Record a Concept Observation — ont

Captures a point-in-time snapshot of a concept evaluation. Unlike audits (which compare human judgment to system), observations are recorded facts about what the system evaluated at a specific moment.

Usage

ont_observe(
  concept_id,
  scope,
  version = NULL,
  filter_expr = NULL,
  observation_type = "snapshot",
  store_details = FALSE,
  triggered_by = "manual",
  observer_id = NULL,
  notes = NULL,
  con = NULL
)

Arguments

concept_id: Character. The concept to observe.
scope: Character. The scope.
version: Integer. The version. If NULL, uses active version.
filter_expr: Character. Optional SQL WHERE clause to filter objects.
observation_type: Character. Type of observation: "snapshot" (default), "scheduled", or "triggered".
store_details: Logical. If TRUE, store object-level details (can be large for big datasets). Default FALSE.
triggered_by: Character. What triggered this observation (e.g., "manual", "schedule", "drift_check").
observer_id: Character. Who/what initiated this observation.
notes: Character. Optional notes.
con: A DBI connection. If NULL, uses the active connection.

Value

A list with observation summary including observation_id, counts, and prevalence rate.

Details

This enables:

Trend analysis: How does concept prevalence change over time?
Comparison: How do different versions or cohorts differ?
Monitoring: Track concept behavior in production

Examples

if (FALSE) { # \dontrun{
ont_connect(":memory:")
# ... setup ...

# Take a snapshot observation
obs <- ont_observe(
  concept_id = "ready_for_discharge",
  scope = "flow",
  triggered_by = "daily_monitor"
)

# Observe with a filter
obs <- ont_observe(
  concept_id = "ready_for_discharge",
  scope = "flow",
  filter_expr = "ward = 'ICU'",
  notes = "ICU-specific observation"
)
} # }