Predictive RPM: Investing in Proactive AI for High-Margin Healthcare

The whole remote patient monitoring (RPM) field is changing. What used to be basic telemetry is now becoming AI-driven predictive triage. For any growth equity or VC investor looking at the space, you have to get this shift to find the real high-growth software platforms instead of getting stuck with hardware sellers. The old model of just shipping a device is over. The new one is about proactive, algorithm-driven solutions.

The Commoditization of Hardware-Centric Remote Monitoring

For years, the RPM market was just about the hardware, devices that collected basic physiological data and sent it off. That model is now a commodity business. The value of shipping a “smart” blood pressure cuff or glucose meter is dropping fast as these things become cheap and ubiquitous, squeezing margins to almost nothing. The Centers for Medicare and Medicaid Services (CMS) definitely helped get the market going with reimbursement policies. CPT codes like 99453 for patient setup and 99454 for 30 days of monitoring made adoption possible CMS RPM CPT code definitions. And in 2026, new codes like 99445 and 99470 are supposed to add more billing flexibility for short-term device use and remote management time. But here’s the catch: these codes pay for the service of monitoring and sending data, not for any advanced analytics or predictive insight. Any company focused only on hardware distribution or sticking data in a simple dashboard is facing brutal price pressure with no way to stand out. Their business looks a lot more like medical device distribution than a scalable software-as-a-medical-device (SaMD) play.

The Rise of Predictive Algorithms: Biofourmis and Cadence as Exemplars

The real investment opportunity is with companies using AI to get ahead of clinical events, moving from reactive alerts to proactive prediction. These platforms take raw data and turn it into actual clinical intelligence, which is what actually improves patient outcomes and makes hospitals run more efficiently. Biofourmis is a prime example of an AI-native firm using predictive analytics for home-based care. Their platform pulls data from all sorts of wearables and medical devices, then applies machine learning to catch subtle changes in a patient’s condition long before an acute event happens. This gives clinicians a chance to intervene early, potentially stopping a hospitalization before it starts. Biofourmis is interpreting data in the context of an individual patient’s baseline and predicting deviations that need attention. This is the jump from simple monitoring to real predictive care management, which is exactly what value-based care models demand. By focusing on generating real-world evidence (RWE) from continuous monitoring, they’re building a data moat that makes their algorithms stronger and very difficult for anyone else to replicate Biofourmis clinical validation studies. Cadence is another leader in this shift. Though known for virtual chronic care, its core strength is predicting clinical deterioration in patients with diseases like heart failure or hypertension. By tracking key parameters and tying into electronic health records, Cadence’s AI can identify patients at high risk of decompensation. This predictive power lets their clinical teams intervene proactively, maybe by adjusting medication, providing education, or scheduling a virtual visit. For these platforms, the key metric is their impact on hospital readmissions for conditions like heart failure. It’s been shown that continuous monitoring programs with predictive capabilities can dramatically cut readmissions Studies on continuous monitoring and readmission reduction. That result directly saves payers money and improves quality metrics for providers, which is what drives real market adoption.

Working through the Spectrum: From “Smart Hardware” to Intelligent Software Platforms

RPM solutions aren’t all the same, so investors have to figure out where a company sits on the spectrum from a basic hardware-plus-service bundle to an advanced, software-only predictive platform. At one end, you had a retailer like Best Buy, which bought Current Health to get a foothold in remote monitoring. But then Best Buy sold Current Health back to its co-founder, Christopher McGhee, in June 2025, who is now steering it as an independent company focused on advanced therapies. The initial retail strategy often leans on supply chain strength to distribute “smart hardware” with a service layer attached (a classic distribution play). Even with a complete platform like Current Health offers, with its own wearables and dashboard, the real test is evolving past basic telemetry to build clinically validated predictive algorithms that can compete with the AI-native companies. Without a strong SaMD component and a clear roadmap to advanced analytics, these kinds of offerings are at risk of being low-margin, service-heavy businesses. In contrast, Biofourmis and Cadence are what higher-margin, scalable software platforms look like. Their value comes from the intelligence in their algorithms, their integration capabilities, and their power to deliver measurable clinical and economic outcomes. These companies are building data moats, constantly refining their predictive models, and showing clear clinical validation. For an investor, that means more defensible IP, a stronger competitive position, and in the end, better exit multiples.

Investment Takeaway: Spotting High-Margin RPM Software Platforms

For growth equity investors and VCs, the job is to find the RPM companies that are truly AI-native or are quickly becoming AI-driven predictive platforms. You have to scrutinize a few key things:

• Clinical Validation Score: Does the company have published outcomes data showing it improves patient health, like reducing hospitalizations or lowering readmission rates? This kind of evidence is what gets payers to sign contracts and providers to trust the tech.
• Regulatory Risk Rating: A company with a clear 510(k) clearance or De Novo classification for its AI algorithms, especially if it includes a Predetermined Change Control Plan (PCCP), has a much lower regulatory risk and signals a mature approach to SaMD.
• Payer Penetration Depth: Can they show strong reimbursement pathways? Getting paid under existing CMS CPT codes is good, but having a value proposition that resonates with value-based care models is what proves real commercial viability.
• Published Outcomes Data: Go beyond the company’s own marketing. Are there peer-reviewed publications and real-world evidence (RWE) that validate the predictive accuracy and clinical utility of their AI? The future of remote patient monitoring is intelligent, proactive intervention, not passive data collection. The companies that master the shift from reactive alerts to predictive triage, using AI to deliver measurable clinical and economic value, are the ones that will lead this market. Investors should be backing the companies building strong data moats and clear paths to high-margin software platforms, not the ones stuck in the hardware business.