Natalia Sniadanko

Poor systems deliver poor results, and, in the case of US healthcare, the pile of problems has been growing for years. From lack of transparency to high costs and administrative inefficiency, the system has created an environment where patients and medical staff suffer. During the global pandemic of COVID-19, all of those pain points have only intensified and got worse.

According to the analytical agency Frost&Sullivan, the market for digital medical solutions in 2021 amounted to $6 billion. At the same time, annual growth approached the 40% mark. This means that in the world’s developed countries, there is a significant growth in electronic medical records, the possibility of remote patient management, and the sale of medicines via the Internet.

Healthcare companies — providers or payers — have historically relied on computers for administrative tasks. However, new use cases have emerged as technology matured and the industry digitized. Today, hardly any clinic operates without a fleet of computers to store and manage patient/facility data, monitor patients and equipment, perform operations, and research.

Smart healthcare is the future of the healthcare system. This revolution is already impacting the daily work of healthcare professionals and the practice of patient care. The changes taking place can provide solutions to many problems. Still, they also require us to rethink how we organize the health system, shifting the focus from treatment to primary prevention. The main feature of the predictive analytics market in the healthcare sector is that it is global and rapidly developing. In 2018 the market size was a modest $2.9 billion. However, it is expected to grow at an average of 28.9% per year, and, according to Meticulous Research, will reach $84.2 billion in 2027. Let’s see how is predictive analytics used in healthcare.

VITech is pleased to reveal the results of the State of data science and ML in healthcare survey that we conducted on LinkedIn in 2019. The survey sought to look into the scope and patterns of data science and machine learning adoption in the healthcare industry. Over 50 qualified respondents represented a variety of company sizes, from startups to corporations with more than 10,000 employees, in the pharma, care sector, biotechnology, and medical device development. Among them: are C-level execs, directors, and VPs (50% of the pool), as well as ML engineers, data scientists, and software developers. The surveyed have a firm grasp of the industry’s challenges and objectives, enabling us to analyze and assess the trends and the actual state of tech in healthcare.

Modern technologies, including Big Data and machine learning, have opened new horizons for the predictive analytics and decision support systems market. With proper use, they will be the next step in the digital transformation of any industry, including healthcare.

Information processes are present in all areas of medicine and healthcare. The visibility of the industry, which functions as a whole, and the management effectiveness depend on order. The essential properties are objectivity, completeness, reliability, adequacy, accessibility, and relevance. The properties of information depend both on the properties of the data and the properties of the methods for extracting it. This is especially important for chronic care management solutions.

Predictive analytics can seriously change the medical industry shortly. Based on the use of big data, this technology will help, for example, prevent the occurrence of chronic diseases in patients and correctly allocate doctors’ resources. For the industry, the load can be reduced, and, in general, its work can be made more predictable.

How does ML respond to the real needs of healthcare organizations? According to research made by Syft in 2018, hospitals spend over $25 billion more than necessary in their supply chains despite having the ability to save an average of 17.7% in their total supply expenses. Artificial intelligence (AI) and machine learning (ML) can decrease costs spent on such stuff.​

The latest developments in medical care mean a lot of pros to everybody, beginning with reduced unnecessary disability and increased life spans to better health equity and life quality. Besides all those benefits, the changing healthcare landscape means a heavy burden, especially for the finances — the USA is planning to spend no less than $6 trillion, or about $17K per person, on healthcare in 2027.

With the ever-increasing volume, variety, and speed of data available, the scientific disciplines have provided us with advanced mathematical tools, processes, and algorithms to use this data in meaningful ways. Data science (DS), machine learning (ML), and artificial intelligence (AI) are three such disciplines. The question often arises: what is the difference here: data science vs machine learning vs AI? Can they be compared at all? Let’s figure it out.

In today’s world, where digital technologies are developing rapidly, it would be surprising if these innovations did not touch the healthcare sector. Mobile healthcare is one of the main trends in this direction. With mobile apps, physicians and pharmacists can provide safer, more effective patient care while allowing patients to self-monitor their treatment and improve adherence to therapy. We propose to find out at what stage this innovation’s development is, its advantages, and what risks accompany the choice of such an approach to healthcare application development.