| 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 | version: "1.0" 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:
- 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.
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:
- 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
