4 posts tagged with "ai"

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?

Artificial Intelligence (AI) in healthcare has long captured our imagination, fueled by portrayals in movies and TV shows where robotic surgeons and virtual assistants are the norm. But beyond science fiction, AI has profoundly impacted real-world healthcare. As it has evolved over the years, AI has transformed from an ambitious idea into a powerful tool, reshaping how we deliver care.

The Three Waves of AI in Healthcare​

The journey of AI in healthcare can be categorized into three distinct waves:

1. The First Wave: Foundations and Early Exploration​

The first wave emerged in the aftermath of World War II with the introduction of Machine Learning and the coining of the term "Artificial Intelligence." During this period, foundational concepts like the Turing Test were developed, laying the groundwork for future advancements. While the early stages of AI were exploratory, they set the stage for what was to come.

Colossus was the world's first electronic programmable computer, at Bletchley Park in Bedfordshire. Bletchley Park Trust/Science & Society Picture Library via Chris Monk

2. The Second Wave: Expert Systems and Rule-Based AI​

The second wave, spanning the 1980s, was marked by the development of expert systems such as MYCIN and DXPlain. These systems aimed to mimic human decision-making using large sets of rules to provide medical recommendations. However, their potential was limited by the complexity of managing thousands of interacting rules and the constraints of the data available at the time, which primarily came from textbooks and human experts.

DXPlain, one of the pioneering expert systems in healthcare, aimed to emulate human decision-making.

3. The Third Wave: AI Renaissance & ChatGPT​

The third and current wave of AI began in the 2010s, characterized by the introduction of deep learning and neural networks. This era has been driven by the availability of large datasets, advances in computing power (especially GPUs), and innovative architectures like transformers which gave AI the ability to understand bodies of text as a whole versus just sequences of words.

The "Attention is All You Need" paper introduced the transformer architecture, revolutionizing natural language processing.

The release of models like GPT-3 and ChatGPT has revolutionized natural language processing and catalyzed the development of AI-driven applications in healthcare, enabling conversational agents, predictive analytics, and personalized care. These same technologies are now being deployed to assist clinicians through prepopulating notes, automating repetitive tasks, and providing decision support.

Imaging, pathology, and radiology are some of the fields where AI is making significant inroads, helping to reduce the time and cost of diagnosis while improving accuracy. For example, AI can now analyze medical images with remarkable precision, often outperforming human experts in specific tasks like detecting diabetic retinopathy or analyzing radiology scans. They are also capable of operating in the background to find opportunistic diagnoses that may have been missed by human eyes. This concept is often overlooked and will become more important as costs continue to come down.

via Avicenna.AI a leading medical imaging AI company

One of the most exciting developments is AI’s ability to integrate multimodal data—combining text, images, biosensor data, and even genomic information to provide a more comprehensive understanding of a patient’s condition. This capability allows AI to go beyond simple image recognition and contribute to the overall diagnostic process, taking into account the patient’s medical history and up to date data.

The Da Vinci Surgical System is a prime example of how AI is transforming surgery (from their website)

While fully autonomous surgery may still be a decade away, the integration of AI into the surgical process is already enhancing precision and outcomes. The da Vinci Surgical System, which combines robotic surgery with AI, is a prime example of how technology is pushing the boundaries of what’s possible.

AI in Outreach and Care Delivery​

At CareLaunch, we recognize the transformative power of AI and are integrating advanced AI capabilities into our CRM platform to support healthcare providers. AI is a strong fit for our mission to streamline patient communication, optimize workflows, and provide actionable insights. For instance, our AI-powered features can predict patient needs based on their historical data, enabling proactive outreach through our CRM. This is particularly valuable in managing chronic conditions, where timely interventions can prevent complications and improve quality of life. At the core of any AI or healthcare based initiative is a strong data foundation. The CareLaunch platform leverages HL7 FHIR standards to ensure that data is interoperable and secure. This also allows for the integration of AI models and other third-party tools to enhance the platform’s capabilities.

Our CRM system takes the guesswork out of patient communication by automating outreach campaigns with data-driven precision. Whether it's reminding patients about upcoming appointments, sending post-visit follow-ups, or launching preventive care initiatives, CareLaunch ensures that every message is sent at the right time to the right patient. This automation reduces administrative burden on healthcare providers, allowing them to focus more on patient care while ensuring that no critical communication is overlooked.

The Mayo Clinic's use of AI in diagnostics offers a real-world example of how AI-driven insights can lead to better patient care, a vision we share and strive to achieve with our platform.

At CareLaunch, we’re redefining what’s possible in healthcare outreach and patient relationship management. Our AI-driven CRM platform is designed to help you deliver personalized care, streamline communications, and ultimately improve patient outcomes. Ready to see how CareLaunch can transform your practice? Schedule a demo today and discover how our innovative solutions can help you connect with your patients in more meaningful ways.

For more insights into how CareLaunch is leveraging AI to transform healthcare, visit our blog or explore our platform to see how we can help you enhance patient care and operational efficiency.

Meta’s Newest Model: Llama 3.1 - Surprises, Impressions, and Use Cases​

This week, Meta released their newest and most advanced open-source model yet, Llama 3.1. This announcement is special for a few reasons. Not only does this advanced model outperform OpenAI’s latest GPT-4o, but Meta has also released it as an “open model”, allowing everyone to build on top of the technology at no cost.

Let's break down some quick impressions, surprises, and interesting use cases.

Surprises:​

1. Model Size and Variants:

   • The size of the model is unprecedented at 405 billion parameters.
   • Meta also released smaller versions of the model, down to 8 billion parameters, which can be run locally on consumer-grade equipment.

2. Math Benchmark Performance:

   • Surprisingly, the 405 billion parameter model underperformed on math benchmarks, which is unexpected as Llama models are typically strong in this area.

3. Software Integration:

   • Llama 3.1 excels at integrating with other software, such as web browsers, enhancing its practical applications.

4. Responsible AI features:

   • On top of being open-source, Meta has implemented several safety measures, including Llama Guard (a multilingual safety model) and Prompt Guard (a prompt injection filter)

Impressions:​

1. Advancement in Open-Source Language Models:

   • Llama 3.1 is considered a significant advancement in open-source language models. The 405 billion parameter version performs exceptionally well on most benchmarks.

2. Free and Permissive License:

   • The model is free and has a permissive license, allowing for fine-tuning and customization, which is seen as a major advantage over closed-source models.

3. Performance of Smaller Versions:

   • Some users found the 8 billion parameter version to be less capable than its Llama 3 counterpart for certain tasks. This is an important consideration for those using tools like Ollama for running models locally.

Use Cases:​

1. Multilingual Dialogue:

   • Llama 3.1 is optimized for multilingual dialogue use cases, making it versatile for global and clinical applications.

2. Research and Development:

   • Meta is making it easier to train new models on Llama 3.1, which could lead to rapid advancements in AI research and development.
   • The model's advanced capabilities make it suitable for reviewing and summarizing medical research, which could aid healthcare professionals in staying up-to-date with the latest findings

3. Advanced reasoning for complex medical queries

   • Llama 3.1, especially the 405 billion parameter model, has strong reasoning capabilities that can be applied to answer complex medical questions or analyze patient data.

4. Conversational Patient Intake:

   • Groq has already demonstrated how Llama 3.1 can be used for conversational patient intake, showcasing its potential in healthcare applications.

5. Developing intelligent healthcare agents

   • Llama 3.1's enhanced tool use and function calling capabilities allow for the creation of complex AI agents that can automate sophisticated healthcare tasks and answer intricate medical queries

6. Customized healthcare applications

   • Healthcare organizations can fine-tune Llama 3.1 models with their proprietary medical data to create specialized AI systems for specific medical domains or use cases

Conclusion:​

The open nature of the model has sparked discussions about potential risks and the need for responsible AI development. Mark Zuckerberg argues that open models are inherently safer, as they can be scrutinized by the community, but others worry about the potential for misuse.

At CareLaunch, we see AI as another tool we can make available to our partners. Rapid advances are happening in the space, and we hope to democratize the availability of these technologies to care providers, both small and large. By integrating advanced models and technologies, we aim to enhance our offerings and support our clients in delivering exceptional care. The potential for creating HIPAA-compliant solutions and improving outreach aligns with our commitment to providing secure and efficient healthcare communication tools.