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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 comparisonsTreatment 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 scoringStatistical Analysis : Hazard ratios, p-values, confidence intervals, survival curves, Cox regression, log-rank testsEvidence Grading : GRADE system (1A/1B/2A/2B/2C), Oxford CEBM levels, quality of evidence assessmentClinical Terminology : SNOMED-CT, LOINC, proper medical nomenclature, trial nomenclatureRegulatory Compliance : HIPAA de-identification, confidentiality headers, ICH-GCP alignmentProfessional 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:
Phase 2/3 Trial Analyses : Biomarker-stratified efficacy and safety analysesSubgroup Analyses : Forest plots showing treatment effects across patient subgroupsCompanion Diagnostic Development : Linking biomarkers to drug responseRegulatory Submissions : IND/NDA documentation with evidence summaries
KOL Education Materials : Evidence-based treatment algorithms for thought leadersMedical Strategy Documents : Competitive landscape and positioning strategiesAdvisory Board Materials : Cohort analyses and treatment recommendation frameworksPublication Planning : Manuscript-ready analyses for peer-reviewed journals
Guideline Development : Evidence synthesis with GRADE methodology for specialty societiesConsensus Recommendations : Multi-stakeholder treatment algorithm developmentPractice Standards : Biomarker-based treatment selection criteriaQuality Measures : Evidence-based performance metrics
RWE Cohort Studies : Retrospective analyses of patient cohorts from EMR dataComparative Effectiveness : Head-to-head treatment comparisons in real-world settingsOutcomes Research : Long-term survival and safety in clinical practiceHealth 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 characteristicsGenerate treatment recommendation reports with evidence grading for clinical guidelines or pharmaceutical strategiesCompare outcomes between patient subgroups with statistical analysis (survival, response rates, hazard ratios)Produce pharmaceutical research documents for drug development, clinical trials, or regulatory submissionsDevelop clinical practice guidelines with GRADE evidence grading and decision algorithmsDocument biomarker-guided therapy selection at the population level (not individual patients)Synthesize evidence from multiple trials or real-world data sourcesCreate 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
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
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 findingsBiomarker Insights (green box): Key molecular subtype findingsClinical Implications (yellow/orange box): Actionable treatment implicationsStatistical Summary (gray box): Hazard ratios, p-values, key statisticsSafety Highlights (red box, if applicable): Critical adverse events or warnings
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 criteriaBiomarker Stratification : Molecular subtypes, genomic alterations, IHC profilesTreatment Exposure : Therapies received, dosing, treatment duration by subgroupOutcome Analysis : Response rates (ORR, DCR), survival data (OS, PFS), DORStatistical Methods : Kaplan-Meier survival curves, hazard ratios, log-rank tests, Cox regressionSubgroup Comparisons : Biomarker-stratified efficacy, forest plots, statistical significanceSafety Profile : Adverse events by subgroup, dose modifications, discontinuationsClinical Recommendations : Treatment implications based on biomarker profilesFigures : Waterfall plots, swimmer plots, survival curves, forest plotsTables : 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 populationKey Recommendations Box (green): Top 3-5 GRADE-graded recommendations by line of therapyBiomarker Decision Criteria Box (blue): Key molecular markers influencing treatment selectionEvidence 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 landscapeTarget Population : Patient characteristics, biomarker criteria, stagingEvidence Review : Systematic literature synthesis, guideline summary, trial dataTreatment Options : Available therapies with mechanism of actionEvidence Grading : GRADE assessment for each recommendation (1A, 1B, 2A, 2B, 2C)Recommendations by Line : First-line, second-line, subsequent therapiesBiomarker-Guided Selection : Decision criteria based on molecular profilesTreatment Algorithms : TikZ flowcharts showing decision pathwaysMonitoring Protocol : Safety assessments, efficacy monitoring, dose modificationsSpecial Populations : Elderly, renal/hepatic impairment, comorbiditiesReferences : Full bibliography with trial names and citations
Output Format MANDATORY FIRST PAGE REQUIREMENT:
Page 1 : Full-page executive summary with 3-5 colored tcolorbox elementsPage 2 : Table of contents (optional)Page 3+ : Detailed sections with methods, results, figures, tables
Primary : LaTeX/PDF with 0.5in margins for compact, data-dense presentationLength : Typically 5-15 pages (1 page executive summary + 4-14 pages detailed content)Style : Publication-ready, pharmaceutical-grade, suitable for regulatory submissionsFirst Page : Always a complete executive summary spanning entire page 1 (see Document Structure section)
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 ratesCompliance : De-identification per HIPAA Safe Harbor, confidentiality notices for proprietary data
Integration This skill integrates with:
scientific-writing : Citation management, statistical reporting, evidence synthesisclinical-reports : Medical terminology, HIPAA compliance, regulatory documentationscientific-schematics : TikZ flowcharts for decision algorithms and treatment pathwaystreatment-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 affairsScope : Population-level analyses, evidence synthesis, guideline developmentFocus : Biomarker stratification, statistical comparisons, evidence gradingOutput : Multi-page analytical documents (5-15 pages typical) with extensive figures and tablesUse Cases : Drug development, regulatory submissions, clinical practice guidelines, medical strategyExample : "Analyze 60 HER2+ breast cancer patients by hormone receptor status with survival outcomes"
Audience : Clinicians, patients, care teamsScope : Individual patient care planningFocus : SMART goals, patient-specific interventions, monitoring plansOutput : Concise 1-4 page actionable care plansUse Cases : Bedside clinical care, EMR documentation, patient-centered planningExample : "Create treatment plan for a 55-year-old patient with newly diagnosed type 2 diabetes"
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
1A : Strong recommendation, high-quality evidence1B : Strong recommendation, moderate-quality evidence2A : Weak recommendation, high-quality evidence2B : Weak recommendation, moderate-quality evidence2C : Weak recommendation, low-quality evidence
Strong : Benefits clearly outweigh risksConditional : Trade-offs exist, patient values importantResearch : Insufficient evidence, clinical trials needed
Best Practices
For Cohort Analyses
Patient Selection Transparency : Clearly document inclusion/exclusion criteria, patient flow, and reasons for exclusionsBiomarker Clarity : Specify assay methods, platforms (e.g., FoundationOne, Caris), cut-points, and validation statusStatistical 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 analysesData 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 guidanceDecision Algorithm Clarity : TikZ flowcharts should be unambiguous with clear yes/no decision pointsSpecial Populations : Address elderly, renal/hepatic impairment, pregnancy, drug interactionsMonitoring Guidance : Specify safety labs, imaging, and frequencyUpdate 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 dataPublication-Ready Formatting : Use 0.5in margins, professional fonts, color-coded sectionsReproducibility : Document all statistical methods to enable replicationConflict of Interest : Disclose pharmaceutical funding or relationships when applicableVisual 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 comparisonstreatment_recommendation_template.tex - Evidence-based clinical practice guidelines with GRADE gradingclinical_pathway_template.tex - TikZ decision algorithm flowcharts for treatment sequencingbiomarker_report_template.tex - Molecular subtype classification and genomic profile reportsevidence_synthesis_template.tex - Systematic evidence review and meta-analysis summaries
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% CIcreate_waterfall_plot.py - Best response visualization for cohort analysescreate_forest_plot.py - Subgroup analysis visualization with confidence intervalscreate_cohort_tables.py - Demographics, biomarker frequency, and outcomes tablesbuild_decision_tree.py - TikZ flowchart generation for treatment algorithmsbiomarker_classifier.py - Patient stratification algorithms by molecular subtypecalculate_statistics.py - Hazard ratios, Cox regression, log-rank tests, Fisher's exactvalidate_cds_document.py - Quality and compliance checks (HIPAA, statistical reporting standards)grade_evidence.py - Automated GRADE assessment helper for treatment recommendations 