What is Artificial Intelligence?
Artificial intelligence (AI) is the ability of a computer to solve problems that are customarily performed by human beings. Artificial intelligence can be used to sort through vast amounts of data and produces insightful information that can be used to guide patient treatment.
The two main areas of AI used in healthcare today are machine learning and deep learning. Machine learning technology uses algorithms to analyze patient data to recognize patterns and provide insights. Deep learning is a subset of machine learning that is more complex, due to the layers of deep neural networks that are designed to mimic the human decision-making process.
Deep neural networks imitate the human brain’s ability to consume data, evaluate patterns, identify missing data, and generate insights. Deep learning technology analyzes data where the outcome is already known. And as data changes, existing patterns are reinforced with expected outcomes while new patterns take the path with the highest probability to the outcome.
For example, a computer is fed a batch of images that may or may not contain tumors. The computer is able to use initial reference data to identify patterns that are similar to known positive diagnoses. Every time it makes an incorrect diagnosis, validated by a human clinician, it “learns” to adjust its criteria a little bit more by using the previous experience to inform its future decision-making. Eventually, it becomes accurate enough to present trusted results to the user.
Examples of the use of Artificial Intelligence in Healthcare:
- Identification of pre-diabetes and people with undiagnosed diabetes which supports CMS covered programs such as the Medicare Diabetes Prevention Program.
- Prediction of preventable visits to the emergency departments and admissions to hospitals.
- Identify patients who are likely to develop chronic conditions in the near future.
The digitization of healthcare data has empowered the use of artificial intelligence. AI is both powered and limited by access to digital patient data. There is remarkable potential in using digital health data to improve patient healthcare. As a result, there are many initiatives to improve interoperability between systems for the purpose of improving patient care.
- In April 2018, CMS announced its Meaningful use program was renamed to “Promoting Interoperability” and the Merit-Based Incentive Payment System (MIPS) Advancing Care Information performance category was renamed the “Promoting Interoperability” performance category.
- CMS will also require providers to use 2015 edition certified EHR technology (CEHRT) in 2019 to demonstrate meaningful use and qualify for federal incentive payments.
- CommonWell Health Alliance, consisting of health IT companies, has created nationwide access to data which will result in patient records that are more complete.
- FHIR standard adoption to improve interoperability between disparate systems.