Re: https://www.hifa.org/dgroups-rss/open-access-review-and-next-steps-2-has...
Dear David,
Thank you for sharing your preprint (that I think was inspired by our recent discussion on HIFA?).
https://www.qeios.com/read/U2SYIR
Predicting the Probability That Open-Access Clinical Literature Saves Lives
Abstract
Whether open-access (OA) clinical literature directly saves lives is frequently debated, yet empirical documentation is scarce because clinical notes rarely record how evidence was accessed. This study synthesizes high-impact cases of OA-enabled clinical change — most notably the SARS-CoV-2 PCR diagnostic protocol and the RECOVERY dexamethasone findings — and develops an expanded Bayesian predictive model estimating the probability that a single clinician reading one OA article saves a life. We integrate three primary evidence bases: (1) clinician-reported rates of practice change following article consultation, (2) the proportion of clinical decisions that influence short- or long-term mortality, and (3) empirically observed mortality reductions following OA-mediated dissemination of life-saving therapeutic evidence. We then extend this model by incorporating additional determinants of diagnostic and therapeutic accuracy, including medical error rates, years of clinical experience, multimorbidity-dependent diagnostic entropy, cognitive load, structural barriers, team-based reliability, guideline adherence, and electronic health record (EHR)–related error susceptibility, formalized in a multilevel Bayesian framework. The core model yields a probability range of p ≈ 0.003–0.02 that a clinician–article encounter prevents one death, corresponding to a Number Needed to Treat (NNT) analog of approximately 50–330 clinician–article encounters. After accounting for heterogeneity in clinical acuity, multimorbidity, and the extended set of clinician and system parameters, hierarchical Bayesian extensions adjust the predictive interval to p ≈ 0.002–0.03 and NNT ≈ 30–500. The integrated analysis demonstrates that OA literature meaningfully increases the probability of life-saving clinical decisions, especially in high-acuity environments where marginal improvements in evidence latency and accuracy have large mortality consequences.
In the introduction you wrote: 'Evaluating whether OA literature “saves lives” therefore requires probabilistic modeling rather than anecdotal case documentation. Given that clinical decision-making is probabilistic, heterogeneous, and distributed across clinicians and settings, the relevant question is: what is the probability that one OA clinical paper read by one clinician contributes to a decision that prevents at least one death?'
I was out of my depth with the Bayesian framework you used so I asked ChatGPT for help. It proposed the following conclusion:
'Open access to medical research modestly increases the chances that a doctor’s decision will save a life — roughly a 0.2–3% chance per article read—which adds up to a substantial impact across healthcare systems.'
Does this align with your observations? I note that the preprint has not yet been reviewed and would encourage fellow HIFA members with a grasp of Bayesian framework to comment/review.
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