Cyft | Blog
When journalists write about the disruptive power of artificial intelligence in healthcare they tend to zero in on radiology and pathology and for good reason. Both trades involve the interpretation of patterns from quantifiable image data - a thing that AI has proven highly capable of in several studies and commercial applications from facial recognition to the classification of hotdogs.
The healthcare AI space is frothy. Billions in venture capital are flowing, nearly every writer on the healthcare beat has at least an article or two on the topic, and there isn’t a medical conference that doesn’t at least have a panel if not a dedicated day to discuss. The promise and potential is very real.And yet, we seem to
A more technical post for those evaluating risk scores for care management, coupled with a real-world example. As healthcare moves toward taking on greater financial risk for keeping people healthy, it is critical for organizations to match people to the interventions they’re most likely to benefit from. This has traditionally meant using various risk scores that are based on claims data.
These days, every care management / value-based care organization has a risk score to help target interventions. Unfortunately, these risk scores often frustrate clinicians by directing them to people who cannot benefit from an intervention – either the person is not actually headed for trouble, or the clinician already knew about that person. Why is that? It turns out most
Healthcare is notorious for its lack of consistent and widely adopted data formats. The one consistent exception is the billing information exchanged between payors and providers. These files are often referred to as “claims.” Because of their ubiquity, many of today’s analytical approaches - from epidemiology to public health, actuarial sciences, business intelligence, and risk scores - rely heavily,
Data scientists like Sid Henriksen, a Ph.D. student nearing graduation, often ask me how they can succeed in healthcare. With Sid's permission, here are a few questions and insights for aspiring healthcare data scientists. How applicable is generic data science in healthcare? The core data science skillset of machine learning, data visualization, and statistics is the foundation of working with all data, healthcare