VITech is pleased to reveal the results of the State of data science and ML in the 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.
Around 50% of respondents surveyed in State of data science and ML in healthcare report they have already adopted AI. However, data remains the key concern. Finding reliable and relevant data, data evaluation, data extraction, data processing and cleansing, and data transfer are significant challenges for AI initiatives. Others wrestle with visualizing large datasets to enable more efficient analytics and decision-making.
At the same time, although AI adoption is overgrowing among the healthcare industry leaders, half of the respondents note their companies do not use any of the data science and machine learning methods to drive either research or business outcomes. However, part of them indicates that they are planning to or are already exploring solutions relevant to their needs. Current requests are trend identification, diagnostic imaging results, prognostics, and predictive analytics.
Machine learning enables healthcare companies to optimize a wide range of business processes, but analytics and image analysis are reported as the most common tasks by 45% of the surveyed.
The respondents report progressively migrating enterprise workloads to the cloud to take advantage of its network of data storage, data processing, and machine learning services. Amazon web services and Microsoft Azure remain the dominant cloud providers in healthcare, with 40% and 30% of the market. They are also the platforms of choice for data science and machine learning tasks, with Python and R as the top open-source programming languages for data analysis.
Among the most repeatedly featured ML platforms and libraries are Keras, PyTorch, Tensorflow, and OpenCV. Django, Docker, SQL Server, and Apache Kafka have been picked as data analysis and data streaming tools of preference, too. Security-wise, the leaders are DarkTrace and Sophos.
REAL Space Navigator, Pharmapendium, Reaxys, and Scifinder are reported for research among healthcare-specific tools. At the same time, Qlik Sense and Spotfire are primarily used for decision-making and data visualization, and Teradata and Databricks — are for data analytics.
For sure, organizations in healthcare continue to rely on a wide range of statistical tools, including the ones developed in-house and Excel sheets.
Around 50% of those organizations and teams who have not applied any data science and machine learning techniques in 2019 plan to kick off their AI adoption journey next year. Primarily, they plan to cover a variety of prediction tasks, from volume/demand predictions and prognostics to predictive toxicology and predictive risk alerts.
Other notable tasks are CAD and image analysis, NGS for pharmacovigilance, staff performance analysis to cut man-hours (i.e. better case processing), clinical trial analysis and optimization, drug design, analysis of sales team data, and PLC communication.
Note: 57% of the surveyed report that their organizations do not struggle with IT any infrastructure challenges; 30% report otherwise.
Predictive analytics in healthcare: benefits and challenges
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. Today, the market for predictive analytics is exceptionally fast-growing. According to a study by AMR, from…
How is predictive analytics used in healthcare: TOP 10 examples
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…
ML-based system or why we use сomputer-aided systems in healthcare
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. The advance in…
Benefits of EHR: Advantages and disadvantages for patients and medical staff
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…
How to provide diagnostics accuracy while lacking time
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.
Human factors in safety control: epidemiological safety at risk
Organizations in every industry must ensure a safe working environment for employees and achieve safety compliance enterprise-wide. However, despite stringent regulations, regular safety drills, and safety management systems, non-fatal and fatal injuries in the workplace are still an issue for businesses. According to National Safety Council research, the total cost of workplace injuries in 2018 reached…
How to achieve earlier lung disease detection: the automated approach
Most of us never think about our breathing; it’s just something we do. For example, research by Lung Foundation Australia shows that almost half of all Australians rarely or never think about their lung health. Despite this, almost two-thirds of Australians reported that they had experienced at least one lung-related health issue.