Deploy medical_prompt_templates.py to backend/ directory

backend/medical_prompt_templates.py

@@ -0,0 +1,728 @@
+"""
+Medical Prompt Templates for MedGemma Synthesis
+Comprehensive templates for generating clinician-level and patient-friendly summaries
+
+Author: MiniMax Agent
+Date: 2025-10-29
+Version: 1.0.0
+"""
+
+from typing import Dict, Any, List, Optional
+from enum import Enum
+
+
+class SummaryType(Enum):
+    """Types of medical summaries that can be generated"""
+    CLINICIAN_TECHNICAL = "clinician_technical"
+    PATIENT_FRIENDLY = "patient_friendly"
+    MULTI_MODAL = "multi_modal"
+    RISK_ASSESSMENT = "risk_assessment"
+
+
+class PromptTemplateLibrary:
+    """
+    Comprehensive library of medical prompt templates for MedGemma
+    Supports all medical modalities with evidence-based generation
+    """
+    
+    @staticmethod
+    def get_clinician_summary_template(
+        modality: str,
+        structured_data: Dict[str, Any],
+        model_outputs: List[Dict[str, Any]],
+        confidence_scores: Dict[str, float]
+    ) -> str:
+        """
+        Generate clinician-level technical summary prompt
+        
+        Features:
+        - Technical medical terminology
+        - Detailed analysis with evidence
+        - Confidence scores and uncertainty
+        - Clinical decision support
+        """
+        
+        if modality == "ECG":
+            return PromptTemplateLibrary._ecg_clinician_template(
+                structured_data, model_outputs, confidence_scores
+            )
+        elif modality == "radiology":
+            return PromptTemplateLibrary._radiology_clinician_template(
+                structured_data, model_outputs, confidence_scores
+            )
+        elif modality == "laboratory":
+            return PromptTemplateLibrary._laboratory_clinician_template(
+                structured_data, model_outputs, confidence_scores
+            )
+        elif modality == "clinical_notes":
+            return PromptTemplateLibrary._clinical_notes_clinician_template(
+                structured_data, model_outputs, confidence_scores
+            )
+        else:
+            return PromptTemplateLibrary._general_clinician_template(
+                structured_data, model_outputs, confidence_scores
+            )
+    
+    @staticmethod
+    def get_patient_summary_template(
+        modality: str,
+        structured_data: Dict[str, Any],
+        model_outputs: List[Dict[str, Any]],
+        confidence_scores: Dict[str, float]
+    ) -> str:
+        """
+        Generate patient-friendly summary prompt
+        
+        Features:
+        - Plain language explanations
+        - Key findings highlighted
+        - Actionable next steps
+        - Reassurance when appropriate
+        """
+        
+        if modality == "ECG":
+            return PromptTemplateLibrary._ecg_patient_template(
+                structured_data, model_outputs, confidence_scores
+            )
+        elif modality == "radiology":
+            return PromptTemplateLibrary._radiology_patient_template(
+                structured_data, model_outputs, confidence_scores
+            )
+        elif modality == "laboratory":
+            return PromptTemplateLibrary._laboratory_patient_template(
+                structured_data, model_outputs, confidence_scores
+            )
+        elif modality == "clinical_notes":
+            return PromptTemplateLibrary._clinical_notes_patient_template(
+                structured_data, model_outputs, confidence_scores
+            )
+        else:
+            return PromptTemplateLibrary._general_patient_template(
+                structured_data, model_outputs, confidence_scores
+            )
+    
+    # ========================
+    # ECG TEMPLATES
+    # ========================
+    
+    @staticmethod
+    def _ecg_clinician_template(
+        data: Dict[str, Any],
+        outputs: List[Dict[str, Any]],
+        confidence: Dict[str, float]
+    ) -> str:
+        """Clinician-level ECG summary template"""
+        
+        intervals = data.get("intervals", {})
+        rhythm = data.get("rhythm_classification", {})
+        arrhythmia_probs = data.get("arrhythmia_probabilities", {})
+        derived = data.get("derived_features", {})
+        
+        overall_confidence = confidence.get("overall_confidence", 0.0)
+        
+        prompt = f"""You are a medical AI assistant generating a comprehensive ECG analysis report for clinicians.
+
+PATIENT CONTEXT:
+- Document ID: {data.get('metadata', {}).get('document_id', 'N/A')}
+- Facility: {data.get('metadata', {}).get('facility', 'N/A')}
+- Recording Date: {data.get('metadata', {}).get('document_date', 'N/A')}
+
+ECG MEASUREMENTS:
+- Heart Rate: {rhythm.get('heart_rate_bpm', 'N/A')} bpm
+- PR Interval: {intervals.get('pr_ms', 'N/A')} ms
+- QRS Duration: {intervals.get('qrs_ms', 'N/A')} ms
+- QT Interval: {intervals.get('qt_ms', 'N/A')} ms
+- QTc Interval: {intervals.get('qtc_ms', 'N/A')} ms
+- RR Interval: {intervals.get('rr_ms', 'N/A')} ms
+
+RHYTHM ANALYSIS:
+- Primary Rhythm: {rhythm.get('primary_rhythm', 'N/A')}
+- Rhythm Regularity: {rhythm.get('heart_rate_regularity', 'N/A')}
+- Detected Arrhythmias: {', '.join(rhythm.get('arrhythmia_types', [])) or 'None'}
+
+ARRHYTHMIA PROBABILITIES:
+- Normal Sinus Rhythm: {arrhythmia_probs.get('normal_rhythm', 'N/A')}
+- Atrial Fibrillation: {arrhythmia_probs.get('atrial_fibrillation', 'N/A')}
+- Atrial Flutter: {arrhythmia_probs.get('atrial_flutter', 'N/A')}
+- Ventricular Tachycardia: {arrhythmia_probs.get('ventricular_tachycardia', 'N/A')}
+- Heart Block: {arrhythmia_probs.get('heart_block', 'N/A')}
+
+ST-SEGMENT & T-WAVE FINDINGS:
+- ST Elevation: {derived.get('st_elevation_mm', 'None detected')}
+- ST Depression: {derived.get('st_depression_mm', 'None detected')}
+- T-wave Abnormalities: {', '.join(derived.get('t_wave_abnormalities', [])) or 'None'}
+- Axis Deviation: {derived.get('axis_deviation', 'Normal')}
+
+AI MODEL OUTPUTS:
+{PromptTemplateLibrary._format_model_outputs(outputs)}
+
+ANALYSIS CONFIDENCE: {overall_confidence * 100:.1f}%
+
+INSTRUCTIONS:
+Generate a comprehensive clinical ECG report with the following sections:
+
+1. TECHNICAL SUMMARY
+   - Concise interpretation of rhythm and intervals
+   - Significance of any abnormal findings
+   
+2. CLINICAL SIGNIFICANCE
+   - Pathophysiological implications
+   - Risk stratification (low/moderate/high)
+   
+3. DIFFERENTIAL DIAGNOSIS
+   - Most likely diagnoses based on findings
+   - Alternative considerations
+   
+4. RECOMMENDATIONS
+   - Immediate actions required (if any)
+   - Follow-up studies or monitoring
+   - Cardiology referral if indicated
+   
+5. CONFIDENCE EXPLANATION
+   - Why the AI confidence is {overall_confidence * 100:.1f}%
+   - Which findings are most/least certain
+   - Limitations of the analysis
+
+Use precise medical terminology. Be evidence-based. Flag any critical findings requiring immediate attention.
+
+Generate the report now:"""
+        
+        return prompt
+    
+    @staticmethod
+    def _ecg_patient_template(
+        data: Dict[str, Any],
+        outputs: List[Dict[str, Any]],
+        confidence: Dict[str, float]
+    ) -> str:
+        """Patient-friendly ECG summary template"""
+        
+        rhythm = data.get("rhythm_classification", {})
+        intervals = data.get("intervals", {})
+        
+        prompt = f"""You are a medical AI assistant explaining ECG results to a patient in simple, clear language.
+
+YOUR ECG RESULTS:
+- Heart Rate: {rhythm.get('heart_rate_bpm', 'N/A')} beats per minute
+- Heart Rhythm: {rhythm.get('primary_rhythm', 'N/A')}
+
+WHAT THIS MEANS:
+Generate a patient-friendly explanation that:
+
+1. WHAT WE FOUND
+   - Explain the heart rate and rhythm in simple terms
+   - Describe any abnormalities without medical jargon
+   
+2. WHAT THIS MEANS FOR YOU
+   - Is this normal or concerning?
+   - What might be causing any abnormalities?
+   
+3. NEXT STEPS
+   - What should you do next?
+   - Do you need to see a doctor urgently?
+   - Any lifestyle changes to consider?
+   
+4. OUR CONFIDENCE
+   - How certain are we about these findings?
+   - Why you should still talk to your doctor
+
+Use everyday language. Be reassuring when appropriate. Be clear about urgency if there are concerns.
+
+Generate the patient explanation now:"""
+        
+        return prompt
+    
+    # ========================
+    # RADIOLOGY TEMPLATES
+    # ========================
+    
+    @staticmethod
+    def _radiology_clinician_template(
+        data: Dict[str, Any],
+        outputs: List[Dict[str, Any]],
+        confidence: Dict[str, float]
+    ) -> str:
+        """Clinician-level radiology summary template"""
+        
+        findings = data.get("findings", {})
+        metrics = data.get("metrics", {})
+        images = data.get("image_references", [])
+        
+        prompt = f"""You are a radiologist AI assistant generating a comprehensive imaging report.
+
+IMAGING STUDY DETAILS:
+- Modality: {', '.join([img.get('modality', 'N/A') for img in images[:3]])}
+- Body Parts: {', '.join([img.get('body_part', 'N/A') for img in images[:3]])}
+- Study Date: {data.get('metadata', {}).get('document_date', 'N/A')}
+
+FINDINGS:
+{findings.get('findings_text', 'N/A')}
+
+IMPRESSION:
+{findings.get('impression_text', 'N/A')}
+
+CRITICAL FINDINGS: {', '.join(findings.get('critical_findings', [])) or 'None'}
+INCIDENTAL FINDINGS: {', '.join(findings.get('incidental_findings', [])) or 'None'}
+
+QUANTITATIVE METRICS:
+- Organ Volumes: {metrics.get('organ_volumes', {})}
+- Lesion Measurements: {len(metrics.get('lesion_measurements', []))} lesions measured
+
+AI MODEL ANALYSIS:
+{PromptTemplateLibrary._format_model_outputs(outputs)}
+
+ANALYSIS CONFIDENCE: {confidence.get('overall_confidence', 0.0) * 100:.1f}%
+
+Generate a structured radiology report with:
+
+1. TECHNIQUE & COMPARISON
+2. FINDINGS (organized by anatomical region)
+3. IMPRESSION
+4. RECOMMENDATIONS
+5. CONFIDENCE ASSESSMENT
+
+Use standard radiology terminology (BI-RADS, Lung-RADS, etc. if applicable).
+
+Generate the report now:"""
+        
+        return prompt
+    
+    @staticmethod
+    def _radiology_patient_template(
+        data: Dict[str, Any],
+        outputs: List[Dict[str, Any]],
+        confidence: Dict[str, float]
+    ) -> str:
+        """Patient-friendly radiology summary template"""
+        
+        findings = data.get("findings", {})
+        images = data.get("image_references", [])
+        
+        prompt = f"""You are explaining imaging results to a patient in clear, simple language.
+
+YOUR IMAGING STUDY:
+- Type of Scan: {', '.join([img.get('modality', 'N/A') for img in images[:3]])}
+- Body Area: {', '.join([img.get('body_part', 'N/A') for img in images[:3]])}
+
+Generate a patient-friendly explanation:
+
+1. WHAT THE SCAN SHOWED
+   - Main findings in simple terms
+   - Any areas of concern
+   
+2. WHAT THIS MEANS
+   - Are the findings normal or abnormal?
+   - What conditions might this suggest?
+   
+3. NEXT STEPS
+   - Do you need additional tests?
+   - Should you see a specialist?
+   - Timeline for follow-up
+   
+4. QUESTIONS TO ASK YOUR DOCTOR
+   - List 3-4 relevant questions
+
+Use everyday language. Explain medical terms when necessary. Be clear about urgency.
+
+Generate the patient explanation now:"""
+        
+        return prompt
+    
+    # ========================
+    # LABORATORY TEMPLATES
+    # ========================
+    
+    @staticmethod
+    def _laboratory_clinician_template(
+        data: Dict[str, Any],
+        outputs: List[Dict[str, Any]],
+        confidence: Dict[str, float]
+    ) -> str:
+        """Clinician-level laboratory results template"""
+        
+        tests = data.get("tests", [])
+        abnormal_count = data.get("abnormal_count", 0)
+        critical_values = data.get("critical_values", [])
+        
+        test_summary = "\n".join([
+            f"- {test.get('test_name', 'N/A')}: {test.get('value', 'N/A')} {test.get('unit', '')} "
+            f"(Ref: {test.get('reference_range_low', 'N/A')}-{test.get('reference_range_high', 'N/A')}) "
+            f"{test.get('flags', [])}"
+            for test in tests[:20]  # Limit to 20 tests
+        ])
+        
+        prompt = f"""You are a clinical laboratory AI assistant generating a comprehensive lab results analysis.
+
+LABORATORY PANEL:
+- Panel Type: {data.get('panel_name', 'General Laboratory Panel')}
+- Collection Date: {data.get('collection_date', 'N/A')}
+- Total Tests: {len(tests)}
+- Abnormal Results: {abnormal_count}
+- Critical Values: {len(critical_values)}
+
+TEST RESULTS:
+{test_summary}
+
+CRITICAL VALUES: {', '.join(critical_values) or 'None'}
+
+AI MODEL ANALYSIS:
+{PromptTemplateLibrary._format_model_outputs(outputs)}
+
+ANALYSIS CONFIDENCE: {confidence.get('overall_confidence', 0.0) * 100:.1f}%
+
+Generate a comprehensive laboratory interpretation with:
+
+1. SUMMARY OF KEY FINDINGS
+   - Normal vs abnormal results
+   - Critical values requiring immediate attention
+   
+2. CLINICAL CORRELATION
+   - Pattern recognition (e.g., renal dysfunction, electrolyte imbalance)
+   - Physiological significance
+   
+3. DIFFERENTIAL DIAGNOSIS
+   - Most likely conditions based on lab pattern
+   
+4. RECOMMENDATIONS
+   - Immediate interventions for critical values
+   - Additional testing needed
+   - Follow-up timeline
+   
+5. CONFIDENCE ASSESSMENT
+   - Reliability of each test result
+   - Need for repeat testing
+
+Generate the interpretation now:"""
+        
+        return prompt
+    
+    @staticmethod
+    def _laboratory_patient_template(
+        data: Dict[str, Any],
+        outputs: List[Dict[str, Any]],
+        confidence: Dict[str, float]
+    ) -> str:
+        """Patient-friendly laboratory results template"""
+        
+        tests = data.get("tests", [])
+        abnormal_count = data.get("abnormal_count", 0)
+        
+        prompt = f"""You are explaining laboratory test results to a patient in simple language.
+
+YOUR LAB RESULTS:
+- Total Tests: {len(tests)}
+- Abnormal Results: {abnormal_count}
+
+Generate a patient-friendly explanation:
+
+1. OVERVIEW
+   - What tests were done and why
+   - Overall picture (mostly normal, some concerns, etc.)
+   
+2. KEY FINDINGS
+   - Which results are normal
+   - Which results are outside the normal range
+   - What each abnormal result means in simple terms
+   
+3. WHAT THIS MEANS FOR YOUR HEALTH
+   - Are these results concerning?
+   - What conditions might they suggest?
+   
+4. NEXT STEPS
+   - Do you need to see your doctor urgently?
+   - Lifestyle changes that might help
+   - Additional tests that might be needed
+   
+5. IMPORTANT NOTES
+   - Lab values can vary based on many factors
+   - Always discuss results with your doctor
+
+Use everyday language. Explain abbreviations. Be clear about urgency.
+
+Generate the patient explanation now:"""
+        
+        return prompt
+    
+    # ========================
+    # CLINICAL NOTES TEMPLATES
+    # ========================
+    
+    @staticmethod
+    def _clinical_notes_clinician_template(
+        data: Dict[str, Any],
+        outputs: List[Dict[str, Any]],
+        confidence: Dict[str, float]
+    ) -> str:
+        """Clinician-level clinical notes summary template"""
+        
+        sections = data.get("sections", [])
+        entities = data.get("entities", [])
+        diagnoses = data.get("diagnoses", [])
+        medications = data.get("medications", [])
+        
+        sections_summary = "\n".join([
+            f"- {section.get('section_type', 'N/A')}: {section.get('content', 'N/A')[:200]}..."
+            for section in sections[:10]
+        ])
+        
+        prompt = f"""You are a clinical documentation AI assistant synthesizing medical notes.
+
+NOTE TYPE: {data.get('note_type', 'Clinical Documentation')}
+DOCUMENTATION DATE: {data.get('metadata', {}).get('document_date', 'N/A')}
+
+CLINICAL SECTIONS:
+{sections_summary}
+
+EXTRACTED ENTITIES:
+- Diagnoses: {', '.join(diagnoses[:10]) or 'None identified'}
+- Medications: {', '.join(medications[:10]) or 'None identified'}
+
+AI MODEL ANALYSIS:
+{PromptTemplateLibrary._format_model_outputs(outputs)}
+
+ANALYSIS CONFIDENCE: {confidence.get('overall_confidence', 0.0) * 100:.1f}%
+
+Generate a comprehensive clinical synthesis with:
+
+1. CLINICAL SUMMARY
+   - Chief complaint and HPI synthesis
+   - Pertinent positives and negatives
+   
+2. ASSESSMENT
+   - Problem list with prioritization
+   - Clinical reasoning
+   
+3. PLAN
+   - Management for each problem
+   - Medications and interventions
+   - Follow-up and monitoring
+   
+4. DOCUMENTATION QUALITY
+   - Completeness assessment
+   - Missing information
+   
+5. CONFIDENCE ASSESSMENT
+
+Generate the clinical synthesis now:"""
+        
+        return prompt
+    
+    @staticmethod
+    def _clinical_notes_patient_template(
+        data: Dict[str, Any],
+        outputs: List[Dict[str, Any]],
+        confidence: Dict[str, float]
+    ) -> str:
+        """Patient-friendly clinical notes summary template"""
+        
+        diagnoses = data.get("diagnoses", [])
+        medications = data.get("medications", [])
+        
+        prompt = f"""You are explaining a clinical visit summary to a patient in clear, simple language.
+
+Generate a patient-friendly visit summary:
+
+1. REASON FOR YOUR VISIT
+   - Why you came to see the doctor
+   
+2. WHAT THE DOCTOR FOUND
+   - Key findings from examination
+   - Test results discussed
+   
+3. YOUR DIAGNOSES
+   - {', '.join(diagnoses[:5]) if diagnoses else 'To be discussed with your doctor'}
+   - What each diagnosis means in simple terms
+   
+4. YOUR TREATMENT PLAN
+   - Medications prescribed
+   - Other treatments or therapies
+   
+5. WHAT YOU NEED TO DO
+   - Follow-up appointments
+   - Tests or procedures needed
+   - Lifestyle changes
+   - Warning signs to watch for
+   
+6. QUESTIONS FOR YOUR DOCTOR
+   - List important questions to ask
+
+Use everyday language. Explain medical terms. Organize by priority.
+
+Generate the patient summary now:"""
+        
+        return prompt
+    
+    # ========================
+    # GENERAL TEMPLATES
+    # ========================
+    
+    @staticmethod
+    def _general_clinician_template(
+        data: Dict[str, Any],
+        outputs: List[Dict[str, Any]],
+        confidence: Dict[str, float]
+    ) -> str:
+        """General clinician-level summary template"""
+        
+        prompt = f"""You are a medical AI assistant generating a comprehensive clinical summary.
+
+DOCUMENT TYPE: {data.get('metadata', {}).get('source_type', 'Medical Document')}
+DOCUMENT DATE: {data.get('metadata', {}).get('document_date', 'N/A')}
+
+AI MODEL ANALYSIS:
+{PromptTemplateLibrary._format_model_outputs(outputs)}
+
+ANALYSIS CONFIDENCE: {confidence.get('overall_confidence', 0.0) * 100:.1f}%
+
+Generate a structured medical summary with:
+1. KEY FINDINGS
+2. CLINICAL SIGNIFICANCE
+3. RECOMMENDATIONS
+4. CONFIDENCE ASSESSMENT
+
+Use appropriate medical terminology.
+
+Generate the summary now:"""
+        
+        return prompt
+    
+    @staticmethod
+    def _general_patient_template(
+        data: Dict[str, Any],
+        outputs: List[Dict[str, Any]],
+        confidence: Dict[str, float]
+    ) -> str:
+        """General patient-friendly summary template"""
+        
+        prompt = f"""You are explaining medical information to a patient in simple, clear language.
+
+Generate a patient-friendly explanation:
+1. WHAT WE FOUND
+2. WHAT THIS MEANS FOR YOU
+3. NEXT STEPS
+4. QUESTIONS TO ASK YOUR DOCTOR
+
+Use everyday language. Be clear and reassuring when appropriate.
+
+Generate the explanation now:"""
+        
+        return prompt
+    
+    # ========================
+    # MULTI-MODAL SYNTHESIS
+    # ========================
+    
+    @staticmethod
+    def get_multi_modal_synthesis_template(
+        modalities: List[str],
+        all_data: Dict[str, Dict[str, Any]],
+        confidence_scores: Dict[str, float]
+    ) -> str:
+        """
+        Generate prompt for multi-modal clinical synthesis
+        Combines multiple document types into unified summary
+        """
+        
+        modality_summaries = []
+        for modality in modalities:
+            data = all_data.get(modality, {})
+            modality_summaries.append(f"- {modality.upper()}: Available with {confidence_scores.get(modality, 0.0)*100:.1f}% confidence")
+        
+        prompt = f"""You are a medical AI assistant synthesizing multiple medical documents into a comprehensive clinical picture.
+
+AVAILABLE DOCUMENTS:
+{chr(10).join(modality_summaries)}
+
+TASK:
+Generate a unified clinical summary that:
+
+1. INTEGRATED CLINICAL PICTURE
+   - Synthesize findings across all modalities
+   - Identify consistent patterns
+   - Flag contradictions or discrepancies
+   
+2. TIMELINE CORRELATION
+   - How findings relate temporally
+   - Disease progression or improvement
+   
+3. COMPREHENSIVE ASSESSMENT
+   - Overall patient status
+   - Risk stratification
+   
+4. COORDINATED CARE PLAN
+   - Unified recommendations
+   - Priority actions
+   - Specialist referrals
+   
+5. CONFIDENCE SYNTHESIS
+   - Overall reliability of the integrated analysis
+   - Areas needing additional investigation
+
+Generate the integrated clinical synthesis now:"""
+        
+        return prompt
+    
+    # ========================
+    # UTILITY METHODS
+    # ========================
+    
+    @staticmethod
+    def _format_model_outputs(outputs: List[Dict[str, Any]]) -> str:
+        """Format model outputs for inclusion in prompts"""
+        if not outputs:
+            return "No specialized model outputs available"
+        
+        formatted = []
+        for idx, output in enumerate(outputs[:5], 1):  # Limit to top 5
+            model_name = output.get("model_name", "Unknown Model")
+            domain = output.get("domain", "general")
+            result = output.get("result", {})
+            
+            # Extract key information from result
+            if isinstance(result, dict):
+                confidence = result.get("confidence", 0.0)
+                summary = result.get("summary", result.get("analysis", "Analysis completed"))[:200]
+                formatted.append(f"{idx}. {model_name} ({domain}): {summary}... [Confidence: {confidence*100:.1f}%]")
+            else:
+                formatted.append(f"{idx}. {model_name} ({domain}): {str(result)[:200]}...")
+        
+        return "\n".join(formatted)
+    
+    @staticmethod
+    def get_confidence_explanation_template(
+        confidence_scores: Dict[str, float],
+        modality: str
+    ) -> str:
+        """Generate prompt for explaining confidence scores"""
+        
+        overall = confidence_scores.get("overall_confidence", 0.0)
+        extraction = confidence_scores.get("extraction_confidence", 0.0)
+        model = confidence_scores.get("model_confidence", 0.0)
+        quality = confidence_scores.get("data_quality", 0.0)
+        
+        if overall >= 0.85:
+            threshold = "AUTO-APPROVED (≥85%)"
+        elif overall >= 0.60:
+            threshold = "REQUIRES REVIEW (60-85%)"
+        else:
+            threshold = "MANUAL REVIEW REQUIRED (<60%)"
+        
+        prompt = f"""Explain the confidence scores for this {modality} analysis to a clinician:
+
+CONFIDENCE BREAKDOWN:
+- Overall Confidence: {overall*100:.1f}% [{threshold}]
+- Data Extraction: {extraction*100:.1f}%
+- Model Analysis: {model*100:.1f}%
+- Data Quality: {quality*100:.1f}%
+
+Generate a brief explanation that:
+1. Why this confidence level?
+2. What factors contributed to the score?
+3. What should the clinician be aware of?
+4. Is human review recommended?
+
+Be concise and practical.
+
+Generate the explanation now:"""
+        
+        return prompt