Use of machine learning to identify patients at risk of sub‑optimal adherence: study based on real‑world data from 10,929 children using a connected auto‑injector device

Background. Our aim was to develop a machine learning model, using real-world data captured from a connected auto-injector device and from early indicators from the first 3 months of treatment, to predict sub-optimal adherence to recombinant human growth hormone (r-hGH) in patients with growth disorders. Methods. Adherence to r-hGH treatment was...

Observed High Adherence to Recombinant Human Growth Hormone Treatment Using a Multi-Component Approach to Improve Adherence in Individuals with Growth Disorders

We explored whether a multi-component approach - using a digital health device, the easypod™ auto-injector, the 'MySupport' patient support programme (PSP) and a Patient Activation Measure® (PAM®) - could improve adherence in patients receiving recombinant human growth hormone (r-hGH). A 13-item PAM was used to assess caregiver self-reported...