Globalization and digitalization places new demands on working life. The ultimate question and challenge is how to react and maintain the Nordic welfare society when the proportion of elderly people in the population is rapidly increasing, and resources are limited. This places new demands on social and health care professionals, who are forced to – irrespective of how it is done – fundamentally change their ways of working (Green 2007). One potential way to overcome this challenge, is the utilization of digital data.
Electronic traces of health related behaviour from individuals and patients have because of the rapid technological advances accumulated at a astonishing pace over the last two decades. This era of big data and data analytics opens up new opportunities in personalized medicine, preventive care, chronic disease management, tele monitoring and managing of patients with implanted devices. This has also led to new and exciting avenues for cross-disciplinary scientific research, combining computer science and health related sciences, to create health analytics. When properly analysed, the rich data accumulating within digital services provide a microscope to study health in an altogether new light, and to ask completely new questions as well as propose new hypothesis about the interplay between behavioural patterns and health and wellbeing.
Big data in healthcare is overwhelming not only because of its volume but also because of the diversity of data types. The data types include the more traditional clinical data from electronic patient records and clinical sensor data, biodata from biobanks, as well as data from more modern wearable measuring devices from outside the traditional clinical context (Raghupathi & Raghupathi 2014; Piwek et al 2016). More and more health data is created with the help of these consumer wearable measuring devices, effortlessly creating masses of digital data on a greater number of elements of human life (Piwek et al 2016: Lupton 2016). An area where health analytics has been said to be revolutionizing is within the realms of health behaviour, where digital footprints and data can be aggregated and analysed as health proxies (Ayers et al 2014; Tana 2018; Tana et al 2018). These new insights would not be accessible without digital data, and the implications for health and welfare are potentially enormous (Ayers et al 2014, Tana 2018; Tana et al 2018). Life is now more or less digital. This means that more and more data is generated and stored digitally about individuals, clients and patients and their behaviour related to health (Birkler & Dahl 2014). For person-centred care, collecting and synthesizing data for a single patient from multiple sources can result in a more complete perspective on health.
Health analytics in general has the potential to transform the way healthcare is provided, and it has been predicted that in the near future there will be a rapid, widespread implementation and use of big data analytics across the healthcare industry (Raghupati & Raghupati 2014). The potential of health analytics have only recently started to take shape, and possibilities span from supporting evidence-based medicine, maximizing research potential and improving patient information to managing population health and empowering patients and clients in health promotion (Weiser & Ellis 2015). Moreover, health analytics have the potential to aid in creating better care, improved population health, and reduced costs (Raghupati & Raghupati 2014; Weiser & Ellis 2015).
The workforce required to sustain the health analytics revolution has been described as inadequate and unlikely to catch up without education and training programs (Weiser & Ellis 2015). In the near future, there will be a high demand for skilled personnel that have an understanding of data analytics and its application within healthcare, and these capable employees are in short supply today (Weiser & Ellis 2015; Wu et al 2016). Although digitalization has many promises and is changing many aspects of health care delivery, its core purpose remains steadfast – health and wellness for individuals and populations (Weiser & Ellis 2015; Wu et al 2016). By understanding the opportunities and challenges that health analytics brings, we can ensure, and improve, this core purpose in the future. However, for health analytics to reach its full potential health care providers on all levels need competence in health analytics.
Health analytics will be an integrated part of future social and health care, but the development places new and great demands on education. These questions as well as the need for a new profession were recently reflected on a seminar arranged by SITRA together with Tallinn University of Technology, Satakunta University of Applied Science and Pori unit of Tampere University of Technology[i] . A new understanding of how large amounts of data can be utilized for service development at a general level and in the care of individuals in prevention, diagnosis and treatment is required. In particular, insight into human behaviour is an important competence for professionals. The approach is based on patients´ ability to take responsibility of their own health and care in a completely new way, and thus new tutorial models and support are needed. There are also open issues regarding the research and utilization of various kind of available data for specific purposes. Already today, there is a large amount of data available, but the questions on how the data can be understood and utilized are many. So far, the greatest advances seems to concern diagnosis of diseases.
It is obvious that competencies needed to understand and utilize large amounts of health data in the care of patients must be considered in the education of future professionals in social and health care. Furthermore ethical issues related to big data emerge as crucial to consider. The environment related to big data generates various ethical challenges, both completely new and already existing ones, which relate to not only the value of health on individual and societal levels, but also to individual rights and other moral requirements (Wade 2007).
Ayers, J.W., Althouse, B.M. and Dredze, M., 2014. Could behavioral medicine lead the web data revolution?. Jama, 311(14), pp.1399-1400..
Birkler, J. and Dahl, M.R., 2014. Den digitala patienten. Liber. 1st edition. Stockholm.
Green, I., (red.) 2007. Future Competencies. Think Tank on Future Competencies. Nordic Network for Adult Learning. https://www.utu.fi/en/units/ffrc/research/project-archive/education/Documents/ntt_rapport_sum_en.pdf (External link) Available 10.4.2018
Grossglauser M., Saner H. Data-driven healthcare: from patterns to actions. European journal of preventive cardiology. 2014 Nov;21(2_suppl):14-7.
Kostkova, P., 2015. Grand challenges in digital health. Frontiers in public health, 3
Lupton, D., 2016. The quantified self. John Wiley & Sons.
Raghupathi, W. and Raghupathi, V., 2014. Big data analytics in healthcare: promise and potential. Health information science and systems, 2(1), p.3.
Tana, J., 2018. An infodemiological study using search engine query data to explore the temporal variations of depression in Finland. Finnish Journal of eHealth and eWelfare, 10(1), pp.133-142.
Tana, J., Kettunen, J., Eirola, E. and Paakkonen H. 2018. Analysis of hourly variations in search engine query volumes for depression-related health information. JMIR Mental Health (forthcoming). doi:10.2196/mental.9152
Wade, D., 2007. Ethics of collecting and using healthcare data. BMJ 334(7608): 1330–1331.
Weiser, Phil and Ellis, Amy. The Information Revolution Meets Health: The Transformative Power and Implementation Challenges of Health Analytics (April 13, 2015). Silicon Flatirons Center, 2015. Available at SSRN: https://ssrn.com/abstract=2593879 or http://dx.doi.org/10.2139/ssrn.2593879 (External link)
Wu, J., Li, H., Cheng, S. and Lin, Z., 2016. The promising future of healthcare services: When big data analytics meets wearable technology. Information & Management, 53(8), pp.1020-1033.
[i] for details about the program see https://media.sitra.fi/2018/02/12135955/health-data-analytics-seminar-14-03-2018-invitation.pdf (External link)