#!/usr/bin/env python3 """ Pixelated Empathy — Medication Management Session Generator Fills Gap 6: psychiatric medication management sessions. 4 categories × 50 sessions = 200 sessions total. Clinician is a Psychiatric Nurse Practitioner (NP), not a therapist. Shorter sessions (8-12 turns) reflecting med management appointments. Usage: python generate_medication_sessions.py [--categories all] [--sessions-per-category 50] [--resume] """ import json import os import random import sys import time import argparse from pathlib import Path from session_base import ( OLLAMA_BASE, THERAPIST_MODEL, PATIENT_MODEL, THERAPIST_TEMP, PATIENT_TEMP, MAX_RETRIES, RETRY_DELAY, STYLE_MAX_RETRIES, FORBIDDEN_OUTPUT_OPENINGS, PLATITUDE_PATTERNS, ROBOTIC_SIGNALS, SYCOPHANCY_MARKERS, THERAPIST_STYLE_PROFILES, PATIENT_SYSTEM, ollama_chat, _check_style, session_exists, ) NP_SYSTEM_BASE = ( "You are a Psychiatric Nurse Practitioner conducting a medication management appointment. " "You are warm but more direct and medical than a therapist. You discuss psychiatric medications, " "their efficacy, side effects, dosage adjustments, and safety. You explain risks and benefits clearly. " "You screen for substance use and drug interactions. You prioritize patient safety and informed consent.\n\n" "CRITICAL — Sound like a REAL human clinician:\n" "- NEVER use formulaic phrases like 'I hear that you feel', 'I want to validate', " "'That sounds really difficult', 'I can see how that would be', or 'What I'm hearing is...'\n" "- Use natural, conversational language — contractions, varied sentence length.\n" "- Be direct about medical matters without being cold.\n" "- NEVER start responses with: " + ", ".join(f"'{p}'" for p in FORBIDDEN_OUTPUT_OPENINGS[:10]) + "\n" "- NEVER use platitudes like: " + ", ".join(f"'{p}'" for p in PLATITUDE_PATTERNS) + "\n" "- NEVER use robotic AI language like: " + ", ".join(f"'{p}'" for p in ROBOTIC_SIGNALS) + "\n" ) CATEGORIES = { "initial_med_consult": { "name": "Initial Medication Consultation", "difficulty": "low-medium", "presentations": [ "first-time SSRI — patient nervous about starting antidepressants, NP explains mechanism, addresses fears", "anxiety medication exploration — patient wants something for anxiety, NP discusses options (SSRI vs buspirone vs short-term benzo)", "ADHD evaluation — adult seeking ADHD evaluation, NP conducts screening, discusses stimulant vs non-stimulant options", "sleep medication consult — patient on insomnia meds, wants to stop, NP discusses tapering and sleep hygiene", ], "patient_personas": [ { "age": "28", "gender": "female", "occupation": "teacher", "presenting": "Therapist recommended meds, nervous about side effects, never taken psych meds before", }, { "age": "35", "gender": "male", "occupation": "engineer", "presenting": "Anxiety interfering with work, wants something to take the edge off, worried about dependence", }, { "age": "31", "gender": "non-binary", "occupation": "designer", "presenting": "Struggled with focus since college, wondering about ADHD, worried about stimulant stigma", }, { "age": "45", "gender": "female", "occupation": "nurse", "presenting": "Been on zolpidem for 2 years, wants to stop, worried about rebound insomnia", }, ], "therapist_techniques": [ "shared decision making — present options, explain pros/cons, let patient choose", "psychoeducation — explain how SSRIs work, timeline for effect, common side effects", "risk-benefit framing — weigh medication risks against untreated condition risks", "fear normalization — acknowledge fear of psych meds, provide factual reassurance", "start low go slow — explain dosing strategy, reassure about gradual onset", "informed consent process — explain off-label use, alternatives, right to refuse", ], "therapist_prompt_addon": ( "You're a Psychiatric NP doing an initial medication consult. You take a brief history, " "discuss medication options, and address fears about psychiatric medications. You explain " "mechanisms in plain language. You're patient with first-timers. You present options as " "choices, not orders. You always explain the timeline (SSRIs take 4-6 weeks) and set " "realistic expectations." ), }, "side_effects_management": { "name": "Side Effects Management", "difficulty": "medium", "presentations": [ "SSRI sexual dysfunction — patient on sertraline, experiencing loss of libido, embarrassed to bring it up", "weight gain on antipsychotic — patient on quetiapine, gained 15 lbs, frustrated and considering stopping", "sedation and fatigue — patient on mirtazapine, can't function during day, considering switching", "GI side effects — patient on new SSRI, nausea and digestive issues in first 2 weeks", "emotional numbing — patient on SSRI, feels 'flat,' can't cry or feel joy, questioning if meds are working", ], "patient_personas": [ { "age": "30", "gender": "male", "occupation": "software developer", "presenting": "On sertraline 3 months, lost all sex drive, embarrassed, relationship suffering", }, { "age": "38", "gender": "female", "occupation": "teacher", "presenting": "On quetiapine for bipolar, gained 15 pounds in 2 months, angry and considering stopping", }, { "age": "42", "gender": "male", "occupation": "truck driver", "presenting": "On mirtazapine, can't stay awake at work, nearly got in accident, desperate", }, { "age": "26", "gender": "female", "occupation": "barista", "presenting": "Started fluoxetine 2 weeks ago, constant nausea, wondering if it's worth it", }, ], "therapist_techniques": [ "side effect assessment — systematically review side effects, normalize as common and often temporary", "dosage adjustment — discuss lowering dose, splitting dosing, or timing changes", "medication switching — explain cross-taper process, what to expect during switch", "augmentation strategies — discuss adding medications to counteract side effects (e.g., bupropion for SSRI sexual dysfunction)", "lifestyle interventions — discuss dietary changes, exercise, timing strategies for specific side effects", "risk-benefit re-evaluation — weigh side effects against therapeutic benefit, patient-centered decision", ], "therapist_prompt_addon": ( "You're a Psychiatric NP managing medication side effects. You take side effects seriously " "and never dismiss them. You're comfortable discussing sexual side effects without awkwardness. " "You explain which side effects are temporary vs persistent. You offer concrete options: " "dose adjustment, switching meds, augmentation. You involve the patient in the decision. " "You never just say 'give it time' when the patient is suffering." ), }, "medication_adherence": { "name": "Medication Adherence", "difficulty": "medium", "presentations": [ "stopped because feeling better — patient stopped meds when symptoms improved, now symptoms returning", "stopped because side effects — patient quit without telling NP, now in withdrawal, scared", "stigma and shame — patient embarrassed about taking psych meds, hides from family, considering stopping", "cost barrier — patient can't afford medication, rationing doses, NP explores alternatives", "intermittent adherence — patient takes meds sometimes, forgets often, NP problem-solves", ], "patient_personas": [ { "age": "33", "gender": "female", "occupation": "marketing manager", "presenting": "Stopped SSRIs 3 weeks ago because she felt better, now crashing, scared and ashamed", }, { "age": "40", "gender": "male", "occupation": "construction worker", "presenting": "Stopped bupropion cold turkey, having brain zaps and mood swings, didn't know you shouldn't quit abruptly", }, { "age": "24", "gender": "male", "occupation": "student", "presenting": "Won't take meds where roommates can see, feels weak for needing them, considering stopping", }, { "age": "50", "gender": "female", "occupation": "home health aide", "presenting": "Can't afford $200/month for brand name, been taking half doses, running out", }, ], "therapist_techniques": [ "motivational interviewing — explore reasons for stopping, validate concerns, evoke adherence motivation", "psychoeducation about relapse — explain why meds need to continue even when feeling better", "tapering protocol — for those who stopped, explain safe restart or taper plan", "stigma exploration — discuss societal attitudes, help patient reframe meds as healthcare, not weakness", "cost navigation — explore generic alternatives, patient assistance programs, sliding scale pharmacies", "practical adherence strategies — pill organizers, phone alarms, tying to daily routine, 90-day supplies", ], "therapist_prompt_addon": ( "You're a Psychiatric NP addressing medication adherence. You never shame patients for stopping " "meds. You explore reasons with curiosity, not judgment. You explain the biology of relapse — " "feeling better means meds are working, not that they're cured. You're practical about barriers " "(cost, stigma, forgetfulness) and offer concrete solutions. You understand that adherence is " "a process, not an event." ), }, "controlled_substance_boundaries": { "name": "Controlled Substance Boundaries", "difficulty": "high", "presentations": [ "early refill request — patient says they 'lost' their stimulant prescription, requesting early refill", "dose escalation pressure — patient on benzodiazepines, pushing for higher dose, signs of tolerance", "drug-seeking patterns — new patient with specific benzo/stimulant request, vague history, insist on controlled substance", "diversion concern — family member says patient is selling their stimulant medication, NP must address", "polysubstance risk — patient on multiple controlled substances, requesting another, NP must assess safety", ], "patient_personas": [ { "age": "27", "gender": "male", "occupation": "sales rep", "presenting": "Called saying Adderall prescription was 'stolen,' wants early refill, third time this year", }, { "age": "34", "gender": "female", "occupation": "nurse", "presenting": "On Xanax 2 years, wants higher dose, gets angry when NP suggests tapering, signs of tolerance", }, { "age": "29", "gender": "male", "occupation": "unemployed", "presenting": "New patient, knows exactly which benzo he wants, vague symptoms, refuses non-controlled alternatives", }, { "age": "22", "gender": "female", "occupation": "student", "presenting": "Mother called saying daughter is selling her Vyvanse, NP needs to address with patient directly", }, { "age": "45", "gender": "male", "occupation": "business owner", "presenting": "Already on opioid pain meds from PCP, asking for benzodiazepines for 'sleep,' high overdose risk", }, ], "therapist_techniques": [ "boundary setting — clear, firm, compassionate refusal when clinically inappropriate", "controlled substance agreement — review PDMP, establish expectations, document everything", "risk assessment — screen for substance use disorder, assess overdose risk, consider naloxone", "motivational interviewing for taper — explore ambivalence about controlled substances, plant seeds for change", "alternative treatment framing — offer non-controlled alternatives with clear rationale", "safety-first approach — when diversion or polysubstance risk identified, prioritize patient safety", ], "therapist_prompt_addon": ( "You're a Psychiatric NP navigating controlled substance management. You set firm, compassionate " "boundaries. You can say no without being punitive. You check the PDMP (prescription drug monitoring " "program). You document everything. You recognize drug-seeking behavior without assuming everyone " "who asks for meds is drug-seeking. You offer alternatives. You prioritize patient safety over " "patient satisfaction. You never prescribe controlled substances when it's not clinically appropriate, " "even under pressure." ), }, } def generate_np_turn(persona, presentation, category, conversation, turn_num, style_profile): """Generate NP response with technique injection.""" technique = random.choice(category["therapist_techniques"]) technique_guidance = ( f"\n\n[INTERNAL CLINICAL GUIDANCE — embody, never state explicitly]: " f"Use this approach naturally: {technique}. " f"Weave it into the conversation — don't announce it. " f"Respond to what the patient actually said." ) style_guidance = ( f"\n\nSTYLE: {style_profile['description']}\n" f"NEVER start with: {', '.join(style_profile['forbidden_openings'])}\n" f"Good examples: {'; '.join(style_profile['good_examples'][:3])}\n" f"MAX {style_profile['max_sentences']} sentences, {style_profile['max_words']} words." ) addon = f"\n\n{category['therapist_prompt_addon']}" system_content = NP_SYSTEM_BASE + technique_guidance + style_guidance + addon messages = [{"role": "system", "content": system_content}, *conversation] return ollama_chat(messages, model=THERAPIST_MODEL, temperature=THERAPIST_TEMP, num_predict=400) def generate_np_turn_validated(persona, presentation, category, conversation, turn_num, style_profile): for attempt in range(STYLE_MAX_RETRIES): output = generate_np_turn(persona, presentation, category, conversation, turn_num, style_profile) passed, reason = _check_style(output, style_profile) if passed: return output print(f" [style retry {attempt + 1}/{STYLE_MAX_RETRIES}] {reason}") return output def generate_med_session(category_key, category, persona, presentation, session_idx): """Generate a complete medication management session.""" min_turns = 8 max_turns = 12 total_turns = random.randint(min_turns // 2, max_turns // 2) * 2 total_patient_turns = total_turns // 2 style_keys = list(THERAPIST_STYLE_PROFILES.keys()) style_profile = THERAPIST_STYLE_PROFILES[style_keys[session_idx % len(style_keys)]] conversation = [] for turn in range(1, total_patient_turns + 1): # Reuse patient turn from session_base logic if turn == 1: direction = f"The patient is arriving for a medication management appointment. Their presenting concern: {presentation}." elif turn <= 3: direction = "The patient is sharing more about their experience with medications." elif turn == total_patient_turns: direction = "Final turn. The patient is wrapping up, maybe asking a final question or expressing a concern." else: direction = "The patient is responding to the NP's explanation or recommendation." conv_text = "" for msg in conversation: role = "Patient" if msg["role"] == "user" else "NP" conv_text += f"{role}: {msg['content']}\n" prompt = f"""You are playing a patient in a medication management appointment. Stay completely in character. PATIENT: Age: {persona["age"]}, Gender: {persona["gender"]}, Occupation: {persona["occupation"]} Presenting concern: {persona["presenting"]} SESSION FOCUS: {category["name"]} — {presentation} DIRECTION FOR THIS TURN: {direction} This is patient turn {turn} of {total_patient_turns}. CONVERSATION SO FAR: {conv_text if conv_text else "(First turn — arriving at appointment.)"} What does the patient say next? Generate ONLY spoken words — no labels, no narration. 2-5 sentences.""" messages = [ {"role": "system", "content": PATIENT_SYSTEM}, {"role": "user", "content": prompt}, ] patient_msg = ollama_chat(messages, model=PATIENT_MODEL, temperature=PATIENT_TEMP, num_predict=250) conversation.append({"role": "user", "content": patient_msg}) np_msg = generate_np_turn_validated(persona, presentation, category, conversation, turn, style_profile) conversation.append({"role": "assistant", "content": np_msg}) session_id = f"medication_{category_key}_{session_idx:04d}" return { "messages": [ {"role": "system", "content": NP_SYSTEM_BASE}, *conversation, ], "metadata": { "source_family": "medication_management", "category": category_key, "category_name": category["name"], "presentation": presentation, "session_id": session_id, "persona_age": persona["age"], "persona_gender": persona["gender"], "persona_occupation": persona["occupation"], "presenting_concern": persona["presenting"], "style_profile": style_profile["description"][:50], "turns": len(conversation), "difficulty": category["difficulty"], "clinician_role": "psychiatric_np", }, } def main(): parser = argparse.ArgumentParser(description="Medication Management Session Generation") parser.add_argument("--categories", default="all", help="Comma-separated category keys or 'all'") parser.add_argument("--sessions-per-category", type=int, default=50) parser.add_argument("--resume", action="store_true") parser.add_argument("--spot-check", type=int, default=None, help="Generate N sessions from first category only") args = parser.parse_args() output_dir = Path("data/medication_sessions") output_dir.mkdir(parents=True, exist_ok=True) output_file = output_dir / "medication_sessions.jsonl" if args.categories == "all": cats = list(CATEGORIES.keys()) else: cats = [c.strip() for c in args.categories.split(",")] if args.spot_check: cats = cats[:1] total_sessions = args.spot_check else: total_sessions = len(cats) * args.sessions_per_category print(f"\n=== MEDICATION MANAGEMENT SESSION GENERATION ===") print(f"Categories: {len(cats)} ({', '.join(cats)})") print(f"Sessions per category: {args.spot_check or args.sessions_per_category}") print(f"Total sessions: {total_sessions}") print(f"Output: {output_file}") print(f"Clinician: {THERAPIST_MODEL} (Psychiatric NP persona)") print(f"Patient: {PATIENT_MODEL}") print(f"Turns: 8-12 (med management appointments)") print() completed = 0 skipped = 0 failed = 0 start_time = time.time() for cat_key in cats: category = CATEGORIES[cat_key] n_sessions = args.spot_check or args.sessions_per_category print(f"\n--- {category['name']} ({cat_key}) ---") for i in range(n_sessions): presentation = category["presentations"][i % len(category["presentations"])] persona = category["patient_personas"][i % len(category["patient_personas"])] session_id = f"medication_{cat_key}_{i:04d}" if args.resume and session_exists(output_file, session_id): skipped += 1 continue try: session = generate_med_session(cat_key, category, persona, presentation, i) with open(output_file, "a") as f: f.write(json.dumps(session) + "\n") completed += 1 elapsed = time.time() - start_time rate = completed / (elapsed / 3600) if elapsed > 0 else 0 remaining = (total_sessions - completed - skipped) / rate if rate > 0 else 0 print( f" ✓ {session_id} {len(session['messages'])} msgs | done: {completed}/{total_sessions} | ~{remaining:.1f}h left" ) except Exception as e: failed += 1 print(f" ✗ {session_id} FAILED: {e}") with open(output_dir / "errors.log", "a") as f: f.write(f"{session_id}: {e}\n") elapsed = time.time() - start_time print(f"\n=== COMPLETE ===") print(f"Generated: {completed}") print(f"Skipped: {skipped}") print(f"Failed: {failed}") print(f"Elapsed: {elapsed / 3600:.1f}h") print(f"Output: {output_file}") if __name__ == "__main__": main()