Anav Sawhney 2/21/25
In healthcare operations, the moments that influence documentation and workflow accuracy often involve subtle details. A brief clarification about a symptom, a data point that affects coding, or a shift in the order in which clinical information is presented can change the shape of an encounter. People working in clinical settings recognize these details at once. Many AI systems still overlook them.
This difference has shaped much of my work at Nudge. After examining operational workflows across practices and health systems, a pattern stands out. Systems that perform well in controlled settings tend to lose stability when exposed to real clinical environments. The issue rarely comes from limited model capability. It comes from the absence of mechanisms that help a system remain aligned with the structure and intent of a live encounter as it unfolds.
In this article, I focus on why these operational details matter, how adaptive feedback architectures support reliability, and what it takes to design AI agents that function as dependable components of healthcare workflows. The goal is to outline what stability requires and how Nudge approaches that challenge.
Where Conversational AI Loses Stability
Across scribing, CDI, billing, after-visit summaries, and analytics, three consistent issues appear. These patterns match results seen in public evaluations of large language model behavior.
Common AI Failure Modes
(Based on public LLM reliability studies from 2023 to 2024)
| Failure Mode | Approximate Frequency | Typical Impact |
| Hallucination | 15% to 30% | Inaccurate or fabricated information |
| Escalation Errors | Roughly 20% | Missed cues or unnecessary escalation |
| Context Drift | 40% to 50% | Repetition, confusion, loss of direction |
Hallucination
This can occur when an AI system generates clinical or administrative information that was not provided. It may introduce unsupported conditions, exam findings, or structured elements. These errors often appear when the system lacks strong internal checks on certainty and context.
Escalation Errors
Escalation relates to workflow signals, not emotional cues. If the organization of clinical information changes or new documentation requirements arise, the system must adjust. Failure to recognize these moments leads to mismatches between what the clinician expects and what the agent produces.
Context Drift
Clinical encounters are not linear. The sequence of history, examination, and planning varies by clinician and specialty. Documentation also shifts based on payer rules, visit type, and clinical emphasis. Without mechanisms that anchor the system across these updates, the AI begins to lose orientation.
These breakdowns reflect the same underlying requirement. Stability depends on feedback that operates throughout the workflow, not only at the beginning or end.
Why Adaptive Feedback Matters in Healthcare
Feedback – Image | Shutterstock
Healthcare environments rely on structured yet flexible workflows. Clinical documentation, coding cues, and administrative processes change frequently, sometimes within the same encounter. Clinicians also vary in how they present information and what they prioritize. Systems that treat these workflows as static tend to produce inconsistent results.
Adaptive feedback allows AI agents to track workflow signals, adjust structure, and maintain integrity in documentation. It helps agents:
- Follow the organization of the encounter as it changes
- Maintain accuracy when extracting or generating structured data
- Prevent small errors from turning into larger inconsistencies
- Preserve continuity across long or complex visits
- Align with the documentation style of each clinician
Communication research points to a similar principle. Meaning often depends on subtle, contextual cues. In healthcare, these cues appear as procedural signals, transitions, corrections, and other workflow-specific markers. Agents must recognize these signals to function reliably.
A Feedback Framework Designed for Healthcare Agents
At Nudge, we design internal feedback systems that match the layered nature of clinical and administrative tasks. Reliable performance comes from understanding that workflows contain multiple types of signals. No single signal determines the correct outcome.
Our framework includes four layers.
1. Structural Feedback
This layer ensures alignment with medical documentation standards, billing rules, template formats, and internal consistency. It serves as the foundation that stabilizes the system.
2. Workflow Feedback
This layer follows the organizational flow of the encounter. In many visits, clinical details appear in varying sequences and may be revisited as the clinician refines documentation. Workflow feedback tracks these shifts and keeps the agent oriented.
3. Reinforcement and Correction
This layer evaluates the system’s own output. It identifies missing elements, excessive content, or misplacements within the note. The agent adjusts immediately, limiting the accumulation of errors.
4. Meta-Level Evaluation
This layer ensures that the final output reflects clinician preferences and practice conventions. It supports long-term consistency across encounters and across providers.
These layers work together to help the agent maintain clarity and precision across varied workflows.
How This Framework Supports a Scribe
The most direct application of this framework is the Nudge AI Scribe. Strong documentation requires both precision and consistent structure, not long text generation.
Recognizing Workflow Transitions
Clinical details from different parts of the note often appear in variable order. Workflow feedback ensures each detail is placed in the correct section of the documentation.
Capturing Billing-Relevant Elements
Details such as severity, chronicity, or risk factors may appear briefly. The agent identifies these elements and incorporates them without stretching or modifying content.
Maintaining Structure
Electronic medical records depend on predictable section organization. Without feedback, AI systems tend to blend or misplace content. A feedback-driven approach preserves structure across the entire note.
Reflecting Clinician Preferences
Documentation styles vary. Some clinicians prefer concise language. Others prefer detailed notes. Meta-level feedback supports these preferences, enabling the scribe to maintain consistency across clinicians.
Reliable performance becomes apparent when the agent produces accurate, structured documentation across many encounters.
How Feedback Supports Other Healthcare Agents
Beyond scribing, healthcare relies on additional workflows that benefit from feedback-driven architectures.
A CDI agent must recognize documentation gaps that influence billing accuracy.
An after-visit summary agent must convert clinical details into clear patient instructions.
An analytics agent requires structured data that remains consistent across encounters.
Each workflow introduces distinct signals. Feedback enables the agent to interpret and respond to those signals accurately.
The Direction Healthcare AI Is Moving
Model capability continues to evolve, but healthcare organizations increasingly evaluate AI systems based on reliability, consistency, and practical value. The next generation of AI agents will be defined by their ability to:
- Maintain structure across varied encounter flows
- Adjust as new information becomes relevant
- Correct errors during generation rather than after the fact
- Follow clinician documentation styles without manual settings
- Reduce administrative burden while improving documentation quality
These strengths come from feedback architectures rather than model size alone.
A Future Shaped by Attentive Systems
Work across healthcare operations has reinforced one principle. Systems that succeed are those that pay close attention to operational signals. They remain steady as workflows shift. They recognize important clinical and administrative details when they appear. They produce documentation and insights that reflect the realities of clinical practice.
Adaptive feedback loops make this possible. They allow AI agents to support clinicians with clarity, reduce administrative burden, and help practices operate more efficiently.
As AI becomes a core component of healthcare operations, the systems that stand out will be those built to stay aligned with actual workflows. That principle continues to guide my work at Nudge. AI should support the way healthcare operates. It should provide the level of reliability that clinical teams expect every day.
About the Author
Anav Sawhney helps lead product and growth efforts at Nudge, where he works on AI systems that support behavioral health practices. His career spans education, research, and technology, with a focus on improving how people and systems communicate. He studied Psychology and PPE at Ashoka University and is based in the San Francisco Bay Area.