Artificial intelligence (AI) and machine learning (ML) have expanded our capabilities to gather the patient insights that allow care plans and interventions to take shape. AI and ML enable 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. For those still honing their understanding of how AI can impact patient outcomes and reimbursement at the end of a performance year, machine learning and other methods are where to start.
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 patterns that physicians might not catch in a single visit that can lead to a significant 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 Physicians and Care Teams with AI Insights
The core of Lightbeam’s AI solution 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. The goal of AI in the healthcare setting is to empower physicians and help them identify other factors affecting a patient’s health that are not easily seen. An optimal workflow must be automatable, easy to digest, and the goals must be trustworthy, actionable, and measurable after the interventions are applied. The workflow will seamlessly integrate with any care management or engagement strategies to ensure a smooth transition.
Matt Westfall is a Data Scientist at Lightbeam.