Sword Health ties 50% of program cost to a member-reported outcome. This page recomputes the published evidence behind that outcome — in your browser, from the sources, with no data from me. Free. Nothing to install, nothing to buy.
“In this whitepaper, we outline how Sword links 50% of program cost to a clinically-validated, member-reported outcome.” swordhealth.com/reports-and-guides/outcome-pricing-claims-validated · 3 Mar 2026
The pricing page that announces the model backs it with five outcome claims and five footnotes. Two of those footnotes carry the surgery and painkiller claims — the two that map most directly onto an employer’s downstream spend:
“47% decrease in the use of prescribed painkillers³ · 50% reduction in costly surgeries⁴”
³ Janela D, Costa F, Molinos M, et al. Long-term outcomes of a fully remote digital musculoskeletal care program. Healthcare (Basel). 2022;10(8):1595. https://doi.org/10.3390/healthcare10081595
⁴ Janela D, Costa F, Molinos M, et al. Impact of digital musculoskeletal care on surgery intent and healthcare utilization. Healthcare (Basel). 2022;10(8):1595. https://doi.org/10.3390/healthcare10081595 swordhealth.com/newsroom/outcome-pricing · footnotes 3 and 4, retrieved 19 Aug 2026
Two different titles. One DOI. Press the button: your browser asks Crossref what that DOI actually is, and what the other three are.
Follow the DOI to the paper. Its abstract reports the headline that a pay-for-outcome trigger would most resemble — the share of people who improved enough to count:
“A completion rate of 74.2% (396/534) … with 66.8% treatment responders considering pain.”
And in the results, the same figure with a qualifier the abstract drops:
“Among completers, 66.8% of participants surpassed the MCIC of 30% reduction in pain.” Janela et al., Healthcare 2022;10(8):1595 — abstract and §3.2.2
534 people started. 396 finished. So “among completers” should mean 66.8% of 396. It cannot. No whole number of people out of 396 rounds to 66.8% — the fraction jumps 66.7% → 66.9% and skips it. Same for 534. Your browser can enumerate every possibility in a millisecond:
The third denominator in that test is not a guess pulled from air. It is printed in the
same section of the paper, in the degrees of freedom of the correlation run on the same pain
variable — r(191), which for a Pearson correlation means n = 193. And 129 of 193
is 66.84%, which rounds to 66.8%.
So the number that anchors “meaningful improvement” is computed over roughly 193 people out of the 534 who started — about 36% of them. The other ~64% are not failures in the figure. They are not in the figure.
| Denominator | People | Responder share | Who is excluded |
|---|---|---|---|
| as published | ~193 | 66.8% | everyone who dropped out, plus everyone missing a pain reassessment |
| completers | 396 | ≈ 32.6% | the 138 who dropped out |
| everyone enrolled | 534 | ≈ 24.2% | nobody |
Lower two rows carry the same ~129 responders over the larger populations — the arithmetic the button prints. If a share of the missing did respond, the true figure sits between; the point is that the published 66.8% is the ceiling, not the estimate, and the page that sets the price does not say so.
The paper behind footnote 4 does not count surgeries. It asks a question:
“Self-reported surgery intent assessed by the question ‘How likely are you to seek surgery to address your condition in the next 12 months?’ (range 0 (not likely)–100 (extremely likely)).” Janela et al. 2022, §2 Methods — secondary outcomes
The reported movement on that question is 70.1%, in a single-arm cohort with no control group, of people with chronic hip pain. There is no 50% in it, no surgery count in it, and no comparison group in it. An employer reading “50% reduction in costly surgeries” is reading a claim about operations. The citation offered leads to a claim about willingness.
Both buttons above would be worthless if they printed a verdict regardless of input. Each carries a control that must come out the other way, and the page marks itself RED if a control agrees with it:
If you are a benefits leader, a stop-loss carrier, a health plan or a consultant with 50% of a contract riding on a member-reported outcome, the question to put in writing is one line long:
Over which population is the outcome rate computed — everyone enrolled, everyone who completed, or everyone who answered the survey — and what happens to a member who stops answering?
A vendor whose engine is honest answers it in a sentence. A number that only exists over its survivors will not survive the question. That gap is the whole of what I do: I take a published number somebody is paid on, recover the population it was computed over, and hand back the recomputation with the arithmetic exposed — so the person writing the cheque can repeat it without me.