# Sepsis Engine Design Decisions

## Why Redis for SIRS state, not a PostgreSQL time-range query

At a medium hospital with 200 concurrent inpatients, each generating five observations
per patient per minute, the sepsis engine processes approximately 17 observation events
per second at steady state.

A PostgreSQL alternative would look like this on every event:

```sql
SELECT observation_code
FROM observations
WHERE encounter_id = :encounterId
  AND observation_code IN ('TEMP_C', 'HEART_RATE', 'RESP_RATE', 'WBC_K_UL')
  AND recorded_at >= NOW() - INTERVAL '30 minutes'
  AND (
      (observation_code = 'TEMP_C'     AND (value > 38.3 OR value < 36.0)) OR
      (observation_code = 'HEART_RATE' AND value > 90)                      OR
      (observation_code = 'RESP_RATE'  AND value > 20)                      OR
      (observation_code = 'WBC_K_UL'  AND (value > 12.0 OR value < 4.0))
  );
```

This query hits the `observations` table on every event. Under load it competes for
I/O with the ingest path writing new rows — both want the same composite index. With
Redis: four `SET`/`DEL` operations and one `MGET`, all O(1), all in-memory. No disk
I/O, no lock contention with the write path.

The TTL enforces the 30-minute sliding window automatically. Without Redis (or an
equivalent in-memory store), a background job would be needed to clean up stale
criteria — another failure point, another deployment concern.

## Why not Apache Flink

Flink is a distributed stream processor designed for stateful computation at scale
(millions of events per second across a fleet). It brings real costs:

- A Flink cluster (JobManager + TaskManagers) is infrastructure that must be deployed,
  monitored, and upgraded independently of the application.
- Flink state backends (RocksDB, heap) add operational complexity that is not
  justified unless the stream volume saturates what a single consumer thread can handle.
- Flink's exactly-once semantics require Kafka transactions, which add latency and
  require tuning separate from the rest of the application.

At a single hospital (200 inpatients, ~17 observations/second), a Kafka consumer +
Redis state store handles the volume with single-digit millisecond latency per event
and no additional infrastructure. The trade-off: if this system needed to scale to a
multi-hospital network with 50,000+ concurrent inpatients (~5,000 observations/second),
Flink would become the right choice. The architecture decision is correct at this scale
and defensible at interview with a clear scale inflection point named.

## Phase 27 — Migration from SIRS to SOFA

**Rationale:** Sepsis-3 (2016) replaced SIRS with SOFA for organ dysfunction assessment.
SIRS is non-specific (post-exercise tachycardia, mild fever). Doctor feedback aligned with
moving sepsis **confirmation** to SOFA delta ≥ 2 while retaining qSOFA as a **bedside screen**.

**What changed:**
- `SirsDetector` / `SirsEvaluator` deleted — no new `SEPSIS_WARNING` alerts
- qSOFA ≥ 2 → `QSOFA_SCREEN` (WARNING, suppressible) with lab-order recommendation
- Sepsis hour-1 bundle triggers from `SOFA_SEPSIS` only (Phase 26 delta ≥ 2)
- Historical `SEPSIS_WARNING` and `QSOFA_WARNING` rows remain queryable

**Redis key patterns after Phase 27:**
- `qsofa:{encounterId}:{code}` — qSOFA screening (30 min TTL)
- `sofa:{encounterId}:{code}` — SOFA lab carry-forward (Phase 26)
- `gcs:{encounterId}:{code}` — GCS components (Phase 25)
- ~~`sirs:{encounterId}:{code}`~~ — removed (legacy keys expire)