Machine learning has shown the potential to revolutionize clinical research and patient care. However, despite growing interest, there are still several challenges ahead before healthcare organizations can widely adopt this technology.
This article will explore how machine learning impacts healthcare, what ongoing barriers remain and how they can be overcome.
Machine Learning In Healthcare
So far, most medical research has been based on a ‘trial and error’ approach rather than a data-driven one. After a drug or treatment is discovered, it must generally go through years of testing before being approved by the FDA.
Machine Learning: A Data-Driven Approach
Machine learning seeks to change this process by making data-driven discoveries. Using machine learning, researchers can now sift through millions of pieces of patient data for patterns that will help them identify new medical treatments.
For example, researchers are using machine learning algorithms to identify patterns in gene expression to identify specific subtypes of cancer. Other researchers are using the technology to predict the risk of heart disease based on electronic health records.
Machine Learning Challenges In Healthcare
Several barriers still stand between machine learning and widespread adoption within healthcare.
Computational And Storage Costs: One of the most significant barriers to using machine learning in healthcare is the high computational and storage costs that come as a result. Researchers must often use supercomputers or cloud-based computing services to process massive amounts of data. This can quickly get expensive for individual hospitals and medical research institutions.
Privacy: Another barrier is that many healthcare organizations and medical researchers are reluctant to share their privacy concerns.
Healthcare Data Governance: Data governance is a third challenge facing the widespread adoption of machine learning within health care. Most hospitals and healthcare insurers have yet to implement an optimal way of sharing patient data, which would allow for effective machine learning.
Data Integrity And Security
The vast amounts of sensitive patient information stored within electronic health records make securing this information a significant challenge. Patient privacy is especially critical due to the increase in cyber-attacks on healthcare organizations.
Machine Learning And HIPAA
HIPAA sets the standards for protecting ePHI (electronically protected health information). Under HIPAA, or Health Insurance Portability and Accountability Act, covered entities must implement appropriate administrative, physical and technical safeguards to secure patient information. The first step is a risk analysis of its environment to map out vulnerabilities.
How Can The Barriers To Machine Learning Be Overcome?
Fortunately, several companies are working on solutions to all three of these challenges and making it easier than ever before for researchers and hospitals to use machine learning technology in the lab and within the clinic. One such company is Intel Nervana.
Intel has developed deep learning technology that significantly reduces computational and storage costs. It is also working towards making it easier for healthcare organizations to securely share their datasets, which will be vital if machine learning realizes its full potential within the industry.
Another company focused on overcoming these barriers is Siemens Healthineers. Siemens offers advanced analytics software that can process massive amounts of data in real-time – to bring personalized medicine to its patients.
These companies are working towards making machine learning an integral part of healthcare research and practice, which will help improve patient care and save lives around the world.