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]

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.

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)

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

1. Report Information Box (using colored tcolorbox)
2. Document type and purpose
3. Date of analysis/report
4. Disease state and patient population
5. Author/institution (if applicable)

6. Analysis framework or methodology

7. Key Findings Boxes (3-5 colored boxes using tcolorbox)

8. Primary Results (blue box): Main efficacy/outcome findings
9. Biomarker Insights (green box): Key molecular subtype findings
10. Clinical Implications (yellow/orange box): Actionable treatment implications
11. Statistical Summary (gray box): Hazard ratios, p-values, key statistics
12. 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:

\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:
4. Report hazard ratios with 95% confidence intervals, not just p-values
5. Include median follow-up time for survival analyses
6. Specify statistical tests used (log-rank, Cox regression, Fisher's exact)
7. Account for multiple comparisons when appropriate
8. Outcome Definitions: Use standard criteria:
9. Response: RECIST 1.1, iRECIST for immunotherapy
10. Adverse events: CTCAE version 5.0
11. Performance status: ECOG or Karnofsky
12. Survival Data Presentation:
13. Median OS/PFS with 95% CI
14. Landmark survival rates (6-month, 12-month, 24-month)
15. Number at risk tables below Kaplan-Meier curves
16. Censoring clearly indicated
17. Subgroup Analyses: Pre-specify subgroups; clearly label exploratory vs pre-planned analyses
18. Data Completeness: Report missing data and how it was handled

For Treatment Recommendation Reports

1. Evidence Grading Transparency:
2. Use GRADE system consistently (1A, 1B, 2A, 2B, 2C)
3. Document rationale for each grade
4. Clearly state quality of evidence (high, moderate, low, very low)
5. Comprehensive Evidence Review:
6. Include phase 3 randomized trials as primary evidence
7. Supplement with phase 2 data for emerging therapies
8. Note real-world evidence and meta-analyses
9. Cite trial names (e.g., KEYNOTE-189, CheckMate-227)
10. Biomarker-Guided Recommendations:
11. Link specific biomarkers to therapy recommendations
12. Specify testing methods and validated assays
13. Include FDA/EMA approval status for companion diagnostics
14. Clinical Actionability: Every recommendation should have clear implementation guidance
15. Decision Algorithm Clarity: TikZ flowcharts should be unambiguous with clear yes/no decision points
16. Special Populations: Address elderly, renal/hepatic impairment, pregnancy, drug interactions
17. Monitoring Guidance: Specify safety labs, imaging, and frequency
18. Update Frequency: Date recommendations and plan for periodic updates

General Best Practices

1. First Page Executive Summary (MANDATORY):
2. ALWAYS create a complete executive summary on page 1 that spans the entire first page
3. Use 3-5 colored tcolorbox elements to highlight key findings
4. No table of contents or detailed sections on page 1
5. Use \thispagestyle{empty} and end with \newpage
6. This is the single most important page - it should be scannable in 60 seconds
7. De-identification: Remove all 18 HIPAA identifiers before document generation (Safe Harbor method)
8. Regulatory Compliance: Include confidentiality notices for proprietary pharmaceutical data
9. Publication-Ready Formatting: Use 0.5in margins, professional fonts, color-coded sections
10. Reproducibility: Document all statistical methods to enable replication
11. Conflict of Interest: Disclose pharmaceutical funding or relationships when applicable
12. 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