Jenny Chen

Like an unfazed young quarterback who keeps firing it downfield despite a spate of interceptions, CMMI keeps tossing around VBC models hoping something will connect. Contrary to its intent, CMMI's programs increased spending by $5.4 billion from 2011-2020 with another increase in net spending of $1.3 billion by 2030. Of the more than 50 models the Center has implemented, only 6 have generated "statistically significant" savings. Only 4 (Home Health Value-Based Purchasing, Prior Auth Model for Non-Emergent Ambulance Transport, Diabetes Prevention Program, and Pioneer ACO) were certified for expansion. CMMI points to the voluntary nature of the programs as a challenge to achieving cost savings. However, CMS hasn't done itself any favors here by aggressively ratcheting down payments and making downside risk >>> upside risk. (Notably, two upcoming programs -- TEAM and Inpatient THA/TKA PROMs are mandatory). Finding the VBC sweet spot was always going to require some trial and error. Perhaps CMMI has learned from the programs that failed or weren't chosen for expansion. TEAM doesn't seem to be all that different from CJR. Making Care Primary aims to closely integrate primary and specialty care (or, more likely, incentivize PCPs to take on more of this work). Not surprisingly, one of the criticisms is lack of provider input. The programs are too complex, too expensive to administer, and aren't transparent. CMMI should take note of several studies demonstrating the success of physician-led VBC efforts. Here are some other thoughts: - Offer financial grants in 5- or 10-year terms for any organization that designs its own VBC program and presents a reasonable and viable clinical/business model. CMMI would do itself a favor by partnering with or at least listening to innovators. - Medicare Advantage complexity and risk adjustment handwaving shouldn't be the only mechanisms by which advanced providers can profitably care for beneficiaries. That program isn't saving money either, but it's the only way many of these approaches can generate meaningful revenue (and most still aren't profitable). - Have regular meetings with those building VBC models, particularly those with strong physician leadership. Those meetings should be transparent and productive of action plans and iterative changes that increase model viability. - Be more open about what your goals and targets are. No one should be a victim of their own success, and if you keep moving the goalposts, you shouldn't be surprised when people get frustrated and drop out. I'm certain that, given fair incentives, a meaningful voice, and an equal seat at the table, healthcare innovators can design sustainable VBC programs. But there needs to be more collaboration and a willingness to listen and engage. Otherwise, you're just slowly adding to the alphabet soup of also-ran programs. #medicine #healthcare #health #vbc #healthcareinnovation #valuebasedcare

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This post highlights some incredibly vital points about the growth and importance of RPM technology in modern healthcare. The emphasis on government reimbursement and the focus on the aging population are particularly crucial as we see an increasing need for sustainable health solutions in the face of a nursing shortage and rising chronic health conditions. At Vironix Health, we are deeply committed to enhancing RPM implementations with state-of-the-art AI algorithms that not only track but also predict patient health events, thereby preventing potential emergencies. If you’re looking to understand how RPM can be tailored to your specific healthcare practice needs, don’t hesitate to reach out. Let's make healthcare proactive together! #HealthTech #DigitalHealth #RPM #InnovativeHealthcare #AgingPopulation

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STAT NEWS ARTICLE- Pumping the Breaks on LLM’s in Healthcare. Under peer-reviewed scrutiny - LLMs don’t seem to meet the all the hype. Stat News has published an important article summarizing the results of several peer reviewed studies. They are concerning. The article is behind a paywall, but a couple quick summaries: 🚨GenAI INCREASED physician time in responding to patient messages by 22%. Only 58% of messages were deemed acceptable to be sent without clinician review. Clinicians had to spend longer reading both patient response and AI response. 🚨Automation Bias appeared in studies. 🚨Less than 50% accuracy in generating the correct diagnostic codes. 🚨Personal Feedback: my colleagues at one hospital have greatly reduced their use of a once well working dictation program. The AI “upgrade”has slowed it down and generates non sensical responses, making their work harder, if not more humorous based on some the responses. For some reason the AI is really fixated on the recommending “Diseases on the Endocrone System” for just about everything. 🤨 ➡️Bottom Line: we need peer-reviewed studies and guardrails to safely understand the boundaries of this rapidly evolving technology. We can’t get caught up industry hype. 💡Thank you to the institutions leading the charge on objectively evaluating these models. Thank you to STAT news for some great journalism.

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