Active
Clinical Summary Generation
Synthesizes patient records, labs, and encounter notes into concise clinical summaries.
Last ExecutionJul 2, 2026 08:14 AM
Success Rate
98.4%
Executions (24h)214
neuro
by
Monin Modi
Monin Modithis is the requirement doc
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neuro · Platform Ops
AI Orchestration
Configure and monitor the AI model pipelines that power clinical insights, prompt routing, and patient recommendations across the neuro platform.
Partial Degradation
Pipeline Overview
AI Workflow Overview
Monitor status, execution history, and reliability across every automated pipeline running on the neuro platform.
Active
Clinical Insight Generation
Derives data-driven clinical insights from normalized patient signals for clinician review.
Last ExecutionJul 2, 2026 08:02 AM
Success Rate
96.1%
Executions (24h)187
Active
Nudge Generation
Produces personalized behavioral nudges for patients based on adherence and activity trends.
Last ExecutionJul 2, 2026 07:55 AM
Success Rate
91.7%
Executions (24h)342
Error
Chatbot / Avatar Support
Powers the patient-facing conversational avatar for real-time support and triage.
Last ExecutionJul 2, 2026 06:41 AM
Success Rate
87.2%
Executions (24h)96
Active
Clinician Copilot
Assists clinicians with charting suggestions and evidence-linked recommendations.
Last ExecutionJul 2, 2026 08:10 AM
Success Rate
99.1%
Executions (24h)121
Inactive
App Data Interpretation
Interprets NEURO World app behavioral signals into normalized clinical context.
Last ExecutionJun 29, 2026 04:18 PM
Success Rate
0%
Executions (24h)0
Active
Wearable Trend Interpretation
Analyzes connected device metrics to surface activity and recovery trends.
Last ExecutionJul 2, 2026 07:48 AM
Success Rate
94.8%
Executions (24h)265
Active
Assessment Trend Interpretation
Tracks longitudinal assessment score trends to flag clinically significant change.
Last ExecutionJul 2, 2026 07:30 AM
Success Rate
95.5%
Executions (24h)78
v2.4.0All changes saved
Workflow Configuration
Define input/output schema mapping, error handling, and safety validation for this workflow.
draft
Valid
Valid JSON schema
Valid
Valid JSON schema
Error Handling Rules
Safety Validation
Timeout Settings
sec
sec
Workflow runs exceeding max execution time are terminated and routed to the configured fallback behavior.
Prompt Templates
Manage reusable prompt templates powering summary, insight, nudge, chatbot, and copilot workflows.
| Template Name | Workflow Type | Version | Last Modified | Actions |
|---|---|---|---|---|
| Clinical Summary DigestPT-101 · Dr. Sarah Chen | Summary | v3.2 | Jun 28, 2026 | |
| Weekly Progress InsightPT-102 · Marcus Wu | Insight | v2.0 | Jun 27, 2026 | |
| Medication Adherence NudgePT-103 · Priya Nair | Nudge | v1.4 | Jun 25, 2026 | |
| Patient Support Chatbot IntroPT-104 · Dr. Sarah Chen | Chatbot | v4.1 | Jun 24, 2026 | |
| Clinician Copilot Draft NotePT-105 · Elena Kaur | Copilot | v1.0 | Jun 22, 2026 | |
| Wearable Trend InsightPT-106 · Marcus Wu | Insight | v2.3 | Jun 20, 2026 |
Clinical Summary DigestPT-101
SummaryWeekly Progress InsightPT-102
InsightMedication Adherence NudgePT-103
NudgePatient Support Chatbot IntroPT-104
ChatbotClinician Copilot Draft NotePT-105
CopilotWearable Trend InsightPT-106
InsightShowing 1–6 of 14
Prompt Engineering7 variables in use
Prompt Template Editor
Author and version the prompts that drive neuro's AI workflows. Insert variable placeholders, preview rendered output with sample patient data, and publish when ready.
Template textSyntax highlighting on · use {{variable}} placeholders
Variable referenceClick to insert at cursor
PreviewRendered with sample data
You are a clinical AI assistant preparing a summary for Dr. Sarah Chen regarding Marcus Ellison. Clinical context: - Diagnosis: Type 2 Diabetes, Hypertension - Risk level: Elevated - Latest assessment score: 78 / 100 - Wearable summary: 6h 42m sleep, 8,120 steps, resting HR 64bpm - Last visit: June 14, 2026 Generate a concise clinical summary highlighting any risk factors that require immediate attention, and recommend one actionable next step. Keep the response under 150 words.
Rendered using sample patient data for preview purposes only. Live output reflects real patient records at runtime.
Sample data used
- {{patient_name}}Marcus Ellison
- {{clinician_name}}Dr. Sarah Chen
- {{diagnosis}}Type 2 Diabetes, Hypertension
- {{assessment_score}}78 / 100
- {{risk_level}}Elevated
- {{wearable_summary}}6h 42m sleep, 8,120 steps, resting HR 64bpm
- {{last_visit_date}}June 14, 2026
LLM Provider Configuration
Connect and validate the language model provider powering neuro’s clinical AI workflows.
Not tested yet
Stored encrypted at rest. Never transmitted to the browser after save.
Security Notice
Credentials are encrypted using AES-256 and stored in a secure vault. Never share your API keys. Rotate credentials regularly for compliance with neuro’s security policy.
LLM Safety & Fallback Rules
Configure guardrails that govern how neuro's AI orchestration layer handles medical content, validates input, filters output, and recovers from provider failures.
Restricted Medical Advice HandlingWhen enabled, the model is prohibited from issuing direct diagnoses, dosages, or treatment instructions. Responses are reframed as clinician-facing suggestions requiring review.
Restriction active
Input Validation RulesChecks applied to every prompt before it is dispatched to a configured LLM provider.
- Block PII in incoming promptsDetects and rejects prompts containing patient identifiers (MRN, DOB, SSN) before they reach the model.
- Reject prompts exceeding token limitPrevents oversized context windows from being submitted to reduce cost and latency risk.
- Sanitize prompt injection attemptsStrips or flags instructions embedded in user/EMR content that attempt to override system prompts.
- Enforce input schema validationRejects malformed payloads that do not match the expected clinical data schema.
Output Filtering RulesPost-processing checks applied to every model response before it reaches the clinical portal.
- Redact PII in model responsesAutomatically masks any patient-identifying data the model may echo back in generated text.
- Append clinical disclaimer footerEvery AI-generated recommendation includes a disclaimer requiring clinician review before action.
- Flag low-confidence responsesResponses below the configured confidence threshold are routed to a clinician queue instead of auto-display.
- Block harmful or non-clinical contentFilters profanity, off-topic, or unsafe generated content before it reaches the clinical portal.
Fallback Behavior on API ErrorDetermines what happens when the active LLM provider times out, rate-limits, or returns an error.
Automatically re-routes the request to the next configured LLM provider in priority order.
AI-Assisted Care Guidance
Clinician Recommendations
Evidence-based recommendations generated from patient wearable data, clinical assessments, and AI insight synthesis. Review, prioritize, and act on guidance tailored to each patient's current care plan.
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