The last time we discussed artificial intelligence (AI) and machine learning, we covered the basics of what they are, what they are not, and how useful they are when applied to the healthcare industry. Today we are discussing the human side of AI’s capabilities; how the insights gathered from these analytics allow care plans and patient interventions to take shape for physician and care team action. AI and machine learning allow healthcare providers to better tailor care and maximize the impact of their in-person visits with patients. These focused interventions give providers the ability to continuously survey data and generate insights that can be crafted into care pathways, empowering care managers and helping them understand an intervention’s success.
The Importance of Machine Learning
AI and machine learning technologies allow clinicians to analyze data, learn about each patient’s medical history, and find the “needles in the haystack.” These are the hidden algorithms that physicians might not catch in a single visit that can lead to a greater amount of positive interventions. The value of machine learning is amplified when it is used to overlay traditional analytics methods. To illustrate this, we will create a patient persona named Jenny. Jenny is female and is part of a group of individuals of average age distributions, risk, and demographic factors. Jenny is also learning how to manage her chronic obstructive pulmonary disease (COPD).
Jenny does a decent job of seeing her primary care doctor, but she has been visiting her grandchildren more often and has been less consistent in scheduling preventative visits. What Jenny does not know is that during flu season, COPD increases the average risk of emergency room admittance. Jenny’s primary care doctor can leverage AI and machine learning to discern her modifiable risk factors, know whether or not she has had her flu shot this year, and with the aid of real-time weather data, surveil the air quality in her county. The AI engine will generate a risk score for her based on the above data to flag that Jenny is now at risk for a COPD-related ER visit within the next 90 days. Machine learning and AI technology consider age, gender, comorbidities, previous procedures, social determinants of health (SDoH), medication compliance, weather patterns, and any other features that might affect an individual’s outcomes in a specified period.
Equip Care Teams with AI Insights
The core of Lightbeam solutions is to help clinicians better understand their population and determine which patients need intervention. Other AI tools help to predict the risk of a patient and their potential outcome; where many fall short is that for machine learning to be a catalyst for improvement, it must be embedded into the existing workflows of the care team. An optimal workflow must be easy to digest, and the goals must be trustworthy, actionable, and measurable after the interventions are applied. This workflow will seamlessly integrate with any care management or engagement strategies, which is why we and our advisory services team stress this to our clients. Our AI solution has advanced in the Centers for Medicare & Medicaid Services (CMS) Artificial Intelligence (AI) Health Outcomes Challenge; Lightbeam is 1 of 25 innovators across the nation to do so.
Alex Gorman is Lightbeam’s Associate Vice President of Business Development.