Some tech buzzwords stir up excitement in some people but mostly trepidation.Artificial intelligence (AI) and machine learning (ML) are two of those terms. Those who understand the benefits of AI and machine learning in health care are ready to implement them, while those who don’t fear that they will be too complicated to put into action. In the health care industry, which can be notoriously slow to adopt technology, there is an even greater degree of uneasiness about AI and machine learning.
But some health care organizations have started implementing machine learning, and the results speak for themselves. This is a technology that will have a major impact on the health care industry in the years to come. Those who are ready to embrace it will save time and money while improving patient care.
What is Machine Learning in Health Care?
Machine learning is a system that observes and analyzes data, examples, and instructions to find patterns and predict future events. It is a subset of artificial intelligence that takes a large sum of information and sorts through it to deliver real-time data.
Machine learning is extremely powerful. It can process data from multiple sources and convert them into a homogenous format that is easy to understand.
A few different machine learning tools exist within the health care industry. The most common are:
- Image processing
- Optical character recognition
- Natural language processing
- Data analytics
Use Cases for Machine Learning in Health Care
There are multiple ways machine learning can be used in the health care industry. It can be implemented anywhere from patient diagnosis to Risk Adjustment. Below are some of the top machine learning applications in health care.
With its ability to consider a large input of variables, machine learning is very good at making predictions based on historical data. When used by physicians, machine learning can intake a patient’s medical history and health data and output a recommended diagnosis. Machine learning techniques can predict disease susceptibility and early diagnosis, as well as the recurrence and progression of diseases. It’s a look-alike model to compare data to millions of other records.
Because the technology can learn to identify small details in CT scans and MRIs, it can also be used to process medical imaging quickly and accurately. The technology can pinpoint patterns that are hard for physicians to see or recognize.
During Risk Adjustment, health plans must analyze huge amounts of patient health data. Machine learning can help coders sort through data quickly with unbelievable accuracy so they can more efficiently complete the Risk Adjustment process.
With tools such as natural language processing, coders can also increase coding accuracy and remove the need for tedious and error-prone. Health plans can also paint a clearer picture of their members’ health, which allows them to properly plan for anticipated health care costs. Machine learning technology for Risk Adjustment supports the coding chart review process because it can quickly identify key documentation elements required for HCC coding. The results are faster and more accurate risk adjustment.
Data has demonstrated that audits performed with the help of AI have higher accuracy rates than those performed without. The increased accuracy helps ensure that health plans will have access to necessary funds to help them manage the complex health conditions of patients. When machine learning can assist trained and certified coders, the result is an efficient and accurate Risk Adjustment program. AI can nearly eliminate the need for 2nd level-reviews and forget about 3LRs.
Part of the reason that drug discovery and development is so difficult is because there is a lot of testing and experimentation that goes into the process. Machine learning can aide drug development by rapidly analyzing a large wealth of data.
Instead of testing on people, machine learning software can process simulations for how a drug would impact the human body. It has already been used to find medicines for some diseases. Because of this, machine learning can help decrease the time it takes to develop a new drug.
Each patient is unique, and their treatments should be, too. Unfortunately, customized treatments aren’t always easy to create. Machine learning uses a patient’s electronic health record, genetic data, and information about their lifestyle to design personalized treatments. Physicians can then work with their patients with individualized treatment plans crafted to their needs and situation.
The health care industry is full of data, both structured and unstructured. The sheer magnitude of information can make it impossible to translate into actionable information – Machine learning is a potential solution. It can be used to help with every step of patient care.
Talix, a patient data analytics platform, has machine learning capabilities that can improve efficiencies and enhance coding performance during the Risk Adjustment process. Learn more at Talix.com.