Three major buzzwords in healthcare information technology are artificial intelligence (AI), machine learning, and deep learning. Since significant noise surrounds these topics, we decided to address what AI actually is and how we use it at Lightbeam within our strategies.
The Differences Between AI and Machine Learning
I want to start by defining what AI is not: AI is not a doctor. AI is not merely a set of rules. Instead, AI is the development of computer systems to aid or enhance a set of tasks that normally require human intelligence. Examples include visual perception, speech recognition, decision-making, and translation between languages.
Machine learning, on the other hand, is a field of study that gives computers the ability to learn without being explicitly programmed for specific functions. Machine learning has recently made a splash in the healthcare space in several use-case categories, such as clinical, operations, and financial.
AI at Lightbeam Health Solutions
Lightbeam uses AI to gain insights from accrued data and compile the entire longitudinal history of our 20.5 million-patient registry with over 5 billion clinical data elements. The engine is continually refining itself through the flow of new data information fed back in real-time. The population health management platform provides intelligence from predictive machine learning to enhance our ability to stratify client population risks. By accurately predicting which patients are high-cost utilizers, a provider can best respond with care management or other strategies to ensure the highest return on engagement.
AI is delivering value. We have already seen exceptional results within use-cases and have witnessed population health vendors even using AI to predict:
- Patients who will be no shows.
- Chronic obstructive pulmonary disease (COPD) patients that will have adverse events, like ER visits and hospital admissions within 90 days.
- Cost outliers for home health, inpatient, and skilled nursing.
For those interested in how AI utilization works, the AI framework begins with the foundation of clinical informatics before adding a combination of AI/ML techniques (recurrent neural networks), like recommender systems. Once the data is analyzed, the identification model provides a suspect list of patients at risk for a particular condition and their probability of being diagnosed within a certain timeframe. The platform is highly adaptive to the latest clinical guidelines; it incorporated the recent landmark hypertension guidelines in only five hours.
Regardless of the use-case, an authentic AI healthcare model is centered on a common goal: improving the quality of care for the patient.
Visit our website to learn more about how we put AI and machine learning to work at Lightbeam Health Solutions.
Read more from Mike Hoxter, Lightbeam’s Chief Technology Officer.