[D] Looking for advice: Modelling a medicine-reminder agent that must decide “remind / wait / notify” under incomplete information[D]

Hi everyone,

I’m researching how to design an AI agent for a medicine-reminder system. The agent has to decide, at each relevant time, whether to:

• send a reminder,
• wait (do nothing for now), or
• notify another person (e.g. caregiver),

when it does not have complete information about the patient (has the dose already been taken? is the person nearby/attentive? are there adherence barriers? etc.).

I’m trying to frame this properly before diving into implementation. Right now I’m looking at it as a sequential decision problem under partial observability (POMDP / belief-state RL territory), but I’m not sure how far that framing is actually useful in practice for this kind of system.

I’d really appreciate any pointers on:

1. Is a POMDP / belief-state approach overkill here, or is it the right formalization? What simpler alternatives (contextual bandits, MDP with engineered features, rule-based + uncertainty thresholds, etc.) have people used successfully for similar “remind vs wait vs escalate” decisions?
2. Papers, open-source projects, or real systems that tackle medication adherence / context-aware reminders with uncertainty or incomplete observations.
3. Common practical pitfalls (reward design, observation noise, alert fatigue, safety/escalation logic, evaluation metrics) that aren’t obvious from the theory.
4. Any recommended starting points for someone new who wants to move from “I understand the concepts” to a small working prototype or simulation.

I’m mainly in research/preparation mode right now, so even high-level advice, key papers, or “here’s what I’d do differently” comments would be very helpful. Thanks!