clinical-decision-support — Автоматизация клинических документов для исследований

--- name: clinical-decision-support description: Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis. allowed-tools: Read Write Edit Bash license: MIT License metadata: skill-author: K-Dense Inc. --- # Clinical Decision Support Documents ## Description Generate professional clinical decision support (CDS) documents for pharmaceutical companies, clinical researchers, and medical decision-makers. This skill specializes in analytical, evidence-based documents that inform treatment strategies and drug development: 1. **Patient Cohort Analysis** - Biomarker-stratified group analyses with statistical outcome comparisons 2. **Treatment Recommendation Reports** - Evidence-based clinical guidelines with GRADE grading and decision algorithms All documents are generated as publication-ready LaTeX/PDF files optimized for pharmaceutical research, regulatory submissions, and clinical guideline development. **Note:** For individual patient treatment plans at the bedside, use the `treatment-plans` skill instead. This skill focuses on group-level analyses and evidence synthesis for pharmaceutical/research settings. **Writing Style:** For publication-ready documents targeting medical journals, consult the **venue-templates** skill's `medical_journal_styles.md` for guidance on structured abstracts, evidence language, and CONSORT/STROBE compliance. ## Capabilities ### Document Types **Patient Cohort Analysis** - Biomarker-based patient stratification (molecular subtypes, gene expression, IHC) - Molecular subtype classification (e.g., GBM mesenchymal-immune-active vs proneural, breast cancer subtypes) - Outcome metrics with statistical analysis (OS, PFS, ORR, DOR, DCR) - Statistical comparisons between subgroups (hazard ratios, p-values, 95% CI) - Survival analysis with Kaplan-Meier curves and log-rank tests - Efficacy tables and waterfall plots - Comparative effectiveness analyses - Pharmaceutical cohort reporting (trial subgroups, real-world evidence) **Treatment Recommendation Reports** - Evidence-based treatment guidelines for specific disease states - Strength of recommendation grading (GRADE system: 1A, 1B, 2A, 2B, 2C) - Quality of evidence assessment (high, moderate, low, very low) - Treatment algorithm flowcharts with TikZ diagrams - Line-of-therapy sequencing based on biomarkers - Decision pathways with clinical and molecular criteria - Pharmaceutical strategy documents - Clinical guideline development for medical societies ### Clinical Features - **Biomarker Integration**: Genomic alterations (mutations, CNV, fusions), gene expression signatures, IHC markers, PD-L1 scoring - **Statistical Analysis**: Hazard ratios, p-values, confidence intervals, survival curves, Cox regression, log-rank tests - **Evidence Grading**: GRADE system (1A/1B/2A/2B/2C), Oxford CEBM levels, quality of evidence assessment - **Clinical Terminology**: SNOMED-CT, LOINC, proper medical nomenclature, trial nomenclature - **Regulatory Compliance**: HIPAA de-identification, confidentiality headers, ICH-GCP alignment - **Professional Formatting**: Compact 0.5in margins, color-coded recommendations, publication-ready, suitable for regulatory submissions ## Pharmaceutical and Research Use Cases This skill is specifically designed for pharmaceutical and clinical research applications: **Drug Development** - **Phase 2/3 Trial Analyses**: Biomarker-stratified efficacy and safety analyses - **Subgroup Analyses**: Forest plots showing treatment effects across patient subgroups - **Companion Diagnostic Development**: Linking biomarkers to drug response - **Regulatory Submissions**: IND/NDA documentation with evidence summaries **Medical Affairs** - **KOL Education Materials**: Evidence-based treatment algorithms for thought leaders - **Medical Strategy Documents**: Competitive landscape and positioning strategies - **Advisory Board Materials**: Cohort analyses and treatment recommendation frameworks - **Publication Planning**: Manuscript-ready analyses for peer-reviewed journals **Clinical Guidelines** - **Guideline Development**: Evidence synthesis with GRADE methodology for specialty societies - **Consensus Recommendations**: Multi-stakeholder treatment algorithm development - **Practice Standards**: Biomarker-based treatment selection criteria - **Quality Measures**: Evidence-based performance metrics **Real-World Evidence** - **RWE Cohort Studies**: Retrospective analyses of patient cohorts from EMR data - **Comparative Effectiveness**: Head-to-head treatment comparisons in real-world settings - **Outcomes Research**: Long-term survival and safety in clinical practice - **Health Economics**: Cost-effectiveness analyses by biomarker subgroup ## When to Use Use this skill when you need to: - **Analyze patient cohorts** stratified by biomarkers, molecular subtypes, or clinical characteristics - **Generate treatment recommendation reports** with evidence grading for clinical guidelines or pharmaceutical strategies - **Compare outcomes** between patient subgroups with statistical analysis (survival, response rates, hazard ratios) - **Produce pharmaceutical research documents** for drug development, clinical trials, or regulatory submissions - **Develop clinical practice guidelines** with GRADE evidence grading and decision algorithms - **Document biomarker-guided therapy selection** at the population level (not individual patients) - **Synthesize evidence** from multiple trials or real-world data sources - **Create clinical decision algorithms** with flowcharts for treatment sequencing **Do NOT use this skill for:** - Individual patient treatment plans (use `treatment-plans` skill) - Bedside clinical care documentation (use `treatment-plans` skill) - Simple patient-specific treatment protocols (use `treatment-plans` skill) ## Visual Enhancement with Scientific Schematics **⚠️ MANDATORY: Every clinical decision support document MUST include at least 1-2 AI-generated figures using the scientific-schematics skill.** This is not optional. Clinical decision documents require clear visual algorithms. Before finalizing any document: 1. Generate at minimum ONE schematic or diagram (e.g., clinical decision algorithm, treatment pathway, or biomarker stratification tree) 2. For cohort analyses: include patient flow diagram 3. For treatment recommendations: include decision flowchart **How to generate figures:** - Use the **scientific-schematics** skill to generate AI-powered publication-quality diagrams - Simply describe your desired diagram in natural language - Nano Banana Pro will automatically generate, review, and refine the schematic **How to generate schematics:** ```bash python scripts/generate_schematic.py "your diagram description" -o figures/output.png ``` The AI will automatically: - Create publication-quality images with proper formatting - Review and refine through multiple iterations - Ensure accessibility (colorblind-friendly, high contrast) - Save outputs in the figures/ directory **When to add schematics:** - Clinical decision algorithm flowcharts - Treatment pathway diagrams - Biomarker stratification trees - Patient cohort flow diagrams (CONSORT-style) - Survival curve visualizations - Molecular mechanism diagrams - Any complex concept that benefits from visualization For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation. --- ## Document Structure **CRITICAL REQUIREMENT: All clinical decision support documents MUST begin with a complete executive summary on page 1 that spans the entire first page before any table of contents or detailed sections.** ### Page 1 Executive Summary Structure The first page of every CDS document should contain ONLY the executive summary with the following components: **Required Elements (all on page 1):** 1. **Document Title and Type** - Main title (e.g., "Biomarker-Stratified Cohort Analysis" or "Evidence-Based Treatment Recommendations") - Subtitle with disease state and focus 2. **Report Information Box** (using colored tcolorbox) - Document type and purpose - Date of analysis/report - Disease state and patient population - Author/institution (if applicable) - Analysis framework or methodology 3. **Key Findings Boxes** (3-5 colored boxes using tcolorbox) - **Primary Results** (blue box): Main efficacy/outcome findings - **Biomarker Insights** (green box): Key molecular subtype findings - **Clinical Implications** (yellow/orange box): Actionable treatment implications - **Statistical Summary** (gray box): Hazard ratios, p-values, key statistics - **Safety Highlights** (red box, if applicable): Critical adverse events or warnings **Visual Requirements:** - Use `thispagestyle{empty}` to remove page numbers from page 1 - All content must fit on page 1 (before `newpage`) - Use colored tcolorbox environments with different colors for visual hierarchy - Boxes should be scannable and highlight most critical information - Use bullet points, not narrative paragraphs - End page 1 with `newpage` before table of contents or detailed sections **Example First Page LaTeX Structure:** ```latex maketitle thispagestyle{empty} % Report Information Box begin{tcolorbox}[colback=blue!5!white, colframe=blue!75!black, title=Report Information] textbf{Document Type:} Patient Cohort Analysis\ textbf{Disease State:} HER2-Positive Metastatic Breast Cancer\ textbf{Analysis Date:} today\ textbf{Population:} 60 patients, biomarker-stratified by HR status end{tcolorbox} vspace{0.3cm} % Key Finding #1: Primary Results begin{tcolorbox}[colback=blue!5!white, colframe=blue!75!black, title=Primary Efficacy Results] begin{itemize} item Overall ORR: 72% (95% CI: 59-83%) item Median PFS: 18.5 months (95% CI: 14.2-22.8) item Median OS: 35.2 months (95% CI: 28.1-NR) end{itemize} end{tcolorbox} vspace{0.3cm} % Key Finding #2: Biomarker Insights begin{tcolorbox}[colback=green!5!white, colframe=green!75!black, title=Biomarker Stratification Findings] begin{itemize} item HR+/HER2+: ORR 68%, median PFS 16.2 months item HR-/HER2+: ORR 78%, median PFS 22.1 months item HR status significantly associated with outcomes (p=0.041) end{itemize} end{tcolorbox} vspace{0.3cm} % Key Finding #3: Clinical Implications begin{tcolorbox}[colback=orange!5!white, colframe=orange!75!black, title=Clinical Recommendations] begin{itemize} item Strong efficacy observed regardless of HR status (Grade 1A) item HR-/HER2+ patients showed numerically superior outcomes item Treatment recommended for all HER2+ MBC patients end{itemize} end{tcolorbox} newpage tableofcontents % TOC on page 2 newpage % Detailed content starts page 3 ``` ### Patient Cohort Analysis (Detailed Sections - Page 3+) - **Cohort Characteristics**: Demographics, baseline features, patient selection criteria - **Biomarker Stratification**: Molecular subtypes, genomic alterations, IHC profiles - **Treatment Exposure**: Therapies received, dosing, treatment duration by subgroup - **Outcome Analysis**: Response rates (ORR, DCR), survival data (OS, PFS), DOR - **Statistical Methods**: Kaplan-Meier survival curves, hazard ratios, log-rank tests, Cox regression - **Subgroup Comparisons**: Biomarker-stratified efficacy, forest plots, statistical significance - **Safety Profile**: Adverse events by subgroup, dose modifications, discontinuations - **Clinical Recommendations**: Treatment implications based on biomarker profiles - **Figures**: Waterfall plots, swimmer plots, survival curves, forest plots - **Tables**: Demographics table, biomarker frequency, outcomes by subgroup ### Treatment Recommendation Reports (Detailed Sections - Page 3+) **Page 1 Executive Summary for Treatment Recommendations should include:** 1. **Report Information Box**: Disease state, guideline version/date, target population 2. **Key Recommendations Box** (green): Top 3-5 GRADE-graded recommendations by line of therapy 3. **Biomarker Decision Criteria Box** (blue): Key molecular markers influencing treatment selection 4. **Evidence Summary Box** (gray): Major trials supporting recommendations (e.g., KEYNOTE-189, FLAURA) 5. **Critical Monitoring Box** (orange/red): Essential safety monitoring requirements **Detailed Sections (Page 3+):** - **Clinical Context**: Disease state, epidemiology, current treatment landscape - **Target Population**: Patient characteristics, biomarker criteria, staging - **Evidence Review**: Systematic literature synthesis, guideline summary, trial data - **Treatment Options**: Available therapies with mechanism of action - **Evidence Grading**: GRADE assessment for each recommendation (1A, 1B, 2A, 2B, 2C) - **Recommendations by Line**: First-line, second-line, subsequent therapies - **Biomarker-Guided Selection**: Decision criteria based on molecular profiles - **Treatment Algorithms**: TikZ flowcharts showing decision pathways - **Monitoring Protocol**: Safety assessments, efficacy monitoring, dose modifications - **Special Populations**: Elderly, renal/hepatic impairment, comorbidities - **References**: Full bibliography with trial names and citations ## Output Format **MANDATORY FIRST PAGE REQUIREMENT:** - **Page 1**: Full-page executive summary with 3-5 colored tcolorbox elements - **Page 2**: Table of contents (optional) - **Page 3+**: Detailed sections with methods, results, figures, tables **Document Specifications:** - **Primary**: LaTeX/PDF with 0.5in margins for compact, data-dense presentation - **Length**: Typically 5-15 pages (1 page executive summary + 4-14 pages detailed content) - **Style**: Publication-ready, pharmaceutical-grade, suitable for regulatory submissions - **First Page**: Always a complete executive summary spanning entire page 1 (see Document Structure section) **Visual Elements:** - **Colors**: - Page 1 boxes: blue=data/information, green=biomarkers/recommendations, yellow/orange=clinical implications, red=warnings - Recommendation boxes (green=strong recommendation, yellow=conditional, blue=research needed) - Biomarker stratification (color-coded molecular subtypes) - Statistical significance (color-coded p-values, hazard ratios) - **Tables**: - Demographics with baseline characteristics - Biomarker frequency by subgroup - Outcomes table (ORR, PFS, OS, DOR by molecular subtype) - Adverse events by cohort - Evidence summary tables with GRADE ratings - **Figures**: - Kaplan-Meier survival curves with log-rank p-values and number at risk tables - Waterfall plots showing best response by patient - Forest plots for subgroup analyses with confidence intervals - TikZ decision algorithm flowcharts - Swimmer plots for individual patient timelines - **Statistics**: Hazard ratios with 95% CI, p-values, median survival times, landmark survival rates - **Compliance**: De-identification per HIPAA Safe Harbor, confidentiality notices for proprietary data ## Integration This skill integrates with: - **scientific-writing**: Citation management, statistical reporting, evidence synthesis - **clinical-reports**: Medical terminology, HIPAA compliance, regulatory documentation - **scientific-schematics**: TikZ flowcharts for decision algorithms and treatment pathways - **treatment-plans**: Individual patient applications of cohort-derived insights (bidirectional) ## Key Differentiators from Treatment-Plans Skill **Clinical Decision Support (this skill):** - **Audience**: Pharmaceutical companies, clinical researchers, guideline committees, medical affairs - **Scope**: Population-level analyses, evidence synthesis, guideline development - **Focus**: Biomarker stratification, statistical comparisons, evidence grading - **Output**: Multi-page analytical documents (5-15 pages typical) with extensive figures and tables - **Use Cases**: Drug development, regulatory submissions, clinical practice guidelines, medical strategy - **Example**: "Analyze 60 HER2+ breast cancer patients by hormone receptor status with survival outcomes" **Treatment-Plans Skill:** - **Audience**: Clinicians, patients, care teams - **Scope**: Individual patient care planning - **Focus**: SMART goals, patient-specific interventions, monitoring plans - **Output**: Concise 1-4 page actionable care plans - **Use Cases**: Bedside clinical care, EMR documentation, patient-centered planning - **Example**: "Create treatment plan for a 55-year-old patient with newly diagnosed type 2 diabetes" **When to use each:** - Use **clinical-decision-support** for: cohort analyses, biomarker stratification studies, treatment guideline development, pharmaceutical strategy documents - Use **treatment-plans** for: individual patient care plans, treatment protocols for specific patients, bedside clinical documentation ## Example Usage ### Patient Cohort Analysis **Example 1: NSCLC Biomarker Stratification** ``` > Analyze a cohort of 45 NSCLC patients stratified by PD-L1 expression (<1%, 1-49%, ≥50%) > receiving pembrolizumab. Include outcomes: ORR, median PFS, median OS with hazard ratios > comparing PD-L1 ≥50% vs <50%. Generate Kaplan-Meier curves and waterfall plot. ``` **Example 2: GBM Molecular Subtype Analysis** ``` > Generate cohort analysis for 30 GBM patients classified into Cluster 1 (Mesenchymal-Immune-Active) > and Cluster 2 (Proneural) molecular subtypes. Compare outcomes including median OS, 6-month PFS rate, > and response to TMZ+bevacizumab. Include biomarker profile table and statistical comparison. ``` **Example 3: Breast Cancer HER2 Cohort** ``` > Analyze 60 HER2-positive metastatic breast cancer patients treated with trastuzumab-deruxtecan, > stratified by prior trastuzumab exposure (yes/no). Include ORR, DOR, median PFS with forest plot > showing subgroup analyses by hormone receptor status, brain metastases, and number of prior lines. ``` ### Treatment Recommendation Report **Example 1: HER2+ Metastatic Breast Cancer Guidelines** ``` > Create evidence-based treatment recommendations for HER2-positive metastatic breast cancer including > biomarker-guided therapy selection. Use GRADE system to grade recommendations for first-line > (trastuzumab+pertuzumab+taxane), second-line (trastuzumab-deruxtecan), and third-line options. > Include decision algorithm flowchart based on brain metastases, hormone receptor status, and prior therapies. ``` **Example 2: Advanced NSCLC Treatment Algorithm** ``` > Generate treatment recommendation report for advanced NSCLC based on PD-L1 expression, EGFR mutation, > ALK rearrangement, and performance status. Include GRADE-graded recommendations for each molecular subtype, > TikZ flowchart for biomarker-directed therapy selection, and evidence tables from KEYNOTE-189, FLAURA, > and CheckMate-227 trials. ``` **Example 3: Multiple Myeloma Line-of-Therapy Sequencing** ``` > Create treatment algorithm for newly diagnosed multiple myeloma through relapsed/refractory setting. > Include GRADE recommendations for transplant-eligible vs ineligible, high-risk cytogenetics considerations, > and sequencing of daratumumab, carfilzomib, and CAR-T therapy. Provide flowchart showing decision points > at each line of therapy. ``` ## Key Features ### Biomarker Classification - Genomic: Mutations, CNV, gene fusions - Expression: RNA-seq, IHC scores - Molecular subtypes: Disease-specific classifications - Clinical actionability: Therapy selection guidance ### Outcome Metrics - Survival: OS (overall survival), PFS (progression-free survival) - Response: ORR (objective response rate), DOR (duration of response), DCR (disease control rate) - Quality: ECOG performance status, symptom burden - Safety: Adverse events, dose modifications ### Statistical Methods - Survival analysis: Kaplan-Meier curves, log-rank tests - Group comparisons: t-tests, chi-square, Fisher's exact - Effect sizes: Hazard ratios, odds ratios with 95% CI - Significance: p-values, multiple testing corrections ### Evidence Grading **GRADE System** - **1A**: Strong recommendation, high-quality evidence - **1B**: Strong recommendation, moderate-quality evidence - **2A**: Weak recommendation, high-quality evidence - **2B**: Weak recommendation, moderate-quality evidence - **2C**: Weak recommendation, low-quality evidence **Recommendation Strength** - **Strong**: Benefits clearly outweigh risks - **Conditional**: Trade-offs exist, patient values important - **Research**: Insufficient evidence, clinical trials needed ## Best Practices ### For Cohort Analyses 1. **Patient Selection Transparency**: Clearly document inclusion/exclusion criteria, patient flow, and reasons for exclusions 2. **Biomarker Clarity**: Specify assay methods, platforms (e.g., FoundationOne, Caris), cut-points, and validation status 3. **Statistical Rigor**: - Report hazard ratios with 95% confidence intervals, not just p-values - Include median follow-up time for survival analyses - Specify statistical tests used (log-rank, Cox regression, Fisher's exact) - Account for multiple comparisons when appropriate 4. **Outcome Definitions**: Use standard criteria: - Response: RECIST 1.1, iRECIST for immunotherapy - Adverse events: CTCAE version 5.0 - Performance status: ECOG or Karnofsky 5. **Survival Data Presentation**: - Median OS/PFS with 95% CI - Landmark survival rates (6-month, 12-month, 24-month) - Number at risk tables below Kaplan-Meier curves - Censoring clearly indicated 6. **Subgroup Analyses**: Pre-specify subgroups; clearly label exploratory vs pre-planned analyses 7. **Data Completeness**: Report missing data and how it was handled ### For Treatment Recommendation Reports 1. **Evidence Grading Transparency**: - Use GRADE system consistently (1A, 1B, 2A, 2B, 2C) - Document rationale for each grade - Clearly state quality of evidence (high, moderate, low, very low) 2. **Comprehensive Evidence Review**: - Include phase 3 randomized trials as primary evidence - Supplement with phase 2 data for emerging therapies - Note real-world evidence and meta-analyses - Cite trial names (e.g., KEYNOTE-189, CheckMate-227) 3. **Biomarker-Guided Recommendations**: - Link specific biomarkers to therapy recommendations - Specify testing methods and validated assays - Include FDA/EMA approval status for companion diagnostics 4. **Clinical Actionability**: Every recommendation should have clear implementation guidance 5. **Decision Algorithm Clarity**: TikZ flowcharts should be unambiguous with clear yes/no decision points 6. **Special Populations**: Address elderly, renal/hepatic impairment, pregnancy, drug interactions 7. **Monitoring Guidance**: Specify safety labs, imaging, and frequency 8. **Update Frequency**: Date recommendations and plan for periodic updates ### General Best Practices 1. **First Page Executive Summary (MANDATORY)**: - ALWAYS create a complete executive summary on page 1 that spans the entire first page - Use 3-5 colored tcolorbox elements to highlight key findings - No table of contents or detailed sections on page 1 - Use `thispagestyle{empty}` and end with `newpage` - This is the single most important page - it should be scannable in 60 seconds 2. **De-identification**: Remove all 18 HIPAA identifiers before document generation (Safe Harbor method) 3. **Regulatory Compliance**: Include confidentiality notices for proprietary pharmaceutical data 4. **Publication-Ready Formatting**: Use 0.5in margins, professional fonts, color-coded sections 5. **Reproducibility**: Document all statistical methods to enable replication 6. **Conflict of Interest**: Disclose pharmaceutical funding or relationships when applicable 7. **Visual Hierarchy**: Use colored boxes consistently (blue=data, green=biomarkers, yellow/orange=recommendations, red=warnings) ## References See the `references/` directory for detailed guidance on: - Patient cohort analysis and stratification methods - Treatment recommendation development - Clinical decision algorithms - Biomarker classification and interpretation - Outcome analysis and statistical methods - Evidence synthesis and grading systems ## Templates See the `assets/` directory for LaTeX templates: - `cohort_analysis_template.tex` - Biomarker-stratified patient cohort analysis with statistical comparisons - `treatment_recommendation_template.tex` - Evidence-based clinical practice guidelines with GRADE grading - `clinical_pathway_template.tex` - TikZ decision algorithm flowcharts for treatment sequencing - `biomarker_report_template.tex` - Molecular subtype classification and genomic profile reports - `evidence_synthesis_template.tex` - Systematic evidence review and meta-analysis summaries **Template Features:** - 0.5in margins for compact presentation - Color-coded recommendation boxes - Professional tables for demographics, biomarkers, outcomes - Built-in support for Kaplan-Meier curves, waterfall plots, forest plots - GRADE evidence grading tables - Confidentiality headers for pharmaceutical documents ## Scripts See the `scripts/` directory for analysis and visualization tools: - `generate_survival_analysis.py` - Kaplan-Meier curve generation with log-rank tests, hazard ratios, 95% CI - `create_waterfall_plot.py` - Best response visualization for cohort analyses - `create_forest_plot.py` - Subgroup analysis visualization with confidence intervals - `create_cohort_tables.py` - Demographics, biomarker frequency, and outcomes tables - `build_decision_tree.py` - TikZ flowchart generation for treatment algorithms - `biomarker_classifier.py` - Patient stratification algorithms by molecular subtype - `calculate_statistics.py` - Hazard ratios, Cox regression, log-rank tests, Fisher's exact - `validate_cds_document.py` - Quality and compliance checks (HIPAA, statistical reporting standards) - `grade_evidence.py` - Automated GRADE assessment helper for treatment recommendations