FinnGen study launched in Finland in December 2017 is a unique…
What is Future Care Finland?
Future Care Finland is a platform that highlights Finnish innovations, technologies and applications that aim to facilitate customized patient care. The current examples are focused on future possibilities of individualized cancer care.Read more
The focus of health care is shifting from reactive treatment of diseases to their prevention. Sophisticated systems biology approaches, where longitudinal data collected from multiple different sources is analysed using new measurement and visualisation technologies, as well as computational and mathematical tools, are expected to further increase our understanding of factors impacting on early transitions from health to disease.
Accumulating knowledge of predisposing genetic alterations, disease pathogenesis and specific risk factors facilitates prevention, detection of diseases in earlier stage and development of personalized treatments. Preventive healthcare pilot projects, which in Finland include e.g. GeneRisk and Digital Health Revolution and abroad e.g. The Hundred Person Wellness Projects, SciLifeLab Wellness Profiling study and Digital health study utilizing wearable biosensors, have raised awareness of individuals´ own role in the management of health, by providing people actionable information of their personal risk factors together with the health behaviour coaching.
Accurate diagnosis provides a foundation for effective, tailored treatment. Information about the molecular aberrations can be used to divide cancers with the same histological origin into subclasses that form distinct clinical entities. Rapid development, decreased costs and increased availability of sequencing-based technologies has provided tools for modern, molecular-level diagnostics.
Whole-slide imaging-based virtual microscopy and automated image analysis tools has accelerated the development of digital pathology. This provides means for digital archiving of tissue samples, efficient data sharing, remote analysis and data mining for research purposes. Artificial intelligence -driven analysis capabilities will further advance the applicability of digital pathology in the identification of therapeutic targets, prediction of patient outcomes and therapeutic responses.
Molecular profiling has facilitated identification of many driver gene mutations that directly or indirectly provide growth advantage to cancer cells. Exploitation of these alterations has led to the development of targeted therapies, including small molecule inhibitors and monoclonal antibodies.
Since the approval of the first targeted therapy (Rituximab) in 1997, ~100 targeted therapies are used to treat many common malignancies today. Depending on the molecular complexity of the cancer, treatment can be provided either as mono – or combination therapy. In addition, the current treatment arsenal for modern customized cancer care includes immunotherapeutic approaches. Immunotherapy is used to either stimulate patients’ own immune system to fight cancer or counteract signals produced by cancer cells that suppress immune responses.
Drug resistance constitutes one of the most challenging hurdle to overcome. This could be achieved by taking into account the clonal architecture and stem cell compartment of the cancer in treatment design.
Treatment effectiveness means achieving the desired changes in a patient’s health and well-being as a result of treatment given in daily health care practice. In the assessment of effectiveness relevant key domains of health and well-being such as mortality, morbidity, symptoms, work and functioning capacity and quality of life are systematically considered.
Patients’ involvement is an integral part of the effectiveness assessment: it is important to evaluate the effectiveness based not only on professional but also patient considerations. Effectiveness assessment allows for a critical review of daily health care methods to evaluate treatments and interventions given as well as the investments made relative to health benefits produced. It allows for comparison of different methods and procedures to find the best ones.
Through effectiveness assessment the use of the selected treatments and interventions can be justified.
Means to follow-up disease symptoms and effects of treatments have developed tremendously during recent years through digitalization. Utilization of different mobile sensors and digital applications allow customized and effective home-based self-monitoring. This leads to shorter inpatient stays while maintaining close contact with the patients and possibility to rapidly identify those whose symptoms require medical attention.
Assessing patient-reported outcomes has emerged to standard care, providing means to collect patients’ perspectives, such as effectiveness of treatment, prevalence and magnitude of symptoms and side effects, as well as quality of life in general. The possibility of continuous, real-time follow-up can be used to replace fixed-term follow-up visits with tailored, demand-based check-ups.
Real world data (RWD) and real world evidence (RWE) are referred to as health-related data and evidence obtained from multiple sources outside of traditional clinical research trials.
Finland has several assets enabling the use of RWD and RWE, such as national registries, disease-specific registries, hospital data lakes and a biobank infrastructure including six regional and three national biobanks. Information obtained from different data sources can be efficiently combined by using the national ID system, providing means to build coherent data entities.
The currently ongoing reforms in Finland aim to advance the use of RWE besides in research, also in e.g. product development, innovations and decision-making.