Case Report

Case Report Presentation

Overview

When a user provides clinical case data and asks for a case report / M&M / morning conference presentation, use this skill to generate a timeline-based python-pptx presentation and an interactive HTML website. The input is typically a folder containing:

• Markdown medical records (醫師記錄/): Admission note, Progress notes, Discharge note, Surgical records, Objective findings
• IO sheets (IO/): PNG screenshots of fluid intake/output records by shift
• Lab data images (LABS/): PNG screenshots of lab trend charts (CBC, SMAC, CRP, etc.)
• Chest X-rays / imaging (CXR/): PNG images with date-based filenames
• Nursing records (護理紀錄/): PDF files
• Medication records: PDF files at root level

The slide structure should be timeline-based — organized chronologically around the clinical course, with adaptive granularity (finer detail for the main focus period, coarser for secondary periods).

Prerequisites

pip3 install python-pptx pymupdf

Workflow

0a. Ask for Presenter Information & Presentation Type

Before starting, ask the user two things in a concise one-liner:

Presenter info: Who is presenting and who is the supervisor? (e.g., "R2 王大明 / VS 李教授") — press Enter to skip.

Presentation type: (1) M&M Conference (2) Case Report (3) Morning Conference — default: M&M

• M&M Conference → ending section uses "Learning Points / Take-home Messages"
• Case Report → ending section uses "Discussion" (literature comparison & analysis)
• Morning Conference → ending section uses "Summary & Key Points"
• If the user skips → check memory for saved user profile; default to M&M Conference

0b. Identify Input & Create Output Folder

Detect input type

The user provides a folder path containing clinical data organized in subdirectories:

import os, glob

user_input = "..."  # folder path provided by user

# Expected structure
records_dir = os.path.join(user_input, "醫師記錄")
io_dir = os.path.join(user_input, "IO")
labs_dir = os.path.join(user_input, "LABS")
cxr_dir = os.path.join(user_input, "CXR")
nursing_dir = os.path.join(user_input, "護理紀錄")

# Find all files
md_files = sorted(glob.glob(os.path.join(records_dir, "*.md"))) if os.path.isdir(records_dir) else []
io_images = sorted(glob.glob(os.path.join(io_dir, "*.png"))) if os.path.isdir(io_dir) else []
lab_images = sorted(glob.glob(os.path.join(labs_dir, "*.png"))) if os.path.isdir(labs_dir) else []
cxr_images = sorted(glob.glob(os.path.join(cxr_dir, "*.png"))) if os.path.isdir(cxr_dir) else []
nursing_pdfs = sorted(glob.glob(os.path.join(nursing_dir, "*.pdf"))) if os.path.isdir(nursing_dir) else []
root_pdfs = sorted(glob.glob(os.path.join(user_input, "*.pdf")))

Create output folder

{PatientName}_case_report/
├── figures/                ← CXR images, lab images copied here
├── images/                 ← IO sheet images if embedded
├── presentation.pptx       ← PPTX presentation
├── presentation.html       ← HTML website (external images)
└── presentation_portable.html  ← HTML website (self-contained base64)

1. Read & Parse All Clinical Records

Markdown records (primary data source)

Read each .md file using the Read tool and extract structured data:

IO sheets (PNG images)

IO sheets are PNG screenshots from the hospital's electronic medical system. Process them:

1. Visually inspect each IO image using the Read tool to understand the format
2. Extract numerical data by reading the values directly from the image:
   • Per shift: 白班 (07:00-14:59), 小夜 (15:00-22:59), 大夜 (23:00-06:59)
   • Categories: 輸液 (IV fluids), 血品 (blood products), 進食 (enteral), 排尿 (urine output), 總輸入/總排出/差值
3. Structure into tables for the fluid resuscitation slides (Pattern G)
4. If OCR is needed for hard-to-read values, use the macOS Vision OCR script from the notebooklm-to-editable-pptx skill

Lab data strategy

• Primary source: Objective finding.md — already has parsed, structured lab values by date
• Lab images (PNG): Use as visual verification and can be embedded as supplementary reference
• DO NOT recreate charts — use the structured data to build formatted tables

Medication records (PDF — 急診用藥紀錄, 住院後用藥簽用記錄)

Extract using PyMuPDF (fitz.open()). Key data to parse:

• IV fluids: LR, NS, D5W — with execution times (not order times). Multiple execution records under the same order number may represent either separate bags or multi-nurse sign-offs — cross-reference with nursing records to disambiguate.
• Blood products: FFP, FP, RBC — doses and execution times
• Antibiotics: Drug name, dose, frequency, start/stop dates, route
• Supportive medications: NaHCO₃, Albumin, diuretics, vasopressors, sedatives

CRITICAL: Use execution timestamps (執行時間), not order timestamps (開立時間), for fluid volume calculations. The same order number with multiple execution records may be the same bag signed by different nurses — verify with nursing records and IO sheets.

CXR and imaging

• Copy all CXR images to figures/ directory with date-based filenames
• These will be used in serial comparison slides (Pattern I)
• Label by actual imaging location — check PACS header (institution name) in the image. Do NOT assume the first CXR is from an outside hospital.

Wound / clinical photos

• Check for existing PPTX files (e.g., trauma team presentations) that may contain clinical photos
• Extract images via shape.image.blob from python-pptx
• Resize large images (>3MB) using sips -Z 2000 -s format jpeg -s formatOptions 80 before embedding in PPTX

2. Build Clinical Timeline

Construct a chronological timeline from all parsed data. The timeline granularity should be adaptive:

Timeline structure (acute/critical care default):
├── Pre-hospital: mechanism, rescue, initial hospital management
├── ED / Arrival (Hour 0): vital signs, primary survey, initial labs
├── Hour 0–8: resuscitation phase 1 (MAIN FOCUS — finest granularity)
├── Hour 8–24: resuscitation phase 2 (MAIN FOCUS)
├── Day 2–3: early ICU course
├── Day 4–7: continued course
└── Day 7+: later course / outcome

For each time point, record:

• Events: procedures, interventions, clinical changes
• Vitals: BP, HR, RR, Temp, SpO2
• Labs: key values with abnormal flags
• Fluid balance: intake by category, output (UOP), cumulative balance
• Medications: new starts, dose changes

3. Design Slides Based on Clinical Timeline

3.5. Text Formatting Rules (CRITICAL)

Same rules as journal-reading skill — apply to ALL python-pptx text:

Never use p.text = "..."

Always use set_run() or p.add_run(). The p.text = pattern does not guarantee formatting.

Never use \n in text strings

Each line must be a separate paragraph with its own run and explicit formatting.

Every text element must have explicit formatting

Every run must set: font.size, font.color.rgb, font.name. Never rely on defaults.

Every paragraph must have explicit alignment (CRITICAL)

Always set p.alignment = PP_ALIGN.LEFT (or CENTER/RIGHT) on every paragraph. Never rely on PowerPoint defaults — they are inconsistent across text boxes, tables, and shapes. Without explicit alignment, text may render LEFT in some boxes and CENTER in others.

# CORRECT — explicit alignment on every paragraph
p = tf.paragraphs[0]
p.alignment = PP_ALIGN.LEFT  # ← MUST SET
set_run(p, "text", font_size, color)

# WRONG — missing alignment → inconsistent rendering
p = tf.paragraphs[0]
set_run(p, "text", font_size, color)  # ← alignment undefined

Alignment conventions:

• Bullets, captions, section numbers, highlight/key-point box text: PP_ALIGN.LEFT
• Vital signs cards, table headers, table data (non-first-column): PP_ALIGN.CENTER
• Slide numbers: PP_ALIGN.RIGHT
• Title/ending slide (Thank You): PP_ALIGN.CENTER
• Content slide titles: PP_ALIGN.LEFT

Prevent text overlap between elements (CRITICAL)

When a slide has multiple vertically stacked elements (bullets + key_point box, timeline + bullets, etc.), calculate the available vertical space before adding content. If content exceeds the space, either:

1. Split into two slides — preferred for dense content
2. Reduce font size — minimum Pt(12) for readability
3. Move the emphasis box lower — but never below top=Inches(6.2) (slide numbers at 7.05)

Rule of thumb for vertical spacing:

• Mini-timeline occupies y=0.92 to y=1.75 (~0.83 inches)
• Content area after timeline: top=Inches(1.85) to top=Inches(5.5) max
• Key_point/highlight box: calculate top = bullets_top + (n_lines × line_height)
• Never stack more than 12–14 bullet lines + 1 emphasis box on a single slide

4. Slide Structure

Do NOT force a fixed slide count. Use as many slides as needed. Prefer more slides with less content each over fewer dense slides.

Layout density principles:

• Max 4–5 bullet points per slide
• Max 1 table + 1–2 figures per slide
• Font sizes for projection: body text ≥ Pt(18), titles ≥ Pt(28), table cells ≥ Pt(14), captions ≥ Pt(12)
• Leave breathing room — generous padding, whitespace between elements

Required slides (always present):

1. Title slide — Case type label (M&M / Case Report / Morning Conference), chief complaint as title, patient demographics summary, presenter info
2. Outline slide — Numbered TOC matching subsequent sections. Use uniform 0.5"×0.5" rounded-square badges (not circles) so 2-digit numbers fit. Badge font: 16pt for 1-digit, 13pt for 2-digit, bold white, vertically + horizontally centered, word_wrap=False, zero margins. Row step 0.6", label font 18pt vertically aligned with badge center. (Implemented in add_outline_slide() in generate_aesthetic_pptx.py.)
3. Patient Profile — Demographics, comorbidities, baseline functional status, social history
4. Mechanism / Presentation — How the injury/illness occurred, pre-hospital care, include outside hospital fluids/medications with execution times
5. Initial Assessment — Arrival vitals (Pattern K cards), primary/secondary survey
6. Injury/Disease Assessment — e.g., TBSA by region (Pattern J) for burns, staging for cancer
7. Wound / Injury Photos — Clinical photos from ER and procedures (Pattern I). Source from existing PPTX (add_picture extraction) or image folder. Always include if available.
8. Initial Labs — Key lab results in table (Pattern H) with abnormal highlighting
9. Initial Imaging — CXR/CT with interpretation. Label by actual source (e.g., "ER Arrival" if taken at receiving hospital, not "Outside Hospital")
10. Treatment Focus slides — Detailed slides on the main focus area (e.g., fluid resuscitation for burns). Use PBD (Post-Burn Day) or post-event timeline, not calendar dates, for time reference
11. Procedure slides — Operative details, clinical photos
12. Daily Course slides (PBD-based) — Each slide MUST include: Weight (with Δ), Labs, IO balance, Rx (daily management), Abx (antibiotics), Vitals from nursing records. Add a mini-timeline indicator (Pattern L) on each slide
13. Lab Trends — Serial lab tables (Pattern H) grouped by system (Renal, Hematology, Inflammatory)
14. Fluid/UOP Trend — PBD-based summary table: In / UOP / mL·kg⁻¹·hr⁻¹ / Balance / Cr / Weight
15. Serial Imaging — Side-by-side comparison (Pattern I)
16. Complications — Summary of complications encountered
17. Outcome — Final status, contributing factors
18. Learning Points / Discussion / Summary — Based on presentation type selected in step 0a
19. Ending slide — "Thank You — Questions?" with presenter info
20. Supplementary slides (after Thank You) — Individual CXR with radiology reports, IO screenshots, SMAC/CBC/CRP screenshots, blood sugar. Marked with "SUPPLEMENTARY" tag and gray accent bar

Case-specific slide templates:

For burns / fluid resuscitation:

• Weight: Use pre-burn weight from op note (not admission weight which includes pre-hospital fluids). Flag the discrepancy explicitly.
• TBSA: Report both ED assessment and post-op reassessment if different.
• Parkland formula: Calculate from burn time (not hospital admission). Account for late presentation — if first 8h window passed, note explicitly. Show mL/kg/%TBSA actual vs expected (4 mL).
• PBD-based fluid slides: Break down by Phase (outside hospital → ER → BU shifts). Show fluid type detail (LR, NS, NaHCO₃, Albumin, blood products) with execution times from 急診用藥紀錄/住院後用藥簽用記錄.
• UOP tracking: Calculate mL/kg/hr using pre-burn weight per PBD phase. Red-highlight when below target.
• Outside hospital fluids: Include in PBD1 total. Note Foley bladder drainage vs sustained UOP.
• Wound/escharotomy photos: Extract from existing PPTX if available (e.g., trauma team PPTX). Use slide.shapes → shape.image.blob to export images.
• Weight trend: Daily weights with Δ from baseline and % change.

For surgical complications:

• Pre-operative assessment slide
• Intra-operative events timeline
• Post-operative complication timeline

For infection / sepsis:

• Antibiotic timeline per PBD — show ER empiric → BU definitive → changes
• Culture results with sensitivity data
• Inflammatory marker trends (CRP, PCT, WBC)

Slide Layout Patterns (16:9, coordinates in Inches)

Reuse patterns A–E from journal-reading skill, plus these new patterns:

Emphasis Box Policy

Same as journal-reading:

• add_key_point() — deep blue box, white text — for KEY FINDING, most important takeaway
• add_highlight_box() — warm yellow box — for supporting context, targets, criteria
• Max 1 emphasis box per slide
• Default font: 14pt, left-aligned (both helpers). Override font_size= only when a slide truly needs larger emphasis. Never use centered alignment for multi-line summary text.

5. Generate the PPTX

Write a self-contained Python script to /tmp/create_case_report.py that inlines all helper functions from both:

• scripts/generate_aesthetic_pptx.py (base helpers)
• scripts/generate_case_report_pptx.py (case-report-specific helpers)

Do NOT import from the helper file paths — copy all function definitions directly.

Script structure:

1. Inline all helpers at the top (base + case-report-specific)
2. Build slides using layout patterns by name in comments
3. Call add_slide_numbers(prs) as the final step before prs.save()
4. Save to {output_dir}/presentation.pptx

Execute the script:

python3 /tmp/create_case_report.py

6. Generate HTML Website

After the PPTX is generated, create an interactive HTML presentation website following the presentation-website skill patterns:

• Use the same content and slide structure as the PPTX
• Follow the HTML template conventions from presentation-website/templates/presentation_template.html
• Include all mandatory features:
  • Dot navigation (right sidebar)
  • Progress bar (top)
  • Keyboard navigation (arrows, space, page up/down, presentation clickers)
  • scroll-snap-type: y mandatory
  • Card-based layout with left accent borders
  • Inline editing mode
  • Double-click source text popup — every content card must have hidden <div class="source-text"> with original record excerpt
  • Presentation mode toggle (P or F5)
  • PDF download via html2canvas + jsPDF (screenshot approach)
  • PPTX download via html2canvas + PptxGenJS
  • Slide counters on every slide ("X / N")
  • Fade-in animations

Generate two versions:

• presentation.html — external images referenced from figures/
• presentation_portable.html — self-contained with base64-encoded images

7. Deliver

Notify the user:

• Output folder path
• Number of PPTX slides and section breakdown
• Number of figures/images embedded
• That both PPTX and HTML are available
• HTML features summary (editing, download, navigation)

Content Guidelines

• Default language: English — use standard medical terminology
• If the user requests Chinese or bilingual, switch accordingly
• De-identify patient data in the presentation — use initials or generic identifiers, not full names
• Preserve exact lab values, vital signs, and clinical data — accuracy is critical
• Use red/bold for abnormal or critical values
• Use green for values returning to normal
• Significant findings should use add_key_point() boxes
• Target values (e.g., UOP target) should use add_highlight_box() boxes

When to Use

This skill applies when a user:

• Provides clinical case data (medical records, IO sheets, lab data, imaging)
• Requests a "case report" / "M&M" / "case presentation" / "mortality and morbidity"
• Mentions "case discussion" / "morning conference case" / "晨會 case"
• Wants a timeline-based presentation of a clinical course
• Provides a folder with 醫師記錄/, IO/, LABS/, CXR/ subdirectories