Topflight Apps

We’re proud to announce that Topflight is a Best of Clutch Finalist. This honor reflects the standout digital experiences we’ve created —showcasing the measurable results we’ve delivered, the technical challenges we’ve solved, and the strong client relationships behind every launch - and we’re proud to be named among the top-performing agencies on Clutch. Thank you Ryan Majoria, M.D. for your faith in us. #BestofClutch

Data provenance might be healthcare's biggest AI blind spot that no one's talking about. EHRs have no consistent way to track where data comes from or when it entered the system, explains Topflight's Scott Rossignol. FHIR write-backs, extract imports, and Smart on FHIR applications all handle information differently - some merely displaying data without true integration. This creates a critical problem for AI: how can we track when AI outputs influenced clinical decisions? With so many integration paths and logical branches, there's no reliable method to record this in a discrete, meaningful way. The implications are enormous for clinical safety, regulatory compliance, and understanding AI's true impact on patient care.

A great chatbot doesn’t just answer a question. It holds a conversation. Even before LLMs, the best conversational systems were built by thinking through the entire flow as a dialogue. As Topflight's Terry Woodward clarifies - that means not just delivering structured answers, but doing so in a way that feels like an intelligent assistant is helping you. This still holds true today. For example, if a user asks for specific structured data, it’s not enough to simply present the answer. A good system will also understand where you are in the conversation, use that context to continue the dialogue, and keep the interaction moving forward naturally. That’s where semantic context and agentic memory come into play. When you design with that in mind, your chatbot doesn't feel like it's starting from scratch with every message. It feels like it remembers you and is actually trying to help. That’s the difference between a bot that works and a bot that feels human.

AI in EHRs often fails because the focus is on the product, not the provider’s workflow. You’re building inside someone else’s system so no matter how great your UI is, it won’t matter if it pulls providers out of their EHR. The key to success? Scott Rossignol at Topflight suggests meeting providers within their existing workflows. While Smart on FHIR and CDS Hooks offer potential, API-based write-backs often prove more effective by embedding insights directly into the tools providers already use. The golden rule in healthcare tech: never add extra steps. Your solution should fit seamlessly into the provider’s routine—not interrupt it.

Creating a chatbot with LLMs is one challenge. Designing one that meets the demands of regulated industries like healthcare or finance is a whole different game. Topflight's Terry Woodward shares this high-level process to make it work: 1. Start with the use case. Defining what you're solving for drives everything else. 2. Map your data needs, especially where PHI or sensitive data is involved. 3. Choose the right infrastructure. This includes vector databases, memory handling, and agentic frameworks that support context retention. 4. Build in traceability. You'll need robust logging and visibility for audits, QA, and continuous improvement. 5. Plan for escalation. Not everything should be automated. Knowing when and how to bring in a human is essential. This kind of structure isn't just technical hygiene. It's how you build reliable, compliant, and scalable AI systems that people trust.

Smart on FHIR requires careful implementation to succeed in clinical settings, shares Scott Rossignol from Topflight. When does a standalone UI make sense? Three specific scenarios: for sensitive information that shouldn't become part of the permanent record, for workflows extending beyond the EHR, or when your application manages a complete sequence of actions that significantly reduces provider time. What providers reject: clicking a button, waiting for your app to load, viewing information, then manually incorporating it elsewhere. This exchange doesn't justify the workflow interruption. The better approach? Embedded views that allow for side-by-side navigation between your application and EHR functionality. This creates a natural workflow for functions like ambient listening where providers need to move between systems.

When you're building a chatbot for a specific domain - especially something high-touch like telehealth - there’s always a tension between structure and flexibility. Topflight's Preston Hoang discusses the importance of structure to guide the conversation toward an outcome. In a scheduling flow, for example, there are certain pieces of information you must collect: name, phone number, appointment reason. Without structure, you risk never getting the data you need. But you also want to preserve the open-ended, conversational feel that LLMs make possible. That’s what makes the experience feel natural and human. The best systems blend both. You keep the guardrails in place to ensure reliability, but allow for enough flexibility so the chatbot doesn’t feel robotic. 

As the demand for imaging services grows, healthcare organizations face radiologist shortages, delaying patient care. Ryan Majoria, M.D., a practicing radiologist in Baton Rouge, encountered this firsthand. His radiologists were overwhelmed by surges in demand with no way to efficiently bring in extra staff or streamline compensation. Ryan needed a solution that could dynamically connect radiologists to available work, distribute studies efficiently, and ensure prompt payments—all while maintaining HIPAA compliance. That's where Topflight came in.

Clinical decision support in EHRs has been failing for 20 years. AI won't magically fix that. The sobering reality: generating recommendations doesn't mean you can deliver them effectively to providers. You might suggest an order but can't help place it, or recommend against a treatment the patient already received at another health system. What separates success from failure? User activation. Scott Rossignol from Topflight shares this key insight: You get ONE chance to prove your value to busy clinicians. If they try your solution and don't immediately see benefits, they'll never return. This moment in healthcare AI is a once-in-a-lifetime opportunity.  The organizations that succeed understand that perfect algorithms mean nothing without flawless implementation and provider adoption.

If you're building with LLMs, especially in high-stakes industries, reducing hallucinations and managing risk is foundational. Terry Woodward from Topflight shares the importance of a layered approach. First, look at the built-in guardrails available through the cloud provider where the application is hosted. These native controls can offer a strong baseline for protection. Next, bring in a specialized guardrail software that monitors the context of the conversation itself - checking whether answers are grounded in the right provenance and align with what should actually be shared. Finally, leverage vector databases where needed. Some platforms, like Vectara, offer built-in hallucination reduction or elimination features, which adds another layer of reliability. The right stack depends on your application, your data, and your risk profile. But getting this right is key to building trustworthy AI experiences.