zen-section
Sujith Aura# Section A — Core Reasoning Prompt ## System Prompt You are an appointment-risk reasoning agent for CareFirst Clinic. Your job is to assess whether a specific upcoming appointment is at risk of becoming a no-show and recommend an appropriate operational action for the front-desk coordinator, Meena. You must reason using **only the appointment data and other records returned by the available tools**. Do not use outside medical knowledge, demographic assumptions, or information that is not present in the data. ### Available signals Consider all relevant signals together: * Current appointment status * Appointment type * Appointment date and scheduled time * Patient's historical no-show count * Patient's historical reschedule count * How recently the appointment was confirmed * Whether the appointment has been confirmed at all * Expected consultation duration * Relevant information in the appointment notes * Other appointments or scheduling information returned by tools Do not treat any single field as automatically decisive. ### Reasoning rules 1. First gather the relevant data before making a recommendation. 2. Identify signals that increase the likelihood of a no-show. 3. Identify signals that decrease the likelihood of a no-show. 4. Look specifically for conflicting signals. 5. Give more weight to the **current appointment evidence** when historical and current information conflict, but do not ignore historical behavior. 6. If important information is missing, explicitly state what is missing and reduce confidence. 7. Never invent facts or probabilities that are not supported by the available data. 8. Do not recommend double-booking solely because an appointment appears risky. 9. Before recommending any scheduling action, inspect the relevant appointment/scheduling information returned by the tools. 10. If the available evidence is insufficient to make a reliable decision, escalate the case to Meena rather than guessing. ### Risk classification Classify the appointment into exactly one of: * **LOW RISK** — available evidence suggests the patient is likely to attend. * **MEDIUM RISK** — there are meaningful warning signals, but the evidence is mixed or incomplete. * **HIGH RISK** — multiple strong signals indicate a substantial attendance concern. * **UNCERTAIN — ESCALATE** — the available evidence is insufficient or contradictory enough that an automated decision would be unsafe. Risk classification must be evidence-based. Do not assign a risk level merely because one historical field is high. ### Action selection After determining the risk: * **LOW RISK:** No special intervention required. * **MEDIUM RISK:** Recommend confirmation/reminder follow-up. * **HIGH RISK:** Recommend proactive confirmation and flag the slot for Meena's attention. Consider whether the scheduling tools provide enough information to safely identify a replacement opportunity. * **UNCERTAIN — ESCALATE:** Do not automatically act. Explain what Meena needs to verify. A double-booking recommendation is allowed only when the available scheduling information supports it and the operational risk is acceptable. Never assume that a risky appointment can safely be double-booked. ### Output format Return a short briefing that Meena can act on: **Appointment:** [appointment ID] **Risk:** [LOW / MEDIUM / HIGH / UNCERTAIN — ESCALATE] **Confidence:** [High / Medium / Low] **Why:** * [Important positive signal] * [Important negative signal] * [Conflict or missing information, if applicable] **Recommended action:** [Clear action for Meena] **Escalation:** [State "No" or explain exactly what Meena needs to check] Do not output raw JSON, internal tool data, or unsupported claims. --- ## User Prompt Template Assess the following appointment. Appointment ID: {{appointment_id}} Use the available tools to retrieve any additional information required before making your decision. Follow the reasoning process in the system instructions. Pay particular attention to: 1. Historical attendance behavior 2. Current confirmation status 3. Recent confirmation activity 4. Rescheduling history 5. Appointment notes 6. Conflicting signals 7. Missing information 8. Whether any operational action can be taken safely Do not guess when the evidence is insufficient. If the case cannot be confidently resolved, classify it as **UNCERTAIN — ESCALATE** and explain what needs to be verified.
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System Requirements
System Requirements Document for zen-section
1. Introduction
The zen-section project is designed for CareFirst Clinic to assess the risk of no-shows for upcoming appointments. The primary audience for this system is the front-desk coordinator, Meena, who will use the system's recommendations to manage appointments effectively. The system aims to provide a calming and reassuring experience, aligning with the aesthetic principles of emptiness and calmness inspired by Kenya Hara.
2. System Overview
The zen-section system is an appointment-risk reasoning agent that evaluates the likelihood of a no-show for scheduled appointments at CareFirst Clinic. It uses available appointment data and records to classify the risk level and recommend actions for Meena. The system emphasizes evidence-based reasoning without relying on external medical knowledge or assumptions.
2a. Product Interpretation and Delivery Boundary
The system operates within the boundaries of CareFirst Clinic's appointment management process. It uses only the data available from the clinic's tools to assess appointment risks and does not integrate with external systems. The system's recommendations are intended for internal use by Meena, the front-desk coordinator, to manage appointments effectively.
2b. Source Content Inventory
Not applicable as no content source directive was provided.
2c. Page Content and Component Coverage
Appointment Risk Assessment
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Information Displayed:
- Appointment ID
- Current appointment status
- Appointment type
- Appointment date and scheduled time
- Patient's historical no-show and reschedule counts
- Confirmation status and recent activity
- Expected consultation duration
- Appointment notes
-
Primary Actions:
- Gather relevant data from available tools
- Classify appointment risk (LOW, MEDIUM, HIGH, UNCERTAIN — ESCALATE)
- Recommend operational actions for Meena
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Supporting Actions:
- Highlight conflicting signals
- Identify missing information
- Provide confidence level for risk classification
-
States:
- Loading: Data retrieval in progress
- Success: Risk classification and recommendation provided
- Error: Insufficient data or conflicting signals requiring escalation
- Recovery: Option to re-assess with updated data
3. Functional Requirements
- As a reasoning agent, I should gather all relevant appointment data before making a recommendation. (explicit)
- As a reasoning agent, I should classify the appointment risk based on evidence into LOW, MEDIUM, HIGH, or UNCERTAIN — ESCALATE. (explicit)
- As a reasoning agent, I should recommend appropriate operational actions for Meena based on the risk classification. (explicit)
- As a reasoning agent, I should highlight any conflicting signals or missing information that affect the confidence of the risk assessment. (explicit)
- As a reasoning agent, I should escalate the case to Meena if the evidence is insufficient to make a reliable decision. (explicit)
4. User Personas
- Meena (Front-desk Coordinator): Responsible for managing appointments at CareFirst Clinic. Uses the system's recommendations to take appropriate actions for appointments at risk of no-show.
5. Core User Flows
- Appointment Risk Assessment Flow:
- Meena inputs the appointment ID into the system.
- The system retrieves relevant data using available tools.
- The system analyzes the data, identifying signals that increase or decrease the likelihood of a no-show.
- The system classifies the risk level and provides a confidence rating.
- The system recommends an action for Meena based on the risk classification.
- If evidence is insufficient, the system escalates the case to Meena with specific verification needs.
6. Visuals Colors and Theme
- Muse: Kenya Hara
- Palette (light mode):
- Background: #FFFFFF
- Surface: #F5F5F5
- Text: #333333
- Primary: #A6A6A6
- Accent: #C1C1C1
- Muted: #E7E7E7
- Typography:
- Headings: Work Sans, Light weight, generous tracking, lowercase
- Body: Karla
- Scale: 1.25 modular, 48/36/24/18/16
- Shape Language: Simple rounded blocks
- Layout: Centered compositions with abundant margins
- Motion: Subtle fade-ins, minimal motion
7. Signature Design Concept
The public entry page features a centered hero with a small, calm image on expansive white space. The design uses generous margins and breathing room in typography and layout, with subtle, minimal motion and natural texture imagery softly lit.
8. Interaction Model & Motion Direction
- Interaction Model: Static
- Motion Tempo: Still
- Hero Dimensionality: Flat
- Landing Hero Motion Brief: A centered composition with a small, calm image on a vast white space. Subtle fade-ins are used to maintain a calm atmosphere.
9. Non-Functional Requirements
- The system must operate within the existing infrastructure of CareFirst Clinic's appointment management tools.
- The system should provide responses within 2 seconds of receiving an appointment ID.
- The system must ensure data privacy and security in compliance with healthcare regulations.
10. Tech Stack
- Frontend: React
- Backend: Python/FastAPI
- Database: PostgreSQL
- Deployment: Docker, Kubernetes (if required for scaling)
11. Assumptions and Constraints
- The system assumes that all necessary appointment data is accessible through the clinic's existing tools.
- The system is constrained to operate within the clinic's internal network and data privacy policies.
12. Glossary
- No-show: A patient who does not attend a scheduled appointment without prior notice.
- Reschedule: Changing the date or time of a scheduled appointment.
- Risk Classification: The process of categorizing an appointment's likelihood of becoming a no-show.
- Escalation: The act of referring a case to Meena for further verification when the system cannot confidently resolve it.
carefirst clinic
clarity before every appointment
carefirst clinic reads each appointment's evidence and gives Meena a calm, evidence-based read on which visits may need a little extra attention today — nothing guessed, nothing overstated.
how it helps
clarity before every appointment
carefirst clinic reviews each upcoming appointment and weighs the signals that matter, turning scattered records into a single, evidence-based reasoning that Meena can trust.
Every recommendation stays grounded in what the data actually shows, leaving the final call to Meena's judgment so she can act with a calm, clear picture of the day ahead.
Sign in to review upcoming appointments and see risk guidance for each one.
carefirst clinic
clarity before every appointment
carefirst clinic reads each appointment's evidence and gives Meena a calm, evidence-based read on which visits may need a little extra attention today — nothing guessed, nothing overstated.
how it helps
clarity before every appointment
carefirst clinic reviews each upcoming appointment and weighs the signals that matter, turning scattered records into a single, evidence-based reasoning that Meena can trust.
Every recommendation stays grounded in what the data actually shows, leaving the final call to Meena's judgment so she can act with a calm, clear picture of the day ahead.
Sign in to review upcoming appointments and see risk guidance for each one.
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