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The program, instituted four decades ago, limits the growth of health care facilities – as a means of protecting the investments of public, non-profit hospitals. Both the House and Senate have pitched overhauls, but Miller quotes state Sen.

One could argue that the bill essentially paralyzes the health care discussion. Senate Bill 106, the governor’s legislation, has already passed the Senate.

Behanji

Today, there’s a move afoot in the Georgia General Assembly to remove these important protections.Repealing the CON laws would allow profiteers to open lucrative surgery and imaging centers – siphoning off revenue streams that are the lifeblood of local hospitals. The end result is to enrich a select few at the expense of the many.

Another reason why the effort to overhaul Georgia’s certificate-of-need system may have stalled: Gov. Brian Kemp’s legislation to craft a waiver that may or may not include an increased draw-down of federal Medicaid dollars.One could argue that the bill essentially paralyzes the health care discussion. Senate Bill 106, the governor’s legislation, has already passed the Senate. It would give state bureaucrats until June 2020 to put something together. What that something is, no one knows.

Public, non-profit hospitals fear doing away with Georgia’s certificate-of-need program will deprive them of income from the well-insured. Since the outset of this year’s legislative session, a Medicaid waiver has been dangled as an offset.But with no way of judging the quality of the carrot, or whether it will appear at all, hospitals are balking at an attempt to wield the stick.

Clinicians are better at interpreting images when they consider a patient’s clinical context.27,38 As clinicians are frequently considering other evidence sources not available to the model (e.g., clinical history, past imaging), it is important to enable them to synthesize a model’s predictions with a patient’s larger clinical scenario. Multimodal models allow an algorithm’s prediction to be separately attributed to image or PT + HP variables. Patient risk profiles have been created from deep-learning representations of EHR data that compute the risk difference attributable to age, gender, and prior hospitalizations.