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
The extent and depth of routine health care data
are growing at an ever-increasing rate, forming huge repositories
of information. These repositories can answer a vast array of questions.
However, an understanding of the purpose of the dataset used and
the quality of the data collected are paramount to determine the
reliability of the result obtained. This Editorial describes the importance of adherence to sound
methodological principles in the reporting and publication of research
using ‘big’ data, with a suggested reporting framework for future Cite this article:
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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:
