Guest post by Get Real Health
Historically, healthcare has largely been provided episodically in response to a triggering event. The prevailing fee for service payment model is both cause and effect of this healthcare delivery model and doesn’t provide appropriate financial incentives for continual health monitoring and prevention. However, the shift to value-based care is changing incentives so healthcare provider organizations can benefit from a proactive and preventative approach to their patients’ health.
One key tool for a proactive approach is continual monitoring of patients who have, or are at significant risk of acquiring, a chronic disease. Using connected home-based devices such as blood pressure monitors, weight scales, and blood glucose meters, means changes in an individual’s health can be detected immediately instead of having to wait for their next encounter with a healthcare professional. By using data networks like Validic™ and CHBase, this information can be delivered to a central location for machine processing.
Currently, machine processing of this type of information is usually limited to simple rules-based processing. If the data matches specific pre-defined static rule (such as a systolic blood pressure reading greater than 145) then an event is triggered. Events can include notifications to the individual’s family members or to healthcare providers. These notifications allow intervention before an acute problem arises, thereby reducing costs and improving the patient’s quality of life.
While a simple rules-based notification system is a powerful tool, it is only the beginning. Recent hardware and software advances in machine learning-based artificial intelligence (AI) are quickly bringing this breakthrough technology closer to market readiness for this type of scenario. There is every reason to believe that within the next 5-10 years, this type of AI will be deployable at commercial scale to perform much more sophisticated processing of patient generated health data. By combining each patient’s data into a complete picture of their health and comparing it to millions of other individuals, the system will be able to train itself to detect potential problems more quickly and suggest better and less-invasive interventions as time goes on.
In short, the combination of patient health monitoring networks, value-based care models, and the approaching availability of AI are poised to dramatically change much of healthcare from a reactive approach to a continual engagement and prevention model.