How to survive the medical misinformation mess is a research paper published in European Journal of Clinical Investigation (2017). On theSindex it has a DataRank of 0.691. It has been cited 99 times.
Most physicians and other healthcare professionals are unaware of the pervasiveness of poor quality clinical evidence that contributes considerably to overuse, underuse, avoidable adverse events, missed opportunities for right care and wasted healthcare resources. The Medical Misinformation Mess comprises four key problems. First, much published medical research is not reliable or is of uncertain reliability, offers no benefit to patients, or is not useful to decision makers. Second, most healthcare professionals are not aware of this problem. Third, they also lack the skills necessary to evaluate the reliability and usefulness of medical evidence. Finally, patients and families frequently lack relevant, accurate medical evidence and skilled guidance at the time of medical decision-making. Increasing the reliability of available, published evidence may not be an imminently reachable goal. Therefore, efforts should focus on making healthcare professionals, more sensitive to the limitations of the evidence, training them to do critical appraisal, and enhancing their communication skills so that they can effectively summarize and discuss medical evidence with patients to improve decision-making. Similar efforts may need to target also patients, journalists, policy makers, the lay public and other healthcare stakeholders.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.691
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.