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hcc-meat-coder / docs /DEMO_SCRIPT.md

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Claude Opus 4.7 (1M context)

Milestone 0.4.0: narrative + polish (blog, demo script, keybinds, calibrator fit)

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# Demo video script — HCC-MEAT Coder (60–90 seconds)

Target length: **75 seconds**. Shoot at 1920×1080 with the browser zoomed to 110%
so the text is readable on a recruiter's tiny laptop screen.

Tools: OBS Studio (free), or QuickTime screen-record on macOS. Narration recorded
separately and mixed — no live audio.

---

## Pre-roll (0:00–0:03)

**Visual:** title card, dark slate background.

> **HCC-MEAT Coder**
> CMS-HCC v28 · MEAT-justified · calibrated · RADV-framed
> *Research demo · synthetic data only*

---

## 0:03–0:15 — Why this exists

**Visual:** cut to the three-panel UI with no note loaded yet.

**Narration:**
> Medicare Advantage plans pay risk-adjustment coders to capture every HCC the
> chart supports — but if the chart doesn't back it, CMS takes it back in a RADV
> audit. So coders read notes and look for **MEAT**: Monitor, Evaluate, Assess,
> Treat. The standard.

---

## 0:15–0:30 — Load a case, run the pipeline

**Visual:**
1. Click the gallery dropdown, pick **"Straightforward DM2 with chronic complications"**.
2. Results populate. Camera lingers on the suspect list.

**Narration:**
> Paste a note — or load an example — and the pipeline returns a ranked suspect
> list. Each HCC shows its RAF weight, a calibrated confidence, and a MEAT
> coverage pill. HCC-18, diabetes with chronic complications: all four MEAT
> categories verified, LOW over-coding risk.

---

## 0:30–0:45 — Drill in, show the MEAT quotes

**Visual:**
1. Click **HCC-18** in the center panel.
2. Right panel opens. Scroll gently through the four MEAT categories.
3. On the left, the note shows the four quotes highlighted by category color.

**Narration:**
> Every piece of MEAT evidence is a byte-exact quote from the note — not a
> summary, not a paraphrase. The verifier drops any quote it can't find in the
> source. Over-coding risk is a five-component transparent heuristic — named
> reasons, not a black box.

---

## 0:45–0:58 — Show a negative case

**Visual:**
1. Gallery dropdown → **"Family history of malignancy"**.
2. Suspect list is empty.

**Narration:**
> Family history alone doesn't code. Neither does "ruled out", "denies", or
> workup mentions. The pipeline filters those before linking, so the coder
> doesn't have to chase a suspect list full of false positives.

---

## 0:58–1:10 — Tabs for metrics + auditability

**Visual:**
1. Click the **Metrics** tab. Linger on the macro-F1 stat and the per-HCC table.
2. Click **Subgroups**. Linger on note-length slices.
3. Click **Calibration** — the placeholder panel is visible ("not fit yet").
4. Click **About**.

**Narration:**
> Every number on screen is reproducible from the eval harness. Subgroup slices
> by note length and scenario tag are in the UI, not hidden in a notebook.
> Calibration is explicit — if the model isn't calibrated yet, the tab says so.

---

## 1:10–1:18 — Close

**Visual:** back to the coding tab; scroll the suspect list to hero the final
frame.

**Narration:**
> Built for a 2026 healthtech portfolio. Public data, single laptop, HF Docker
> Space. Repo and demo links in the description.

---

## End card (1:18–1:22)

**Visual:** static text.

> **github.com/chaaarlieyap/hcc-meat-coder**
> **huggingface.co/spaces/charlesyapai/hcc-meat-coder**

---

## Notes for the recording session

- Close every Chrome tab except the demo. Disable any browser extension that
  could overlay UI (1Password, Grammarly, LastPass).
- Use Cmd+Shift+P in DevTools → "Show rulers" to confirm the 16:9 aspect
  before recording.
- Pre-populate the gallery: run `scripts/generate_meat_templates.py` so the
  gallery has at least the 10 staple cases.
- Do two silent passes first to lock timing before recording narration.
- Final export: H.264 MP4, 30fps, 1080p. Target file size < 20 MB so it
  embeds cleanly in the Space README.

	# Demo video script — HCC-MEAT Coder (60–90 seconds)

	Target length: 75 seconds. Shoot at 1920×1080 with the browser zoomed to 110%
	so the text is readable on a recruiter's tiny laptop screen.

	Tools: OBS Studio (free), or QuickTime screen-record on macOS. Narration recorded
	separately and mixed — no live audio.

	---

	## Pre-roll (0:00–0:03)

	Visual: title card, dark slate background.

	> HCC-MEAT Coder
	> CMS-HCC v28 · MEAT-justified · calibrated · RADV-framed
	> Research demo · synthetic data only

	---

	## 0:03–0:15 — Why this exists

	Visual: cut to the three-panel UI with no note loaded yet.

	Narration:
	> Medicare Advantage plans pay risk-adjustment coders to capture every HCC the
	> chart supports — but if the chart doesn't back it, CMS takes it back in a RADV
	> audit. So coders read notes and look for MEAT: Monitor, Evaluate, Assess,
	> Treat. The standard.

	---

	## 0:15–0:30 — Load a case, run the pipeline

	Visual:
	1. Click the gallery dropdown, pick "Straightforward DM2 with chronic complications".
	2. Results populate. Camera lingers on the suspect list.

	Narration:
	> Paste a note — or load an example — and the pipeline returns a ranked suspect
	> list. Each HCC shows its RAF weight, a calibrated confidence, and a MEAT
	> coverage pill. HCC-18, diabetes with chronic complications: all four MEAT
	> categories verified, LOW over-coding risk.

	---

	## 0:30–0:45 — Drill in, show the MEAT quotes

	Visual:
	1. Click HCC-18 in the center panel.
	2. Right panel opens. Scroll gently through the four MEAT categories.
	3. On the left, the note shows the four quotes highlighted by category color.

	Narration:
	> Every piece of MEAT evidence is a byte-exact quote from the note — not a
	> summary, not a paraphrase. The verifier drops any quote it can't find in the
	> source. Over-coding risk is a five-component transparent heuristic — named
	> reasons, not a black box.

	---

	## 0:45–0:58 — Show a negative case

	Visual:
	1. Gallery dropdown → "Family history of malignancy".
	2. Suspect list is empty.

	Narration:
	> Family history alone doesn't code. Neither does "ruled out", "denies", or
	> workup mentions. The pipeline filters those before linking, so the coder
	> doesn't have to chase a suspect list full of false positives.

	---

	## 0:58–1:10 — Tabs for metrics + auditability

	Visual:
	1. Click the Metrics tab. Linger on the macro-F1 stat and the per-HCC table.
	2. Click Subgroups. Linger on note-length slices.
	3. Click Calibration — the placeholder panel is visible ("not fit yet").
	4. Click About.

	Narration:
	> Every number on screen is reproducible from the eval harness. Subgroup slices
	> by note length and scenario tag are in the UI, not hidden in a notebook.
	> Calibration is explicit — if the model isn't calibrated yet, the tab says so.

	---

	## 1:10–1:18 — Close

	Visual: back to the coding tab; scroll the suspect list to hero the final
	frame.

	Narration:
	> Built for a 2026 healthtech portfolio. Public data, single laptop, HF Docker
	> Space. Repo and demo links in the description.

	---

	## End card (1:18–1:22)

	Visual: static text.

	> github.com/chaaarlieyap/hcc-meat-coder
	> huggingface.co/spaces/charlesyapai/hcc-meat-coder

	---

	## Notes for the recording session

	- Close every Chrome tab except the demo. Disable any browser extension that
	could overlay UI (1Password, Grammarly, LastPass).
	- Use Cmd+Shift+P in DevTools → "Show rulers" to confirm the 16:9 aspect
	before recording.
	- Pre-populate the gallery: run `scripts/generate_meat_templates.py` so the
	gallery has at least the 10 staple cases.
	- Do two silent passes first to lock timing before recording narration.
	- Final export: H.264 MP4, 30fps, 1080p. Target file size < 20 MB so it
	embeds cleanly in the Space README.