AI is used as an example of a capability hindered by the lack of access. But of course, lack of access causes greater harm than just slowing AI adoption.
Painfully little has been written for non-technical healthcare leaders whose job it is to successfully execute in the real world with real returns. It’s time to address that gap for two reasons.
We usually deal with smaller sets of rich but messy data (sample sizes in the hundreds or thousands). 10k rows vs 10M rows of claims data tend to be equally useful (or useless) for most problems.
I asked LinkedIn friends to submit their questions related to AI in healthcare in preparation for an upcoming keynote at this year’s HIMSS in Vegas. I promised to try to answer the questions they submitted.
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.
As healthcare organizations move 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.
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.
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.
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.
Cyft is pleased to recognize the publication of three peer-reviewed studies by leading health services researches that used the research precursor to Cyft to address important clinical questions.