Scientific advances provide hopeful front page news these days. Data, and in some cases a complete lack of data, affect how headlines are shaped in both life sciences and high technology. Here are a few key recurring issues to keep in mind when reading the headlines about new technology developments in medicine and IT.
Getting Data – Faster, Farther Reach, High Quality
There is always evolving scientific debate, which is constructive and healthy. Good debate and data helps produce scientific consensus, which then allows society to move forward in tackling medical challenges. Pressing challenges in data gathering include getting quality data faster, and extending the reach of data collection.
Quality data leads to better science, public health policy and IP. In the absence of fast, good data, we can see that ill-informed dogma has more opportunity to dominate. A drug, such as hydroxychloroquine, can be touted as a treatment by some physician and politician headline-chasers, until contradictory data eventually can provide a complete answer. On another public health issue, masking, people fiercely debate the pros and cons of masking when there is little clinical data proving a significant safety benefit. Anecdotal evidence and poor-quality information abound. Public health professionals and scientists may respond to dubious information in a tepid manner because they lack the best ammunition to strongly respond, which is solid, peer-reviewed data and scientific consensus. Sources of potentially bad scientific data, such as many non-reviewed preprints need to be vetted and nipped in the bud early. There is a rush to get good data in place, but in the meantime, the general public does not withhold judgment. Fast, quality data is an essential tool to builds scientific consensus until it is strong enough to defeat mainstream assertions of 'fake news' (there will always be fringe elements to deal with regardless).
On the tech side, software is helping to expand the reach of data collection. For example, contract tracing applications are going to be critical for the "tracing" part of the "test, trace, isolate" mantra that is necessary to address COVID-19 outbreaks. AI and machine learning are also expanding the ability to process vast amounts of health data and other data to assist in tracking, preventing, and treating virus.
Making Data Available for Public Review and Critique
Science by press release is occurring on critical vaccine and medicine issues. There are questions about data quality and robustness if the full data set is not peer reviewed. A product may be impressive or it may not – it is impossible to tell from incomplete information. For example, the key data from Gilead's remdesivir clinical trial was the subject of a company press release, but was publicly released for physicians to review until several weeks after the FDA provided emergency use authorization. The US FDA also put out its own press release linking to its letter of authorization of the drug, without much clinical data backup. Eventually, the remdesivir data was published in the New England Journal of Medicine, and physicians and scientists dove in and eagerly digested the pros and cons.
Moderna also put out a press release on its phase 1 data for its vaccine, which is a phase that focuses on testing safety and toxicity, not efficacy. Vaccine experts are calling for the data to be released for review.
Companies tend to keep data confidential for business reasons. However, business reasons should not be paramount in a public health emergency. It is possible to generate data, protect IP and publish. Universities and other public research institutes do this all the time. If companies have taken steps to protect their IP (which they would have done quickly), it is reasonable to ask that the data be released. Positive press releases may increase stock prices and encourage investment, but they do little to further scientific knowledge or educate the public.
Communicating Data to the Public Effectively
If there is not a significant effort to communicate science to the public then the data may not achieve its full impact. Sensible readers need to be able to understand context in order to assess new information. See our earlier article about the need to make more COVID-19 data available. Even if there is significant effort, it can still be overridden by contrary, misleading forces of communication if a good communication strategy is not in place. This is counterintuitive to science-minded people, whose first common sense inclination is that there is no good reason why anyone would contradict data and scientific consensus. However, science can be misrepresented as a tool for political or financial gain.
For example, hydroxychloroquine sales continue to soar, in part based on media coverage of endorsements, even though there is a lack of evidence of efficacy and clear evidence of side-effects. Anti-vaxxers still have a public audience and attract mainstream and online media attention. Headlines often follow an agenda unrelated to good data and can confuse the public. It is clear that science communication professionals need to be considered a partner in best practices of generation and use of data. The data may speak for itself to those in the know, but it needs a platform and advocates to effectively reach the general public1.
Data is key to the short- and long-term response to the COVID-19 pandemic. Extra efforts need to be taken to collect good data to support innovation, IP and public health initiatives. Communication of data has to remain a high priority to try to counteract misinformation.
Footnotes
1 Lack of communication of the huge R&D effort that goes into collecting data and developing treatments also leads to some members of the public mistakenly viewing drug companies and their IP are adversaries to the public interest, as opposed to a source of essential new diagnostics and treatments.
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