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A local healthcare organisation providing healthcare to 288,000 residents in Sweden struggled with a longstanding budget deficit. Several attempts to overcome the demanding financial situation have failed. A decommissioning programme was launched, and two years later, an evaluation indicated positive outcomes. The aim of this study was to explore factors politicians and public servants perceived as enablers to the successful implementation of the programme.

A deductive content analysis approach using a framework of factors facilitating successful implementation of decommissioning decisions was applied to analyse interviews with 18 informants.

Important factors were: (1) a review report contributing to the clarity of evidence, which (2) made the clarity of the rationale for change undeniable and (3) strengthened the political support for change. Additional factors were: (4) the strength of executive leadership, (5) the strength of clinical leadership supported by (6) the quality of project management and (7) a cultural and behavioural change seen as an important outcome for the path forward. A way to maximise the potential for a successful implementation of a large-scale decommissioning programme is to build a shared vision and a collaboration grounded in convincing evidence. Include public servants with a clinical background in the executive leadership team to contribute with legitimacy, competence, and trust in the decommissioning programme’s intention.

The paper addresses the limited knowledge of best practices in decommissioning processes and contributes empirical knowledge from a successful case.

Technology-enabled healthcare focuses on providing better information flow and coordination in healthcare operations. Technology-enabled health services enable hospitals to manage their resources effectively, maintain continuous patient engagement and provide seamless services without compromising their perceived quality.

This study investigates the role of technology-enabled health services in improving perceived healthcare quality among patients. Data are collected from the users (n = 418) of health platforms offered in multi-specialty hospitals. Multiple learners are employed to accurately represent the users' perceived quality regarding the perceived usefulness of the features provided via these digital health platforms.

The best-fitted model using a decision tree classifier (accuracy = 0.86) derives the accurate significance of features offered in the digital health platform in fostering perceived healthcare quality. Diet and lifestyle recommendations (30%) and chatting with health professionals (11%) are the top features offered in digital health platforms that primarily influence the perceived quality of healthcare among users.

The predictability of perceived quality with the individual features existing in the digital health platform, the significance of the features on the perceived healthcare quality and the prediction rules showing the combined effect of features on healthcare quality can help healthcare managers accelerate digital transformation in hospitals by improving their digital health platform, designing and offering new health packages while strengthening their e-infrastructure.

The study represents perceived healthcare quality with the features offered in digital health platforms using machine learners based on users' post-pandemic experience. By advancing digital platforms with more patient-centric features using emerging technologies, this model can further foresee its impact on the perceived quality of healthcare, offering valuable directions to healthcare service providers. The study is limited to focusing on digital health platforms that can deal with people's general healthcare needs.

This paper explores the Adoption of Technological Innovation (ATI) in the healthcare industry. It investigates how the literature has evolved, and what are the emerging innovation dimensions in the healthcare industry adoption studies.

We followed a mixed-method approach combining bibliometric methods and topic modeling, with 57 papers being deeply analyzed.

Our results identify three latent topics. The first one is related to the digitalization in healthcare with a specific focus on the COVID-19 pandemic. The second one groups up the word combinations dealing with the research models and their constructs. The third one refers to the healthcare systems/professionals and their resistance to ATI.

The study’s sample selection focused on scientific journals included in the Academic Journal Guide and in the FT Research Rank. However, the paper identifies trends that offer managerial insights for stakeholders in the healthcare industry.

ATI has the potential to revolutionize the health service delivery system and to decentralize services traditionally provided in hospitals or medical centers. All this would contribute to a reduction in waiting lists and the provision of proximity services.

The originality of the paper lies in the combination of two methods: bibliometric analysis and topic modeling. This approach allowed us to understand the ATI evolutions in the healthcare industry.

This paper brings attention to the role of hybrid middle managers. In particular it explores the relationship of organisational purpose and role requirements.

The primary research question for the original research was: What does it mean to hybrid managers to lead and deal with imposed changes (restructuring) to services? A novel narrative approach based on a synthesis of Czarniawska, Gabriel and Boje was applied. Accounts from interviews were condensed into narratives by initially using the categories defined by Gabriel (2000) as epic, tragic, comic and romantic and then further categorised into stories, themes and a serial (Czarniawska, 1997). The final stage of the three-way synthesised narrative approach incorporated Boje’s (2001) notion of “antenarrative” to include pre-emplotment elements.

Four narratives are provided that give insight to the nature of the struggles the hybrid middle managers were in the midst of. A struggle to address incongruent demands being placed on them that cause tension with their sense of purpose, organisational goals and their hybrid clinical roles and management roles. In the midst of these struggles the narratives illustrate the dynamic of ethical resistance that seeks a way forward. However, this appears to come at a health and well-being cost to the middle managers.

The paper offers up the notion of an added third bind to the traditional double, that of “ethical resistance”, a struggle to align organisational purpose with clinical and management role requirements. Theorising this third bind provides a new insight into understanding the context and dynamics of the hybrid middle manager role and behaviour. Indeed, the idea of ethical resistance may cause a revision of how resistance is understood.

To explore the mechanisms of the implementation strategy, “oilcloth sessions” and understand and explain the ripple effects of oilcloth sessions as a strategy to implement a new emergency department.

A qualitative design was used whereby data were collected using field notes from an ethnographic study of the oilcloth sessions and follow-up semi-structured interviews with staff, managers and key employees who participated in the oilcloth sessions. The data analysis was inspired by the realist evaluation approach of generative causality proposed by Pawson and Tilley.

The primary ripple effect was that the oilcloth sessions were used for different purposes than the proposed program theory, including being used as: (1) a stage, (2) a battlefield, (3) a space for imagination and (4) a strategic management tool influencing the implementation outcomes. The results bring essential knowledge that may help to explain why and how a well-defined implementation strategy has unplanned outcomes.

Unintended outcomes of implementation strategies are an underexplored issue. This study may help implementation researchers rethink the activities required to reduce unintended negative outcomes or explore potential unplanned outcomes and, in this way, hinder or enhance outcomes, effectiveness and sustainability. Future studies within implementation research should incorporate attention to unintended outcomes to fully understand the impact of implementation strategies.

As part of a national plan to govern professional and organizational development in Norwegian specialist healthcare, the country’s hospital clinics are tasked with constructing development plans. Using the development plan as a case, the paper analyzes how managers navigate and legitimize the planning process among central actors and deals with the contingency of decisions in such strategy work.

This study applies a qualitative research design using a case study method. The material consists of public documents, observations and single interviews, covering the process of constructing a development plan at the clinical level.

The findings suggest that the development plan was shaped through a multilevel translation process consisting of different contending rationalities. At the clinical level, the management had difficulties in legitimizing the process. The underlying tension between top-down and bottom-up steering challenged involvement and made it difficult to manage the contingency of decisions.

The findings are relevant to public sector managers working on strategy documents and policymakers identifying challenges that might hinder the fulfillment of political intentions.

This paper draws on a case from Norway; however, the findings are of general interest. The study contributes to the academic discussion on how to consider both the health authorities’ perspective and the organizational perspective to understand the manager’s role in handling the contingency of decisions and managing paradoxes in the decision-making process.

Despite the unparalleled importance of value within healthcare, value-based models remain underutilised in the procurement of medical devices. Research is needed to understand what factors incentivise standard, low-priced device purchasing as opposed to value-adding devices with potentially higher overall health outcomes. Framed in agency theory, we examine the conditions under which different actors involved in purchasing decisions select premium-priced, value-adding medical devices over low-priced, standard medical devices.

We conducted 2 × 2 × 2 between-subjects scenario-based vignette experiments on three UK-based online samples of managers (n = 599), medical professionals (n = 279) and purchasing managers (n = 449) with subjects randomly assigned to three treatments: (1) cost-saving incentives, (2) risk-sharing contracts and (3) stronger (versus weaker) clinical evidence.

Our analysis demonstrates the harmful effects of intra-organisational cost-saving incentives on value-based purchasing (VBP) adoption; the positive impact of inter-organisational risk-sharing contracts, especially when medical professionals are involved in decision-making; and the challenge of leveraging clinical evidence to support value claims.

Our results demonstrate the need to align incentives in a context with multiple intra- and inter-organisational agency relationships at play, as well as the difficulty of reducing information asymmetry when information is not easily interpretable to all decision-makers. Overall, the intra-organisational agency factors strongly influenced the choices for the inter-organisational agency relationship.

We contribute to VBP in healthcare by examining the role of intra- and inter-organisational agency relationships and incentives concerning VBP (non-) adoption. We also examine how the impact of such mechanisms differs between medical and purchasing (management) professionals.

Staff shortages in the healthcare sector increase the competition for qualified staff. A magnet hospital is intended to attract, and retain healthcare professionals. This article aims to investigate the challenges related to implementation of a magnet hospital model, and given these challenges, to analyse the interplay between different organisational levels in a Swedish hospital.

The data collection followed the implementation of a magnet hospital model and consisted of 14 meeting observations, 31 interviews and 13 document analyses.

The model implementation was driven by a top-down approach, with accompanying bottom-up activities, involving healthcare professionals, to ensure adaption to the hospital’s conditions at different organisational levels. The findings revealed that the model was more appealing to top management, seeking a standardised solution to attract and retain nurses. Clinic managers preferred tailor-made solutions for managing their employee resourcing challenges. Difficulties in translating and contextualising the model to the hospital’s conditions created challenges at every organisational level. Some were contained within a level while others spread to the organisational level below and turned into something else.

Apart from unique empirical material depicting the implementation of a magnet hospital model as an effort to attract and retain healthcare professionals, the value of this study lies in the attention given to the challenges that arise when responsibility for implementing a management model is shifted from top management to change agents tasked with facilitating and executing the organisational change.

The healthcare sector relies on knowledge management systems to improve knowledge flows and effectively capture, leverage and share knowledge with several organizational stakeholders. However, knowledge as a resource represents a social construct that involves additional managerial complexities and challenges, including undesirable knowledge behaviours. The aim of the current study is to provide insight on how knowledge management systems, knowledge hoarding, knowledge hiding and task conflict shape the quality of care provided by hospitals. We propose and test an original revealing model.

We follow a quantitative approach to address the structural relationship between variables using a combination of factor analysis and multiple regression analysis. The model is tested adopting a structural equation modelling approach and using survey data conducted to 318 healthcare professionals working in Portuguese hospitals.

The main findings suggest that knowledge hiding is positively related to task conflict in hospitals, and task conflict negatively influences quality of care. Knowledge management systems directly and indirectly (via knowledge hoarding) promote quality of care. Moreover, knowledge management systems also mitigate the negative influence of task conflict over quality of care. We propose a final corollary on the relevant role of HRM as the backstage for the model.

Our research offers a novel insight into an overlap of organizational behaviour and healthcare management research. It provides an original framework on knowledge management systems, counterproductive knowledge behaviours and task conflict in hospital settings.

Our research offers a novel insight into an overlap of organizational behaviour and healthcare management research. It provides an original framework on knowledge management systems, counterproductive knowledge behaviours and task conflict in hospital settings.

This study aims to identify and assess the factors challenging the integration of artificial intelligence (AI) technologies in healthcare workplaces.

The study utilized a mixed approach, that starts with a literature review, then developing and testing a questionnaire survey of the factors challenging the integration of AI technologies in healthcare workplaces. In total, 46 factors were identified and classified under 6 groups. These factors were assessed by four different stakeholder categories: facilities managers, medical staff, operational staff and patients/visitors. The evaluations gathered were examined to determine the relative importance index (RII), importance rating (IR) and ranking of each factor.

All 46 factors were assessed as “Very Important” through the overall assessment by the four stakeholder categories. The results indicated that the most important factors, across all groups, are “AI ability to learn from patient data”, “insufficient data privacy measures for patients”, “availability of technical support and maintenance services”, “physicians’ acceptance of AI in healthcare”, “reliability and uptime of AI systems” and “ability to reduce medical errors”.

Determining the importance ratings of the factors can lead to better resource allocation and the development of strategies to facilitate the adoption and implementation of these technologies, thus promoting the development of innovative solutions to improve healthcare practices.

This study contributes to the body of knowledge in the domain of technology adoption and implementation in the medical workplace, through improving stakeholders’ comprehension of the factors challenging the integration of AI technologies.