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unisound-llm

unisound-followup-mgmt

by Unisound-LLM · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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Install in OpenClaw
/install unisound-followup-mgmt
Description
检后随访管理。根据体检报告,生成结构化随访计划(时间节点/随访内容/健康指导),支撑体检闭环服务(JSON + 面向受检者的随访通知)。
README (SKILL.md)

检后随访管理

概述

面向体检中心/健康管理机构,给定受检者体检报告,本技能会:

  • 生成按时间节点(1个月/3个月/6个月/1年)组织的随访计划
  • 明确每个时间点需复查的项目、就诊科室和目标值
  • 提供针对性的健康指导要点(饮食/运动/用药依从性等)
  • 列出预警条件(出现哪些症状需立即就医)
  • 给出下次年度体检的重点关注项目

本技能是体检服务闭环的核心环节,实现"检查—发现—跟踪—改善"全链条管理。

数据安全、隐私与伦理声明

  • 最小必要原则:仅处理制定随访计划所必需的体检数据;不要求包含直接身份标识。
  • 严格脱敏:发送前对可识别身份信息进行脱敏处理。
  • 不做本地持久化:仅在内存中短暂处理;本次调用结束即销毁
  • 医疗边界:随访计划为健康管理建议,具体就诊方案由接诊医生决定。

输入格式

纯文本(UTF-8),建议提供体检报告原文,例如:

受检者:女,42岁
体检日期:2026年5月
主要发现:
- 血压:135/86mmHg(临界高血压)
- 空腹血糖:6.0mmol/L(临界偏高)
- TG:2.1mmol/L(轻度升高)
- 宫颈液基细胞学:ASCUS(不典型鳞状细胞)
- 乳腺超声:右乳3mm结节,BI-RADS 3类
- 甲状腺超声:TI-RADS 2类
- 骨密度:T值-1.2(骨量减少)
其余各项:正常

也支持 JSON 格式(包含 text/content/report 字段的对象)。

快速开始

# 从 skills 目录运行
python3 health-exam/post-exam-mgmt/followup-mgmt/scripts/run.py \
  --input data/health-exam-followup/case-001.txt \
  --appkey \x3Cyour-appkey>

# 保存输出到文件
python3 health-exam/post-exam-mgmt/followup-mgmt/scripts/run.py \
  --input data/health-exam-followup/case-001.txt \
  --appkey \x3Cyour-appkey> \
  --output runs/health-exam-followup/case-001.json

参数说明

  • --input PATH必填。体检报告文件路径(txt 或 json,UTF-8)。
  • --appkey STRING必填。调用内部医疗大模型的鉴权 key,由平台分配。
  • --output PATH:输出文件路径(默认:打印到 stdout)。
  • --base URL:内部大模型 base URL(默认:https://maas-api.hivoice.cn/v1)。
  • --model STRING:模型名称(默认:u1-insuremed)。
  • --timeout SECONDS:HTTP 超时秒数;0 表示一直等待(默认:0)。
  • --encoding STRING:输入文件编码(默认:utf-8)。

输出约定

输出分两部分:

JSON 结构

{
  "followup_summary": {
    "total_followup_items": 5,
    "highest_priority": "宫颈ASCUS需6个月内行HPV检测+TCT复查",
    "exam_date": "2026年5月"
  },
  "followup_schedule": [
    {
      "timepoint": "1个月内",
      "items": [
        {
          "issue": "宫颈ASCUS",
          "followup_action": "妇科就诊,行HPV分型检测",
          "target_value": "明确是否高危型HPV阳性",
          "channel": "医院就诊"
        }
      ]
    },
    {
      "timepoint": "6个月后",
      "items": [
        {
          "issue": "右乳结节(BI-RADS 3)",
          "followup_action": "乳腺超声复查",
          "target_value": "结节无增大",
          "channel": "本机构复查或医院超声科"
        }
      ]
    }
  ],
  "health_coaching_points": [
    {
      "topic": "代谢管理",
      "key_messages": ["低盐低糖饮食", "增加有氧运动", "每日监测血压"],
      "materials_to_provide": ["高血压预防手册", "血糖管理指南"]
    }
  ],
  "alert_conditions": ["出现持续头痛、视物模糊或血压≥160/100mmHg时立即就诊"],
  "next_annual_exam_focus": ["血压、血糖、血脂", "妇科(宫颈+乳腺)", "骨密度"]
}

随访通知:以"【随访通知】"开头,用友好语言向受检者说明需要跟进的事项和时间安排。

依赖

运行环境

  • Python 3.7+(仅使用标准库,无需额外安装)

外部 API

  • 内部医疗大模型:https://maas-api.hivoice.cn/v1/chat/completions

备注

  • followup_schedule 按紧急程度和时间从近到远排序
  • 对于影像学可疑发现(乳腺结节/甲状腺结节等),会给出明确的随访时间窗
  • 发布约束:示例输入、运行输出均放在 skill 包外(data/runs/),skill 目录内仅保留可发布的核心文件
Usage Guidance
Review before installing or using: only run this skill if you trust the configured LLM provider, manually remove direct identifiers from reports, protect the app key, and verify the publisher/package identity. Treat generated follow-up plans as health-management guidance that should be reviewed by qualified medical staff.
Capability Assessment
Purpose & Capability
The skill coherently reads a user-selected health exam report and generates a follow-up plan, but the input is inherently sensitive medical data.
Instruction Scope
The documentation promises de-identification before sending, but the included script embeds the raw report text into the LLM prompt and sends it to the configured API.
Install Mechanism
There is no package install step and the script uses only Python standard library, but registry/package identity fields do not fully align, so publisher provenance should be verified.
Credentials
The script reads only the user-provided input path and optionally writes to a user-provided output path; the main environmental exposure is the external API submission.
Persistence & Privilege
No background persistence is shown, but the skill requires an API app key even though registry metadata declares no primary credential.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install unisound-followup-mgmt
  3. After installation, invoke the skill by name or use /unisound-followup-mgmt
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release: Structured health exam follow-up management skill - Generates time-based follow-up plans (1/3/6/12 months) based on exam reports - Provides clear itemized rechecks, departments, targets, and health coaching points - Lists alert symptoms and key focus areas for the next annual check-up - Ensures strong data privacy: minimal data use, de-identification, and no local storage - Outputs both structured JSON and human-friendly follow-up notifications
Metadata
Slug unisound-followup-mgmt
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is unisound-followup-mgmt?

检后随访管理。根据体检报告,生成结构化随访计划(时间节点/随访内容/健康指导),支撑体检闭环服务(JSON + 面向受检者的随访通知)。 It is an AI Agent Skill for Claude Code / OpenClaw, with 55 downloads so far.

How do I install unisound-followup-mgmt?

Run "/install unisound-followup-mgmt" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is unisound-followup-mgmt free?

Yes, unisound-followup-mgmt is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does unisound-followup-mgmt support?

unisound-followup-mgmt is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created unisound-followup-mgmt?

It is built and maintained by Unisound-LLM (@unisound-llm); the current version is v1.0.0.

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