Dear David,
Thanks for your message with your prompt to ChatGPT and its response. I find this interesting in two ways. First, it potentially informs us to what extent access to the full text of research might save lives. Second, it is an opportunity for us to look at the potential and the limitations of ChatGPT.
Your prompt: 'One of my colleagues made the following statement, but I was wondering if you had any insights or could answer the question with reference to published literature… for example… is there a published paper that proves open access literature helped save a patient or helped treat a patient? It would be fine to have a body of work that is open access to influence policy in a way that saved lives— that would be an appropriate reference as well: here is the original response that I am asking you to consider an answer answering this question—- “However, to date we have been unable to identify a single example of how access to the full text of a research paper saved a life, or even contributed directly to clinical decision-making. We asked ChatGPT, who could not identify an example either.”'
My comment on the prompt: The original objective was to 'identify a single example of how access to the full text of a research paper saved a life, or even contributed directly to clinical decision-making'. As I said in my original message, we are not asking about free/open access to derivatives of health research: clinical guidelines, formularies, training materials, manuals, decision aids. I think we would all agree that these are vital to deliver quality health care and thereby save lives. Our query is specifically about access to the full text of a research paper in a clinical context. It would eb interesting to see examples in a policy context as well, but these are outside the remit of the original query.
ChatGPT then gives five examples. It would be interesting to look at each of these in turn to see whether any of them respond to the original query.
I'll start with the first example:
"#1: The first widely used COVID 19 (SARS-CoV-2) PCR protocol was published OA in Eurosurveillance on 23 Jan 2020 (Victor M. Corman et al.). Labs worldwide stood up diagnostic testing using that paper’s primers/protocols—core to triage, isolation, and care."
Helpfully ChatGPT gives us the reference: Corman VM, Landt O, Kaiser M, et al. Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Eurosurveillance. 2020 Jan 23;25(3):2000045. doi: 10.2807/1560-7917.ES.2020.25.3.2000045.
Here is the abstract:
Abstract
Background: The ongoing outbreak of the recently emerged novel coronavirus (2019-nCoV) poses a challenge for public health laboratories as virus isolates are unavailable while there is growing evidence that the outbreak is more widespread than initially thought, and international spread through travellers does already occur.
Aim: We aimed to develop and deploy robust diagnostic methodology for use in public health laboratory settings without having virus material available.
Methods: Here we present a validated diagnostic workflow for 2019-nCoV, its design relying on close genetic relatedness of 2019-nCoV with SARS coronavirus, making use of synthetic nucleic acid technology.
Results: The workflow reliably detects 2019-nCoV, and further discriminates 2019-nCoV from SARS-CoV. Through coordination between academic and public laboratories, we confirmed assay exclusivity based on 297 original clinical specimens containing a full spectrum of human respiratory viruses. Control material is made available through European Virus Archive – Global (EVAg), a European Union infrastructure project.
Conclusion: The present study demonstrates the enormous response capacity achieved through coordination of academic and public laboratories in national and European research networks.
My comment on #1: This study is clearly irrelevant to my original query. It does not provide an example of how access to the full text of a research paper saved a life, or even contributed directly to clinical decision-making.
I'll have a look at the other four examples given by ChatGPT. If you or other HIFA members would like to do the same, this would be great - we can compare notes.
Many thanks, Neil
HIFA profile: Neil Pakenham-Walsh is coordinator of HIFA (Healthcare Information For All), a global health community that brings all stakeholders together around the shared goal of universal access to reliable healthcare information. HIFA has 20,000 members in 180 countries, interacting in four languages and representing all parts of the global evidence ecosystem. HIFA is administered by Global Healthcare Information Network, a UK-based nonprofit in official relations with the World Health Organization. Email: neil@hifa.org