OpenClaw Health and Symptom Tracker: Daily Signals for Trigger Discovery
Track food intake and symptoms with reminder-based logging to identify potential triggers over time.
0) TL;DR (3-minute launch)
- Symptom patterns are hard to spot when logs are scattered across notes and chats.
- Workflow in short: Meal/symptom reminder → capture structured entry (time, intake, symptoms, severity) → normalize and append to tracker → detect short-term correlations → flag patterns for review → generate weekly clinician-ready summary
- Start fast: Track one condition scope first (for example GI or migraine) to reduce noise.
- Guardrail: Do not provide medical diagnosis or treatment instructions.
1) What problem this solves
Symptom patterns are hard to spot when logs are scattered across notes and chats. This workflow standardizes daily capture and surfaces likely triggers without over-claiming causality.
2) Who this is for
- Operators responsible for health logging decisions
- Builders who need repeatable habit analytics workflows
- Teams that want automation with explicit human checkpoints
3) Workflow map
Meal/symptom reminder
-> capture structured entry (time, intake, symptoms, severity)
-> normalize and append to tracker
-> detect short-term correlations
-> flag patterns for review
-> generate weekly clinician-ready summary4) MVP setup
- Track one condition scope first (for example GI or migraine) to reduce noise
- Use fixed severity scale and symptom taxonomy
- Log time-of-day and context for every entry
- Export weekly summary with raw logs + pattern hints
- Keep manual override for incorrect auto-classification
5) Prompt template
You are my symptom logging assistant. On each entry: 1) capture symptoms, severity, and timing 2) record recent food/activity context 3) detect repeated co-occurrence patterns 4) produce a conservative summary with uncertainty labels Never claim medical diagnosis; focus on tracking evidence and trends.
6) Cost and payoff
Cost
Primary costs are model calls, integration maintenance, and periodic prompt tuning.
Payoff
Faster execution cycles, fewer context switches, and clearer decision quality over time.
Scale
Add role-specific subagents, stronger evaluation metrics, and staged automation permissions.
7) Risk boundaries
- Do not provide medical diagnosis or treatment instructions
- Mark correlations as tentative unless user confirms longer trend
- Protect sensitive health data and avoid unnecessary sharing
9) FAQ
How quickly can this workflow deliver value?
Most teams see meaningful results within 1-2 weeks when they keep the initial scope narrow and measurable.
What should stay manual at the beginning?
Keep ambiguous, high-risk, or customer-impacting actions behind explicit human approval until quality is proven.
How do we prevent automation drift over time?
Review logs weekly, sample outputs, and tune prompts/rules as data patterns and business goals change.
What KPI should we track first?
Track one leading metric (speed or coverage) plus one quality metric (accuracy, escalation rate, or user satisfaction).