Concepts
Cancer, treated as an observability problem
The mental model behind the app — why this maps cleanly to logs, metrics, events, and outcomes.
Cancer journeys generate a lot of small, scattered signals: how you're feeling, what your labs look like, when you started a treatment, what a scan showed. The same problem shape comes up in software observability — fast-moving streams that only make sense when you put them on the same timeline.
Teloma treats those signals the same way:
| Symptoms | logs — frequent, free-form, time-stamped |
| Labs & vitals | metrics — numeric, comparable over time |
| Diagnosis · biopsy · treatment · imaging · adverse events | events / traces |
| Pathology · genomics · imaging impression | system state |
| Treatment response & recurrence | outcomes |
| Research engine | analytics layer (opt-in, de-identified) |
Everything you log in Teloma writes a row into your timeline. That means you and your care team always have a single, chronological view of the journey instead of a folder of PDFs and a memory of what happened when.