Remote Cardiac Monitor Data and Artificial Intelligence: Transforming Clinical Applications for Heart Patients
Cardiovascular diseases remain the number one killer of people in the world, resulting in 31 percent of all global deaths (17.9 million per year), and are the most expensive condition to treat. However, AI and machine learning technologies are being developed to make care pathways, treatment and real-time visualization of cardiac anomalies and subsequent therapy more effective. Artificial intelligence (AI) and machine learning capabilities may provide numerous advantages over traditional analytics and clinical decision-making techniques, and cardiology is likely to benefit tremendously from these advancements as they mature.
“As machine learning-based algorithms become more precise and accurate by interacting with data and programmed information, these technologies will allow care teams to gain unprecedented insights into diagnostics, care processes, treatment variability and patient outcomes, especially in regard to cardiac care,” said Stuart Long, CEO of InfoBionic, the leading digital health company that created the MoMe Kardia remote cardiac monitoring platform.
“AI algorithm-based cardiac devices can procure tremendous amounts of data, providing for the ability to match up what physicians are seeing to long-term patterns and possibly detect subtle improvements that can impact care,” noted Long.
Leveraging AI for clinical decision support, risk scoring and early alerting is one of the most promising areas of development for this revolutionary approach to data analysis. Powering new tools and systems can help make clinicians more aware of nuances, more efficient when delivering care, and more likely to curb a patient’s developing health problems.
AI is ushering in new clinical quality and breakthroughs in patient care. For example, at the Cleveland Clinic, a customized algorithm developed by clinicians analyzes data, including blood pressure, heart rate and oxygen saturation levels, to flag the patients that are at highest risk of deterioration. The ultimate goal is to provide front-line clinicians notice of serious cardiac events before they happen. Moreover, the precision now possible with cardiovascular imaging, combined with “big data” from the electronic health record and pathology, is likely to lead to tremendous cases of cardiac disease management and personalized therapy.
Healthcare consulting firm, Frost & Sullivan, projects a 40 percent growth rate for AI in healthcare between 2016 and 2021, and said AI has the potential to improve outcomes by as much as 40 percent, while reducing the costs of treatment by as much as 50 percent.