DigiImpact

The aim of the research group is to promote value-based healthcare by producing researched information on the impact of digitalization and its measurement in health care and social welfare.

The impacts of digitalization are evaluated from the perspectives of different stakeholders in both specialised and primary healthcare.

The research utilizes the patient-specific health outcomes, as well as patient experience and cost data.

The research group consists of multidisciplinary experts who work in Finnish and international hospitals, universities, and companies.

References:

1. Lakoma S, Härkönen H, Verho A, Torkki P, Leskelä RL, Pennanen P,Laukka E, Jansson M. Impact of Digital Services on Healthcare and Social Welfare – An Umbrella Review.
2. Pennanen P, Jansson M, Torkki P, ym. Digitaalisten palvelujen vaikutukset sosiaali- ja terveydenhuollossa. Valtioneuvoston selvitys- ja tutkimustoiminnan julkaisusarja 2023:52. http://urn.fi/URN:ISBN:978-952-383-059-2
3. Pennanen P, Jansson M, Torkki P, ym. Digitalisaation vaikutukset sote-palveluissa. Policy Brief 2023: 32. Valtioneuvoston selvitys- ja tutkimustoiminnan artikkelisarja 32/2023.
4. Accounting for Healthcare: The Digital Transition to Value-Based Healthcare. In Gillian Vesty, Miia Jansson, Tarek Rana, Kerryn-Butler Henderson (eds).
5. Vesty G, Kokshagina O, Jansson M, Cheong F, Butler-Henderson K. Ac-counting, valuing and investing in healthcare: dealing with outdated ac-counting models. Meditari Accountancy Research 2023; 1(31); 52–77.
6. Digital Health Knowledge Network (2023–2024)

The aim of the research group is to improve risk-based prevention, early detection and personalized care by describing and evaluating the needs, opportunities, and effects of emerging technologies in acute and critical care.

The development process ranges from in vitro -studies to preclinical testing and in vivo -studies. Evaluation include experimental (RCT) and observational research methods. In addition, we use multimodal data from various sources to develop new machine learning-based prediction models to identify an individual who would benefit from a certain type of treatment based on the predicted disease course.

The research group consists of multidisciplinary experts who work in Finnish and international companies, hospitals, universities and research organizations.

References:

1. Jansson M, Liedes H, Vuorinen A-L, Harjumaa M, Similä H, Koivisto J, Puhto A-P, Vesty G, He H-G, Pikkarainen M. Short-term effects of a digital patient journey solution on patient-reported outcomes and health care utilization in arthroplasty: a pragmatic randomized controlled trial. Finnish Journal of EHealth and EWelfare 2023; 15(3):305–321.
2. Frondelius T, Atkova I, Miettunen J, Jansson M. Early prediction of ventilator-associated pneumonia with machine learning models: A systematic review and meta-analysis of prediction model performance. European Journal of Internal Medicine, 2023; S0953-6205(23)00406-5.
3. Frondelius T, Atkova I, Miettunen J, Rello J, Jansson MM. Diagnostic and prognostic prediction models in ventilator-associated pneumonia: systematic review and meta-analysis of prediction modelling studies. J Crit Care 2022; 67: 44–56.
4. Jansson M, Rubio J, Gavaldà R, Rello J. Artificial Intelligence for clinical decision support in Critical Care, required and accelerated by COVID-19. Anaesthesia, Critical Care & Pain Medicine. 2020;39:691–693.
5. Jansson MM, Harjumaa M, Puhto A-P, Pikkarainen M. Patients’ satisfaction and experiences during elective primary fast-track total hip and knee arthroplasty journey: A qualitative study. Journal of Clinical Nursing 2020; 29:567–582.
6. Jansson MM, Harjumaa M, Puhto A-P, Pikkarainen M. Healthcare professionals’ perceived problems in fast-track hip and knee arthroplasty: results of a qualitative interview study. Journal of Orthopaedic Surgery and Research 2019; 14: 294.
7. Jansson MM, Harjumaa M, Puhto A-P, Pikkarainen M. Healthcare professionals’ proposed eHealth needs in elective primary fast-track total hip and knee arthroplasty journey: A qualitative interview study. Journal of Clinical Nursing 2019;28: 4434–4446.

The aim of the research group is to improve the continuity of care by evaluating and developing the usability of health information systems.

Usability is evaluated from the perspectives of different stakeholders, considering the sociotechnical perspective.

The evaluation considers the identification and development of factors affecting usability and the quality of health information exchange.

The research utilizes survey, interview and register research.

References:

1. Hyvämäki P, Sneck S, Kääriäinen M, Jansson MM. Interorganizational health information exchange related patient safety incidents: qualitative analysis of patient safety incident reports. International Journal of Medical Informatics 2023;174:105045.
2. Jansson M, Liisanantti J, Ala-Kokko T, Reponen J. The negative impact of interface design, customizability, inefficiency, malfunctions, and information retrieval on user experience: a national usability survey of ICU clinical information systems in Finland. International Journal of Medical Informatics 2022; 259: 104680.
3. Jansson MM, Ohtonen P, Alalääkkölä T, Heikkinen J, Mäkiniemi M, Lahtinen S, Lahtela R, Ahonen M, Jämsä S, Liisanantti J. Artificial Intelligence-based technologies in surgical scheduling: Content validity as-sessment of required functionalities. BMC Health Services Research 2022; 22: 1513.
4. Hyvämäki P, Kääriäinen M, Tuomikoski A, Pikkarainen M, Jansson M. Registered nurses’ and medical doctors’ experiences of health information exchange in interorganizational care transitions affecting patient safety: Qualitative review. Journal of Patient Safety 2022;18:210–224.
5. Frondelius T, Atkova I, Miettunen J, Rello J, Jansson MM. Diagnostic and prognostic prediction models in ventilator-associated pneumonia: systematic review and meta-analysis of prediction modelling studies. Journal of Critical Care 2022; 67: 44–56.
6. Jansson M, Rubio J, Gavaldà R, Rello J. Artificial Intelligence for clinical decision support in Critical Care, required and accelerated by COVID-19. Anaesthesia, Critical Care & Pain Medicine. 2020;39:691–693.