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3. THEME 2: Advancing prevention through digital health

3.1 Artificial intelligence in social and health care in Finland

Jukka Lähesmaa, Ministry of social affairs and health Finland
In previous years, AI develop­ment primarily focused on various diagnostics, predictions, and partly information production support use cases, applying machine and deep learning methods. However, over the past couple of years, the focus of development has shifted to use cases related to information structuring, summary production, and decision support.
Artificial intelligence is expected to have a significant impact on productivity and employment in various industries. However, the estimations vary on how significant the impacts will be. For example, Acemoglu (2024) estimates the impact on annual growth in total productivity at only 0.07 percentage. On the other hand, Investment bank Goldman Sachs (2024) has estimated that the percentage could be as high as 1.5.
In social and health care sector there are high expectations that AI can improve the services to the customers, support professionals in clinical as well as administrative tasks and provide possibilities for cost savings. However, at the same time many of the real-life AI solutions are still in the piloting phase and large-scale implementations are only underway. Little research evidence on the effects of artificial intelligence is available.
The Finnish national AI ecosystem for social and health services is an informal network of stakeholders in the social and healthcare sector, including authorities, businesses, and researchers (DigiFinland 2025a). The ecosystem brings together over 250 organizations with a shared mission: to responsibly harness the power of AI to enhance health and social services nationwide for patients and the community. The ecosystem is not a rigid structure but a dynamic, voluntary network. It thrives on collaboration, shared learning, and experimentation. Key national actors support its coordination.
For the AI ecosystem, a cornerstone of the 2025 plan is a series of pilot projects (see Table 1) designed to test AI solutions in real-world settings, offering valuable insights into their effectiveness, scalability, and legal implications. Funding is provided by the Ministry and the wellbeing services counties for the pilots that are expected to pave the way for broader national adoption.
Table 1 The pilot projects in the Finnish AI ecosystem.
Finnish Health AI Ecosystem pilot projects
AI-assisted assessment of child service needs
AI-based prediction of functional capacity changes
Professional's AI assistant
AI-based compilation of client background and risk information
Automatic clinical information system documentation
LingAI real-time interpretation
AI-based real-time interpretation solution
50% productivity increase in digital obesity treatment using AI tools
AI in cancer PET imaging
Development and implementation of an AI-powered medication risk tool
A key enabler of this collaboration is the Wellbeing Counties own AI Network group. The group meets every three weeks to share updates, identify common challenges, and foster partnerships among its members. It plays a crucial role in maintaining a real-time overview of AI development across the country and ensuring that knowledge flows freely between regions. Wellbeing Counties AI Network maintains status information of AI development in the wellbeing counties. The goal is to gather information about AI-related development already carried out and ongoing to enable transparency and information sharing. The pilot goals are categorized based on the AI applicated area and related use cases. In May 2025, almost 150 current projects related to the AI development have been documented in wellbeing services counties. Of these 25 percent have reached implementation while the rest are in a piloting or a development phase. (UNA 2025)
The content of AI development projects has been analysed in relation to the use case categories and use cases described in the Mapping of Potential AI Use Cases feasibility report (DigiFinland 2024). In previous years, AI development primarily focused on various diagnostics, predictions, and partly information production support use cases, applying machine and deep learning methods. However, over the past couple of years, the focus of development has shifted to use cases related to information structuring, summary production, and decision support.
In spring 2025, the ecosystem undertook a vision-building initiative to define a shared national direction for AI in the social affairs and health sectors. This process resulted in a white paper (DigiFinland 2025b) that focused on the most promising AI use cases but also outlines ethical principles and policy recommendations. Based on the vision, a road map for health AI development will be created.

References

Acemoglu, D. (2024): The Simple Macroeconomics of AI, NBER Working Paper 32487, May 2024. https://www.nber.org/system/files/working_papers/w32487/w32487.pdf
Goldman Sachs (2024): AI is showing "very positive" signs of eventually boosting GDP and productivity. Available from: https://www.goldmansachs.com/insights/articles/AI-is-showing-very-positive-signs-of-boosting-gdp
DigiFinland 2025a. AI Ecosystem in Social and Health Services. Available from: https://digifinland.fi/sote-tekoalyn-ekosysteemi/
UNA 2025. The State of AI development in Finland’s Wellbeing Services Counties. 12.5.2025, Una Oy. Available from: https://unaoy.fi/uncategorized/ai-development-in-finland/
DigiFinland 2024. Tekoäly hyvinvointialueilla: sosiaali- ja terveydenhuollon käyttötapaukset ja kansallinen edistäminen [Artificial intelligence in the wellbeing counties: social and health care use cases and national progress, in Finnish]. Final report 3/2024. https://digifinland.fi/wp-content/uploads/2024/03/DigiFinland_tekoaly_loppuraportti_210324.pdf
DigiFinland 2025b. SOTE-tekoälyn ekosysteemin tekoälyvisio 2035. Available from: https://digifinland.fi/wp-content/uploads/2025/06/SOTE-tekoalyn_ekosysteemin_tekoalyvisio_2035_final.pdf

3.1.2 Artificial Intelligence Creates New Competency Demands in Wellbeing Services Counties

Johan Sanmark, Tandem Health, and Enni Sanmark, Helsinki University Hospital and University of Helsinki, Finland

Harnessing Artificial Intelligence for Health Care Transformation

In recent years, rapidly advancing artificial intelligence (AI) technologies have demonstrated substantial potential to transform social and health care systems. AI’s capacity to process vast quantities of data and generate new content enables the automation of cognitive tasks, more efficient allocation of resources, and enhanced decision-making support (McKinsey & Company 2025, PwC 2025). In Finland, the safe adoption of AI is facilitated by unique structural advantages, including comprehensive digital patient records, extensive quality registries, and a robust research infrastructure.
Within publicly funded health care, available resources are expected to decline relative to service demand in the coming years. As a result, Wellbeing Services Counties face increasing pressure to enhance efficiency and reform their operational models, and AI is widely regarded as one possible solution. AI can reduce clinicians’ documentation burden e.g., through automation (Stults et al. 2025), improve the consistency and quality of diagnostic processes (Takita et al. 2025), and support decision-making both in clinical practice and in organizational leadership. Furthermore, AI is anticipated to transform both public health prevention strategies and individualized treatment decisions (Panteli et al. 2025, Sharma et al. 2024).
From the perspective of health care professionals, the range of potential AI applications can be structured based on use purpose. In addition, there are numerous other use cases, such as facilitating multilingual communication and accelerating or optimizing radiological workflows, that may remain invisible to front-line professionals but nevertheless hold considerable significance for the broader implementation of the technology.

State of Knowledge on Workforce Competence

To date, assessments of AI-related competencies within Wellbeing Services Counties have been highly limited. To our knowledge, the first such evaluation was a survey conducted by Keusote in August–September 2025, assessing staff use of AI and their self-perceived competence (Keusote 2025). The survey received responses from 437 employees across various sectors. Among respondents, 43% had used AI at least once in their work, yet only 21% of those users rated their AI competence as good or excellent. The primary barriers to adoption were a lack of knowledge regarding available applications and insufficient training.
In spring 2024, the Western Uusimaa Wellbeing Services County examined the impact of an AI tool on the work efficiency of professional translators (Martikainen et al. 2025). The study revealed substantial variation in staff capacity to adopt and benefit from the tool: one translator improved productivity by 102%, whereas another experienced a 2% decline.
Globally, research evidence on workforce AI competencies remains scarce. A systematic review by Garquez-Garcia et al. (2025) sought to identify the key competency requirements for the adoption of AI among health care professionals. Of 1,489 articles identified, only seven were deemed relevant and included in the final analysis. The review highlighted five essential domains of competence: Fundamentals of AI, Ethical and legal considerations, Data analysis and management, Communication and teamwork, Evaluation of AI tools

Regulatory Framework for AI Competencies

Legislation imposes comprehensive obligations on Wellbeing Services Counties to ensure adequate AI-related competencies among their personnel. Under the AI Act, any organization deploying AI applications in its operations must be able to document and demon­strate that staff have received sufficient training in AI literacy, commensurate with their roles and responsibilities (European Union 2024). Moreover, national legislation in Finland, including the Occupational Safety and Health Act (738/2002) and the Act on Health Care Professionals (559/1994), requires employers to provide guidance and create the conditions necessary for learning the use of new tools.

Discussion

AI tools are expected to become substantially more prevalent in health care over the coming years, among both administrative personnel and professio­nals directly involved in patient care. Realizing the full potential of AI will require not only the redesign of workflows but also strong staff motivation and the capacity to competently use new technologies. However, current evidence suggests that gaps in AI-related competence persist at both the leadership level of Wellbeing Services Counties and among front-line users.

References

738/2002 Työturvallisuuslaki. Helsinki: Finlex; 2002. Section 14.
559/1994 Laki terveydenhuollon ammattihenkilöistä. Helsinki: Finlex; 1994. Section 18.
European Union. Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union L, 2024 Jul 12: Article 4.
Gazquez-García J, Sánchez-Bocanegra CL, Sevillano JL. AI in the Health Sector: Systematic Review of Key Skills for Future Health Professionals. JMIR Med Educ. 2025 Feb 5;11:e58161.
Keusote. Vajaa puolet työntekijöistä käyttää tekoälyä – yli 80% kaipaa koulutusta [Internet]. Hyvinkää: Keski-Uudenmaan hyvinvointialue; 2025 [cited 2025 Sep 29]. Available from: https://www.keusote.fi/vajaa-puolet-tyontekijoista-kayttaa-tekoalya-yli-80-kaipaa-koulutusta/
Martikainen M, Smolander K, Sanmark J, Sanmark E. Evaluation of Generative Artificial Intelligence Implementation Impacts in Social and Health Care Language Translation: Mixed Methods Case Study. JMIR Form Res. 2025 Sep 17;9:e73658.
McKinsey & Company. The economic potential of generative AI: The next productivity frontier. 2023. [viitattu 27.6.2025].
Panteli D, et al. Artificial intelligence in public health: promises, challenges, and an agenda for policy makers and public health institutions. Lancet Public Health. 2025;10(5):e428-e432.
PwC. PwC’s 2025 AI Business Predictions [Internet]. PwC. 2025 [viitattu 27.6.2025]. Available from:  https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.html
Sharma A, Lysenko A, Jia S, et al. Advances in AI and machine learning for predictive medicine. J Hum Genet. 2024;69:487–497.
Stults CD, Deng S, Martinez MC, et al. Evaluation of an Ambient Artificial Intelligence Documentation Platform for Clinicians. JAMA Netw Open. 2025;8(5):e258614. 
Takita H, Kabata D, Walston SL, et al. A systematic review and meta-analysis of diagnostic performance comparison between generative AI and physicians. npj Digit Med. 2025;8:175. 

3.2 Promising evidence on the value of eHealth inter­ventions and digital tools

Vesa Jormanainen, Ministry of social affairs and health Finland
Measuring and under­standing the economic value and performance of eHealth tools is essential to under­standing the outcome and best uses of such technologies.”
eHealth represents one of the pillars of the modern healthcare system and a strategy involving the use of digital tools to assist an increasing number of patients and reducing healthcare costs. Measuring and understanding the economic value and performance of eHealth tools is, therefore, essential to understanding the outcome and best uses of such techno­logies (Biancuzzi et al. 2023). However, only few economic evaluations of eHealth technologies among elderly have been published (Sanyal et al. 2018). Despite its potential, eHealth implementations have often faced significant challenges, with high failure rates reported in both developed and developing countries (Palm et al. 2025).
Early assessments suggested promising results. In 2010–2011, published syste­matic reviews on eHealth interventions concluded that high-quality evidence on health and economic benefits was still lacking (Ekeland et al. 2010, Black et al. 2011). Systematic reviews on eHealth interventions in somatic diseases published in 2009–2012 concluded that eHealth is effective or cost-effective but did not significantly improve quality of life and all-cause mortality (Elbert et al. 2014). In measuring eHealth interventions, the included reviews differed substantially in terms of study populations, intervention components, comparison groups, and outcome measures.
A large-scale cluster randomized trial failed to show cost-effectiveness of telehealth compared to usual care among patients with long-term conditions. The Whole System Demonstrator trial involved 3230 patients with diabetes mellitus, COPD, and CHF in 2008–2009 in England, and showed that eHealth interventions are associated with lower mortality and emergency admission rates (Steventon et al. 2012). Net benefit analyses of costs and outcomes using quality adjusted life years (QALY) gain showed that the incremental cost per QALY (ICER) of telehealth when added to usual care was £92,000 (Henderson et al. 2013). The QALY gains by patients using telehealth in addition to usual care were similar than those by patients receiving usual care only, and total costs associated with the telehealth intervention were higher.
A systematic review summarised the evidence on the cost-effectiveness of digital health interventions published in scientific literature in 2016–2020 (Gentili et al. 2022). Findings showed a growing body of evidence and suggested a generally favorable effect in terms of costs and health outcomes. However, due to the heterogeneity across study methods, the comparison between interventions remained difficult.
Another recent structured review on eHealth interventions published in 2019–2021 found that there is still a lack of consensus regarding the recommended models to map and report their economic outcomes and performance (Biancuzzi et al. 2023). As in earlier systematic reviews, several diseases were the object of detailed clinical trials and protocols using various eHealth tools, leading to various economic outcomes, especially in the COVID-19 post-pandemic era.

References

Biancuzzi H, Dal Mas F, Bidoli C, et al. Economic and performance evaluation of E-Health before and after the pandemic era: a literature review and future perspectives. Int J Environ Res Public Health 2023;20:4038. https://doi.org/10.3390/ijerph20054038  
Black AD, Car J, Pagliari C, et al.  The impact of eHealth on the quality and safety of health care: a systematic overview. PLoS Med 2011;8: e1000387. https://doi.org/10.1371/journal.pmed.1000387  
Gentili A, Failla G, Melnyk A, et al. The cost-effectiveness of digital health interventions: a systematic review of the literature. Front Public Health 2022;10:78713. https://doi.org/10.3389/fpubh.2022.787135  
Ekeland AG, Bowes A, Flottorp S. Effectiveness of telemedicine: a systematic review of reviews. Int J Med Inform 2010;79:736–771. https://doi.org/10.1016/j.ijmedinf.2010.08.006  
Elbert NJ, van Os-Medendorp H, van Renselaar W, et al. Effectiveness and cost-effectiveness of eHealth interventions in somatic diseases: a systematic review of systematic reviews and meta-analyses. J Med Internet Res 2014;16:e110. https://doi.org/10.2196/jmir.2790  
Henderson C, Knapp M, Fernández J-L, et al. Cost effectiveness of telehealth for patients with long term conditions (Whole Systems Demonstrator telehealth questionnaire study): nested economic evaluation in a pragmatic, cluster randomized controlled trial. BMJ 2013;346:f1035. https://doi.org/10.1136/bmj.f1035  
Palm K, Brantnell A, Peolsson M, et al. National eHealth strategies: a comparative study of nine OECD health systems. BMC Health Serv Res 2025;25:269. https://doi.org/10.1186/s12913-025-12411-7  
Sanyal C, Stolee P, Juzwishin D, et al.  Economic evaluations of eHealth technologies: a systematic review. PLoS ONE 2018;13: e0198112. https://doi.org/10.1371/journal.pone.0198112  
Steventon A, Bardsley M, Billings J, et al. Effect of telehealth on use of secondary care and mortality: findings from the Whole System Demonstrator cluster randomised trial. BMJ 2012;344:e3874. https://doi.org/10.1136/bmj.e3874

3.2.1 Measurement and Effectiveness Evaluation of Digital Health Services – From Current Perspectives to Future Directions

Elina Laukka, University of Helsinki and Oulu University of Applied Sciences, and Paulus Torkki, University of Helsinki, Finland

Introduction

Digital health services (DHSs) are increasingly recognized as valuable tools for improving the overall quality and efficiency of health and social care. However, DHSs encompass a broad spectrum of solutions from remote consultations and chat-based support to cutting-edge technologies like artificial intelligence and the metaverse. In addition, the aim may vary from replacing a physical contact to supporting long-term self-care of a chronic disease. This diversity poses significant challenges for assessing their effectiveness, making it difficult for policymakers to make well-informed decisions about their adoption, use, and potential replacement (Härkönen et al., 2024; Laukka et al., 2025).
In Finland, the number of digital clinics has grown rapidly, now covering over 80% of the population (www.sotedatalab.fi). Despite their expansion, it remains unclear whether these services are truly effective or whether they might even lead to increased use of care with less severe needs, although the common aim is to increase cost-effectiveness by offering digital solutions to suitable needs. DHSs operate through varying mechanisms: some aim to reduce the number of visits by offering remote alternatives, while others take a more preventive approach, encouraging early-stage consultations to avoid deterioration of health. This latter model may be particularly beneficial in managing chronic conditions.

Current Understanding of the Effective­ness of Digital Health Services in Finland

The findings of the Finnish studies have been synthesized using the Quintuple Aim framework.

Improving Population Health

Users of digital clinics are typically younger, more highly educated, and predominantly female compared to health centre users, and the digital divide has to be considered also when assessing the impacts on population health. Digital clinics are well-suited for the treatment of uncomplicated infections, whereas health centre visits are more often associated with chronic conditions. Approximately 70–85% of health problems addressed in digital clinics have reportedly been successfully resolved (Lakoma et al., 2024; Dahlberg et al., 2025).
Hakanen et al. (2023) found no difference in outcomes between patients using digital care pathways compared to physical visits among tonsillectomy patients. In Finland, early experiences of digital care pathways for chronic patients seem promising in terms of improving outcomes, supporting the evidence from the literature (Lakka et a., 2023; Kokkonen et al., 2024; Turkkila et al., 2025).

Enhancing Patient Experiences

One of the most reported benefits of DHS in literature has been improved patient experience. In Finland, both the population survey by Finnish Institute for Health and Welfare as well as many studies have reported high patient satisfaction or experience towards DHS (Parikka et al., 2020; Pennanen et al., 2023). The most frequently reported benefit is that digital services facilitate the use of healthcare services independent of time and location. In addition, the improved access has been reported both in primary and in secondary care.

Advancing Health Equity

The digital divide is a real topic in terms of equity. Many studies report increased use among young, well-educated people and limitations or concerns towards use of DHS among e.g., older or handicapped populations (Pennanen et al., 2023; Heponiemi et al., 2023). On the other hand, the improved access may reduce the equity. As the DHS are still shaping the healthcare system, the development of health equity should be carefully monitored and considered.

Improving Provider Wellbeing

Evidence on professional experiences with DHS in Finland remains limited. It is also known that there is a divide among professionals: a relatively small group is responsible for a large share of digital clinic visits.

Reducing Care Costs

Digital care appears to improve cost-efficiency. Studies have reported 20–50% cost savings, depending on the patient group and context (Hakanen et al., 2023; Kokkonen et al., 2024; Lakoma et al., 2024; Dahlberg et al., 2025). Replacing physical visits with digital contacts enhances cost-efficiency, although in primary care this may sometimes lead to additional physical visits. In specialized care, both preopera­tive and follow-up visits have successfully been replaced by digital consultations. In some patient groups (e.g., heart failure), improved continuity of care has also reduced the need for emergency visits and even hospitalizations.

Conclusions and recommendations

Future research should place greater emphasis on evaluating the cost-effectiveness of DHSs. A holistic understanding of their overall impact requires the integration of clinical outcomes, patient experiences, and economic evaluations. Additionally, incorporating the perspectives of equity and professionals’ experience would broaden the analysis in line with the Quintuple Aim framework. To support this, healthcare policymakers must enable systematic data collection that facilitates robust analysis and empowers researchers to produce high-quality, actionable evidence. This evidence should then guide the strategic development and continuous improvement of DHSs.

References

Dahlberg, A., Jukarainen, S., Kaartinen, T., & Orre, P. (2025). Cost minimization analysis of digital-first healthcare pathways in primary care. npj Digital Medicine, 8(1), 546.
Hakanen, O., Tolvi, M., & Torkki, P. (2023). Cost analysis of face-to-face visits, virtual visits, and a digital care pathway in the treatment of tonsillitis patients. American journal of otolaryngology, 44(4), 103868.
Heponiemi, T., Gluschkoff, K., Leemann, L., Manderbacka, K., Aalto, A. M., & Hyppönen, H. (2023). Digital inequality in Finland: access, skills and attitudes as social impact mediators. New Media & Society, 25(9), 2475–2491.
Härkönen, H., Lakoma, S., Verho, A., Torkki, P., Leskelä, R-L., Pennanen, P., Laukka, E., Jansson, M. (2024) Impact of digital services on healthcare and social welfare: An umbrella review. International journal of nursing studies, 152, 104692.
Kokkonen, J., Mustonen, P., Heikkilä, E., Leskelä, RL., Pennanen, P., Krühn, K., Jalkanen, A., Laakso, JP., Kempers, J., Väisänen, S., Torkki, P. (2024) Effectiveness of Telemonitoring in Reducing Hospitalization and Associated Costs for Patients With Heart Failure in Finland: Nonrandomized Pre-Post Telemonitoring Study. JMIR Mhealth Uhealth, 12, e51841
Lakka, T. A., Aittola, K., Järvelä-Reijonen, E., Tilles-Tirkkonen, T., Männikkö, R., Lintu, N., ... & Pihlajamäki, J. (2023). Real-world effectiveness of digital and group-based lifestyle interventions as compared with usual care to reduce type 2 diabetes risk–A stop diabetes pragmatic randomised trial. The Lancet Regional Health–Europe, 24.
Lakoma, S., Pasanen, H., Lahdensuo, K., Pehkonen, J., Viinikainen, J., & Torkki, P. (2024). Quality of the digital gp visits and characteristics of the users: retrospective observational study. Scandinavian Journal of Primary Health Care, 42(4), 686–694.
Laukka, E., Härkönen, H., Lakoma, S., Jansson, M., Torkki, P. (2024) Outcomes and Economic Effects of Digital Health Services: An Umbrella Review, Value in Health, 27(12).
Parikka, S., Koskela, T., Ikonen, J., Kilpeläinen, H., Hedman, L., Koskinen, S., Lounamaa, A. (2020) Kansallisen terveys-, hyvinvointi ja palvelututkimus FinSoten perustulokset. Available from: thl.fi/finsote
Pennanen, P., Jansson, M., Torkki, P., Harjumaa, M., Pajari, I., Laukka, E., ... & Leskelä, R. L. (2023). Digitaalisten palvelujen vaikutukset sosiaali-ja terveydenhuollossa [Impact of digital services in health and social care]. Valtioneuvoston selvitys- ja tutkimustoiminnan julkaisusarja 2023:52. http://urn.fi/URN:ISBN:978-952-383-059-2
Turkkila, E., Pekkala, T., Merikallio, H., Merikukka, M., Heikkilä, L., Hukkanen, J., ... & Savolainen, M. J. (2025). Five-year follow-up of a randomized weight loss trial on a digital health behaviour change support system. International Journal of Obesity, 49(5), 949.

3.3 Telehealth supporting health care delivery towards integrated care

Sari Palojoki and Riikka Vuokko, Ministry of social affairs and health Finland
Integrating telehealth into existing care processes can be challenging. It is crucial that the transition to tele­health applications is managed carefully to maxi­mize the benefits for all stake­holders, including patients, health care providers, and the broader health care system.
Telehealth has potential to promote healthcare service delivery. The World Health Organization (WHO) describes telehealth as “the delivery of health-care services, where distance is a critical factor, by all health-care professionals using information and communi­ca­tion techno­logies for the exchange of valid infor­mation for diagnosis, treatment, and prevention of disease and injuries, research and evaluation, and for the continuing education of health-care providers, all in the interests of advancing the health of individuals and their communities” (WHO 2022). Therefore, telehealth can be deployed for different use purposes, for example, to enhance access to services and to increase communication between the person and care professional or for consultation amongst the health care professionals. Drivers for introducing telehealth solutions may be manifold, such as the increase of the service need caused by the aging population or the simultaneous challenge of not having enough care personnel. Especially, during Covid 19 pandemic telehealth solutions were widely implemented to increase access to health services. Typically, telehealth solutions are expected to increase efficiency of the services. (OECD 2023; WHO 2022)
In Nordic context, telehealth solutions have been successfully piloted and assessed in Swedish priority project Healthcare and Care Through Distance Spanning Solution (VOPD) 2018–2021 and its continuation until 2025, the Integrated Health and Care Project iHAC. In the projects, a number of common challenges and experiences have been documented. Integrated health and social care with the citizens perspective in focus is prevailing in all Nordic countries. Focusing on citizen perspective means services at home and flexibility to access services across distances. One key point is to refine a comprehensive service model, especially so that a telehealth solution will be inte­grated as part of the health services. (Nordic Welfare Center 2020 & 2022) These initiatives illustrate a shared Nordic commitment to ensuring that digital solutions serve as enablers of person-centred, equitable, and efficient care. The systematic development of telehealth services contributes not only to improved access but also to strengthened continuity of care and more resilient health systems. The lessons learned form a valuable foundation for scaling solutions regionally and provide strategic directions for future collaboration in building sustainable, digitally enhanced health and social care across the Nordic countries.
However, evidence on telehealth outcomes is still somewhat limited. Telehealth outcomes, such as, clinical efficacy, patient and provider satisfaction, and cost-effectiveness as well as patient safety are all equally vital in assessing the overall success of telehealth initiatives. Telehealth outcomes are not always consistent or well-integrated in health services, with factors such as device stability and reliability, patient education, accountability, and reim­burse­ment issues impacting the effectiveness of remote patient monitoring. While the cost-effectiveness of various telehealth interventions has been studied, there is limited data on their long-term efficiency compared to conventional medical practices. Additionally, the cost of investment and ongoing maintenance, particularly when multiple stakeholders are involved, may pose significant challenges. Ensuring sustainable implementation requires addressing these financial and logistical barriers while optimizing resource allocation. (Palojoki, Lehtonen, Vuokko 2025)

References

World Health Organization. Consolidated telemedicine implementation guide, November 2022, ISBN 978-92-4-005918-4.
OECD. The COVID-19 Pandemic and the Future of Telemedicine, 2023. OECD Health Policy Studies, OECD Publishing, Paris, https://doi.org/10.1787/ac8b0a27-en
Healthcare and care through distance spanning solutions. Bengt Andersson, Niclas Forsling, Judit Hadnagy, Sofia H Berggren. Nordic Welfare Center 2020, ISBN: 978-91-88213-52-5.
Integrated Healthcare and Care through distance spanning solutions – for increased service accessibility. Eirin Rødseth, Annabelle-Jane Yabsley, Truls Tunby Kristiansen, Siri Bjørvig. Nordic Welfare Centre 2022, ISBN: 978-91-88213-95-2, https://doi.org/10.52746/DXAI1711
Palojoki S, Lehtonen L, Vuokko R. Validation of Telehealth Outcome Categories for Patient Safety: Systematic Literature Review. JMIR Med Inform 2025;13:e75486. DOI: 10.2196/75486, PMID: 41100713.

3.3.1 Advancing Remote Consultations in Finland

Paula Veikkolainen, Kaisa Kujansivu and Aino Rubini, The Remote Consultation Working Group of the Finnish Society of Telemedicine and eHealth, Finland
Remote consultations refer to patient-professional interactions conducted via electronic or other non-face-to-face means (MeSH 2024). Remote practices have become increasingly common in Finland and across the Europe, especially accelerated by the COVID-19 pandemic (Kyytsönen et al. 2021, Knudsen et al. 2024). Despite their growing use, variation in practices and lack of unified guidelines create challenges for organisations, clinicians, and patients alike. While remote care can offer significant benefits in terms of accessibility and efficiency, its success depends on thoughtful implementation, clinical safety, and appropriate use. (WHO 2022)
The Remote Consultation Working Group of the Finnish Society of Telemedicine and eHealth (FSTeH) was established in spring 2024 to support the development and dissemination of best practices for remote consultations in Finnish healthcare. Its multidisciplinary member­ship includes professionals from public and private healthcare providers, occupational health services, and academia. The group’s key objectives include:
  1. Developing national-level practical guidelines for remote consultations
  2. Supporting education and training through webinars, statements, and courses
  3. Promoting multidisciplinary collaboration and knowledge exchange
In January 2025, the group held its first educational event – the course “Everything a Doctor Needs to Know About Telemedicine” – at the Finnish Medical Convention in Helsinki, the largest professional healthcare event in Finland and the year’s main continuing education forum for physicians. The course drew a strong turnout, with 171 participants, and received positive feedback. Building on this success, the group’s proposal for a follow-up session focusing on remote consulta­tions in primary health care, aimed at deepening the discussion and showcasing practical examples, was accepted for inclusion in the 2026 Convention program.
The working group is committed to identifying and promoting emerging best practices that support safe, effective, and equitable remote care. While detailed national guidelines are still under develop­ment, the group has outlined key thematic areas that are central to high-quality remote consultations. These include clinical appropriateness, digital competence among professionals, patient-centred communication and interaction in digital environ­ments, privacy and data security, and standardized protocols for, for example, documen­tation, follow-up, and escalation - such as ordering laboratory or imaging examinations, initiating an in-person consultation, referring a patient to a specialist, or alerting emergency services. To ensure these practices are grounded in evidence and remain responsive to real-world needs, the group closely follows developments in the field, including clinical outcomes, technological advances, and research, as well as feedback from professionals and patients.
The remote consultations should complement, not replace, traditional care, and that digitalization must bring added value to clinical workflows and patient outcomes. To ensure safe, effective, and equitable remote care, it is essential to train clinicians in both communi­cation and digital competencies (Carrillo de Albornoz et al. 2022).  At the same time, ongoing discussion is needed among professionals, policymakers, and patients on how remote practices can be integrated meaningfully and sustainably into healthcare systems. The Remote Consultation Working Group remains open to new members and actively contributes to both national and Nordic-level dialogues on digital health policy, education, and service design.

References

Remote Consultation - MeSH Descriptor Data (2024). Available from: https://meshb.nlm.nih.gov/record/ui?ui=D019114
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