The findings of this study have important practical implications for higher education institutions and industries that send and receive clinical graduates for clinical internships to gain more skills. More emphasis should be on encouraging learners to learn vicariously in addition to other forms of learning experiences available during clinical internships.

The study explains that the graduates' higher engagement in clinical career exploration and decision-making was based on a higher level of vicarious learning during internships. The results suggest that higher education institutions and healthcare service providers can derive greater benefits from more emphasis on promoting vicarious learning during clinical internships.

Digital transformations are altering service models and care delivery methods in healthcare. Artificial Intelligence (AI) represents the next wave of transformation in healthcare. This study aims to understand patient perceptions of AI and its impact on value co-creation.

A conceptual model was developed to investigate how value co-creation operant resources (digital self-efficacy and relational service quality) impact value co-creation engagement (shared decision-making) and value co-creation outcomes (anticipatory AI value co-creation and intention to adopt AI). Data were collected from 332 respondents and analyzed using structural equation modeling.

The results indicate that the value co-creation process for AI technologies is a function of inputs, experiences and AI outputs. Operant resources were found to be positively associated with shared decision-making. However, not all operant resources directly and positively impacted AI outcomes. The indirect and positive mediated relationships through shared decision-making to AI outcomes suggest an interactive AI value co-creation process.

AI technologies are still in early stages of consumer adoption in healthcare. Future research is warranted that investigates the validity of the model through maturing service life cycles.

Customer perceptions of new digital innovations are formed in the context of previous digital experiences. Marketers need to understand how customers view their current non-AI technologies. Strong engagement and perceived value of current technologies will help ease customers into the usage of AI technologies.

This study investigates the unique stages of the value co-creation process for AI technologies in healthcare. The results demonstrate that the value co-creation process is a function of inputs, tech-enabled experiences and AI outputs.

Supplier selection along with continuous evaluation of their performance is a crucial activity in healthcare supply chain management for effective utilization of scarce resources while providing quality service at an affordable price, and minimizing chances of stock-out, avoiding serious consequences on the illness or fatality of the patients. Presence of both qualitative and quantitative evaluation criteria, set of potential suppliers and participation of different stakeholders with varying interest make healthcare supplier selection a challenging task which can be effectively solved using any of the multi-criteria decision making (MCDM) methods.

To deal with various qualitative criteria, like cost, quality, delivery performance, reliability, responsiveness and flexibility, this paper proposes integration of grey system theory with a newly developed MCDM tool, i.e. mixed aggregation by comprehensive normalization technique (MACONT) to identify the best performing supplier for pharmaceutical items in a healthcare unit from a pool of six competing alternatives based on the opinions of three healthcare professionals.

While assessing importance of the six evaluation criteria and performance of the alternative healthcare suppliers against those criteria using grey numbers, and exploring use of three normalization procedures and two aggregation operations of MACONT method, this integrated approach singles out S5 as the most compromised healthcare supplier for the considered problem. A sensitivity analysis of its ranking performance against varying values of both balance parameters and preference parameters also validates its solution accuracy and robustness.

This integrated approach can thus efficiently solve healthcare supplier selection problems based on qualitative evaluation criteria in uncertain group decision making environment. It can also be deployed to deal with other decision making problems in the healthcare sector, like supplier selection for healthcare devices, performance evaluation of healthcare units, ranking of physicians etc.