HealthTech is the future of the health system in order to transform it into more efficient, more accessible and higher quality one. This industry like no other seems to have the highest social impact and it’s inevitable to invest in it heavily.
Looking at the pace in which world’s population is growing and the aging index, it seems obvious that the demand for healthcare will be rising in coming years and decades. What is even more enhancing, is the prevalence rate for chronical diseases among people that need professional medical treatment in a long-term perspective.
The list of technologies that are already being applied into healthcare system is long, starting from for instance digitization, cloud solutions, robotization, Internet of medical things (IoMT), ending with VR technologies and advanced artificial intelligence. Application of digital technologies in health industry works on a very wide spectrum, primarily with data analysis which can contribute to better diagnosis and adjusting optimal treatment for a patient, supporting and training hospital personnel, as well as managing hospitals and clinics based on data. Thanks to that, health industry can become more patient-centered, ready to meet market expectations.
There is especially big potential for the use of machine learning technology in this sector, because it is designed to work with very complex data. The biggest benefit of using machine learning in healthcare is that it results in the most accurate results possible. Taking advantage of that fact can save effort, time and money.
A very interesting case for machine learning is to analyze operational parameters of hospitals and clinics, as there are algorithms to predict patients’ flow, occupancy rate of equipment, or hospitalization times, which when provided as a holistic solution can help such an institution plan, allocate and manage resources optimally. It entails dealing with complex variables such as demographic, socioeconomic, physiological and pathological aspects that vary in different geographies. For traditional prediction methods it’s far too complicated, however this is exactly where machine learning shows its potential.
A solution like that has already been introduced in France, in Assistance Publique-Hôpitaux de Paris (AP-HP), where team of machine learning engineers developed a solution for, initially, 4 hospitals. Basing on 10 years data series collected by the institutions, regarding patience flow as well as external, such as meteorological data, flu patterns or public holidays, they have built a solution that predicts admission rate for next 15 days, which allows the hospitals manage their resources accordingly. (read more)
Another area where machine learning can be particularly value-adding is integration to medical diagnostic systems, whether image based or based on electrophysiological signals, it has enormous potential. Combining advanced methods of image and signal processing with machine learning algorithms, makes it possible to design diagnosis solutions that are proven to be accurate and robust in pathologies such as cancers, strokes, Alzheimer's, Parkinson's and other neurological disfunctions.
There are numerous researches being run in the area of medical diagnosis and Stanford University is the one to deliver an algorithm that was trained to successfully diagnose skin cancer using deep learning. The significance of this research is high, as it was studied that early detection can ensure 97% survival rate in the following five years, however the chances for recovery decline with time, reaching only ca. 20% at the advanced stages of disease. The algorithm was trained on 130 000 images of skin changes representing over 2 000 different diseases. The results of testing the algorithm against a group of certified dermatologists who reviewed 370 images, was that it obtained as precise results as the doctors'. (read more)
“Technology-enabled smart healthcare” is the goal for the future.
Technology opens a new range of possibilities for changing the way people access and rely on healthcare systems. It surely will contribute to improving well-being of populations around the world and will prove that there is no better option, but to go hand in hand with artificial intelligence that can work for the common good.
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