Emma Brulin

This project investigates the role of Artificial Intelligence in future-proofing long-term care (LTC). The sector faces multiple challenges: an ageing population, limited productivity gains in a labour-intensive sector, labour shortages, and the crisis of traditional modes of care provision amid rising female employment rates. This project explores AI’s potential to help the sector respond to long-standing and growing workforce challenges, improve its value proposition and care quality. We will inform industry standards, care models, and share best practices to inform viable and high-quality care solutions.The project focus is on algorithmic management (AM) as an element of AI and its effects on job & care quality. AM may alter the dynamics of the manager-worker-care recipient. However, existing literature rarely engages systematically with the job quality-AM/AI nexus. By offering a research study that compares five countries’ (Austria, Belgium, Sweden, Spain and the UK) this project will systematically investigate efforts to address issues affecting the sustainability of LTC. A key research focus is to explore the challenges and potential of AM/AI to (i) address recruitment & retention challenges through enhancing working conditions and (ii) improve care quality with potentially better outcomes for workers, care recipients and their families, using company case studies in three subsectors within LTC. Special attention will be paid to certain dimensions of job quality relevant to AM/AI: intrinsic elements (OH&S, work intensification, surveillance, consultative rights, and voice) and extrinsic elements (wages, working hours, benefits, employment conditions, skills).Empirically, the project will strengthen the LTC and job quality literature by exploring the less researched role of how public and private care providers across LTC regimes have embraced AM/AI. It will advance the understanding of the effects of deploying AM/AI at workplace level to shed light on the dynamics between AM and job quality within LTC. Theoretically, it will extend the job quality and LTC literature by refining and reframing present job indicators in the context of AM/AI with the aim to develop viable solutions to address labour shortage challenges. On the societal level the project contribute to national & EU policy agendas on the digital transformation of care work.

The SustaInHealth Network aims to foster interdisciplinary collaboration among researchers to develop resilient and sustainable healthcare systems. By integrating the three pillars of healthcare: i) professionals’ well-being, ii) organisational practices, and iii) quality of care, the network seeks to create a holistic understanding of healthcare environments.The network´s goal is to develop an interdisciplinary strategic research agenda with two main objectives: 1) identifying research gaps and strategies to jointly address these gaps and 2) ensuring the transfer of knowledge into successful and sustainable interventions and practices. The network addresses the fragmented nature of current healthcare research by promoting interdisciplinary collaboration to align the three pillars of healthcare.Recognising the dynamic nature of healthcare systems and the three pillars of healthcare, SustaInHealth gathers researchers from various disciplines and institutions who seek to acknowledge the importance of a healthy workforce as a cornerstone of quality care in their research.The network facilitates knowledge exchange among researchers, stakeholders, and practitioners, aiming to synthesise efforts across all three pillars for long-term sustainability. SustaInHealth promotes collaboration and knowledge transfer between disciplines, institutions and junior and senior researchers. International collaborations, particularly with the Healthy Healthcare network, extend the network’s reach and provide opportunities for global insights.We will hold regular online and physical meetings and workshops to ensure ongoing dialogue and the development of research ideas, with a focus on practical implementation. The SustaInHealth Network will serve as a platform for discussing and improving research methods and theories, ultimately contributing to a more resilient and sustainable healthcare system.

In this application, we propose that Professor Siw Tone Innstrand do a research visit to the Unit of Occupational Medicine at the Institute of Environmental Medicine (IMM) at Karolinska Institutet. Siw Tone Innstrand is professor and director of the interdisciplinary Center for Health Promotion Research at the Department of Psychology at the Norwegian University of Science and Technology (NTNU) has specialist expertise in intervention and implementation research, which is valuable to the research environment at the Unit of Occupational Medicine. In addition, Siw Tone is an organisational psychologist, which is a discipline we do not currently have at the Unit, and her sharing of this knowledge is therefore valuable.We have designed a collaborative program for the research visit that emphasizes sharing scientific expertise and qualifications between Siw Tone and researchers at the Unit of Occupational Medicine, specifically in Emma Brulins’ research group. The program includes presenting research and participating in discussions in seminars both at the institution and in various networks. It also includes hosting a workshop to discuss how we can align epidemiological methods, occupational medicine, and organisational psychology in research. In addition, we will work on two manuscripts and a grant proposal for further collaboration.One essential aim of the visit is to build a research network to create resilient healthcare in the Nordic countries by harmonising survey data and developing interventions. Both Siw Tone and Emma Brulin (PI) have a broad national and international network that we will invite to the network on Resilient healthcare in the Nordic countries. An interdisciplinary Nordic network will be able to inform local and regional governmental policy with multi-country data on resilient healthcare.

Research problem and specific questionsFollowing the release of OpenAI’s ChatGPT in November 2022, this artificial intelligence (AI) has never been out of the headlines. ChatGPT is an example of a Large Language Model (LLM) that can generate content based on training on large data sets & algorithms. LLMs can summarize complex data and write responses in a requested conversational style or literacy level. This technology is particularly relevant in a healthcare context especially the era of patient online record access (ORA), an innovation Sweden has been at the forefront of worldwide & where overburdened healthcare professionals (HCPs) desire assistance with documentation. However, LLMs also embed errors & biases. The purpose of this timely interdisciplinary project is to investigate whether LLMs might be used to alleviate documentation burdens & assist patients. The project has 3 aims:AIM 1: To investigate Swedish HCPs’ experiences & understanding of LLMs in documentation in clinical practiceAIM 2: To investigate patients’ experiences & understanding of LLMs in interpreting their health dataAIM 3: To investigate how healthcare might ethically & safely adopt these tools to benefit HCPs & patientsData and method: The project will survey the experiences & opinions of participants using mixed-methods online surveys for AIM1&2. AIM1 will leverage a nationwide survey of HCPs that is already being conducted

AIM2 will use a nationwide survey through the national patient portal 1177.se, managed by Inera AB, to solicit patients’ opinions. AIM3 will triangulate results and involve ethical analysis. AIM3 will also use an online 3-round Delphi methodology to poll experts for recommendations on the ethical, safe adoption of LLMs in health documentation.Societal relevance and utilisation: In December 2023 the EU proposed the world’s first rules for LLM chatbots with makers of generative AI stipulating consumers must be fully informed that they are engaging with AI. A fundamental problem is we do not know when or how HCPs & patients are using LLMs nor how LLMs can be ethically, safely & effectively integrated into healthcare.Plan for project realisation: The project will be divided into 5 work packages designed to be efficiently & effectively managed by a complement of 7 interdisciplinary research personnel plus 1 Ph.D. student & supported by an international Patient Advisory Group & an LLM Advisory Group.

Research problem and specific questionsA collective agreement HÖK 22 with stricter rules guaranteeing workers a minimum of 11h rest between shifts will be implemented this autumn. As a result, quick returns (&lt

11h of rest) are expected to decrease. Quick returns are associated with insufficient recovery and sick leave, but also facilitate information transfer between shifts. HÖK 22 may affect both the work, health and private life of many health- and social care employees. This project investigates factors impacting the implementation of HÖK 22, and its effects on working hours, work environment and health.Data and methodSchedules before and after implementing HÖK 22 will be compared using hospital working time data in two regions. Effects of reduced quick returns on sick leave, health and work environment, and moderating factors will be studied with data from the healthcare register and regional employee surveys. Employees’ views of the change will be studied with a survey distributed to nurses and assistant nurses. Qualitative interviews with HR-representatives, ward managers, staffing assistants, nurses and assistant nurses will provide understanding of the implementation process. The mixed method approach will provide a comprehensive understanding of why and how changes occur.Societal relevance and utilisationThe sectors of health- and social care face difficulties recruiting and keeping staff. Sick leave due to stress related conditions are increasing, with health- and social care employees being over represented. Sustainable work hours are essential for recovery, work satisfaction and work-home balance. The implementation of HÖK 22 provides a unique opportunity to conduct a large-scale study on the effects of reducing quick returns on work time schedules, health and work environment, as well as factors facilitating or hindering implementation, which is of importance for future work time interventions. Plan for project realisationThe project will be conducted in collaboration with the societal partners and hospital management. In work package 1 we will identify the effects of HÖK 22 on work hour schedules, work environment, safety and employee health. In work package 2 we will evaluate the implementation process of HÖK 22, with the aim of identifying facilitators and obstacles of change. The funds applied for will be used to cover employment costs and dissemination of results, for example in the form of a handbook on work time change.

Hälso- och sjukvården (HSV) står inför omfattande utmaningar ex. åldrande befolkning, ökade kostnader och framtida pandemier. För att klara utmaningarna måste HSV rikta resurser för att personal ska behålla hälsa och vilja stanna i sitt yrke. Faktorer som orsakar och upprätthåller ohälsa för personal inom HSV måste identifieras och åtgärdas. Projektets syften är att 1) identifiera utvecklingen av arbetsmiljö och hälsa för personal inom HSV samt att identifiera riskfaktorer i arbetsmiljön, 2) kartlägga relationen mellan arbetsmiljö och hälsa för HSV personal och god och säker vård för patienter, och 3) studera kort- och långsiktiga effekter av C OVID-19 pandemin på HSV personals arbetsmiljö och hälsa samt på patientsäkerheten. Projektet börjar 2023 och pågår i fyra år. Vi kommer att skapa den Svenska Longitudinella databasen för studier av Arbetsmiljö och Hälsa inom HSV (LOHHC S studien) samt en öppen registerbaserad kohort. LOHHC S utgörs av ett representativt urval av läkare, sjuksköterskor och undersköterskor som arbetar inom Svensk HSV. Data kommer att samlas in varje år. Enkäten innehåller validerade frågor om psykosocial arbetsmiljö och hälsa. LOHHC S data kommer att länkas till registerdata på individnivå (syfte 1) och organisationsnivå (syfte 2). Den öppna kohorten kommer att användas för att identifiera förekomst av sjukskrivning. För syfte 1 kommer logistisk regression och individuella växt modeller (growth models) användas och för syfte 2 flernivå analyser. För syfte 3 upprepas dessa modeller med fokus på C OVID-19 pandemin. Resultat från föreliggande studie kan bidra till att resurser kan styras till förmån för en mer hållbar HSV där personal arbetar under hälsosamma förhållande och vården är god och säker. För att möta framtida utmaningar inom HSV är detta avgörande. För forskarsamhället kommer LOHHC S studien att utgöra ett viktigt bidrag för framtida forskning. Det finns, oss veterligen, inte någon liknande kohort vare sig nationellt eller internationellt.