| 11. |
- Neher, Margit, et al.
(author)
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Perspectives of Policy Makers and Service Users Concerning the Implementation of eHealth in Sweden : Interview Study
- 2022
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In: Journal of Medical Internet Research. - : JMIR Publications. - 1438-8871. ; 24:1
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Journal article (peer-reviewed)abstract
- BACKGROUND: Increasing life spans of populations and a growing demand for more advanced care make effective and cost-efficient provision of health care necessary. eHealth technology is often proposed, although research on barriers to and facilitators of the implementation of eHealth technology is still scarce and fragmented. OBJECTIVE: The aim of this study is to explore the perceptions concerning barriers to and facilitators of the implementation of eHealth among policy makers and service users and explore the ways in which their perceptions converge and differ. METHODS: This study used interview data from policy makers at different levels of health care (n=7) and service users enrolled in eHealth interventions (n=25). The analysis included separate qualitative content analyses for the 2 groups and then a second qualitative content analysis to explore differences and commonalities. RESULTS: Implementation barriers perceived by policy makers were that not all service users benefit from eHealth and that there is uncertainty about the impact of eHealth on the work of health care professionals. Policy makers also perceived political decision-making as complex; this included problems related to provision of technical infrastructure and lack of extra resources for health care digitalization. Facilitators were policy makers' conviction that eHealth is what citizens want, their belief in eHealth solutions as beneficial for health care practice, and their belief in the importance of health care digitalization. Barriers for service users comprised capability limitations and varied preferences of service users and a mismatch of technology with user needs, lack of data protection, and their perception of eHealth as being more time consuming. Facilitators for service users were eHealth technology design and match with their skill set, personal feedback and staff support, a sense of privacy, a credible sender, and flexible use of time.There were several commonalities between the 2 stakeholder groups. Facilitators for both groups were the strong impetus toward technology adoption in society and expectations of time flexibility. Both groups perceived barriers in the difficulties of tailoring eHealth, and both groups expressed uncertainty about the care burden distribution. There were also differences: policy makers perceived that their decision-making was very complex and that resources for implementation were limited. Service users highlighted their need to feel that their digital data were protected and that they needed to trust the eHealth sender. CONCLUSIONS: Perceptions about barriers to and facilitators of eHealth implementation varied among stakeholders in different parts of the health care system. The study points to the need to reach an enhanced mutual understanding of priorities and overcome challenges at both the micro and macro levels of the health care system. More well-balanced decisions at the policy-maker level may lead to more effective and sustainable development and future implementation of eHealth.
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| 12. |
- Neher, Margit, et al.
(author)
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Preparedness to Implement Physical Activity and Rehabilitation Guidelines in Routine Primary Care Cancer Rehabilitation : Focus Group Interviews Exploring Rehabilitation Professionals' Perceptions
- 2021
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In: Journal of Cancer Education. - : Springer. - 0885-8195 .- 1543-0154. ; 36:4, s. 779-786
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Journal article (peer-reviewed)abstract
- To explore primary care professionals' perceptions of physical activity and other cancer rehabilitation practice in cancer survivors, investigating the preparedness to implement guidelines regarding cancer rehabilitation. We collected qualitative data through seven semi-structured focus group interviews with 48 rehabilitation professionals, with mean 9 years of experience in primary care rehabilitation (32 physiotherapists, 15 occupational therapists, and 1 rehabilitation assistant) in a primary care setting. Data was analyzed using content analysis. Primary care rehabilitation professionals expressed limited experience of cancer survivors, experienced lack of knowledge of cancer-related disability, and had doubts concerning how to treat cancer survivors. They also experienced uncertainty about where to find collaboration and support in the healthcare system outside their own rehabilitation clinic. There is a need to combine different implementation strategies to tackle multiple barriers for effective cancer survivor rehabilitation in primary care, to boost individual rehabilitation professionals' knowledge and self-efficacy, to clarify roles and responsibilities for cancer rehabilitation across levels of care, and to develop and strengthen organizational bridges to provide adequate access to rehabilitation for cancer survivors.
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| 13. |
- Neher, Margit, 1959-, et al.
(author)
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Selecting theories, models and frameworks
- 2024. - 1
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In: Implementation Science. - Oxon : Routledge. - 9781040016091 - 9781003318125 ; , s. 135-145
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Book chapter (peer-reviewed)abstract
- Implementation science researchers have a multitude of theories, models and frameworks (TMFs) at their disposal. These TMFs provide a rich source of potentially inspiring ways to plan, conduct and evaluate implementation projects. They can enhance researchers’ ability to generalize findings, promote shared understanding and advance the field. However, despite their many strengths, TMFs tend to be underused, used superficially or misused. There is also a tendency to use convenient or familiar TMFs. This chapter addresses the challenges of identifying and using appropriate TMFs and describes the T-CaST (Theory Comparison and Selection Tool), a tool that was developed to facilitate the selection of TMFs. © 2024 selection and editorial matter, Per Nilsen; individual chapters, the contributors.
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| 14. |
- Nilsen, Per, 1960-, et al.
(author)
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A Framework to Guide Implementation of AI in Health Care : Protocol for a Cocreation Research Project
- 2023
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In: JMIR Research Protocols. - Toronto : JMIR Publications. - 1929-0748. ; 12
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Journal article (peer-reviewed)abstract
- Background: Artificial intelligence (AI) has the potential in health care to transform patient care and administrative processes, yet health care has been slow to adopt AI due to many types of barriers. Implementation science has shown the importance of structured implementation processes to overcome implementation barriers. However, there is a lack of knowledge and tools to guide such processes when implementing AI-based applications in health care.Objective: The aim of this protocol is to describe the development, testing, and evaluation of a framework, “Artificial Intelligence-Quality Implementation Framework” (AI-QIF), intended to guide decisions and activities related to the implementation of various AI-based applications in health care.Methods: The paper outlines the development of an AI implementation framework for broad use in health care based on the Quality Implementation Framework (QIF). QIF is a process model developed in implementation science. The model guides the user to consider implementation-related issues in a step-by-step design and plan and perform activities that support implementation. This framework was chosen for its adaptability, usability, broad scope, and detailed guidance concerning important activities and considerations for successful implementation. The development will proceed in 5 phases with primarily qualitative methods being used. The process starts with phase I, in which an AI-adapted version of QIF is created (AI-QIF). Phase II will produce a digital mockup of the AI-QIF. Phase III will involve the development of a prototype of the AI-QIF with an intuitive user interface. Phase IV is dedicated to usability testing of the prototype in health care environments. Phase V will focus on evaluating the usability and effectiveness of the AI-QIF. Cocreation is a guiding principle for the project and is an important aspect in 4 of the 5 development phases. The cocreation process will enable the use of both on research-based and practice-based knowledge.Results: The project is being conducted within the frame of a larger research program, with the overall objective of developing theoretically and empirically informed frameworks to support AI implementation in routine health care. The program was launched in 2021 and has carried out numerous research activities. The development of AI-QIF as a tool to guide the implementation of AI-based applications in health care will draw on knowledge and experience acquired from these activities. The framework is being developed over 2 years, from January 2023 to December 2024. It is under continuous development and refinement.Conclusions: The development of the AI implementation framework, AI-QIF, described in this study protocol aims to facilitate the implementation of AI-based applications in health care based on the premise that implementation processes benefit from being well-prepared and structured. The framework will be coproduced to enhance its relevance, validity, usefulness, and potential value for application in practice. © 2023 The Author(s).
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| 15. |
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| 16. |
- Nilsen, P., et al.
(author)
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A learning perspective on implementation
- 2022
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In: Implementation Science. - London : Taylor & Francis. - 9780367626112 - 9780367626136 ; , s. 169-170
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Book chapter (other academic/artistic)abstract
- For many healthcare practitioners, implementing an evidence-based practice presents a few interlinked learning challenges: acquiring evidence-based practice skills to be able to problem-solve when faced with clinical uncertainty; adopting specific evidence-based practices, for example, interventions with proven effectiveness; and abandonment of non-evidence-based practices. The essay describes two modes of learning and uses these as lenses for analysing the challenges of implementing an evidence-based practice in healthcare. Adaptive learning involves a gradual shift from slower, deliberate behaviours to faster, smoother, and more efficient behaviours. Developmental learning is conceptualized as a process in the “opposite” direction, whereby more or less automatically enacted behaviours become deliberate and conscious. The mechanisms by which the two modes of learning occur are explained with reference to habit theory.
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| 17. |
- Nilsen, Per, 1960-, et al.
(author)
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Implementation from a learning perspective
- 2020
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In: Handbook on implementation science. - Cheltenham, UK : Edward Elgar Publishing. - 9781788975988 - 9781788975995 ; , s. 409-421
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Book chapter (peer-reviewed)abstract
- The Handbook on Implementation Science provides an overview of the field’s multidisciplinary history, theoretical approaches, key concepts, perspectives, and methods. By drawing on knowledge concerning learning, habits, organizational theory, improvement science, and policy research, the Handbook offers novel perspectives from a broad group of international experts in the field representing diverse disciplines. The editors seek to advance implementation science through careful consideration of current thinking and recommendations for future directions.
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| 18. |
- Nilsen, Per, et al.
(author)
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Implementation of Evidence-Based Practice From a Learning Perspective
- 2017
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In: Worldviews on Evidence-Based Nursing. - : WILEY. - 1545-102X .- 1741-6787. ; 14:3, s. 192-199
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Journal article (peer-reviewed)abstract
- IntroductionFor many nurses and other health care practitioners, implementing evidence-based practice (EBP) presents two interlinked challenges: acquisition of EBP skills and adoption of evidence-based interventions and abandonment of ingrained non-evidence-based practices. AimsThe purpose of this study to describe two modes of learning and use these as lenses for analyzing the challenges of implementing EBP in health care. MethodsThe article is theoretical, drawing on learning and habit theory. ResultsAdaptive learning involves a gradual shift from slower, deliberate behaviors to faster, smoother, and more efficient behaviors. Developmental learning is conceptualized as a process in the opposite direction, whereby more or less automatically enacted behaviors become deliberate and conscious. ConclusionAchieving a more EBP depends on both adaptive and developmental learning, which involves both forming EBP-conducive habits and breaking clinical practice habits that do not contribute to realizing the goals of EBP. Linking Evidence to ActionFrom a learning perspective, EBP will be best supported by means of adaptive learning that yields a habitual practice of EBP such that it becomes natural and instinctive to instigate EBP in appropriate contexts by means of seeking out, critiquing, and integrating research into everyday clinical practice as well as learning new interventions best supported by empirical evidence. However, the context must also support developmental learning that facilitates disruption of existing habits to ascertain that the execution of the EBP process or the use of evidence-based interventions in routine practice is carefully and consciously considered to arrive at the most appropriate response.
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| 19. |
- Nilsen, Per, 1960-, et al.
(author)
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Towards evidence-based practice 2.0 : leveraging artificial intelligence in healthcare
- 2024
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In: Frontiers in Health Services. - Lausanne : Frontiers Media S.A.. - 2813-0146. ; 4
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Research review (peer-reviewed)abstract
- Background: Evidence-based practice (EBP) involves making clinical decisions based on three sources of information: evidence, clinical experience and patient preferences. Despite popularization of EBP, research has shown that there are many barriers to achieving the goals of the EBP model. The use of artificial intelligence (AI) in healthcare has been proposed as a means to improve clinical decision-making. The aim of this paper was to pinpoint key challenges pertaining to the three pillars of EBP and to investigate the potential of AI in surmounting these challenges and contributing to a more evidence-based healthcare practice. We conducted a selective review of the literature on EBP and the integration of AI in healthcare to achieve this.Challenges with the three components of EBP: Clinical decision-making in line with the EBP model presents several challenges. The availability and existence of robust evidence sometimes pose limitations due to slow generation and dissemination processes, as well as the scarcity of high-quality evidence. Direct application of evidence is not always viable because studies often involve patient groups distinct from those encountered in routine healthcare. Clinicians need to rely on their clinical experience to interpret the relevance of evidence and contextualize it within the unique needs of their patients. Moreover, clinical decision-making might be influenced by cognitive and implicit biases. Achieving patient involvement and shared decision-making between clinicians and patients remains challenging in routine healthcare practice due to factors such as low levels of health literacy among patients and their reluctance to actively participate, barriers rooted in clinicians' attitudes, scepticism towards patient knowledge and ineffective communication strategies, busy healthcare environments and limited resources.AI assistance for the three components of EBP: AI presents a promising solution to address several challenges inherent in the research process, from conducting studies, generating evidence, synthesizing findings, and disseminating crucial information to clinicians to implementing these findings into routine practice. AI systems have a distinct advantage over human clinicians in processing specific types of data and information. The use of AI has shown great promise in areas such as image analysis. AI presents promising avenues to enhance patient engagement by saving time for clinicians and has the potential to increase patient autonomy although there is a lack of research on this issue.Conclusion: This review underscores AI's potential to augment evidence-based healthcare practices, potentially marking the emergence of EBP 2.0. However, there are also uncertainties regarding how AI will contribute to a more evidence-based healthcare. Hence, empirical research is essential to validate and substantiate various aspects of AI use in healthcare. ©2024 The Authors
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