Summary: A majority of the patient information patients and care teams use to make health care decisions is effectively “locked” in clinical notes such as those written or dictated by physicians. Natural Language Processing techniques have been maturing to extract these concepts, fill information gaps, and support health care’s clinical, operational, and financial objectives. I delivered this webinar to summarize some of Atrius Health’s work using the Linguamatics I2E platform.
analytics
Forget Big Data. It’s Time to Talk About Small Data.
With all of the talk of “big data,” it can be hard to remember that there was ever any other kind of data. If you’re not talking about big data — you know, the 4 V’s: volume, variety, velocity, and veracity — you should go back to running your little science fair experiments until you’re ready to get serious. Prevalent though this message may be, it has, at least in health care, stunted our ability to focus on and capture the hidden 5th V of big data: value.
Identification of High Risk Commercial Patients for Population Management (Epic XGM 2015)
Just got back from Epic XG
M 2015 presenting some of the work I have been doing at Atrius Health in predicting high risk patients.
Some of the session details (slides below):
Summary: Atrius Health expects a large proportion of commercially insured patients to shift into accountable care arrangements in the near future. The presenters will describe their work to develop new risk models for commercial patients, using both financial claims and Epic data, and compare these against other risk models.
Craig Monsen 