Most health services in Australia are sitting on several years of patient comments that nobody has read from start to finish. The Likert scores get tabulated and put in the quarterly pack. The free text gets skimmed by whoever is assembling that pack, and the rest is archived. This post is part of our Practical AI in Health series, and if you are looking for a first AI project that is cheap, visible to your board and nowhere near a clinical decision, this is the one we usually point people to.
The data is already sitting there
If you run an Australian hospital you are probably already collecting AHPEQS, the ACSQHC's 12-item patient experience question set, which includes a free-text comment field. One implementation study covered 86,180 surveys across 36 private hospitals over 18 months. Nothing new needs to be collected for a theme-mining project. The corpus exists.
The volumes are worth knowing before you scope anything. In a provincial health system's inpatient survey analysis, 43.4% of adult patients and 46.9% of paediatric caregivers left a free-text comment, and topic modelling on those comments produced 86 adult and 35 paediatric topics, including elements of care that no existing survey question asked about. That last part is the argument for doing this at all. Closed items can only measure what someone thought to ask.
