Healthcare as an industry is changing how we think of and how we measure "value." The government is leading this shift through alternative payment models that incentivize based on quality over fee-for-service. The idea is to drive better care for individuals and populations while lowering cost. Most within the industry agree that paying providers based on outcomes rather than the quantity of services is a good thing. However, the best way to drive this shift is a political hot button and a major area of debate.
Looking out at cruising altitude, the emerging value-focused geography looks like a series of programs, mandates, and incentives that link provider performance with payment. According to the Centers for Medicare & Medicaid Services (CMS), this movement started in 2008 with the Medicare Improvements for Patient & Providers Act (MIPPA). The programs that have been enabled by MIPPA and subsequent legislation including the Affordable Care Act (ACA) are focused on some flavor of payment model that penalizes and/or incentives based on target performance measures. These programs fall within the larger CMS topography that includes multiple levers across standards, networks, reporting, certifications, and other innovations, which underpin CMS’s quality strategy.
There is one thing that just about every CMS quality program has in common: they are predicated on prevention. For example, the Hospital Readmission Reduction (HRR) program focuses on preventing the readmission of a patient. The Hospital Acquired Conditions (HAC) program encourages hospitals to prevent events like pressure ulcers and surgical site infections.
The problem with mandates as a stand-alone approach to driving prevention is that - while they provide incentives - they don't deliver the tools that actually help providers stop target events and improve health outcomes. As is often the case, technology is emerging that will help address the "prevention gap" and enable improvements across the care giving continuum. Most notable are predictive analytic solutions.
Let's start with a quick definition. "Predictive analytics" is an industry buzzword that has been diluted and twisted by marketers to apply to solutions that are neither predictive nor analytic. When we talk about "predictive analytics" we are referring to technology that uses machine learning (also called artificial intelligence) to account for thousands of variables and make more precise and actionable predictions. This sounds like sci-fi, but it's not. In fact, you probably used machine-learning technology within the last 24 hours when you opened your browser and searched for the latest news or gadget. And machine learning is the reason why that lawn mower ad popped up in your social media feed before you mentioned the need for a new lawn mower to your spouse.
Machine-learning technologies have been around for years and leveraged by other industries with great success. It is just now that healthcare is starting to catch up and use the same power already leveraged by organizations from consumer goods to financial institutions.
- By Jvion Health