Token导航 LogoToken导航TokenDH.com
效率只读clawhub未标认证来源可访问clear审计通过

medication-reconciliation药物协调

Agent Skill

medication-reconciliation 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

4,406

周安装

180

GitHub Stars

公开资料未说明

下载量

1,426
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:medication-reconciliation(药物协调)
来源仓库:https://github.com/aipoch-ai/medication-reconciliation
安装命令:
openclaw skills install medication-reconciliation
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install medication-reconciliation

简介

将患者入院前药物清单与住院订单进行比较,以自动识别遗漏或重复的药物并提高用药安全性。

SKILL.md

name
medication-reconciliation
description
Compare patient pre-admission medication lists with inpatient orders to automatically identify omitted or duplicated medications and improve medication safety.
license
MIT
skill-author
AIPOCH

Medication Reconciliation

Compare patient pre-admission medication lists with inpatient orders to automatically identify omitted or duplicated medications and improve medication safety.

Medical Disclaimer: This tool is for reference only. Final medication decisions must be confirmed by qualified medical staff. All patient data must comply with applicable data protection regulations (e.g., HIPAA).

Quick Check

python -m py_compile scripts/main.py
python scripts/main.py --help

When to Use

  • Use this skill when comparing pre-admission medication lists against inpatient orders to detect omissions or duplicates.
  • Use this skill when generating structured reconciliation reports for clinical handover or pharmacy review.
  • Do not use this skill as a substitute for pharmacist or physician review of medication orders.

Workflow

  1. PHI Check: Before processing, prompt the user to confirm data has been de-identified: "Please confirm that the input files have been de-identified or that you have authorization to process this patient data under applicable regulations (e.g., HIPAA) before proceeding."
  2. Confirm patient ID, pre-admission medication list, and inpatient orders are available.
  3. Validate that both input files are well-formed and patient IDs match.
  4. Run the reconciliation script or apply the manual comparison path.
  5. Return a structured report separating continued, discontinued, new, and duplicate medications.
  6. Dose-change detection: When a drug appears in both lists with different dose strings, flag it as dose_changed with a warning: "Dose change detected — verify with prescribing physician before proceeding."
  7. Flag warnings for critical drug classes (anticoagulants, hypoglycemics, antihypertensives, antiepileptics).
  8. If inputs are incomplete, state exactly which fields are missing and request only the minimum additional information.

Usage

# Basic usage
python scripts/main.py --pre-admission pre_meds.json --inpatient orders.json --output report.json

# Use example data
python scripts/main.py --example

# Verbose output
python scripts/main.py --pre-admission pre_meds.json --inpatient orders.json --verbose

Parameters

ParameterTypeRequiredDescription
--pre-admissionfile pathYesJSON file of pre-admission medications
--inpatientfile pathYesJSON file of inpatient orders
--outputfile pathNoOutput report path (default: stdout)
--exampleflagNoRun with built-in example data
--verboseflagNoInclude detailed matching rationale

Output Format

The reconciliation report separates results into:

  • continued — medications present in both lists (same drug, same dose)
  • dose_changed — same drug present in both lists but with different dose strings (⚠️ requires physician verification)
  • discontinued — pre-admission medications absent from inpatient orders
  • new_medications — inpatient orders not in pre-admission list
  • duplicates — same drug appearing multiple times
  • warnings — critical drug class alerts

Dose-change example:

{
  "dose_changed": [
    {
      "drug": "Metformin",
      "pre_admission_dose": "500mg",
      "inpatient_dose": "1000mg",
      "warning": "Dose change detected — verify with prescribing physician before proceeding."
    }
  ]
}

Scope Boundaries

  • This skill compares structured medication data; it does not interpret clinical appropriateness.
  • This skill does not access live pharmacy systems or EHR databases.
  • This skill does not replace pharmacist verification or physician sign-off.

Stress-Case Rules

For complex multi-constraint requests, always include these explicit blocks:

  1. Assumptions
  2. Inputs Used
  3. Reconciliation Result
  4. Warnings and Critical Flags
  5. Risks and Manual Checks

Error Handling

  • If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
  • If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
  • If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
  • Do not fabricate medication data, citations, or execution outcomes.

Input Validation

This skill accepts: pre-admission medication lists and inpatient order files (JSON format) for a single patient encounter.

If the request does not involve medication list comparison — for example, asking to prescribe medications, interpret drug interactions clinically, or access live EHR systems — do not proceed with the workflow. Instead respond:

"medication-reconciliation is designed to compare pre-admission and inpatient medication lists to flag omissions and duplicates. Your request appears to be outside this scope. Please provide structured medication input files, or use a more appropriate clinical tool."

Response Template

Use the following fixed structure for non-trivial requests:

  1. Objective
  2. Inputs Received
  3. Assumptions
  4. Workflow
  5. Deliverable
  6. Risks and Limits
  7. Next Checks

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

95.61%
按下载量换算1,363

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills