Digital Patient Decision Aids for Endometriosis Management: A Scoping Review
Océane Pittet1, Marion Delvallée2,3, Nicola Pluchino4
1Department of Ambulatory Care, Unisanté, University Center for Primary Care and Public Health, University of Lausanne, Lausanne, Vaud, Switzerland.
Background:
Endometriosis treatment requires women to navigate complex, preference-sensitive decisions. Patient Decision Aids (PtDAs) help patients make value-aligned choices. However, the scope and quality of digital PtDAs for endometriosis, and the cap acity of conversational AI platforms to act as PtDAs, remain unclear.
Objectives:
Systematically map digital PtDAs for women of reproductive age with endometriosis, describe their content, development, and evaluation, and assess quality and replicability.
Search Strategy:
Electronic databases, Google Scholar, grey literature, and web searches were conducted from inception to July 2025.
Selection Criteria:
We included digital PtDAs for women aged 18-49 with a clinical diagnosis of endometriosis that met the minimum criteria established to qualify as a PtDA. Additionally, we developed a prompt to generate five PtDAs using conversational AI platforms, mirroring patient or clinician decision-support queries.
Data Collection And Analysis:
Two independent reviewers extracted data per Joanna Briggs Institute (JBI) scoping reviews methodology. IPDAS criteria (requirements for PtDAs) and TIDieR items (intervention reporting) were applied; data were summarised descriptively and qualitatively.
Results:
Ten PtDAs were included (five expert-developed; five AI-generated). Overall, most addressed pharmacological and surgical options, while AI-generated PtDAs included more complementary therapies. All described the health condition, decision, and options with balanced pros/cons, but most failed on important IPDAS criteria for high-quality PtDAs. Most expert-developed PtDAs also lacked transparent development reporting and had not been formally evaluated.
Conclusions:
Few digital PtDAs for endometriosis were identified; most showed limited adherence to IPDAS criteria, poor reporting transparency, and absent formal evaluation.