How NHS waits have moved since April 2024: a data analysis

The political shorthand on NHS waits is binary: either "the list is coming down" or "the list is still growing". The reality, which we can now see in the data, is that both of those statements are true at once — about different specialties, in different directions, at the same time.

We took the full window of NHS England RTT data we hold, computed the national median wait for each of the 18 user-facing specialties for every published month, and fitted a simple linear trend to each. The results are below — recomputed on every monthly publication, no hand-typing.

So the binary framing fails immediately. A majority of specialties are moving meaningfully in one direction or the other, but they aren't all moving the same way. Headlines about "NHS waiting list improving" or "NHS waiting list growing" pick whichever specialty fits the story.

What changed in the latest NHS data

The trend above is the long view. This is the short one: the newest month of NHS England figures against the month before, recomputed each time a month is added.

What the trajectory looks like for every specialty

Each tile above is one specialty. The sparkline is the national median-of-medians per month, oldest left, newest right. The colour-coded label is the OLS slope across the window: improving (slope ≤ −0.1 weeks/month), worsening (slope ≥ +0.1 weeks/month), or flat. Click any tile to open the latest monthly national ranking for that specialty.

A few patterns are visible immediately:

• Specialties where the bottleneck is theatre throughput plus an ageing workforce (trauma & orthopaedics, ophthalmology) tend to move slowly. The slope is rarely zero, but it's rarely steep either.
• Specialties where the bottleneck is outpatient capacity (dermatology, rheumatology, gastroenterology) are more volatile — a small change in clinic productivity moves the median noticeably.
• Specialties tied to diagnostic bottlenecks (cardiology, neurology) tend to track the DM01 diagnostic queues with a lag.

The biggest single swing

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The specialty with the largest absolute slope is shown above as a single line — same data, but plotted in full so the magnitude of the move is visible. A move of more than half a week of national median wait across the window is large; a move of more than a week is dramatic.

Winners and losers

Two cautions on this table. First, "improving" doesn't mean "fast" — it means "less slow than it was". A specialty with a 30-week median and a slope of −0.5 wks/month is still long, just less long than it used to be. Second, "worsening" doesn't mean "in crisis" — a specialty with a 6-week median and a slope of +0.2 wks/month is still well within target, just less comfortably so.

If you want absolute waits, browse the national rankings; the relative trend is most useful for spotting the direction of travel, not for benchmarking the current level.

What the trend doesn't tell you

Three things this analysis deliberately doesn't say.

1. Why the trend is moving. The slope alone tells you the direction; the cause is multi-factorial and outside the scope of a data piece. (We've written a separate explainer on why NHS waits are long.)
2. Who's responsible. Trust-level performance is shaped by decisions made years earlier — workforce planning, capital investment, the contractual structure of consultants' job plans, local social-care provision. Attributing this year's slope to this year's politics is almost always wrong.
3. Whether the trend will continue. Linear extrapolation past the published window is unsupported by this analysis. Some trends will accelerate, some will reverse, some will plateau. Don't quote a future date from the slope.

What the trend does tell you, reliably, is the direction the median is moving across the published window, and the relative size of the move. That's enough to inform a conversation, a policy question, or a personal decision.

How the analysis was computed

• Data source: NHS England RTT statistics, England.NHS.UK > RTT waiting times, incomplete-pathway file, full published window we hold. Loaded into the waiting_times table by the monthly pipeline the day the data drops.
• Aggregation: for each specialty × month, we compute the national median of trust medians (D-010, R-DATA-1 — using a median-of-medians proxy because patient-volume isn't weighted in the raw publication). Filters: median_weeks > 0 and pathway_type = 'incomplete'.
• Trend: ordinary-least-squares fit of median_weeks against month index. Slope is reported in weeks-per-month. "Net change" multiplies the slope by the window length minus one.
• Classification thresholds: improving = slope ≤ −0.1 wks/month; worsening = slope ≥ +0.1 wks/month; otherwise flat. Across the full window, that threshold is a net change of 0.1 weeks for every month after the first.
• Reproducibility: every number above is computed at build time by the helpers in /web/lib/insights-data.ts and refreshes daily. No figures are typed into the prose.

If you'd like to verify a specific specialty's slope, the easiest cross-check is to open the /national/[specialty] page and inspect the trend chart there — same data, plotted directly.

What this means for patients

If your specialty is improving, that's the direction of travel, but your wait today is still your wait today. If your specialty is worsening, your local trust may be moving faster or slower than the national figure — trust-level variance is enormous, as we showed in our companion analysis on trust variation.

In both cases, the most useful thing is to see your own options side-by-side rather than relying on the national headline. The trend is informative; your trust-level wait is decisive.

Compare your local options

• Browse all 18 specialty rankings →
• Compare NHS trauma & orthopaedics waits →
• Compare NHS gynaecology waits →
• Search by procedure and postcode →

The data this article is built on is the same data the comparison tool uses. If the trend interests you, the per-trust numbers are one click away.