It was about an hour after the release of the Center for Medicare & Medicaid Services’ proposed rule for FY19, which contained the big Patient-Driven Payment Model reveal, that a wave of bold and somewhat uninformed statements surfaced.
In the past, our skilled nursing world has revolved around a sun of its own, known as resource utilization groups (particularly the lucrative high-therapy ones). Now, however, according to the details of the new Medicare fee-for-service reimbursement model known as the Patient Driven Payment Model (PDPM), it seems that we’ll soon be revolving around a different sun altogether: ICD-10 codes.
There is something you and your team must do immediately, the success of which will make or break your transition to the government’s new payment model. Relax. Refresh. Recharge. Only with a clear mind, renewed spirit and body will you create the best, most effective transition plan.
Averages can help or hinder your evaluation of performance. Adding a few additional pieces of data can really support your efforts to properly see opportunity and manage risk.
Returning from an early morning walk down to the harbor of this sleepy Cape Cod town, it’s hard not to feel hopeful. My geriatric pups Melvin and Theodore couldn’t get enough of the sea smells and feel of the sand under their little Shih Tzu paws.
Grandma sure liked her bingo. She excelled at bingo and most card games. Even in games that seemed to involve no skill at all, Grandma almost always emerged the winner.
Perhaps you think that the new therapy-payment bombshell (RCS-1) has turned out to be a dud. I hope to make the point that it was not.
We don’t know when, and we don’t know how, but when Payroll-Based Journal staffing data replaces the data currently being captured from CMS Form 671 for calculating Five-Star staffing, we are likely going to take several lumps.
Exceptional SNF utilized appropriate structure, process and outcome measures. Despite this, however, it might have better been called “Mediocre SNF.”
It’s a loaded question: How good does your data need to be? If we’re honest with ourselves, we must admit that we’re not perfect. Nor are the systems we create to help minimize error perfect.
