| 1. |
- Karnehed, Sara, 1979-, et al.
(författare)
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Kan samproduktion av framtidens teknik bidra till en hållbar arbetsmiljö för sjuksköterskor?
- 2023
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Konferensbidrag (refereegranskat)abstract
- Svensk primärvård står inför stora utmaningar med en åldrande befolkning och ett ökat antal personer som vårdas i hemmet (Landers et al., 2016). Digital teknik implementeras med förhoppning om att förbättra kommunikationen mellan vårdpersonal och underlätta möjligheterna till egenvård och tillgänglighet för patienter (Socialstyrelsen, 2021). Tidigare studier visar att användningen av digital teknik kan förändra det professionella landskapet (Petersson, 2020) och påverka arbetsmiljö och arbetets innehåll (Ertner, 2019). Trots att teknik som implementeras inom vården bör vara anpassad till hälso- och sjukvårdspersonalens arbete och värderingar (Palmer et al., 2019; Reed et al., 2019) är sjuksköterskor sällan involverade i beslut kring utformning eller implementering av ny teknik (von Gerich et al., 2022). Det behövs mer kunskap om hur digitaliseringen kan ske i samproduktion med sjuksköterskor och utformas så att en god arbetsmiljö bibehålls.Presentationen syftar till att beskriva sjuksköterskors arbete och arbetsmiljö inom hemsjukvården och hur dessa kunskaper kan användas vid utvecklingen och implementeringen av framtida digital teknik.Individuella semi-strukturerade intervjuer har genomförts med 20 sjuksköterskor som arbetar på vårdcentral och inom hemsjukvård i två halländska kommuner. Intervjuerna har analyserats genom kvalitativ innehållsanalys (Hsieh & Shannon, 2005). Implementeringsteoretiska ramverk används för att undersöka hur kunskapen kan inkorporeras vid innovation och implementering av digitala tekniker inom vårdverksamheter (Nilsen, 2015).Preliminära resultat kommer att presenteras vid konferensen.ReferenserErtner, S. M. (2019). Enchanting, evoking, and affecting: the invisible work of technology implementation in homecare. Nordic Journal of Working Life Studies, 9(S5), 33-47.Hsieh, H.-F., & Shannon, S. E. (2005). Three Approaches to Qualitative Content Analysis. Qualitative health research, 15(9), 1277-1288.Landers, S., Madigan, E., Leff, B., Rosati, R. J., McCann, B. A., Hornbake, R., MacMillan, R., Jones, K., Bowles, K., Dowding, D., Lee, T., Moorhead, T., Rodriguez, S., & Breese, E. (2016). The Future of Home Health Care: A Strategic Framework for Optimizing Value. Home Health Care Management & Practice, 28(4), 262-278.Nilsen, P. (2015). Making sense of implementation theories, models and frameworks. Implementation science : IS, 10(1), 53-53.Palmer, V. J., Weavell, W., Callander, R., Piper, D., Richard, L., Maher, L., Boyd, H., Herrman, H., Furler, J., & Gunn, J. (2019). The Participatory Zeitgeist: an explanatory theoretical model of change in an era of coproduction and codesign in healthcare improvement. Medical humanities, 45(3), 247-257.Petersson, L. (2020). Paving the way for transparency: How eHealth technology can change boundaries in healthcare Lund University].Reed, J. E., Howe, C., Doyle, C., & Bell, D. (2019). Successful healthcare improvements from translating evidence in complex systems (SHIFT-Evidence): simple rules to guide practice and research. International journal for quality in health care, 31(3), 238-244.Socialstyrelsen. (2021). E-hälsa och välfärdsteknik i kommunerna 2021. Uppföljning av den digitala utvecklingen i socialtjänsten och den kommunala hälso-och sjukvården.von Gerich, H., Moen, H., Block, L. J., Chu, C. H., DeForest, H., Hobensack, M., Michalowski, M., Mitchell, J., Nibber, R., & Olalia, M. A. (2022). Artificial Intelligence-based technologies in nursing: A scoping literature review of the evidence. International Journal of Nursing Studies, 127, 104153.
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| 2. |
- Karnehed, Sara, 1979-, et al.
(författare)
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Developers' beliefs and values – a discursive analysis of e-health technology in home healthcare
- 2023
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Konferensbidrag (refereegranskat)abstract
- BackgroundThe implementation of e-health is transforming healthcare. The acknowledged benefits of digitalization are quality improvement, patient empowerment, and increased efficiency. The mobility of e-health makes it especially suitable for home healthcare. eMar is a common e-health technology used in Swedish home healthcare. Decisions about technology design are governed by developers’ perceptions of intended users. These perceptions can be identified in the description and promotion of a specific product.PurposeThe purpose of the presentation is to contribute to increased knowledge about the values entailed in a specific eMar used in Swedish home healthcare, and furthermore to discuss how these values conform with existing national missions such as people-centered care.MethodInformation consisting of sales materials about a specific eMar used in several Swedish municipalities has been analyzed through critical discourse analysis to visualize values embedded in the eMar.FindingsPreliminary results show that the provider of the specific eMar describes care in terms borrowed from the industrial sector, such as shift changes and production of care. Good and safe care is defined as the right person receiving the right medicine at the right time. Furthermore, the app is advertised as a tool for monitoring assuming that the performance of tasks can be influenced through the remote control of the employee. The eMar is described as representing new and modern technologies that are expected to raise the status of healthcare professions and facilitate the recruitment of employees.
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| 3. |
- Neher, Margit, 1959-, et al.
(författare)
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Innovation in healthcare : leadership perceptions about the innovation characteristics of artificial intelligence—a qualitative interview study with healthcare leaders in Sweden
- 2023
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Ingår i: Implementation Science Communications. - London : BioMed Central (BMC). - 2662-2211. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- Background: Despite the extensive hopes and expectations for value creation resulting from the implementation of artificial intelligence (AI) applications in healthcare, research has predominantly been technology-centric rather than focused on the many changes that are required in clinical practice for the technology to be successfully implemented. The importance of leaders in the successful implementation of innovations in healthcare is well recognised, yet their perspectives on the specific innovation characteristics of AI are still unknown. The aim of this study was therefore to explore the perceptions of leaders in healthcare concerning the innovation characteristics of AI intended to be implemented into their organisation.Methods: The study had a deductive qualitative design, using constructs from the innovation domain in the Consolidated Framework for Implementation Research (CFIR). Interviews were conducted with 26 leaders in healthcare.Results: Participants perceived that AI could provide relative advantages when it came to care management, supporting clinical decisions, and the early detection of disease and risk of disease. The development of AI in the organisation itself was perceived as the main current innovation source. The evidence base behind AI technology was questioned, in relation to its transparency, potential quality improvement, and safety risks. Although the participants acknowledged AI to be superior to human action in terms of effectiveness and precision in some situations, they also expressed uncertainty about the adaptability and trialability of AI. Complexities such as the characteristics of the technology, the lack of conceptual consensus about AI, and the need for a variety of implementation strategies to accomplish transformative change in practice were identified, as were uncertainties about the costs involved in AI implementation.Conclusion: Healthcare leaders not only saw potential in the technology and its use in practice, but also felt that AI’s opacity limits its evidence strength and that complexities in relation to AI itself and its implementation influence its current use in healthcare practice. More research is needed based on actual experiences using AI applications in real-world situations and their impact on clinical practice. New theories, models, and frameworks may need to be developed to meet challenges related to the implementation of AI in healthcare. © 2023, The Author(s).
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| 4. |
- Nilsen, Per, 1960-, et al.
(författare)
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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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Ingår i: JMIR Research Protocols. - Toronto : JMIR Publications. - 1929-0748. ; 12
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Tidskriftsartikel (refereegranskat)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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| 5. |
- Petersson, Lena, 1968-, et al.
(författare)
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Developing an ethical model for guidance the implementation of AI in healthcare
- 2023
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Ingår i: 10th Nordic Health Promotion Research Conference 2023. Sustainability and the impact on health and well-being. - Halmstad : Halmstad University Press. - 9789189587410 ; , s. 84-84
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Konferensbidrag (refereegranskat)abstract
- Background: Artificial intelligence (AI) is predicted to improve healthcare, increase efficiency, save time and resources. However, research shows an urgent need to develop guidance to ensure that the use of AI in healthcare is ethically acceptable.Purpose: To develop an ethical model to support AI implementation in practice.Methods: The study used an explorative and empirically driven qualitative design. Individual interviews were conducted with 18 healthcare professionals from two emergency departments in Sweden where the county council has developed an AI application to predict the risk for unexpected mortality within 30 days after visiting an emergency department. A deductive analysis based on ethical theory i.e virtue, deontology and consequentialism, was used.Findings: The developed model shows how the healthcare professionals use ethical reasoning in relation to the implementation of AI. In relation to virtue ethics, moral considerations in relation to the use of AI were mentioned. In relation to deontology, considerations were mentioned on actions performed based on information acquired from the technology and adherence to specific duties, roles and responsibilities. In relation to consequentialism, considerations about how to provide better resources more rapidly in an equal way and how the technology can be adjusted to each patients’ individual needs and preferences in order to support decisions, self-determination, and actions that are in the patients best interest.Conclusions: Our findings provide an ethical model demonstrating the relevance of virtue, deontology and consequentialism when AI are to be implemented in practice.
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| 6. |
- Petersson, Lena, 1968-, et al.
(författare)
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Ethical considerations in implementing AI for mortality prediction in the emergency department : Linking theory and practice
- 2023
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Ingår i: Digital Health. - London : Sage Publications. - 2055-2076. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Background: Artificial intelligence (AI) is predicted to be a solution for improving healthcare, increasing efficiency, and saving time and recourses. A lack of ethical principles for the use of AI in practice has been highlighted by several stakeholders due to the recent attention given to it. Research has shown an urgent need for more knowledge regarding the ethical implications of AI applications in healthcare. However, fundamental ethical principles may not be sufficient to describe ethical concerns associated with implementing AI applications.Objective: The aim of this study is twofold, (1) to use the implementation of AI applications to predict patient mortality in emergency departments as a setting to explore healthcare professionals’ perspectives on ethical issues in relation to ethical principles and (2) to develop a model to guide ethical considerations in AI implementation in healthcare based on ethical theory.Methods: Semi-structured interviews were conducted with 18 participants. The abductive approach used to analyze the empirical data consisted of four steps alternating between inductive and deductive analyses. Results: Our findings provide an ethical model demonstrating the need to address six ethical principles (autonomy, beneficence, non-maleficence, justice, explicability, and professional governance) in relation to ethical theories defined as virtue, deontology, and consequentialism when AI applications are to be implemented in clinical practice.Conclusions: Ethical aspects of AI applications are broader than the prima facie principles of medical ethics and the principle of explicability. Ethical aspects thus need to be viewed from a broader perspective to cover different situations that healthcare professionals, in general, and physicians, in particular, may face when using AI applications in clinical practice. © The Author(s) 2023.
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| 7. |
- Petersson, Lena, 1968-, et al.
(författare)
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Ethical Perspectives on Implementing AI to Predict Mortality Risk in Emergency Department Patients : A Qualitative Study
- 2023
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Ingår i: Caring is sharing - exploiting the value in data for health and innovation. - Amsterdam : IOS Press. - 9781643683881 - 9781643683898 ; , s. 676-677
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Konferensbidrag (refereegranskat)abstract
- Artificial intelligence (AI) is predicted to improve health care, increase efficiency and save time and recourses, especially in the context of emergency care where many critical decisions are made. Research shows the urgent need to develop principles and guidance to ensure ethical AI use in healthcare. This study aimed to explore healthcare professionals' perceptions of the ethical aspects of implementing an AI application to predict the mortality risk of patients in emergency departments. The analysis used an abductive qualitative content analysis based on the principles of medical ethics (autonomy, beneficence, non-maleficence, and justice), the principle of explicability, and the new principle of professional governance, that emerged from the analysis. In the analysis, two conflicts and/or considerations emerged tied to each ethical principle elucidating healthcare professionals' perceptions of the ethical aspects of implementing the AI application in emergency departments. The results were related to aspects of sharing information from the AI application, resources versus demands, providing equal care, using AI as a support system, trustworthiness to AI, AI-based knowledge, professional knowledge versus AI-based information, and conflict of interests in the healthcare system. © 2023 European Federation for Medical Informatics (EFMI) and IOS Press.
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| 8. |
- Petersson, Lena, 1968-, et al.
(författare)
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Expected values of implementing AI in healthcare – A Qualitative study
- 2023
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Ingår i: Nordic Health Promotion Research Conference 2023. - Halmstad.
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Konferensbidrag (refereegranskat)abstract
- Background: Artificial intelligence (AI) is often presented as a technology that will change healthcare and be useful inclinical work in disease prediction, diagnosis, and precision health. More knowledge is needed regarding the value of AI applications based on the perspectives of healthcare leaders to understand their roles as gatekeepers and facilitatorsfor successful implementation.The purpose of the study: To explore healthcare leaders’ perceptions of the value of AI applications in clinical work.Methods: The study had an explorative qualitative approach. Individual interviews were conducted from October2020 to May 2021 with 26 healthcare leaders with different experiences in implementing AI in clinical practice in acounty council in Sweden. Inductive qualitative content analysis was used, and eight sub-categories and threecategories emerged.Findings: The value of AI applications in clinical care was described in terms of expected benefits for patients as toolssupporting person-centered information and individualized self-management. The expected benefits for healthcareprofessionals included decision-support in diagnostics, risk assessments, and treatment recommendations but alsoproviding warning systems and second opinions in clinical work. On an organizational level, the benefits comprisedpatient safety and decision-support in prioritizing healthcare resources in and across healthcare organizations.Conclusions: The healthcare leaders perceived that AI applications would provide value on different levels inhealthcare for patients, healthcare professionals, and organizations. Across these levels, the implementation of AI cansupport person-centeredness, patient self-management, quality of care, patient safety, and resource optimization.
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| 9. |
- Petersson, Lena, 1968-, et al.
(författare)
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Healthcare Leaders' Perceptions of the Usefulness of AI Applications in Clinical Work : A Qualitative Study
- 2023
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Ingår i: Caring is sharing - exploiting the value in data for health and innovation. - Amsterdam : IOS Press. ; , s. 678-679
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Konferensbidrag (refereegranskat)abstract
- Artificial intelligence (AI) is often presented as a technology that changes healthcare and is useful in clinical work in disease prediction, diagnosis, treatment effectiveness, and precision health. This study aimed to explore healthcare leaders' perceptions of the usefulness of AI applications in clinical work. The study was based on qualitative content analysis. Individual interviews were conducted with 26 healthcare leaders. The usefulness of AI applications in clinical care was described in terms of expected benefits for 1) patients as supporting individualized self-management and person-centered information support tools 2) healthcare professionals in terms of providing decision-support in diagnostics, risk assessments, treatment recommendations, warning systems, and as a new colleague supporting the clinical work, and 3) organizations as providing patient safety and decision-support in prioritizing healthcare resources in organizing healthcare.
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| 10. |
- Petersson, Lena, 1968-, et al.
(författare)
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Implementering av AI i hälso- och sjukvården – ledares gränsarbete kan förändra professionella gränser
- 2023
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Konferensbidrag (refereegranskat)abstract
- Just nu pågår en digital transformation av svensk hälso- och sjukvård och artificiell intelligens (AI) är tänkt att vara lösningen på många av de utmaningar sjukvården står inför. I en kunskapssammanställning från Myndigheten för arbetsmiljökunskap (MYNAK) (2020) om digitalisering och arbetsmiljö påtalas att den snabba tekniska utvecklingen kommer att förändra arbetsmiljö och yrkesrollers karaktär. Professionellas arbete är traditionellt omgärdat av gränser och att upprätthålla gränserna kring det egna kunskapsområdet är en grundläggande del av professionens utveckling. Digitalisering och implementering av olika former av teknik kan förändra professionella gränser och därmed generera så kallat gränsarbete (Petersson, 2020) som kan indelas i tre former; konkurrenskraftigt gränsarbete, kollaborativt gränsarbete och konfigurativt gränsarbete (Langley et al. 2019). De tre formerna av gränsarbete är ofta sammanflätade i praktiken, men konfigurativt gränsarbete kan dock beskrivas som en kraft som driver de andra två kategorierna av gränsarbete, eftersom det riktar sig emot andras aktiviteter i syfte att utforma gränser för förändring mellan grupper (Langley et al., 2019). Denna studie fokuserar på hur gränserna kring vårdprofessionernas arbete kan förändras vid implementering av AI och på vilket konfigurativt gränsarbete som aktörer på ledningsnivån i ett sjukvårdssystem förutser kommer att ske när sjukvården blir mer datadriven genom användning av AI-analyser.Vi genomförde semistrukturerade intervjuer med 26 ledare som var i en position att potentiellt påverka implementeringen och användningen av AI i en svensk region. Intervjuerna analyserades med hjälp av kvalitativ innehållsanalys. Analysen i studien fokuserar på den konfigurativa formen av gränsarbete.Sammantaget visar resultatet att ledarna beskriver olika typer av konfigurativt gränsarbete. Ledarna har makten att bedriva gränsarbete som förändrar gränserna kring vårdpersonalens arbete och de beskriver att de, medvetet eller omedvetet, vill förändra gränserna kring i första hand läkarnas arbete vid implementeringen av AI i hälso- och sjukvården.ReferenserLangley, A., Lindberg, K., Mork, B. E., Nicolini, D., Raviola, E., Walter, L. (2019). Boundary work among groups, occupations, and organization: From Cartography to process. Academy of Management Annals, 13(2): 704–736.Mynak (2020). Framtidens arbetsmiljö – trender, digitalisering och anställningsformer. 2020:3. www.mynak.se.Petersson, L. (2020). Paving the way for transparency: How eHealth technology can change boundaries in healthcare. Lund: Department of Design Sciences, Faculty of Engineering, Lund University.
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