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Decision intelligence report

Care & routine tracking

The missing-data score confuses different kinds of events

Multi-event activity tracker — synthetic demoOctober 25, 2026
Executive summary

What this dataset says

37.1% of source cells are blank, but 24 blanks occur in fields that are always empty within their event type. Review the schema before treating them as data loss.

Event types
5

Shared columns, different meanings

Days with records
2 / 2

Presence does not prove full-day coverage

Type-pattern blanks
24

26 total blank cells

Cross-midnight sleeps
1

Do not assign whole duration to start date

Evidence

Findings supported by the data

0124 / 26

Validate completeness by event type

A blank end time on a feed is not equivalent to a blank end time on a sleep. Always-empty fields suggest conditional applicability, not proof that they are optional. Confirm the source schema; this small sample cannot establish required fields.

02Date-boundary audit

Midnight boundaries can distort daily totals

1 sleep records cross calendar dates. Split intervals at midnight before comparing calendar-day sleep. Check time zone, overlapping intervals, and units before summing durations.

03Logging coverage is unknown

Do not confuse an unlogged day with zero activity

0 dates inside the observed span have no records. Even active dates may be partial. No daily baseline, health interpretation, or feeding/sleep relationship is claimed from incomplete logging.

Composition

Recorded events by type

Sleep3
Feed3
Diaper2
Meds1
Growth1
Action plan

Recommended next steps

1

Review the evidence before acting

Use the calculations, scenarios, and limitations in the executive summary. Validate denominators and row grain before making decisions.

Method & scope

How to use this report

Analysis performed

Patlr profiled 10 rows and 7 source fields, detected 5 record groups, and added 8 analysis-ready fields while preserving every original column.

Interpretation boundary

Results describe patterns in recorded events. They do not establish causation and are not medical guidance. Missing or inconsistently recorded events can affect daily measures.

Evidence-only mode

Patlr does not add external facts. AI-assisted classifications are retained only when paired with an exact quote from the submitted text; unsupported output is left blank. Interpretations should still be validated before decisions.