diff --git a/src/app/api/illness/insights/route.ts b/src/app/api/illness/insights/route.ts
index 48960abdd..938ebf3b4 100644
--- a/src/app/api/illness/insights/route.ts
+++ b/src/app/api/illness/insights/route.ts
@@ -11,6 +11,7 @@
  * contributes no gap rather than a wrong one. Born-gated + owner-scoped.
  */
 import { NextRequest } from "next/server";
+import pLimit from "p-limit";
 
 import { prisma } from "@/lib/db";
 import { apiHandler, requireAuth } from "@/lib/api-handler";
@@ -29,6 +30,9 @@ import { resolveUserTimezone } from "@/lib/measurements/consolidation-base";
 const MS_PER_DAY = 24 * 60 * 60 * 1000;
 /** Cap the per-episode gap computations so a heavy history can't fan out. */
 const MAX_GAP_COMPUTATIONS = 24;
+/** Run the per-episode correlations under a bounded concurrency budget so
+ *  the up-to-24 candidates don't serialise into ~500 round-trips. */
+const EPISODE_GAP_CONCURRENCY = 4;
 
 export const GET = apiHandler(async (request: NextRequest) => {
   const { user } = await requireAuth();
@@ -69,19 +73,24 @@ export const GET = apiHandler(async (request: NextRequest) => {
   const gapCandidates = rows
     .filter((r) => r.resolvedAt !== null && r.lifecycle !== "CHRONIC_ONGOING")
     .slice(0, MAX_GAP_COMPUTATIONS);
-  const gapById = new Map<string, number | null>();
-  for (const r of gapCandidates) {
-    const derived = await computeEpisodeCorrelation(
-      user.id,
-      { id: r.id, onsetAt: r.onsetAt, resolvedAt: r.resolvedAt, lifecycle: r.lifecycle },
-      tz,
-      now,
-    );
-    gapById.set(
-      r.id,
-      derived.status === "ok" ? derived.value.recoveryGapDays : null,
-    );
-  }
+  const limit = pLimit(EPISODE_GAP_CONCURRENCY);
+  const gapEntries = await Promise.all(
+    gapCandidates.map((r) =>
+      limit(async (): Promise<[string, number | null]> => {
+        const derived = await computeEpisodeCorrelation(
+          user.id,
+          { id: r.id, onsetAt: r.onsetAt, resolvedAt: r.resolvedAt, lifecycle: r.lifecycle },
+          tz,
+          now,
+        );
+        return [
+          r.id,
+          derived.status === "ok" ? derived.value.recoveryGapDays : null,
+        ];
+      }),
+    ),
+  );
+  const gapById = new Map<string, number | null>(gapEntries);
 
   const episodes: RetrospectiveEpisode[] = rows.map((r) => ({
     id: r.id,
diff --git a/src/lib/illness/correlation-read.ts b/src/lib/illness/correlation-read.ts
index d37c42240..a17f64909 100644
--- a/src/lib/illness/correlation-read.ts
+++ b/src/lib/illness/correlation-read.ts
@@ -24,6 +24,7 @@
  * Rollup DAY buckets are minted at the UTC midnight of the user-tz day, so
  * keying their `bucketStart` through `dayKeyForUserTz` is exact.
  */
+import pLimit from "p-limit";
 import type { MeasurementType } from "@/generated/prisma/client";
 import { prisma } from "@/lib/db";
 import { readBestGranularityRollups } from "@/lib/rollups/measurement-read-wmy";
@@ -43,6 +44,13 @@ import {
 
 const MS_PER_DAY = 24 * 60 * 60 * 1000;
 
+/**
+ * Bound the per-vital fan-out so a single episode's scan can't pin the
+ * shared Prisma pool. Mirrors the W-POOL `p-limit(4)` discipline the
+ * analytics summaries slice applies.
+ */
+const VITAL_SCAN_CONCURRENCY = 4;
+
 /** The episode shape the read layer needs (a subset of the Prisma row). */
 export interface EpisodeForCorrelation {
   id: string;
@@ -88,16 +96,30 @@ export async function computeEpisodeCorrelation(
   // mitigation — the same pattern the derived route uses).
   const coverage = await probeRollupCoverage(userId);
 
+  // Attempt every scan type under a bounded concurrency cap. The two
+  // windows per type (baseline + episode) are independent reads, so they
+  // run paired rather than serially (halving the per-type round-trips).
+  // The day-log fever read shares the same fan-out budget.
+  const limit = pLimit(VITAL_SCAN_CONCURRENCY);
+  const [perType, dayLogFever] = await Promise.all([
+    Promise.all(
+      ILLNESS_SCAN_TYPES.map((type) =>
+        limit(async () => {
+          const [baseline, episodeWin] = await Promise.all([
+            readWindowMeans(userId, type, baselineStart, baselineEnd, coverage, tz, now),
+            readWindowMeans(userId, type, preOnsetStart, end, coverage, tz, now),
+          ]);
+          return { type, baseline, episodeWin };
+        }),
+      ),
+    ),
+    limit(() => readDayLogFever(episode.id, preOnsetStart, end, tz)),
+  ]);
+
   const series: VitalSeries[] = [];
   let anyLive = false;
   let anyDay = false;
-
-  // We attempt every scan type. An untracked vital returns empty points and
-  // is dropped below. Each read is a single windowed query, so attempting all
-  // types is bounded.
-  for (const type of ILLNESS_SCAN_TYPES) {
-    const baseline = await readWindowMeans(userId, type, baselineStart, baselineEnd, coverage, tz, now);
-    const episodeWin = await readWindowMeans(userId, type, preOnsetStart, end, coverage, tz, now);
+  for (const { type, baseline, episodeWin } of perType) {
     if (baseline.points.length === 0 && episodeWin.points.length === 0) continue;
     if (baseline.source === "live" || episodeWin.source === "live") anyLive = true;
     if (baseline.source === "DAY" || episodeWin.source === "DAY") anyDay = true;
@@ -111,11 +133,6 @@ export async function computeEpisodeCorrelation(
     });
   }
 
-  // Union the canonical journaling-fever path: the illness day-log `feverC`
-  // series over the episode window. A user logging 39.2 °C for days with no
-  // HealthKit thermometer must still escalate. Per-day MAX.
-  const dayLogFever = await readDayLogFever(episode.id, preOnsetStart, end, tz);
-
   const source: "DAY" | "live" | "none" = anyDay
     ? "DAY"
     : anyLive