Leading Quality and Safety on the Frontline - A Case Study of Department Leaders in Nursing Homes.

Purpose: The role of healthcare leaders is becoming increasingly complex, and carries great responsibility for patients, employees, and the quality of service delivery. This study explored the barriers and enablers that department leaders in nursing homes encounter when managing the dual responsibilities in Health, Safety and Environment (HSE) and Quality and Patient Safety (QPS). Methodology: Case study design with data collected through semi structured interviews with 16 department leaders in five Norwegian municipalities. We analyzed the data using qualitative content analysis. Results: Data analysis resulted in four themes explaining what department leaders in nursing homes experience as barriers and enablers when handling the dual responsibility of HSE and QPS: Temporal capacity: The importance of having enough time to create a health-promoting work environment that ensures patient safety. Relational capacity: Relationships have an impact on work process and outcomes. Professional competence: Competence affects patient safety and leadership strategies. Organizational structure: Organizational frameworks influence how the dual responsibilities are handled. Conclusion: Evidence from this study showed that external contextual factors (eg, legislations and finances) and internal factors (eg, relationships and expectations) are experienced as barriers and enablers when department leaders are enacting the dual responsibility of HSE and QPS. Of these, relationships were found to be the most significant contributor.

Target Product Profile for a Machine Learning-Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study.

BACKGROUND: Diabetic eye screening (DES) represents a significant opportunity for the application of machine learning (ML) technologies, which may improve clinical and service outcomes. However, successful integration of ML into DES requires careful product development, evaluation, and implementation. Target product profiles (TPPs) summarize the requirements necessary for successful implementation so these can guide product development and evaluation. OBJECTIVE: This study aims to produce a TPP for an ML-automated retinal imaging analysis software (ML-ARIAS) system for use in DES in England. METHODS: This work will consist of 3 phases. Phase 1 will establish the characteristics to be addressed in the TPP. A list of candidate characteristics will be generated from the following sources: an overview of systematic reviews of diagnostic test TPPs; a systematic review of digital health TPPs; and the National Institute for Health and Care Excellence's Evidence Standards Framework for Digital Health Technologies. The list of characteristics will be refined and validated by a study advisory group (SAG) made up of representatives from key stakeholders in DES. This includes people with diabetes; health care professionals; health care managers and leaders; and regulators and policy makers. In phase 2, specifications for these characteristics will be drafted following a series of semistructured interviews with participants from these stakeholder groups. Data collected from these interviews will be analyzed using the shortlist of characteristics as a framework, after which specifications will be drafted to create a draft TPP. Following approval by the SAG, in phase 3, the draft will enter an internet-based Delphi consensus study with participants sought from the groups previously identified, as well as ML-ARIAS developers, to ensure feasibility. Participants will be invited to score characteristic and specification pairs on a scale from "definitely exclude" to "definitely include," and suggest edits. The document will be iterated between rounds based on participants' feedback. Feedback on the draft document will be sought from a group of ML-ARIAS developers before its final contents are agreed upon in an in-person consensus meeting. At this meeting, representatives from the stakeholder groups previously identified (minus ML-ARIAS developers, to avoid bias) will be presented with the Delphi results and feedback of the user group and asked to agree on the final contents by vote. RESULTS: Phase 1 was completed in November 2023. Phase 2 is underway and expected to finish in March 2024. Phase 3 is expected to be complete in July 2024. CONCLUSIONS: The multistakeholder development of a TPP for an ML-ARIAS for use in DES in England will help developers produce tools that serve the needs of patients, health care providers, and their staff. The TPP development process will also provide methods and a template to produce similar documents in other disease areas. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50568.

Managing patient safety and staff safety in nursing homes: exploring how leaders of nursing homes negotiate their dual responsibilities-a case study.

Objective: Within healthcare, the role of leader is becoming more complex, and healthcare leaders carry an increasing responsibility for the performance of employees, the experience and safety of patients and the quality of care provision. This study aimed to explore how leaders of nursing homes manage the dual responsibility of both Health, Safety and Environment (HSE) and Quality and Patient Safety (QPS), focusing particularly on the approaches leaders take and the dilemmas they face. In addition, we wanted to examine how leaders experience and manage the challenges of HSE and QPS in a holistic way. Design/setting: The study was designed as a case study. Data were collected through semi structured individual interviews with leaders of nursing homes in five Norwegian municipalities. Participants: 13 leaders of nursing homes in urban and rural municipalities participated in this study. Results: Data analysis resulted in four themes explaining how leaders of nursing homes manage the dual responsibility of HSE and QPS, and the approaches they take and the dilemmas they face: 1.Establishing good systems and building a culture for a work environment that promotes health and patient safety.2.Establish channels for internal and external collaboration and communication.3.Establish room for maneuver to exercise leadership.4.Recognizing and having the mandate to handle possible tensions in the dual responsibility of HSE and QPS. Conclusions: The study showed that leaders of nursing homes who are responsible for ensuring quality and safety for both patients and staff, experience tensions in handling this dual responsibility. They acknowledged the importance of having time to be present as a leader, to have robust systems to maintain HSE and QPS, and that conflicting aspects of legislation are an everyday challenge.

Managing risk and resilience in autonomous and intelligent systems: Exploring safety in the development, deployment, and use of artificial intelligence in healthcare.

Autonomous and intelligent systems (AIS) are being developed and deployed across a wide range of sectors and encompass a variety of technologies designed to engage in different forms of independent reasoning and self-directed behavior. These technologies may bring considerable benefits to society but also pose a range of risk management challenges, particularly when deployed in safety-critical sectors where complex interactions between human, social, and technical processes underpin safety and resilience. Healthcare is one safety-critical sector at the forefront of efforts to develop and deploy intelligent technologies, such as through artificial intelligence (AI) systems intended to automate key aspects of healthcare tasks such as reading medical images to identify signs of pathology. This article develops a qualitative analysis of the sociotechnical sources of risk and resilience associated with the development, deployment, and use of AI in healthcare, drawing on 40 in-depth interviews with participants involved in the development, management, and regulation of AI. Qualitative template analysis is used to examine sociotechnical sources of risk and resilience, drawing on and elaborating Macrae's (2022, Risk Analysis, 42(9), 1999-2025) SOTEC framework that integrates structural, organizational, technological, epistemic, and cultural sources of risk in AIS. This analysis explores an array of sociotechnical sources of risk associated with the development, deployment, and use of AI in healthcare and identifies an array of sociotechnical patterns of resilience that may counter those risks. In doing so, the SOTEC framework is elaborated and translated to define key sources of both risk and resilience in AIS.

Healthcare leaders' and elected politicians' approach to support-systems and requirements for complying with quality and safety regulation in nursing homes - a case study.

BACKGROUND: Healthcare leaders play an important and complex role in managing and handling the dual responsibility of both Health, Safety and Environment (HSE) for workers and quality and patient safety (QPS). There is a need for better understanding of how healthcare leaders and decision makers organize and create support structures to handle these combined responsibilities in practice. The aim of this study was to explore how healthcare leaders and elected politicians organize, control, and follow up the work of HSE and QPS in a Norwegian nursing home context. Moreover, we explore how they interpret, negotiate, and manage the dual responsibility and possible tensions between employee health and safety, and patient safety and quality of service delivery. METHODS: The study was conducted in 2022 as a case study exploring the experience of healthcare leaders and elected politicians in five municipalities responsible for providing nursing homes services in Norway. Elected politicians (18) and healthcare leaders (11) participated in focus group interviews (5) and individual interviews (11). Data were analyzed using inductive thematic analysis. RESULTS: The analysis identified five main themes explaining how the healthcare leaders and elected politicians organize, control, and follow up the work of HSE and QPS: 1. Establish frameworks and room for maneuver in the work with HSE and QPS. 2. Create good routines and channels for communication and collaboration. 3. Build a culture for a health-promoting work environment and patient safety. 4. Create systems to handle the possible tensions in the dual responsibility between caring for employees and quality and safety in service delivery. 5. Define clear boundaries in responsibility between politics and administration. CONCLUSIONS: The study showed that healthcare leaders and elected politicians who are responsible for ensuring sound systems for quality and safety for both patients and staff, do experience tensions in handling this dual responsibility. They acknowledge the need to create systems and awareness for the responsibility and argue that there is a need to better separate the roles and boundaries between elected politicians and the healthcare administration in the execution of HSE and QPS.

Naming the "baby" or the "beast"? The importance of concepts and labels in healthcare safety investigation.

This paper focuses on concepts and labels used in investigation of adverse events in healthcare. The aim is to prompt critical reflection of how different stakeholders frame investigative activity in healthcare and to discuss the implications of the labels we use. We particularly draw attention to issues of investigative content, legal aspects, as well as possible barriers and facilitators to willingly participate, share knowledge, and achieve systemic learning. Our message about investigation concepts and labels is that they matter and influence the quality of investigation, and how these activities may contribute to system learning and change. This message is important for the research community, policy makers, healthcare practitioners, patients, and user representatives.

Evaluating a system-wide, safety investigation in healthcare course in Norway: a qualitative study.

OBJECTIVE AND SETTING: National, system-wide safety investigation represents a new approach to safety improvement in healthcare. In 2019, a new master's level course in Safety Investigation in Healthcare was established to support the training and development of a new team of investigators from an independent investigatory body. The course was established at one Norwegian university and a total of 19 students were enrolled and completed the course. The aim of this study was to qualitatively evaluate the course, and the objectives were to explore the students' needs and expectations prior to the course conduct, and their experiences and suggestions for improvements after course completion. DESIGN: The study design was a qualitative explorative study with individual and focus group interviews. Data collection included five individual interviews prior to course participation and two focus group interviews, after course participation, with a total sample size of 13 participants. Data were analysed according to thematic analysis. RESULTS: The results showed a need for a common conceptual foundation for the multidisciplinary team of safety investigators who were all employed in the same investigatory body. Course participation contributed to create reflexive spaces for the participants and generated new knowledge about the need for a broad range of investigatory tools and approaches. This contrasted with the initial aspiration among the participants to have a recipe for how to conduct safety investigations. CONCLUSIONS: Course participation contributed to a common language among a highly multidisciplinary group of safety investigators and supported building a culture of collaborative learning. The need for additional activities to further develop a safety investigation curriculum in healthcare was identified. It is recommended that such a curriculum be co-created with independent investigators, safety scientists, patients and users, and healthcare professionals to ensure a strong methods repertoire and a sound theoretical backdrop for investigatory practice.

Exploring the nature of adaptive capacity for resilience in healthcare across different healthcare contexts; a metasynthesis of narratives.

Adaptive capacity has been described as instrumental for the development of resilience in healthcare. Yet, our theoretical understanding of adaptive capacity remains relatively underdeveloped. This research therefore aims at developing a new understanding of the nature of adaptive capacity by exploring the following research questions: 1. What constitutes adaptive capacity across different healthcare contexts? and 2. What type of enabling factors support adaptive capacity across different healthcare contexts? The study used a novel combination of qualitative methods featuring a metasynthesis of narratives based on empirical research to contribute understanding of adaptive capacity across different healthcare contexts. The findings show that adaptive capacity was found to include four forms: reframing, aligning, coping, and innovating. A framework illustrating the relatedness between the identified forms, in terms of resources, change and enablers, is provided. Based on these findings, a new definition of adaptive capacity for resilience in healthcare is proposed.

Capacities for resilience in healthcare; a qualitative study across different healthcare contexts.

BACKGROUND: Despite an emerging consensus on the importance of resilience as a framework for understanding the healthcare system, the operationalization of resilience in healthcare has become an area of continuous discussion, and especially so when seeking operationalization across different healthcare contexts and healthcare levels. Different indicators for resilience in healthcare have been proposed by different researchers, where some indicators are coincident, some complementary, and some diverging. The overall aim of this article is to contribute to this discussion by synthesizing knowledge and experiences from studies in different healthcare contexts and levels to provide holistic understanding of capacities for resilience in healthcare. METHODS: This study is a part of the first exploratory phase of the Resilience in Healthcare programme. The exploratory phase has focused on screening, synthesising, and validating results from existing empirical projects covering a variety of healthcare settings. We selected the sample from several former and ongoing research projects across different contexts and levels, involving researchers from SHARE, the Centre for Resilience in Healthcare in Norway. From the included projects, 16 researchers participated in semi-structured interviews. The dataset was analysed in accordance with grounded theory. RESULTS: Ten different capacities for resilience in healthcare emerged from the dataset, presented here according to those with the most identified instances to those with the least: Structure, Learning, Alignment, Coordination, Leadership, Risk awareness, Involvement, Competence, Facilitators and Communication. All resilience capacities are interdependent, so effort should not be directed at achieving success according to improving just a single capacity but rather at being equally aware of the importance and interrelatedness of all the resilience in healthcare capacities. CONCLUSIONS: A conceptual framework where the 10 different resilience capacities are presented in terms of contextualisation and collaboration was developed. The framework provides the understanding that all resilience capacities are associated with contextualization, or collaboration, or both, and thereby contributes to theorization and guidance for tailoring, making operationalization efforts for the identified resilience capacities in knowledge translation. This study therefore contributes with key insight for intervention development which is currently lacking in the literature.

Toward Successful Implementation of Artificial Intelligence in Health Care Practice: Protocol for a Research Program.

BACKGROUND: The uptake of artificial intelligence (AI) in health care is at an early stage. Recent studies have shown a lack of AI-specific implementation theories, models, or frameworks that could provide guidance for how to translate the potential of AI into daily health care practices. This protocol provides an outline for the first 5 years of a research program seeking to address this knowledge-practice gap through collaboration and co-design between researchers, health care professionals, patients, and industry stakeholders. OBJECTIVE: The first part of the program focuses on two specific objectives. The first objective is to develop a theoretically informed framework for AI implementation in health care that can be applied to facilitate such implementation in routine health care practice. The second objective is to carry out empirical AI implementation studies, guided by the framework for AI implementation, and to generate learning for enhanced knowledge and operational insights to guide further refinement of the framework. The second part of the program addresses a third objective, which is to apply the developed framework in clinical practice in order to develop regional capacity to provide the practical resources, competencies, and organizational structure required for AI implementation; however, this objective is beyond the scope of this protocol. METHODS: This research program will use a logic model to structure the development of a methodological framework for planning and evaluating implementation of AI systems in health care and to support capacity building for its use in practice. The logic model is divided into time-separated stages, with a focus on theory-driven and coproduced framework development. The activities are based on both knowledge development, using existing theory and literature reviews, and method development by means of co-design and empirical investigations. The activities will involve researchers, health care professionals, and other stakeholders to create a multi-perspective understanding. RESULTS: The project started on July 1, 2021, with the Stage 1 activities, including model overview, literature reviews, stakeholder mapping, and impact cases; we will then proceed with Stage 2 activities. Stage 1 and 2 activities will continue until June 30, 2026. CONCLUSIONS: There is a need to advance theory and empirical evidence on the implementation requirements of AI systems in health care, as well as an opportunity to bring together insights from research on the development, introduction, and evaluation of AI systems and existing knowledge from implementation research literature. Therefore, with this research program, we intend to build an understanding, using both theoretical and empirical approaches, of how the implementation of AI systems should be approached in order to increase the likelihood of successful and widespread application in clinical practice. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/34920.

Realizing the potential of artificial intelligence in healthcare: Learning from intervention, innovation, implementation and improvement sciences.

Introduction: Artificial intelligence (AI) is widely seen as critical for tackling fundamental challenges faced by health systems. However, research is scant on the factors that influence the implementation and routine use of AI in healthcare, how AI may interact with the context in which it is implemented, and how it can contribute to wider health system goals. We propose that AI development can benefit from knowledge generated in four scientific fields: intervention, innovation, implementation and improvement sciences. Aim: The aim of this paper is to briefly describe the four fields and to identify potentially relevant knowledge from these fields that can be utilized for understanding and/or facilitating the use of AI in healthcare. The paper is based on the authors' experience and expertise in intervention, innovation, implementation, and improvement sciences, and a selective literature review. Utilizing knowledge from the four fields: The four fields have generated a wealth of often-overlapping knowledge, some of which we propose has considerable relevance for understanding and/or facilitating the use of AI in healthcare. Conclusion: Knowledge derived from intervention, innovation, implementation, and improvement sciences provides a head start for research on the use of AI in healthcare, yet the extent to which this knowledge can be repurposed in AI studies cannot be taken for granted. Thus, when taking advantage of insights in the four fields, it is important to also be explorative and use inductive research approaches to generate knowledge that can contribute toward realizing the potential of AI in healthcare.

Learning from the Failure of Autonomous and Intelligent Systems: Accidents, Safety, and Sociotechnical Sources of Risk.

Efforts to develop autonomous and intelligent systems (AIS) have exploded across a range of settings in recent years, from self-driving cars to medical diagnostic chatbots. These have the potential to bring enormous benefits to society but also have the potential to introduce new-or amplify existing-risks. As these emerging technologies become more widespread, one of the most critical risk management challenges is to ensure that failures of AIS can be rigorously analyzed and understood so that the safety of these systems can be effectively governed and improved. AIS are necessarily developed and deployed within complex human, social, and organizational systems, but to date there has been little systematic examination of the sociotechnical sources of risk and failure in AIS. Accordingly, this article develops a conceptual framework that characterizes key sociotechnical sources of risk in AIS by reanalyzing one of the most publicly reported failures to date: the 2018 fatal crash of Uber's self-driving car. Publicly available investigative reports were systematically analyzed using constant comparative analysis to identify key sources and patterns of sociotechnical risk. Five fundamental domains of sociotechnical risk were conceptualized-structural, organizational, technological, epistemic, and cultural-each indicated by particular patterns of sociotechnical failure. The resulting SOTEC framework of sociotechnical risk in AIS extends existing theories of risk in complex systems and highlights important practical and theoretical implications for managing risk and developing infrastructures of learning in AIS.

Balancing adaptation and innovation for resilience in healthcare - a metasynthesis of narratives.

BACKGROUND: Adaptation and innovation are both described as instrumental for resilience in healthcare. However, the relatedness between these dimensions of resilience in healthcare has not yet been studied. This study seeks to develop a conceptual understanding of adaptation and innovation as a basis for resilience in healthcare. The overall aim of this study is therefore to explore how adaptation and innovation can be described and understood across different healthcare settings. To this end, the overall aim will be investigated by identifying what constitutes adaptation and innovation in healthcare, the mechanisms involved, and what type of responses adaptation and innovation are associated with. METHODS: The method used to develop understanding across a variety of healthcare contexts, was to first conduct a narrative inquiry of a comprehensive dataset from various empirical settings (e.g., maternity, transitional care, telecare), that were later analysed in accordance with grounded theory. Narrative inquiry provided a contextually informed synthesis of the phenomenon, while the use of grounded theory methodology allowed for cross-contextual comparison of adaptation and innovation in terms of resilience in healthcare. RESULTS: The results identified an imbalance between adaptation and innovation. If short-term adaptations are used too extensively, they may mask system deficiencies and furthermore leave the organization vulnerable, by relying too much on the efforts of a few individuals. Hence, short-term adaptations may end up a barrier for resilience in healthcare. Long-term adaptations and innovation of products, processes and practices proved to be of a lower priority, but had the potential of addressing the flaws of the system by proactively re-organizing and re-designing routines and practices. CONCLUSIONS: This study develops a new conceptual account of adaptation and innovation as a basis for resilience in healthcare. Findings emerging from this study indicate that a balance between adaptation and innovation should be sought when seeking resilience in healthcare. Adaptations can furthermore be divided into short-term and long-term adaptations, creating the need to balance between these different types of adaptations. Short-term adaptations that adopt the pattern of firefighting can risk generating complex and unintended outcomes, but where no significant changes are made to organization of the system. Long-term adaptations, on the other hand, introduce re-organization of the system based on feedback, and therefore can provide a proactive response to system deficiencies. We propose a pattern of adaptation in resilience in healthcare: from short-term adjustments, to long-term reorganizations, to innovations.

Investigating Hospital Supervision: A Case Study of Regulatory Inspectors' Roles as Potential Co-creators of Resilience.

OBJECTIVES: The aim of this study was to explore if, and in what ways, there has been changes in the supervisory approach toward Norwegian hospitals due to the implementation of a new management and quality improvement regulation (Regulation on Management and Quality Improvement in the Healthcare Services, hereinafter referred to as "Quality Improvement Regulation"). Moreover, we aimed to understand how inspectors' work promotes or hampers resilience potentials of adaptive capacity and learning in hospitals. METHODS: The study design is a case study of implementation and impact of the Quality Improvement Regulation. We performed a document analysis, and conducted and analyzed 3 focus groups and 2 individual interviews with regulatory inspectors, recruited from 3 county governor offices who are responsible for implementation and supervision of the Quality Improvement Regulation in Norwegian regions. RESULTS: Data analysis resulted in 5 themes. Informants described no substantial change in their approach owing to the Quality Improvement Regulation. Regardless, data pointed to a development in their practices and expectations. Although the Norwegian Board of Health Supervision, at the national level, occasionally provides guidance, supervision is adapted to specific contexts and inspectors balance trade-offs. Informants expressed concern about the impact of supervision on hospital performance. Benefits and disadvantage with positive feedback from inspectors were debated. Inspectors could nurture learning by improving their follow-up and add more hospital self-assessment. CONCLUSIONS: A nondetailed regulatory framework such as the Quality Improvement Regulation provides hospitals with room to maneuver, and self-assessment might reduce resource demands. The impact of supervision is scarce with an unfulfilled potential to learn from supervision. The Government could contribute to a shift in focus by instructing the county governors to actively reflect on and communicate positive experiences from, and smart adaptations in, hospital practice.

Hospital managers' perspectives with implementing quality improvement measures and a new regulatory framework: a qualitative case study.

A new regulatory framework to support local quality and safety efforts in hospitals was introduced to the Norwegian healthcare system in 2017. This study aimed to investigate hospital managers' perspectives on implementation efforts and the resulting work practices, to understand if, and how, the new Quality Improvement Regulation influenced quality and safety improvement activities. DESIGN: This article reports one study level (the perspectives of hospital managers), as part of a multilevel case study. Data were collected by interviews and analysed according to qualitative content analysis. SETTING: Three hospitals retrieved from two regional health trusts in Norway. PARTICIPANTS: 20 hospital managers or quality advisers selected from different levels of hospital organisations. RESULTS: Four themes were identified in response to the study aim: (1) adaptive capacity in hospital management and practice, (2) implementation efforts and challenges with quality improvement, (3) systemic changes and (4) the potential to learn. Recent structural and cultural changes to, and development of, quality improvement systems in hospitals were discovered (3). Participants however, revealed no change in their practice solely due to the new Quality Improvement Regulation (2). Findings indicated that hospital managers are legally responsible for quality improvement implementation and participants described several benefits with the new Quality Improvement Regulation (2). This related to adaptation and flexibility to local context, and clinical autonomy as an inevitable element in hospital practice (1). Trust and a safe work environment were described as key factors to achieve adverse event reporting and support learning processes (4). CONCLUSIONS: This study suggests that a lack of time, competence and/or motivation, impacted hospitals' implementation of quality improvement efforts. Hospital managers' autonomy and adaptive capacity to tailor quality improvement efforts were key for the new Quality Improvement Regulation to have any relevant impact on hospital practice and for it to influence quality and safety improvement activities.

Health Economic and Safety Considerations for Artificial Intelligence Applications in Diabetic Retinopathy Screening.

Systematic screening for diabetic retinopathy (DR) has been widely recommended for early detection in patients with diabetes to address preventable vision loss. However, substantial manpower and financial resources are required to deploy opportunistic screening and transition to systematic DR screening programs. The advent of artificial intelligence (AI) technologies may improve access and reduce the financial burden for DR screening while maintaining comparable or enhanced clinical effectiveness. To deploy an AI-based DR screening program in a real-world setting, it is imperative that health economic assessment (HEA) and patient safety analyses are conducted to guide appropriate allocation of resources and design safe, reliable systems. Few studies published to date include these considerations when integrating AI-based solutions into DR screening programs. In this article, we provide an overview of the current state-of-the-art of AI technology (focusing on deep learning systems), followed by an appraisal of existing literature on the applications of AI in ophthalmology. We also discuss practical considerations that drive the development of a successful DR screening program, such as the implications of false-positive or false-negative results and image gradeability. Finally, we examine different plausible methods for HEA and safety analyses that can be used to assess concerns regarding AI-based screening.

Exploring links between resilience and the macro-level development of healthcare regulation- a Norwegian case study.

BACKGROUND: The relationship between quality and safety regulation and resilience in healthcare has received little systematic scrutiny. Accordingly, this study examines the introduction of a new regulatory framework (the Quality Improvement Regulation) in Norway that aimed to focus on developing the capacity of hospitals to continually improve quality and safety. The overall aim of the study was to explore the governmental rationale and expectations in relation to the Quality Improvement Regulation, and how it could potentially influence the management of resilience in hospitals. The study applies resilience in healthcare and risk regulation as theoretical perspectives. METHODS: The design is a single embedded case study, investigating the Norwegian regulatory healthcare regime. Data was collected by approaching three regulatory bodies through formal letters, asking them to provide internal and public documents, and by searching through open Internet-sources. Based on this, we conducted a document analysis, supplemented by interviews with seven strategic informants in the regulatory bodies. RESULTS: The rationale for introducing the Quality Improvement Regulation focused on challenges associated with implementation, lack of management competencies; need to promote quality improvement as a managerial responsibility. Some informants worried that the generic regulatory design made it less helpful for managers and clinicians, others claimed a non-detailed regulation was key to make it fit all hospital-contexts. The Government expected hospital managers to obtain an overview of risks and to adapt risk management and quality improvement measures to their specific context and activities. CONCLUSIONS: Based on the rationale of making the Quality Improvement Regulation flexible to hospital context, encouraging the ability to anticipate local risks, along with expectations about the generic design as challenging for managers and clinicians, we found that the regulators did consider work as done as important when designing the Quality Improvement Regulation. These perspectives are in line with ideas of resilience. However, the Quality Improvement Regulation might be open for adaptation by the regulatees, but this may not necessarily mean that it promotes or encourages adaptive behavior in actual practice. Limited involvement of clinicians in the regulatory development process and a lack of reflexive spaces might hamper quality improvement efforts.

Defining the boundaries and operational concepts of resilience in the resilience in healthcare research program.

BACKGROUND: Understanding the resilience of healthcare is critically important. A resilient healthcare system might be expected to consistently deliver high quality care, withstand disruptive events and continually adapt, learn and improve. However, there are many different theories, models and definitions of resilience and most are contested and debated in the literature. Clear and unambiguous conceptual definitions are important for both theoretical and practical considerations of any phenomenon, and resilience is no exception. A large international research programme on Resilience in Healthcare (RiH) is seeking to address these issues in a 5-year study across Norway, England, the Netherlands, Australia, Japan, and Switzerland (2018-2023). The aims of this debate paper are: 1) to identify and select core operational concepts of resilience from the literature in order to consider their contributions, implications, and boundaries for researching resilience in healthcare; and 2) to propose a working definition of healthcare resilience that underpins the international RiH research programme. MAIN TEXT: To fulfil these aims, first an overview of three core perspectives or metaphors that underpin theories of resilience are introduced from ecology, engineering and psychology. Second, we present a brief overview of key definitions and approaches to resilience applicable in healthcare. We position our research program with collaborative learning and user involvement as vital prerequisite pillars in our conceptualisation and operationalisation of resilience for maintaining quality of healthcare services. Third, our analysis addresses four core questions that studies of resilience in healthcare need to consider when defining and operationalising resilience. These are: resilience 'for what', 'to what', 'of what', and 'through what'? Finally, we present our operational definition of resilience. CONCLUSION: The RiH research program is exploring resilience as a multi-level phenomenon and considers adaptive capacity to change as a foundation for high quality care. We, therefore, define healthcare resilience as: the capacity to adapt to challenges and changes at different system levels, to maintain high quality care. This working definition of resilience is intended to be comprehensible and applicable regardless of the level of analysis or type of system component under investigation.