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:

• Patient Cohort Analysis - Biomarker-stratified group analyses with statistical outcome comparisons

• 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)

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:

• Generate at minimum ONE schematic or diagram (e.g., clinical decision algorithm, treatment pathway, or biomarker stratification tree)

• For cohort analyses: include patient flow diagram

• 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:

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):

Document Title and Type

• Main title (e.g., "Biomarker-Stratified Cohort Analysis" or "Evidence-Based Treatment Recommendations")

• Subtitle with disease state and focus

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

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:

\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:

• Report Information Box: Disease state, guideline version/date, target population

• Key Recommendations Box (green): Top 3-5 GRADE-graded recommendations by line of therapy

• Biomarker Decision Criteria Box (blue): Key molecular markers influencing treatment selection

• Evidence Summary Box (gray): Major trials supporting recommendations (e.g., KEYNOTE-189, FLAURA)

• 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

• Patient Selection Transparency: Clearly document inclusion/exclusion criteria, patient flow, and reasons for exclusions

• Biomarker Clarity: Specify assay methods, platforms (e.g., FoundationOne, Caris), cut-points, and validation status

• 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

• Outcome Definitions: Use standard criteria:

• Response: RECIST 1.1, iRECIST for immunotherapy

• Adverse events: CTCAE version 5.0

• Performance status: ECOG or Karnofsky

• 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

• Subgroup Analyses: Pre-specify subgroups; clearly label exploratory vs pre-planned analyses

• Data Completeness: Report missing data and how it was handled

For Treatment Recommendation Reports

• 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)

• 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)

• Biomarker-Guided Recommendations:

• Link specific biomarkers to therapy recommendations

• Specify testing methods and validated assays

• Include FDA/EMA approval status for companion diagnostics

• Clinical Actionability: Every recommendation should have clear implementation guidance

• Decision Algorithm Clarity: TikZ flowcharts should be unambiguous with clear yes/no decision points

• Special Populations: Address elderly, renal/hepatic impairment, pregnancy, drug interactions

• Monitoring Guidance: Specify safety labs, imaging, and frequency

• Update Frequency: Date recommendations and plan for periodic updates

General Best Practices

• 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

• De-identification: Remove all 18 HIPAA identifiers before document generation (Safe Harbor method)

• Regulatory Compliance: Include confidentiality notices for proprietary pharmaceutical data

• Publication-Ready Formatting: Use 0.5in margins, professional fonts, color-coded sections

• Reproducibility: Document all statistical methods to enable replication

• Conflict of Interest: Disclose pharmaceutical funding or relationships when applicable

• 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