Four NHS trusts have now admitted their discharge delay figures were wrong. Not slightly wrong. The kind of wrong where numbers fall from the thousands to zero overnight, then climb straight back up within weeks, a pattern no functioning hospital produces naturally. Those figures sat underneath NHS England’s proudest claim about its £330 million Palantir contract: a 15 per cent fall in delayed discharges, held up as proof the Federated Data Platform was working.
A Financial Times investigation has now found irregularities in the discharge data of 42 per cent of all NHS trusts, across four years of records. The UK’s statistics regulator, the Office for Statistics Regulation, is investigating how the figures were used to justify the technology. A cross-party group of MPs has written to ministers urging the government to use the contract’s break clause, which the government can exercise from February 2027. And NHS England’s own chief executive, Sir Jim Mackey, told a select committee this week that he had personally challenged whether the benefits claims “have been objective and can be fully stood up if challenged.”
The person running the organisation just told Parliament, on the record, that he isn’t confident the headline number survives scrutiny. That’s a long way past a minor caveat.
It doesn’t stop at discharge delays either. A separate Freedom of Information request from the campaign group Foxglove found that close to a third of trusts using the platform’s scheduling tool carried out fewer procedures after adopting it than before, and that a single trust, Chelsea and Westminster, accounted for 84 per cent of the reported fall in outpatient waiting lists across the entire programme. One hospital’s good year, dressed up as a national result.
I’ve sat in enough steering committees to know exactly how this happens. And it isn’t really a story about Palantir.
The Data Was Never Built to Do This Job
Charles Tallack, formerly head of operational research and evaluation at NHS England, put it plainly: the evidence for the platform’s impact “looked increasingly flimsy.” His reasoning matters more than the headline. “The delayed discharge dataset may be suitable for day-to-day management purposes, but not for evaluation,” he said.
That’s the whole story in one sentence. NHS England’s own website admits the data undergoes only “minimal validation”, because the speed of collection doesn’t allow for more; it’s explicitly badged as “fit-for-purpose” for NHS management information.
It was built so ward managers could see who needs discharging today, not so a select committee could weigh whether a £330 million technology contract earned its keep. Those are two different jobs, needing two different levels of rigour. Somewhere along the way, one got quietly substituted for the other.
Every transformation leader has watched this substitution happen. Operational dashboards get repurposed as benefit trackers because they’re already there, already live, already familiar to the room. Nobody sits down and consciously decides to treat management data as evaluation-grade evidence. It just drifts that way, one board pack at a time, until a number designed to flag today’s bottleneck is being quoted as proof a nine-figure programme delivered its business case.
When the Numbers Look Too Clean, Get Suspicious
A drop from thousands of delayed discharges to zero, then straight back up, should never have made it into a report unchallenged. That’s not an improvement curve. That’s a data pipeline breaking.
I’d go further: any benefit metric that moves in a straight line, with no noise, no seasonality, no awkward months, should raise your suspicion before it raises your confidence. Real operational change is messy. It has plateaus, regressions, a bad winter, a strike, a system outage. A number that behaves too perfectly is usually telling you something broke upstream, not that something improved downstream. And a national result that traces back to one outperforming site, as the Foxglove data suggests happened here, is a local win being marketed as a systemic one.
Whoever Owns the Contract Shouldn’t Own the Evidence
The underlying dataset sits with NHS England, not Palantir. But that’s precisely the point worth stressing. The organisation whose reputation, and whose vendor relationship, depended on this figure looking good was also the organisation compiling it, with minimal quality checks, and no independent evaluation running alongside it until the regulator forced the question.
One NHS official told the FT that trusts “are being asked to put their name to statements about improvements before the tools are fully embedded and before the evaluations are done.” Read that twice. Governance failed here. Data quality is just where it happened to show up first, and I’ve seen it inside plenty of transformation programmes that had nothing to do with the NHS or with Palantir.
If the same team that needs the benefit case to land is also the team producing the evidence for it, the incentive to tell a good story will always beat the incentive to tell the true one.
Three Questions Worth Asking Before You Quote a Benefit Number Externally
Before any number from your programme reaches a board pack, a press release, or a select committee, it’s worth asking:
Was this dataset designed to answer the question I’m now asking of it, or was it designed for something else entirely and repurposed under pressure?
Who compiled this figure, and do they have a stake in it looking good?
Would this number survive an independent audit conducted by someone with no relationship to the programme?
If you can’t answer all three with confidence, what you’ve got is a hypothesis pretending to be a benefits case.
The Real Cost Isn’t the Contract
NHS England will likely survive this, whatever happens to the Palantir contract when the break clause opens in 2027. What’s harder to repair is trust in the next number this organisation, or any organisation, puts in front of Parliament, staff, or the public. Sir Jim Mackey said an objective review “would be helpful and necessary” but would take months. That’s months of every subsequent claim being read with one eyebrow raised.
Build your evaluation evidence with the same rigour you’d want turned on you, before someone else turns it on for you.