2 posts tagged with "technology"

The Quiet Revolution: Why GTM Infra is the Missing Piece in Healthcare Ops​

Beyond the CRM: Why the Next Wave of GTM Innovation Starts With the Data Layer

The best GTM strategies don’t start with a sales deck, they start with clean, accessible data.

If you’ve ever read a RevOps playbook and thought, “This is great, but what about clinical operations? What about providers trying to stand up care programs on the fly, in a regulated minefield, using duct-taped tools from 2006?”—you’re not alone.

The latest post from The Signal, "The Platform Democratizing GTM Engineering", nails something we’ve believed for a long time at CareLaunch: GTM is no longer just about marketing and sales. It’s about execution at scale, and that starts with infrastructure.

At CareLaunch, we call this the foundational data layer for care delivery. But if you squint, it’s really just healthcare’s version of GTM infra.

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1. From Sales Ops to Care Ops: Same Bottlenecks, New Stakes​

Traditional GTM teams struggle to move quickly because data lives in silos, tooling requires engineering support, and processes are too brittle to adapt mid-launch.

Sound familiar?

Healthcare orgs especially digital health startups and multi site provider groups face the same challenges, but with higher stakes. Instead of missed leads, it’s missed follow-ups. Instead of lost MRR, it’s dropped patients. Instead of poor sales attribution, it’s poor outcomes tracking.

That’s why we built CareLaunch to be more than a CRM. It’s a composable platform that sits at the center of care coordination, patient communications, outcomes measurement and yes, growth.

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2. Why “GTM Infra” Isn’t Just for SaaS​

Platforms like Common Paper and Retool are changing how non-technical teams launch, iterate, and optimize. They’ve cracked the code by giving go-to-market teams access to developer-grade tools without the overhead.

CareLaunch is doing the same, but for healthcare.

Need to spin up a new intake flow? Launch a self-pay offering? Send an outcomes survey based on a FHIR encounter? You don’t need six tools and a sprint cycle. You need the ability to build in the flow of work. And you need data you can trust, structured and ready for use across every part of your business.

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3. Data as Leverage, Not Liability​

You’ve heard the interoperability promise before: “integrated,” “connected,” “talking systems.”

Too often, that just means more complicated interfaces.

We went the other way. CareLaunch is FHIR-native, built on a composable architecture that streams structured data (in real-time) to BigQuery. No glue code. No data silos.

This matters more than it sounds. Because this isn’t just CRM data. We’re talking:

• Clinical observations
• PROs and patient adherence
• Resource utilization
• Health economic endpoints
• Signals across intake, messaging, and care coordination

When you structure this natively, you unlock next level GTM: signal stacking, async models, outcomes based workflows, and analytics that cross the aisle—from clinical to commercial.

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4. Move Fast. Stay Compliant. Scale Intelligently.​

We didn’t just wrap a pretty UI around clunky EHR infrastructure. We built a care first GTM engine that’s HIPAA compliant, composable, and ready to scale.

That means:

• No waiting on your dev team to launch a new service line
• No more vendor Frankenstack to glue together chat, intake, scheduling, and reporting
• No more flying blind on what’s working or not in your funnel

You can go from MVP to multi-state rollout with a platform that flexes as you do.

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The TL;DR​

If you’re building in healthcare and care delivery is your product, then GTM isn’t a downstream afterthought, it’s baked into everything you do.

CareLaunch gives you the infrastructure to move like a SaaS company and scale like a system. One platform, every patient touchpoint, all in one place.

Want to see what healthcare GTM infra actually looks like in practice?

Book a demo—or better yet, start building.

Artificial intelligence (AI) is no longer the future of healthcare, it’s here, reshaping the industry in ways that are both exciting and unsettling. As we dive deeper into 2025, one thing is clear: healthcare organizations must go all-in on AI or risk irrelevance. Let’s explore the practical applications, unresolved challenges, and predictions for the future of AI in healthcare.

The New Reality: AI as Healthcare’s Backbone​

Forget the sci-fi visions of robot doctors. In the real world, AI’s biggest impact is already behind the scenes, where it’s quietly transforming healthcare administration and operations. Hospitals and clinics are embracing AI to streamline paperwork, manage patient flow, and even forecast supply shortages. Yet, this pragmatic adoption is just the beginning.

Game-Changing Applications​

Here’s how AI is making waves:

• Coding and Documentation: AI tools are slashing the time and errors in medical coding, saving billions annually.
• Revenue Cycle Management: From claim denials to billing optimizations, AI is turning healthcare’s financial nightmare into a manageable dream.
• Patient Flow Optimization: Hospitals like Cleveland Clinic have leveraged AI to reduce patient wait times and improve bed utilization, saving millions in the process.
• Predictive Inventory Management: AI’s ability to predict supply needs prevents shortages and overstocking, ensuring resources are always where they’re needed most.

The Controversial Side: AI as a Double-Edged Sword​

While AI’s potential is undeniable, the path forward isn’t without controversy. Ethical questions loom large: Who owns the data? How do we prevent algorithmic bias? And what happens to the human touch in healthcare?

• Data Privacy Wars: With data breaches hitting headlines, can we trust AI with sensitive patient information? While AI strengthens security, its reliance on massive datasets makes it a tempting target for cybercriminals.
• Human vs. Machine: A Pew study reveals 60% of Americans feel uneasy about AI’s role in their healthcare. This distrust isn’t misplaced, given that even small errors in AI-driven diagnostics could have devastating consequences.

Predictions: What’s Next?​

The future of AI in healthcare will be defined by three major trends:

1. Hyper-Personalized Medicine: AI will use genomics and lifestyle data to tailor treatments, creating a world where one-size-fits-all medicine becomes obsolete.
2. AI in Diagnostics: Expect AI to dominate in early disease detection. Think Alzheimer’s identified a decade earlier, or cancer spotted before symptoms emerge.
3. Decentralized Healthcare: With AI-driven telehealth and mobile health apps, care will become increasingly accessible, especially in underserved regions.

But here’s the our take: By 2035, we predict that healthcare organizations not leveraging AI at scale will either merge with AI-first competitors or close their doors entirely. The AI revolution in healthcare isn’t optional, it’s existential.

Making AI Work: The Playbook​

For those ready to embrace AI, here’s how to do it right:

1. Start with Low-Hanging Fruit: Automate repetitive administrative tasks first. This delivers quick wins and builds organizational confidence.
2. Collaborate, Don’t Dictate: AI works best when integrated with human expertise. Engage clinicians early to avoid resistance.
3. Invest in Data Hygiene: Clean, high-quality data is the lifeblood of AI. Skimp here, and your AI initiatives will flounder.
4. Future-Proof Your Systems: Adopt scalable AI solutions to stay ahead as technology evolves.

Conclusion: All-In or Left Behind?​

AI in healthcare is a revolution, not an evolution. The organizations that thrive will be those bold enough to go all-in. This isn’t just about efficiency or cost-savings—it’s about redefining what’s possible in patient care. So, are you ready to embrace the AI future, or will you be one of the laggards left behind?