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aionboarding/ai-patient-onboarding/app/ai/conversation/next_best_action.py
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3.1 KiB
Python

"""
NextBestActionEngine (Rules 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91):
- Conversational State Machine Evaluation
- Priority Scoring & Action Selection
- Cooldown & Intent Deduplication
- Status-aware field tracking (reported, confirmed_none, unknown, declined, skipped, not_asked)
"""
from typing import Dict, Any, List, Optional, Tuple
class NextBestActionEngine:
"""
Evaluates intake state and determines the next-best action and target question.
"""
def calculate_priority_score(
self,
field: str,
current_state: Dict[str, Any],
is_psych: bool = False,
asked_intents: Optional[List[str]] = None
) -> float:
"""
Calculates question priority score based on Rule 83:
priority = safety_weight + clinical_importance + symptom_completeness
+ specialty_relevance + information_value
- repetition_penalty - already_answered_penalty - unnecessary_question_penalty
"""
score = 0.0
asked_intents = asked_intents or []
weights = {
"emergency": 100.0,
"crisis": 100.0,
"reason_for_visit": 50.0,
"symptoms": 50.0,
"duration": 45.0,
"severity": 30.0,
"medical_history": 35.0,
"medications": 30.0,
"allergies": 25.0,
"previous_episodes": 15.0,
"family_history": 10.0,
"additional_information": 5.0,
}
score += weights.get(field, 10.0)
# Repetition penalty (Rule 84 & 85)
if field in asked_intents:
score -= 60.0
# Specialty relevance bonus
if is_psych and field in ["duration", "medical_history"]:
score += 10.0
return score
def determine_next_action(
self,
current_state: Dict[str, Any],
is_emergency: bool = False,
is_crisis: bool = False,
is_psych: bool = False,
missing_fields: Optional[List[str]] = None
) -> Tuple[str, Optional[str]]:
"""
Returns (action_type, target_field)
Action types (Rule 82):
- SAFETY_ACTION
- CLARIFY
- ASK_QUESTION
- COMPLETE
- HANDLE_REFUSAL
- HANDLE_UNKNOWN
"""
if is_crisis or is_emergency:
return "SAFETY_ACTION", None
# Check for state conflicts requiring clarification (Rule 67)
if current_state and current_state.get("conflicts"):
return "CLARIFY", current_state["conflicts"][-1].get("field")
missing_fields = missing_fields or []
if not missing_fields:
return "COMPLETE", None
# Score missing fields using priority score (Rule 83)
scored_fields = []
for field in missing_fields:
score = self.calculate_priority_score(field, current_state, is_psych=is_psych)
scored_fields.append((score, field))
scored_fields.sort(key=lambda x: x[0], reverse=True)
best_field = scored_fields[0][1]
return "ASK_QUESTION", best_field