Health economic evaluations potentially provide valuable information to clinicians, health care administrators, and policy makers regarding the financial implications of decisions about the care of patients. The highest quality research should be used to inform decisions that have direct impact on the access to care and the outcome of treatment. However, economic analyses are often complex and use research methods which are relatively unfamiliar to clinicians. Furthermore, health economic data have substantial national, regional, and institutional variability, which can limit the external validity of the results of a study. Therefore, minimum guidelines that aim to standardise the quality and transparency of reporting health economic research have been developed, and instruments are available to assist in the assessment of its quality and the interpretation of results. The purpose of this editorial is to discuss the principal types of health economic studies, to review the most common instruments for judging the quality of these studies and to describe current reporting guidelines. Recommendations for the submission of these types of studies to The Bone & Joint Journal are provided. Cite this article: Bone Joint J 2016;98-B:147–51

The OpenAI chatbot ChatGPT is an artificial intelligence (AI) application that uses state-of-the-art language processing AI. It can perform a vast number of tasks, from writing poetry and explaining complex quantum mechanics, to translating language and writing research articles with a human-like understanding and legitimacy. Since its initial release to the public in November 2022, ChatGPT has garnered considerable attention due to its ability to mimic the patterns of human language, and it has attracted billion-dollar investments from Microsoft and PricewaterhouseCoopers. The scope of ChatGPT and other large language models appears infinite, but there are several important limitations. This editorial provides an introduction to the basic functionality of ChatGPT and other large language models, their current applications and limitations, and the associated implications for clinical practice and research.

Cite this article: Bone Joint J 2023;105-B(6):587–589.

The effective capture of outcome measures in the healthcare setting can be traced back to Florence Nightingale’s investigation of the in-patient mortality of soldiers wounded in the Crimean war in the 1850s.

Only relatively recently has the formalised collection of outcomes data into Registries been recognised as valuable in itself.

With the advent of surgeon league tables and a move towards value based health care, individuals are being driven to collect, store and interpret data.

Following the success of the National Joint Registry, the British Association of Spine Surgeons instituted the British Spine Registry. Since its launch in 2012, over 650 users representing the whole surgical team have registered and during this time, more than 27 000 patients have been entered onto the database.

There has been significant publicity regarding the collection of outcome measures after surgery, including patient-reported scores. Over 12 000 forms have been directly entered by patients themselves, with many more entered by the surgical teams.

Questions abound: who should have access to the data produced by the Registry and how should they use it? How should the results be reported and in what forum?

Cite this article: Bone Joint J 2015;97-B:871–4.