Task: Solution Plan for Smart and Dynamic Anamnesis Module

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Problem Statement:

We aim to build a Smart and Dynamic Anamnesis System to enable a comprehensive understanding of a patient’s condition when they visit the practice. The system should dynamically adapt to each patient's symptoms and responses, providing tailored questions that lead to detailed and actionable insights for the doctor.

The final output must provide the doctor with a clear and structured overview of the patient's condition and should integrate seamlessly with the existing database structure. Patient responses should be stored in the following tables:

• symptoms
• current_medications
• diagnosis
• patient (for personal information)
• allergies

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Additional Features

1. Incorporate AI:

   • You are free to leverage any kind of AI to enhance the anamnesis process. Examples include:
     • NLP to interpret free-text symptom descriptions and map them to structured fields.
     • Machine learning to recommend follow-up questions based on patterns in patient responses or historical data.
     • AI models to prioritize or flag critical responses for immediate doctor attention.

2. Question Catalogue Utilization:

   • Assume you have access to a comprehensive question catalogue that includes all possible questions relevant to a patient's anamnesis.
   • Use this catalogue to:
     • Dynamically generate patient-specific questions based on initial responses.
     • Select appropriate follow-ups while avoiding unnecessary or redundant questions.

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Requirements

1. Dynamic and Tailored Question Flow:

• Propose a design for a question flow that adapts based on patient responses. For example:
  • If the patient mentions a specific symptom (e.g., chest pain), follow-up questions should dive into specifics (e.g., duration, severity, associated symptoms).
  • Different paths should exist for acute symptoms (e.g., fever, injury) vs. chronic conditions (e.g., diabetes, hypertension).
• Explain how AI could be used to optimize or automate question selection.

2. Output Data Format and Storage:

• Detail how patient responses will be parsed and stored in the existing database tables:
  • symptoms: Store a structured list of reported symptoms with relevant metadata (e.g., duration, severity).
  • current_medications: Include fields for drug name, dosage, frequency, and duration.
  • diagnosis: Capture potential diagnoses based on responses, mapping to appropriate codes if applicable.
  • patient: Collect and validate personal details such as age, gender, and contact information.
  • allergies: Store known allergies with a focus on substances and reactions.

3. Doctor-Facing Summary:

• Propose a format for the doctor’s view of the anamnesis results:
  • Clear and concise overview of the patient’s symptoms, medications, and potential diagnoses.
  • Highlight critical information such as severe allergies or urgent symptoms.
  • Enable the doctor to drill down into specific details (e.g., medication history).

4. Usability and UI/UX:

• Plan an interface that is intuitive for patients of varying technical abilities:
  • Ensure that the questions are easy to understand and respond to.
  • Include progress indicators and save functionality (to prevent data loss in case of interruptions).

5. Integration with Existing Database:

• Explain how the module will interact with the database to write responses directly to the relevant tables.
• Ensure compatibility with existing schemas and propose any schema updates if needed.

6. AI Utilization and Future-Proofing:

• Suggest how AI can be embedded in the solution, such as:
  • Dynamic generation of follow-up questions.
  • Analysis of patient input to provide recommendations to doctors.
  • Continuous improvement of question selection based on feedback loops.
• Propose how the module could evolve to include further advanced features.

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Deliverables

1. Architecture and Flow Design:

   • Diagram showing the dynamic question flow and its interaction with the backend.
   • Explanation of how branching logic works to tailor the question flow using the question catalogue and AI.

2. AI Integration Proposal:

   • Explain how AI can be used to dynamically adapt questions and process responses.
   • Outline potential tools, libraries, or models you would use.

3. Data Mapping and Storage Plan:

   • Mapping document detailing how each type of patient response is stored in the relevant tables.
   • Include examples of database entries for symptoms, medications, and allergies.

4. Doctor’s Summary View Proposal:

   • Mockup or wireframe of the summary screen presented to the doctor after the anamnesis is completed.

5. UI/UX Design Proposal:

   • Outline of patient-facing UI components with a focus on usability and accessibility.

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Assessment Criteria

• Dynamic Question Flow Design: Creativity and practicality in tailoring the anamnesis to individual patients.
• AI Integration: Effectiveness and feasibility of AI’s role in optimizing question flows and structuring data.
• Database Integration: Accuracy and completeness in mapping patient responses to the existing database structure.
• Doctor-Facing Summary: Clarity and usability of the proposed summary view.
• UI/UX Design: Patient-friendliness and adaptability of the proposed interface.
• Future Scalability: Vision for how the solution can evolve to incorporate AI or other advanced features.