The actor keeps returning to the same data with one question: what needs my attention, and why? The domain can answer some of it in advance — there is consensus about what counts as a problem (e.g. a rule/policy violation) — yet a general view leaves the actor to re-derive those judgements by inference on every visit. A problem-curated view organises what is shown around the detected problems instead of the data types: a checklist of candidate problems, each backed by an explicit rule. It is case of a purpose-keyed view taken to its limit.
Anatomy
The checklist is the interface. Each entry is a detected problem; the list doubles as index, navigation, status display, and audit trail. Selecting a problem filters the collection to the relevant slice, magnified and annotated with why it was flagged — the provenance is explicit, so the view can always say why it looks the way it does, and the reason moves with the actor from noticing into investigating.
Commitments
- Annotate the judgement moment. Flags belong where judgement is already being exercised — the review, the rounds, the scheduled check — and stay out of exploration, where they pre-empt the very judgement they are meant to support. The lesson is empirical: the same detection rules failed as ambient dashboards and succeeded attached to code review. Showing flags only when guidance is solicited is one honest implementation; the rule is the moment, not the request.
- Keep the escape hatch structural. The general exploratory view stays reachable alongside the curated one — required, not optional. Rules never capture the whole judgement: even where they are possible, the ground truth behind them is partly heuristic, so there is always a remainder they don’t encode. The escape hatch is where the remainder gets noticed — and where the next rule is usually discovered.
- Watch the framing. The same checklist reads as scaffolding when it teaches what to look for, and as fossilised process when it caps what anyone looks for.
Resources & references
- Zhang, Ehrmann, Mazwi, Eytan, Ghassemi & Chevalier (2022) Get to the point! Problem-based curated data views to augment care for critically ill patients — In-Sight: the clinical exemplar; source of the checklist anatomy and the escape-hatch commitment
- Sadowski, Aftandilian, Eagle, Miller-Cushon & Jaspan (2018) Lessons from building static analysis tools at Google — Tricorder: the independent developer-tooling exemplar; the judgement-moment lesson, the effective-false-positive definition, the noise budget
- Johnson, Song, Murphy-Hill & Bowdidge (2013) Why don’t software developers use static analysis tools to find bugs? — false positives and warning presentation as the adoption barriers
- Ruskin & Hueske-Kraus (2015) Alarm fatigue: impacts on patient safety — the decay curve the upkeep consequence guards against
- Zhang, Wang & Yi (2025) Exploring collaboration patterns and strategies in human-AI co-creation through the lens of agency — decomposes the anatomy into five agency control mechanisms: context awareness, accept/dismiss gatekeeping, adaptive scaffolding, rule-based transparency, iterative feedback
Consequences
- what needs attention arrives as a rule-backed checklist — index, navigation, status display and audit trail at once — and every flag can say why it fired
- the rule set is a standing maintenance duty — to its reader a false positive is any flag they did not want to see, so flags need a feedback channel and a noise budget, or the curve is alarm fatigue, then suppression: the checklist still renders after trust has left it
- whatever the rules don't encode sails past; the general exploratory view remains the place to notice it
Related patterns
Instantiates
- Purpose-keyed view — problem-curated view takes the design-time keying to its limit: the advance decision about what matters made explicit as detection rules, spanning the watch and the chase
Serves
- Assistance — perceiving — problem-curated view's detection rules do the noticing and carry their reasons; judgement and action stay with the actor, which is where next-best-action systems would pick up
Enacts
- Adaptability — the view, not the content, responds — the framing shifts with what the detection rules find, before the actor selects anything