AI-powered efficiency: How Momentum helped doctors spend more time with patients
Summary
In the fast-paced world of healthcare, every minute counts. This is the story of how we helped a premier concierge care service harness the power of AI to give doctors more time for what matters most - their patients.
Setting the scene
In concierge medicine, time is the most precious commodity. For patients, it means faster access, unrushed appointments, and care that truly feels personal. For doctors, it’s the chance to practice medicine the way it should be—focused on people, not paperwork.
Our client, a premium concierge medical practice, built their reputation on exactly that: speed, quality, and undivided attention. But behind the scenes, they were losing ground. For every patient visit, doctors spent up to 16 minutes buried in clinical documentation—chasing details, navigating templates, and wrestling with systems that slowed them down.
It wasn’t just frustrating. It was breaking the promise of their model. Less face time with patients. More risk of burnout. And mounting financial exposure from incomplete notes, insurance denials, and potential surprise billing. They knew something had to change.
What we were up against
In theory, concierge medicine should protect doctors from the administrative overload that weighs down the wider healthcare system. But even in this high-touch model, paperwork creeps in.
The clinic’s physicians were drowning in post-visit documentation. Every patient interaction triggered a race to capture the right details, structure the notes correctly, and avoid costly mistakes—all while trying to move on to the next appointment.
The impact was hitting on three levels:
- Time loss: Up to 16 minutes per visit, cutting deeply into patient-facing hours.
- Financial risk: Incomplete or inconsistent notes increased the chance of insurance denials and out-of-pocket billing surprises for patients.
- Physician burnout: The very system designed to offer doctors more breathing room was now draining it away.
The team explored standard speech-to-text tools, but they weren’t built for clinical workflows. The notes required heavy post-editing. The process stayed slow. And none of the options solved the root problem: how to make clinical documentation truly ambient, accurate, and fast—without pulling doctors away from their patients.
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Five things that had to work perfectly
The team wasn’t just looking to speed up documentation. They wanted to reclaim time at scale—to make every patient visit feel as personal and attentive as the concierge model promised.
To achieve that, they set out to:
Cut documentation time by over 90%
transforming a 16-minute task into something near-instant
Eliminate post-visit editing
by generating structured, high-quality clinical notes in real time
Protect financial health
by improving the accuracy and consistency of documentation, reducing the risk of insurance denials and surprise billing
Safeguard patient trust
with fully HIPAA and SOC 2-compliant solutions that integrated securely with existing systems
Improve physician well-being
by meaningfully reducing the burden of administrative work and giving doctors their time—and focus—back.
Building the strategy
We knew this wasn’t just a technical challenge—it was a workflow challenge. To make AI work in real clinical settings, it had to feel invisible.
Seamless. Effortless. Anything less would add friction instead of removing it.
We started by embedding ourselves in the clinic’s daily routines. Through deep-dive workshops and direct observation, we mapped the real flow of patient visits—not the idealized version on paper, but what actually happened in the room.
This is where most solutions fail: they don’t fit the way doctors really work.
From there, we built a strategy around three pillars:
Design for the way clinicians think
The solution needed to capture natural, conversational input — not force doctors to change how they speak or structure information.
Automate without losing control
AI should generate structured notes in real time, but always keep the clinician in the driver’s seat.
Build for trust from day one
Data security, HIPAA compliance, and system resilience weren’t add-ons—they were foundational.
How we got it done
Methodology
We knew that to tackle these challenges, we needed a approach that was both innovative and adaptable. That's why we combined Design Sprints with Agile methodologies. This powerful combo allowed us to quickly prototype and test solutions, ensuring we stayed aligned with our client's evolving needs.
Process
Our journey unfolded in three key stages:
Workshops and Analysis
We started by rolling up our sleeves and diving deep into the client's world. Through intensive workshops, we mapped out their needs, clarified priorities, and defined the scope of our project. But we didn't stop there. We put on our detective hats and analyzed the doctors' daily routines, identifying bottlenecks and areas ripe for optimization. This groundwork was crucial in ensuring our solutions would truly make a difference where it mattered most.
AI Solution Exploration
With a clear understanding of the challenges, we set out to find the perfect AI sidekick for their doctors. After exploring various options, we struck gold with Nabla, a cutting-edge ambient AI assistant. Nabla stood out for its ability to automate documentation while enhancing patient-doctor communication, all while playing nice with healthcare standards.
Implementation Architecture
Armed with insights from the Nabla team, we crafted an implementation architecture that would minimize setup time and streamline the structure of post-visit notes. But we didn't just think about the present - our design was built to grow with our client's platform, accommodating their expanding patient base while keeping data secure.
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The tech behind the magic
In the world of healthcare tech, choosing the right tools can make or break a project. Here's why we picked our tech lineup:
Python
The backbone of the system’s real-time data processing and AI integration. Python handled everything from connecting with Nabla’s AI engine to managing the secure data flow between the mobile app and the backend.
Flutter
A fast, cross-platform framework that allowed us to build seamless, responsive interfaces across web and mobile. The clinic’s physicians could access the AI-powered documentation tools wherever they worked—no learning curve, no friction.
Nabla AI
An ambient AI solution purpose-built for healthcare. Nabla didn’t just transcribe speech — it generated structured, SOAP-format clinical notes in near real-time, dramatically reducing the need for manual corrections.
WebSocket Buffering
To ensure reliability even in low-connectivity environments, we implemented WebSocket buffering. This made it possible to process and sync data in sub-10-second windows, without interruptions to the clinical workflow.
HIPAA & SOC 2 Compliance
From day one, the entire system was built to meet the strictest healthcare data protection standards. Every component of the architecture, from storage to data handling, was designed to ensure full HIPAA and SOC 2 alignment—no compromises.
Bringing it all together
Turning strategy and technology into impact required precision, partnership, and relentless iteration:
Pilot Launch
We started small—on a single care team—embedding the AI documentation tool into real patient visits. This gave us live feedback loops: AI accuracy, clinician experience, note quality, connectivity resilience.
Iterative Refinement
Over weekly sprints, we refined AI prompt phrasing, adjusted note templates, and fine-tuned buffering thresholds—based on real-world usage. Every conversation sharpened the model’s ability to capture clinical detail with minimal manual edits.
Seamless System Integration
Working closely with the clinic’s IT and EHR vendor, we connected the AI-generated SOAP notes directly into the patient record system. No extra uploads or manual copy-paste—clinicians just reviewed and signed, keeping workflows smooth.
Training & Change Management
We led on-site and virtual training sessions, providing scenario-based coaching to help physicians trust and control the AI. Quick reference guides and support channels reinforced learning and eased adoption.
Performance Monitoring
Using analytics dashboards, we tracked key metrics—note generation time, AI accuracy rates, doctor sign-off delays, and EHR throughput. This real-time monitoring ensured rapid identification and resolution of any issues across teams.
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The complexities beneath the surface
Bringing AI into real clinical workflows always comes with hidden complexity. Here’s what we faced—and how we solved it.
Reducing documentation time
Doctors were spending up to 16 minutes per patient visit just on clinical notes. We needed to cut that to under a minute without sacrificing accuracy. Through real-time note generation, WebSocket buffering, and iterative prompt refinement, we eliminated the documentation backlog and made clinical notes available almost instantly.
Making AI work with medical language
Off-the-shelf AI tools struggled to handle clinical language—missing key phrases, misinterpreting SOAP note structures, and failing to recognize specialty-specific terms. By systematically collecting edge cases during pilot sessions and working in weekly sprints, we tuned the prompts, adjusted templates, and tailored the AI to the way real clinicians speak.
Handling low-connectivity environments
During home visits and inside busy clinics, network interruptions were common. We built reliable WebSocket buffering to handle temporary disconnections. The system paused, resumed, and synced data automatically—with no loss of clinical information.
Building system-wide trust
AI adoption raised critical questions about workflow disruption and patient data security. We addressed this by embedding HIPAA and SOC 2 compliance at every level, from encrypted transport to secure storage. We also integrated the solution directly into the existing EHR workflow, so clinicians could review and sign notes without switching systems.
Driving user confidence
Even the best AI won’t succeed if users don’t trust it. We ran targeted training sessions, shared live accuracy metrics, and provided clear performance dashboards. As users saw faster review times and fewer manual edits, adoption accelerated across the care team.
What we achieved together
In just a few short weeks, our AI-powered documentation tool transformed the clinic’s operations.
Average note generation time plummeted from 16 minutes to under one minute, with clinical notes appearing in the EHR within 10 seconds of the patient encounter.
AI accuracy consistently exceeded 90%, cutting manual edits by approximately 80%.
As documentation burdens fell, physician focus and satisfaction began to rebound—clinicians reported feeling both freer and more effective during patient visits.
Financially, more precise and timely notes reduced insurance denials and surprise billing incidents.
Equally important, automated compliance measures and secure, encrypted workflows reinforced trust across the team.
The result wasn’t just faster documentation—it was a meaningful shift in how care was delivered, empowering doctors to spend more time with patients and less time on paperwork.
This project shows what’s possible when AI meets real healthcare challenges—with the right strategy, the right team, and a clear focus on impact. We didn’t just talk about AI, we made it work. From automating routine processes to supporting clinical decisions, we helped turn artificial intelligence from a buzzword into a practical, scalable solution.
If you’re exploring how AI can drive efficiency and better outcomes in your healthcare product or system, let’s talk.
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