test_patients.py

# test_patients.py - Test patient data for Testing Lab

from typing import Dict, Any, Tuple

class TestPatientData:
    """Class for managing test patient data"""
    
    @staticmethod
    def get_patient_types() -> Dict[str, str]:
        """Returns available test patient types with descriptions"""
        return {
            "elderly": "👵 Elderly Mary (76 years old, complex comorbidity)",
            "athlete": "🏃 Athletic John (24 роки, відновлення після травми)",
            "pregnant": "🤰 Pregnant Sarah (28 років, вагітність з ускладненнями)"
        }
    
    @staticmethod
    def get_elderly_patient() -> Tuple[Dict[str, Any], Dict[str, Any]]:
        """Повертає дані для літнього пацієнта з множинними захворюваннями"""
        clinical_data = {
            "patient_summary": {
                "active_problems": [
                    "Essential hypertension (uncontrolled)",
                    "Type 2 diabetes mellitus with complications", 
                    "Chronic kidney disease stage 3",
                    "Falls risk - history of 3 falls last year"
                ],
                "current_medications": [
                    "Amlodipine 10mg daily",
                    "Metformin 1000mg twice daily",
                    "Lisinopril 20mg daily",
                    "Furosemide 40mg daily"
                ],
                "allergies": "Penicillin - rash, NSAIDs - GI upset"
            },
            "vital_signs_and_measurements": [
                "Blood Pressure: 165/95 (last visit)",
                "Weight: 78kg", 
                "BMI: 31.2 kg/m²"
            ],
            "critical_alerts": [
                "High fall risk - requires mobility assessment",
                "Uncontrolled hypertension and diabetes"
            ],
            "assessment_and_plan": "76-year-old female with multiple cardiovascular risk factors and functional limitations."
        }
        
        lifestyle_data = {
            "patient_name": "Mary",
            "patient_age": "76",
            "conditions": ["essential hypertension", "type 2 diabetes", "high fall risk"],
            "primary_goal": "Improve mobility and independence while managing chronic conditions safely",
            "exercise_preferences": ["chair exercises", "gentle walking"],
            "exercise_limitations": [
                "High fall risk - balance issues", 
                "Limited endurance due to heart condition",
                "Requires walking frame for mobility"
            ],
            "dietary_notes": [
                "Diabetic diet - needs simple carb counting",
                "Low sodium for hypertension"
            ],
            "personal_preferences": [
                "very cautious due to fall anxiety",
                "needs frequent encouragement"
            ],
            "journey_summary": "Elderly patient with complex medical needs seeking to maintain independence.",
            "last_session_summary": "",
            "progress_metrics": {
                "exercise_frequency": "0 times/week - afraid to move",
                "fall_incidents": "3 in past 12 months"
            }
        }
        
        return clinical_data, lifestyle_data
    
    @staticmethod
    def get_athlete_patient() -> Tuple[Dict[str, Any], Dict[str, Any]]:
        """Повертає дані для спортсмена після травми"""
        clinical_data = {
            "patient_summary": {
                "active_problems": [
                    "ACL reconstruction recovery (3 months post-op)",
                    "Post-surgical knee pain and swelling",
                    "Anxiety related to return to sport"
                ],
                "current_medications": [
                    "Ibuprofen 400mg as needed for pain",
                    "Physiotherapy exercises daily"
                ],
                "allergies": "No known drug allergies"
            },
            "vital_signs_and_measurements": [
                "Blood Pressure: 118/72",
                "Weight: 82kg (lost 3kg since surgery)",
                "BMI: 24.0 kg/m²"
            ],
            "critical_alerts": [
                "Do not exceed physiotherapy exercise guidelines",
                "No pivoting or cutting movements until cleared"
            ],
            "assessment_and_plan": "24-year-old male athlete 3 months post ACL reconstruction."
        }
        
        lifestyle_data = {
            "patient_name": "John",
            "patient_age": "24",
            "conditions": ["ACL reconstruction recovery", "sports performance anxiety"],
            "primary_goal": "Return to competitive football safely and regain pre-injury fitness",
            "exercise_preferences": ["weight training", "swimming", "cycling"],
            "exercise_limitations": [
                "No pivoting or cutting movements yet",
                "Must follow physiotherapy protocol strictly"
            ],
            "dietary_notes": [
                "High protein intake for muscle recovery",
                "Anti-inflammatory foods"
            ],
            "personal_preferences": [
                "highly motivated and goal-oriented",
                "impatient with slow recovery process"
            ],
            "journey_summary": "Motivated athlete recovering from major knee surgery.",
            "last_session_summary": "",
            "progress_metrics": {
                "knee_flexion_range": "120 degrees (target: 135+)",
                "return_to_sport_timeline": "3-4 months if progress continues"
            }
        }
        
        return clinical_data, lifestyle_data
    
    @staticmethod
    def get_pregnant_patient() -> Tuple[Dict[str, Any], Dict[str, Any]]:
        """Повертає дані для вагітної пацієнтки з ускладненнями"""
        clinical_data = {
            "patient_summary": {
                "active_problems": [
                    "Pregnancy 28 weeks gestation",
                    "Gestational diabetes mellitus (diet-controlled)",
                    "Pregnancy-induced hypertension (mild)"
                ],
                "current_medications": [
                    "Prenatal vitamins with iron",
                    "Additional iron supplement 65mg daily"
                ],
                "allergies": "No known drug allergies"
            },
            "vital_signs_and_measurements": [
                "Blood Pressure: 142/88 (elevated for pregnancy)",
                "Current weight: 78kg",
                "Weight gain: 10kg (appropriate)"
            ],
            "critical_alerts": [
                "Monitor blood pressure - risk of preeclampsia",
                "Avoid exercises lying flat on back after 20 weeks"
            ],
            "assessment_and_plan": "28-year-old female, 28 weeks pregnant with gestational diabetes."
        }
        
        lifestyle_data = {
            "patient_name": "Sarah",
            "patient_age": "28",
            "conditions": ["pregnancy 28 weeks", "gestational diabetes"],
            "primary_goal": "Maintain healthy pregnancy with good blood sugar control",
            "exercise_preferences": ["prenatal yoga", "walking", "swimming"],
            "exercise_limitations": [
                "No lying flat on back after 20 weeks",
                "Monitor heart rate - shouldn't exceed 140 bpm"
            ],
            "dietary_notes": [
                "Gestational diabetes diet - controlled carbohydrates",
                "Small frequent meals to manage blood sugar"
            ],
            "personal_preferences": [
                "motivated to have healthy pregnancy",
                "anxious about blood sugar control"
            ],
            "journey_summary": "Second pregnancy with gestational diabetes.",
            "last_session_summary": "",
            "progress_metrics": {
                "blood_glucose_control": "diet-controlled, monitoring 4x daily"
            }
        }
        
        return clinical_data, lifestyle_data
    
    @classmethod
    def get_patient_data(cls, patient_type: str) -> Tuple[Dict[str, Any], Dict[str, Any]]:
        """Універсальний метод для отримання даних пацієнта за типом"""
        if patient_type == "elderly":
            return cls.get_elderly_patient()
        elif patient_type == "athlete":
            return cls.get_athlete_patient()
        elif patient_type == "pregnant":
            return cls.get_pregnant_patient()
        else:
            raise ValueError(f"Невідомий тип пацієнта: {patient_type}")