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.
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.
The Cyft holiday getaway was supposed to be a chance for this new team to forget about the daily grind.
We learned that a team of health services researchers at Dartmouth College recently published a study in the Journal of Patient Safety using an early research version of Cyft to detect falls in inpatient notes. No one on their research team is a data scientist and using a relatively small sample size they outperformed previous efforts at this task by a considerable margin. They completely excluded us from the work – not even asking for our advice.
The transition to value-based care has made it more important than ever for healthcare organizations to use their data to determine how best to allocate limited resources to achieve high quality care. In response, an onslaught of business intelligence vendors have descended on healthcare with data warehouses, reporting tools, and dashboard analytics that have led to tremendous efficiency in other industries.