Assessment waiting lists - the bottleneck isn't where most people think

Waiting lists for neurodevelopmental assessments have become a defining problem in country after country. In the US, demand for adult ADHD evaluations rose by roughly 37 percent between 2020 and 2024 according to Johns Hopkins research, and waits of six months to two years are common - with private evaluations often costing thousands of dollars. In the UK, multi-year NHS waiting lists for ADHD assessment are widely reported. The demand curve is not bending down.

The standard answer is more resources: more clinicians, more clinics, more funding. But when we ran a structured needs validation with psychologists in a Swedish region in the spring of 2026, a different picture emerged. Time in an assessment splits roughly 50/50 between patient-facing and testing time, and the time spent processing, evaluating and writing. It is the second half that is the bottleneck.

The clinical history was singled out as the most time-consuming part of the work. Moving information from records, questionnaires and test results into a finished report was described as both slow and mentally taxing - especially the final step of pulling everything together, when the source material is scattered and the assessment has stretched over two to three months.

One insight from that validation captures the problem particularly well: reports are often written more so that the rest of the care system can trust the assessment than for the patient, who is mainly interested in the summary. A large share of the writing is quality assurance and handover - important, but not something that requires the clinician's unique expertise in every sentence.

That means a significant share of assessment capacity is hiding inside existing workflows. If the time spent compiling and writing can be cut substantially, throughput rises without hiring a single additional clinician - and without reducing patient-facing time. It is a different kind of capacity investment than opening new clinics, and a much faster one.

What matters is how the time is cut. Judgment and control must stay with the clinician: AI compiles, structures and suggests, but never makes decisions and never diagnoses. Used that way, the technology frees time for what actually requires the profession - meeting the patient, weighing the evidence and standing behind the conclusion.

That is exactly the principle Edith Care is built on: supporting the whole assessment flow from first conversation to finished report, keeping the assessment with the clinician and letting the tool do the heavy lifting. Waiting lists are a system problem - but part of the solution is already inside the clinic.