What is langcare-medication-adherence?

A clinical analysis skill that checks whether a patient is receiving prescribed medicines regularly by examining prescription fill records. It calculates proportion of days covered (the percentage of days a medicine was available) and finds refill gaps.

Use it to review prescriptions and dispensing records, calculate adherence for each medicine, identify gaps, and assess possible effects on treatment outcomes.

It turns medication refill history into an adherence report and helps connect missed supplies with clinical results such as blood pressure or A1c.

langcare-medication-adherence — Skill by langcare/langcare-mcp-fhir

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add langcare/langcare-mcp-fhir --skill medication-adherence

Clone the repo

git clone --depth 1 https://github.com/langcare/langcare-mcp-fhir

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/langcare/langcare-mcp-fhir/medication-adherence/github.svg)](https://agentmods.dev/skills/langcare/langcare-mcp-fhir/medication-adherence)

Your own site

<a href="https://agentmods.dev/skills/langcare/langcare-mcp-fhir/medication-adherence"><img src="https://agentmods.dev/badge/skills/langcare/langcare-mcp-fhir/medication-adherence/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/langcare/langcare-mcp-fhir/medication-adherence"><img src="https://agentmods.dev/badge/skills/langcare/langcare-mcp-fhir/medication-adherence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 66 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 677 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00066	$0.00677
Opus 5.5	$0.00026	$0.00271
Sonnet 5.5	$0.00013	$0.00135
Haiku 4.5	$0.00007	$0.00068

Measured 2mo ago against content hash f02b175951a6, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

langcare-medication-adherence scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

cma/skills/medication-management/medication-adherence/SKILL.md · 58 lines

How it starts

The opening of the file, as written. The whole thing — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Medication Adherence Assessment

When to Use This Skill

Use when a clinician needs to evaluate whether a patient is taking medications as prescribed by analyzing fill history, calculating adherence metrics, and identifying therapy gaps.

Clinical Workflow

Use fhir_search to pull active MedicationRequest resources for the patient's prescribed medication regimen
Use fhir_search to pull MedicationDispense resources for fill history with dates and quantities
Calculate Proportion of Days Covered (PDC) for each medication: PDC = (days supply dispensed / days in period) x 100
Identify refill gaps: periods where days supply ran out before next fill
Cross-reference adherence findings with clinical outcomes (e.g., uncontrolled A1c with poor metformin PDC, elevated BP with antihypertensive gaps)
Present adherence report with per-medication PDC, gap analysis, and clinical impact assessment

FHIR Resources

MedicationRequest -- Active prescriptions defining the intended regimen
MedicationDispense -- Fill history: medicationCodeableConcept, quantity, daysSupply, whenHandedOver, status
Observation -- Clinical outcomes (A1c, BP, LDL) correlated with adherence

FHIR Query Examples

Pull Fill History

fhir_search(resourceType="MedicationDispense", queryParams="patient=[patient-id]&_sort=-whenhandedover&_count=200")

Pull Active Prescriptions

fhir_search(resourceType="MedicationRequest", queryParams="patient=[patient-id]&status=active&_count=100")

Clinical Guidelines

CMS Star Ratings use PDC >= 80% as the adherence threshold for diabetes, hypertension, and cholesterol medications
WHO defines five dimensions of adherence: social/economic, health system, condition-related, therapy-related, patient-related
NCQA HEDIS medication adherence measures: ADM (antidepressants), SPC (statins), PBD (beta-blockers post-discharge)

Interpretation Guide

PDC >= 80%: adherent. PDC 50-79%: partially adherent. PDC < 50%: non-adherent
Calculate PDC over the most recent 12 months or since therapy start, whichever is shorter
Flag early refills (>7 days early) which may indicate stockpiling or diversion
Flag medications with no fills despite active prescription (never-filled prescriptions)
Correlate adherence gaps with clinical events: ED visits, hospitalizations, worsening lab values

Read the full file on GitHub · 58 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2mo ago First seen · 58 lines · 66 tokens per session scan A f02b175951a6

Subscribe to this mod's changes

langcare-medication-adherence is a skill published in the GitHub repository langcare/langcare-mcp-fhir (71 stars, last pushed 6mo ago), licensed MIT. It adds 66 tokens to every session and 677 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

langcare-medication-adherence next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
langcare-medication-adherencelangcare/langcare-mcp-fhir	Skill	71	6mo ago	MIT	A	66	Claude Code, Codex
imagegenopenai/codex	Skill	133k	yesterday	Apache-2.0	A	113	Codex
playwright-climicrosoft/playwright	Skill	99k	yesterday	Apache-2.0	A	19	Claude Code
local-ai-agentsmicrosoft/ai-agents-for-beginners	Skill	79k	4d ago	MIT	A	200	Claude Code, Codex
systematic-debuggingobra/superpowers	Skill	313k	3d ago	MIT	A	21	Claude Code
next-cache-components-adoptionvercel/next.js	Skill	143k	yesterday	MIT	A	95	Claude Code
next-partial-prefetching-adoptionvercel/next.js	Skill	143k	yesterday	MIT	A	103	Claude Code