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task

Prefect task priming the EHR serving layer during the background flow.

Extracts patient demographic and code data from the EHR datasource and persists the combined records to the ehr_data cache table, in the same two modes v1 supports (see EHRQueryConfig.mode).

What differs from v1 is what happens when the EHR cannot be reached. v1 is a gate: it raises, and because the step is step 1 of a sequential step loop (flows/dag/executor.py) nothing downstream runs — no inference, no calculations, no criteria_matching, no pointer flip. v3 is a primer instead: it stores whatever it did fetch, reports how the run went on its result, and returns a real cache accessor either way, so downstream steps read this run's rows where they exist and earlier runs' rows where they do not.

Best-effort behaviour is unconditional here rather than a config flag, because _semantic_config hashes every config field including defaults (flows/dag/hashing.py): adding a flag would move the ehr_query partition for every template using the config, including the one that keeps fail-fast on v2 and whose ehr_data cache must survive. Which template gets which behaviour is answered by the version in the YAML.

Module​

Functions​

ehr_criteria_query_task​

def ehr_criteria_query_task(    ehr_data_resource: EHRDataResource | None,    cache: CacheProtocol,    task_hash: str,    config: EHRQueryConfig | None = None,    datasource: BaseSource | None = None,    filenames: list[str] | None = None,    patient_ids: CacheAccessor | None = None,) ‑> EHRQueryResult:

Prime the EHR cache for the current run, without failing the run.

Arguments

  • ehr_data_resource: Configured EHR resource (NextGen or FHIR R4). None means no EHR reached this pod's DAG setup: the run continues, nothing is queried, and the result reports EHRRunOutcome.NOT_CONFIGURED.
  • cache: Cache backend to write ehr_data rows to.
  • task_hash: Hash identifying the current task run; written into every row so re-runs upsert via session.merge.
  • config: Step config. config.mode selects between filename-driven and patient-ID-cache-driven lookups, config.fetch_appointments controls whether appointment and encounter history is fetched, and config.observation_codes/config.observation_categories fetch coded Observations matching those codes/categories (merged and deduplicated when both are set). If neither is set, every core FHIR observation-category is fetched by default — see EHRQueryConfig.fetch_options. When config itself is None the defaults are used so existing programmatic callers do not have to pass one.
  • datasource: Imaging datasource. Required in "filename" mode.
  • filenames: Imaging file IDs to process. Required in "filename" mode.
  • patient_ids: Cache accessor over the ehr_patient_ids table (typically wired from ehr_patient_lister.cache in the YAML). Required in "patient_id_cache" mode.

Returns The step result, always carrying a CacheAccessor over this run's ehr_data partition — including when no row was stored, since an unwired accessor costs criteria_matching its EHR columns entirely, and EHR criteria then read as scan evidence with provenance unknown rather than as UNKNOWN EHR evidence.

Raises

  • ValueError: If config.mode is not one this step supports, or the mode's required inputs are missing. A misconfigured step is not an EHR outage.