This review article considers the rising demand for patient-reported outcome measures (PROMs) in modern ophthalmic research and clinical practice. We review what PROMs are, how they are developed and chosen for use, and how their quality can be critically appraised. We outline the progress made to develop PROMs in each clinical subspecialty. We highlight recent examples of the use of PROMs as secondary outcome measures in randomized controlled clinical trials and consider the impact they have had. With increasing interest in using PROMs as primary outcome measures, particularly where interventions have been found to be of equivalent efficacy by traditional outcome metrics, we highlight the importance of instrument precision in permitting smaller sample sizes to be recruited. Our review finds that while there has been considerable progress in PROM development, particularly in cataract, glaucoma, medical retina, and low vision, there is a paucity of useful tools for less common ophthalmic conditions. Development and validation of item banks, administered using computer adaptive testing, has been proposed as a solution to overcome many of the traditional limitations of PROMs, but further work will be needed to examine their acceptability to patients, clinicians, and investigators.

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IntroductionStandards for Reporting of Diagnostic Accuracy Study (STARD) was developed to improve the completeness and transparency of reporting in studies investigating diagnostic test accuracy. However, its current form, STARD 2015 does not address the issues and challenges raised by artificial intelligence (AI)-centred interventions. As such, we propose an AI-specific version of the STARD checklist (STARD-AI), which focuses on the reporting of AI diagnostic test accuracy studies. This paper describes the methods that will be used to develop STARD-AI.Methods and analysisThe development of the STARD-AI checklist can be distilled into six stages. (1) A project organisation phase has been undertaken, during which a Project Team and a Steering Committee were established; (2) An item generation process has been completed following a literature review, a patient and public involvement and engagement exercise and an online scoping survey of international experts; (3) A three-round modified Delphi consensus methodology is underway, which will culminate in a teleconference consensus meeting of experts; (4) Thereafter, the Project Team will draft the initial STARD-AI checklist and the accompanying documents; (5) A piloting phase among expert users will be undertaken to identify items which are either unclear or missing. This process, consisting of surveys and semistructured interviews, will contribute towards the explanation and elaboration document and (6) On finalisation of the manuscripts, the group’s efforts turn towards an organised dissemination and implementation strategy to maximise end-user adoption.Ethics and disseminationEthical approval has been granted by the Joint Research Compliance Office at Imperial College London (reference number: 19IC5679). A dissemination strategy will be aimed towards five groups of stakeholders: (1) academia, (2) policy, (3) guidelines and regulation, (4) industry and (5) public and non-specific stakeholders. We anticipate that dissemination will take place in Q3 of 2021.