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Science Has A Systemic Problem, Not an Innovation Problem

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Science Has A Systemic Problem, Not an Innovation Problem

Curator: Stephen J. Williams, Ph.D.

    A recent email, asking me to submit a survey, got me thinking about the malaise that scientists and industry professionals frequently bemoan: that innovation has been stymied for some reason and all sorts of convuluted processes must be altered to spur this mythical void of great new discoveries…..  and it got me thinking about our current state of science, and what is the perceived issue… and if this desert of innovation actually exists or is more a fundamental problem which we have created.

The email was from an NIH committee asking for opinions on recreating the grant review process …. now this on the same day someone complained to me about a shoddy and perplexing grant review they received.

The following email, which was sent out to multiple researchers, involved in either NIH grant review on both sides, as well as those who had been involved in previous questionnaires and studies on grant review and bias.  The email asked for researchers to fill out a survey on the grant review process, and how to best change it to increase innovation of ideas as well as inclusivity.  In recent years, there have been multiple survey requests on these matters, with multiple confusing procedural changes to grant format and content requirements, adding more administrative burden to scientists.

The email from Center for Scientific Review (one of the divisions a grant will go to before review {they set up review study sections and decide what section a grant should be  assigned to} was as follows:

Update on Simplifying Review Criteria: A Request for Information

https://www.csr.nih.gov/reviewmatters/2022/12/08/update-on-simplifying-review-criteria-a-request-for-information/

NIH has issued a request for information (RFI) seeking feedback on revising and simplifying the peer review framework for research project grant applications. The goal of this effort is to facilitate the mission of scientific peer review – identification of the strongest, highest-impact research. The proposed changes will allow peer reviewers to focus on scientific merit by evaluating 1) the scientific impact, research rigor, and feasibility of the proposed research without the distraction of administrative questions and 2) whether or not appropriate expertise and resources are available to conduct the research, thus mitigating the undue influence of the reputation of the institution or investigator.

Currently, applications for research project grants (RPGs, such as R01s, R03s, R15s, R21s, R34s) are evaluated based on five scored criteria: Significance, Investigators, Innovation, Approach, and Environment (derived from NIH peer review regulations 42 C.F.R. Part 52h.8; see Definitions of Criteria and Considerations for Research Project Grant Critiques for more detail) and a number of additional review criteria such as Human Subject Protections.

NIH gathered input from the community to identify potential revisions to the review framework. Given longstanding and often-heard concerns from diverse groups, CSR decided to form two working groups to the CSR Advisory Council—one on non-clinical trials and one on clinical trials. To inform these groups, CSR published a Review Matters blog, which was cross-posted on the Office of Extramural Research blog, Open Mike. The blog received more than 9,000 views by unique individuals and over 400 comments. Interim recommendations were presented to the CSR Advisory Council in a public forum (March 2020 video, slides; March 2021 video, slides). Final recommendations from the CSRAC (report) were considered by the major extramural committees of the NIH that included leadership from across NIH institutes and centers. Additional background information can be found here. This process produced many modifications and the final proposal presented below. Discussions are underway to incorporate consideration of a Plan for Enhancing Diverse Perspectives (PEDP) and rigorous review of clinical trials RPGs (~10% of RPGs are clinical trials) within the proposed framework.

Simplified Review Criteria

NIH proposes to reorganize the five review criteria into three factors, with Factors 1 and 2 receiving a numerical score. Reviewers will be instructed to consider all three factors (Factors 1, 2 and 3) in arriving at their Overall Impact Score (scored 1-9), reflecting the overall scientific and technical merit of the application.

• Factor 1: Importance of the Research (Significance, Innovation), numerical score (1-9)
• Factor 2: Rigor and Feasibility (Approach), numerical score (1-9)
• Factor 3: Expertise and Resources (Investigator, Environment), assessed and considered in the Overall Impact Score, but not individually scored

Within Factor 3 (Expertise and Resources), Investigator and Environment will be assessed in the context of the research proposed. Investigator(s) will be rated as “fully capable” or “additional expertise/capability needed”. Environment will be rated as “appropriate” or “additional resources needed.” If a need for additional expertise or resources is identified, written justification must be provided. Detailed descriptions of the three factors can be found here.

Now looking at some of the Comments were very illuminating:

I strongly support streamlining the five current main review criteria into three, and the present five additional criteria into two. This will bring clarity to applicants and reduce the workload on both applicants and reviewers. Blinding reviewers to the applicants’ identities and institutions would be a helpful next step, and would do much to reduce the “rich-getting-richer” / “good ole girls and good ole boys” / “big science” elitism that plagues the present review system, wherein pedigree and connections often outweigh substance and creativity.

I support the proposed changes. The shift away from “innovation” will help reduce the tendency to create hype around a proposed research direction. The shift away from Investigator and Environment assessments will help reduce bias toward already funded investigators in large well-known institutions.

As a reviewer for 5 years, I believe that the proposed changes are a step in the right direction, refocusing the review on whether the science SHOULD be done and whether it CAN BE DONE WELL, while eliminating burdensome and unhelpful sections of review that are better handled administratively. I particularly believe that the de-emphasis of innovation (which typically focuses on technical innovation) will improve evaluation of the overall science, and de-emphasis of review of minor technical details will, if implemented correctly, reduce the “downward pull” on scores for approach. The above comments reference blinded reviews, but I did not see this in the proposed recommendations. I do not believe this is a good idea for several reasons: 1) Blinding of the applicant and institution is not likely feasible for many of the reasons others have described (e.g., self-referencing of prior work), 2) Blinding would eliminate the potential to review investigators’ biosketches and budget justifications, which are critically important in review, 3) Making review blinded would make determination of conflicts of interest harder to identify and avoid, 4) Evaluation of “Investigator and Environment” would be nearly impossible.

Most of the Comments were in favor of the proposed changes, however many admitted that it adds additional confusion on top of many administrative changes to formats and content of grant sections.

Being a Stephen Covey devotee, and just have listened to  The Four Principles of Execution, it became more apparent that issues that hinder many great ideas coming into fruition, especially in science, is a result of these systemic or problems in the process, not at the level of individual researchers or small companies trying to get their innovations funded or noticed.  In summary, Dr. Covey states most issues related to the success of any initiative is NOT in the strategic planning, but in the failure to adhere to a few EXECUTION principles.  Primary to these failures of strategic plans is lack of accounting of what Dr. Covey calls the ‘whirlwind’, or those important but recurring tasks that take us away from achieving the wildly important goals.  In addition, lack of  determining lead and lag measures of success hinder such plans.

In this case a lag measure in INNOVATION.  It appears we have created such a whirlwind and focus on lag measures that we are incapable of translating great discoveries into INNOVATION.

In the following post, I will focus on issues relating to Open Access, publishing and dissemination of scientific discovery may be costing us TIME to INNOVATION.  And it appears that there are systemic reasons why we appear stuck in a rut, so to speak.

The first indication is from a paper published by Johan Chu and James Evans in 2021 in PNAS:

 

Slowed canonical progress in large fields of science

Chu JSG, Evans JA. Slowed canonical progress in large fields of science. Proc Natl Acad Sci U S A. 2021 Oct 12;118(41):e2021636118. doi: 10.1073/pnas.2021636118. PMID: 34607941; PMCID: PMC8522281

 

Abstract

In many academic fields, the number of papers published each year has increased significantly over time. Policy measures aim to increase the quantity of scientists, research funding, and scientific output, which is measured by the number of papers produced. These quantitative metrics determine the career trajectories of scholars and evaluations of academic departments, institutions, and nations. Whether and how these increases in the numbers of scientists and papers translate into advances in knowledge is unclear, however. Here, we first lay out a theoretical argument for why too many papers published each year in a field can lead to stagnation rather than advance. The deluge of new papers may deprive reviewers and readers the cognitive slack required to fully recognize and understand novel ideas. Competition among many new ideas may prevent the gradual accumulation of focused attention on a promising new idea. Then, we show data supporting the predictions of this theory. When the number of papers published per year in a scientific field grows large, citations flow disproportionately to already well-cited papers; the list of most-cited papers ossifies; new papers are unlikely to ever become highly cited, and when they do, it is not through a gradual, cumulative process of attention gathering; and newly published papers become unlikely to disrupt existing work. These findings suggest that the progress of large scientific fields may be slowed, trapped in existing canon. Policy measures shifting how scientific work is produced, disseminated, consumed, and rewarded may be called for to push fields into new, more fertile areas of study.

So the Summary of this paper is

• The authors examined 1.8 billion citations among 90 million papers over 241 subjects
• found the corpus of papers do not lead to turnover of new ideas in a field, but rather the ossification or entrenchment of canonical (or older ideas)
• this is mainly due to older paper cited more frequently than new papers with new ideas, potentially because authors are trying to get their own papers cited more frequently for funding and exposure purposes
• The authors suggest that “fundamental progress may be stymied if quantitative growth of scientific endeavors is not balanced by structures fostering disruptive scholarship and focusing attention of novel ideas”

The authors note that, in most cases, science policy reinforces this “more is better” philosophy”,  where metrics of publication productivity are either number of publications or impact measured by citation rankings.  However, using an analysis of citation changes occurring in large versus smaller fields, it becomes apparent that this process is favoring the older, more established papers and a recirculating of older canonical ideas.

“Rather than resulting in faster turnover of field paradigms, the massive amounts of new publications entrenches the ideas of top-cited papers.”  New ideas are pushed down to the bottom of the citation list and potentially lost in the literature.  The authors suggest that this problem will intensify as the “annual mass” of new publications in each field grows, especially in large fields.  This issue is exacerbated by the deluge on new online ‘open access’ journals, in which authors would focus on citing the more highly cited literature. 

We maybe at a critical junction, where if many papers are published in a short time, new ideas will not be considered as carefully as the older ideas.  In addition,

with proliferation of journals and the blurring of journal hierarchies due to online articles-level access can exacerbate this problem

As a counterpoint, the authors do note that even though many molecular biology highly cited articles were done in 1976, there has been extremely much innovation since then however it may take a lot more in experiments and money to gain the level of citations that those papers produced, and hence a lower scientific productivity.

This issue is seen in the field of economics as well

Ellison, Glenn. “Is peer review in decline?” Economic Inquiry, vol. 49, no. 3, July 2011, pp. 635+. Gale Academic OneFile, link.gale.com/apps/doc/A261386330/AONE?u=temple_main&sid=bookmark-AONE&xid=f5891002. Accessed 12 Dec. 2022.

Abstract: 

Over the past decade, there has been a decline in the fraction of papers in top economics journals written by economists from the highest-ranked economics departments. This paper documents this fact and uses additional data on publications and citations to assess various potential explanations. Several observations are consistent with the hypothesis that the Internet improves the ability of high-profile authors to disseminate their research without going through the traditional peer-review process. (JEL A14, 030)

The facts part of this paper documents two main facts:

1. Economists in top-ranked departments now publish very few papers in top field journals. There is a marked decline in such publications between the early 1990s and early 2000s.

2. Comparing the early 2000s with the early 1990s, there is a decline in both the absolute number of papers and the share of papers in the top general interest journals written by Harvard economics department faculty.

Although the second fact just concerns one department, I see it as potentially important to understanding what is happening because it comes at a time when Harvard is widely regarded (I believe correctly) as having ascended to the top position in the profession.

The “decline-of-peer-review” theory I allude to in the title is that the necessity of going through the peer-review process has lessened for high-status authors: in the old days peer-reviewed journals were by far the most effective means of reaching readers, whereas with the growth of the Internet high-status authors can now post papers online and exploit their reputation to attract readers.

Many alternate explanations are possible. I focus on four theories: the decline-in-peer-review theory and three alternatives.

1. The trends could be a consequence of top-school authors’ being crowded out of the top journals by other researchers. Several such stories have an optimistic message, for example, there is more talent entering the profession, old pro-elite biases are being broken down, more schools are encouraging faculty to do cutting-edge research, and the Internet is enabling more cutting-edge research by breaking down informational barriers that had hampered researchers outside the top schools. (2)

2. The trends could be a consequence of the growth of revisions at economics journals discussed in Ellison (2002a, 2002b). In this more pessimistic theory, highly productive researchers must abandon some projects and/or seek out faster outlets to conserve the time now required to publish their most important works.

3. The trends could simply reflect that field journals have declined in quality in some relative sense and become a less attractive place to publish. This theory is meant to encompass also the rise of new journals, which is not obviously desirable or undesirable.

The majority of this paper is devoted to examining various data sources that provide additional details about how economics publishing has changed over the past decade. These are intended both to sharpen understanding of the facts to be explained and to provide tests of auxiliary predictions of the theories. Two main sources of information are used: data on publications and data on citations. The publication data include department-level counts of publications in various additional journals, an individual-level dataset containing records of publications in a subset of journals for thousands of economists, and a very small dataset containing complete data on a few authors’ publication records. The citation data include citations at the paper level for 9,000 published papers and less well-matched data that is used to construct measures of citations to authors’ unpublished works, to departments as a whole, and to various journals.

Inside Job or Deep Impact? Extramural Citations and the Influence of Economic Scholarship

Josh Angrist, Pierre Azoulay, Glenn Ellison, Ryan Hill, Susan Feng Lu. Inside Job or Deep Impact? Extramural Citations and the Influence of Economic Scholarship.

JOURNAL OF ECONOMIC LITERATURE

Abstract

Does academic economic research produce material of general scientific value, or do academic economists write only for peers? Is economics scholarship uniquely insular? We address these questions by quantifying interactions between economics and other disciplines. Changes in the influence of economic scholarship are measured here by the frequency with which other disciplines cite papers in economics journals. We document a clear rise in the extramural influence of economic research, while also showing that economics is increasingly likely to reference other social sciences. A breakdown of extramural citations by economics fields shows broad field influence. Differentiating between theoretical and empirical papers classified using machine learning, we see that much of the rise in economics’ extramural influence reflects growth in citations to empirical work. This growth parallels an increase in the share of empirical cites within economics. At the same time, some disciplines that primarily cite economic theory have also recently increased citations of economics scholarship.

Citation

Angrist, Josh, Pierre Azoulay, Glenn Ellison, Ryan Hill, and Susan Feng Lu. 2020. “Inside Job or Deep Impact? Extramural Citations and the Influence of Economic Scholarship.” Journal of Economic Literature, 58 (1): 3-52.DOI: 10.1257/jel.20181508

VOL. 58, NO. 1, MARCH 2020

(pp. 3-52)

So if innovation is there but it may be buried under the massive amount of heavily cited older literature, do we see evidence of this in other fields like medicine?

Why Isn’t Innovation Helping Reduce Health Care Costs?

• Eli M. Cahan
• Bob Kocher
• Roger Bohn

JUNE 4, 202010.1377/forefront.20200602.168241

 

Source: https://www.healthaffairs.org/do/10.1377/forefront.20200602.168241/#:~:text=Performance%2Denhancing%20innovations%20frequently%20create,prices%20compared%20to%20existing%20alternatives 

 

National health care expenditures (NHEs) in the United States continue to grow at rates outpacing the broader economy: Inflation- and population-adjusted NHEs have increased 1.6 percent faster than the gross domestic product (GDP) between 1990 and 2018. US national health expenditure growth as a share of GDP far outpaces comparable nations in the Organization for Economic Cooperation and Development (17.2 versus 8.9 percent).

Multiple recent analyses have proposed that growth in the prices and intensity of US health care services—rather than in utilization rates or demographic characteristics—is responsible for the disproportionate increases in NHEs relative to global counterparts. The consequences of ever-rising costs amid ubiquitous underinsurance in the US include price-induced deferral of care leading to excess morbidity relative to comparable nations.

These patterns exist despite a robust innovation ecosystem in US health care—implying that novel technologies, in isolation, are insufficient to bend the health care cost curve. Indeed, studies have documented that novel technologies directly increase expenditure growth.

Why is our prolific innovation ecosystem not helping reduce costs? The core issue relates to its apparent failure to enhance net productivity—the relative output generated per unit resource required. In this post, we decompose the concept of innovation to highlight situations in which inventions may not increase net productivity. We begin by describing how this issue has taken on increased urgency amid resource constraints magnified by the COVID-19 pandemic. In turn, we describe incentives for the pervasiveness of productivity-diminishing innovations. Finally, we provide recommendations to promote opportunities for low-cost innovation.

 

 

Net Productivity During The COVID-19 Pandemic

The issue of productivity-enhancing innovation is timely, as health care systems have been overwhelmed by COVID-19. Hospitals in Italy, New York City, and elsewhere have lacked adequate capital resources to care for patients with the disease, sufficient liquidity to invest in sorely needed resources, and enough staff to perform all of the necessary tasks.

The critical constraint in these settings is not technology: In fact, the most advanced technology required to routinely treat COVID-19—the mechanical ventilator—was invented nearly 100 years ago in response to polio (the so-called iron lung). Rather, the bottleneck relates to the total financial and human resources required to use the technology—the denominator of net productivity. The clinical implementation of ventilators has been illustrative: Health care workers are still required to operate ventilators on a nearly one-to-one basis, just like in the mid-twentieth century. 

High levels of resources required for implementation of health care technologies constrain the scalability of patient care—such as during respiratory disease outbreaks such as COVID-19. Thus, research to reduce health care costs is the same kind of research we urgently require to promote health care access for patients with COVID-19.

Types Of Innovation And Their Relationship To Expenditure Growth

The widespread use of novel medical technologies has been highlighted as a central driver of NHE growth in the US. We believe that the continued expansion of health care costs is largely the result of innovation that tends to have low productivity (exhibit 1). We argue that these archetypes—novel widgets tacked on to existing workflows to reinforce traditional care models—are exactly the wrong properties to reduce NHEs at the systemic level.

Exhibit 1: Relative productivity of innovation subtypes

Source: Authors’ analysis.

Content Versus Process Innovation

Content (also called technical) innovation refers to the creation of new widgets, such as biochemical agents, diagnostic tools, or therapeutic interventions. Contemporary examples of content innovation include specialty pharmaceuticals, molecular diagnostics, and advanced interventions and imaging.

These may be contrasted with process innovations, which address the organized sequences of activities that implement content. Classically, these include clinical pathways and protocols. They can address the delivery of care for acute conditions, such as central line infections, sepsis, or natural disasters. Alternatively, they can target chronic conditions through initiatives such as team-based management of hypertension and hospital-at-home models for geriatric care. Other processes include hiring staff, delegating labor, and supply chain management.

Performance-Enhancing Versus Cost-Reducing Innovation

Performance-enhancing innovations frequently create incremental outcome gains in diagnostic characteristics, such as sensitivity or specificity, or in therapeutic characteristics, such as biomarkers for disease status. Their performance gains often lead to higher prices compared to existing alternatives.  

Performance-enhancing innovations can be compared to “non-inferior” innovations capable of achieving outcomes approximating those of existing alternatives, but at reduced cost. Industries outside of medicine, such as the computing industry, have relied heavily on the ability to reduce costs while retaining performance.

In health care though, this pattern of innovation is rare. Since passage of the 2010 “Biosimilars” Act aimed at stimulating non-inferior innovation and competition in therapeutics markets, only 17 agents have been approved, and only seven have made it to market. More than three-quarters of all drugs receiving new patents between 2005 and 2015 were “reissues,” meaning they had already been approved, and the new patent reflected changes to the previously approved formula. Meanwhile, the costs of approved drugs have increased over time, at rates between 4 percent and 7 percent annually.

Moreover, the preponderance of performance-enhancing diagnostic and therapeutic innovations tend to address narrow patient cohorts (such as rare diseases or cancer subtypes), with limited clear clinical utility in broader populations. For example, the recently approved eculizimab is a monoclonal antibody approved for paroxysmal nocturnal hemoglobinuria—which effects 1 in 10 million individuals. At the time of its launch, eculizimab was priced at more than $400,000 per year, making it the most expensive drug in modern history. For clinical populations with no available alternatives, drugs such as eculizimab may be cost-effective, pending society’s willingness to pay, and morally desirable, given a society’s values. But such drugs are certainly not cost-reducing.

Additive Versus Substitutive Innovation

Additive innovations are those that append to preexisting workflows, while substitutive innovations reconfigure preexisting workflows. In this way, additive innovations increase the use of precedent services, whereas substitutive innovations decrease precedent service use.

For example, previous analyses have found that novel imaging modalities are additive innovations, as they tend not to diminish use of preexisting modalities. Similarly, novel procedures tend to incompletely replace traditional procedures. In the case of therapeutics and devices, off-label uses in disease groups outside of the approved indication(s) can prompt innovation that is additive. This is especially true, given that off-label prescriptions classically occur after approved methods are exhausted.

Eculizimab once again provides an illustrative example. As of February 2019, the drug had been used for 39 indications (it had been approved for three of those, by that time), 69 percent of which lacked any form of evidence of real-world effectiveness. Meanwhile, the drug generated nearly $4 billion in sales in 2019. Again, these expenditures may be something for which society chooses to pay—but they are nonetheless additive, rather than substitutive.

Sustaining Versus Disruptive Innovation

Competitive market theory suggests that incumbents and disruptors innovate differently. Incumbents seek sustaining innovations capable of perpetuating their dominance, whereas disruptors pursue innovations capable of redefining traditional business models.

In health care, while disruptive innovations hold the potential to reduce overall health expenditures, often they run counter to the capabilities of market incumbents. For example, telemedicine can deliver care asynchronously, remotely, and virtually, but large-scale brick-and-mortar medical facilities invest enormous capital in the delivery of synchronous, in-house, in-person care (incentivized by facility fees).

The connection between incumbent business models and the innovation pipeline is particularly relevant given that 58 percent of total funding for biomedical research in the US is now derived from private entities, compared with 46 percent a decade prior. It follows that the growing influence of eminent private organizations may favor innovations supporting their market dominance—rather than innovations that are societally optimal.

Incentives And Repercussions Of High-Cost Innovation

Taken together, these observations suggest that innovation in health care is preferentially designed for revenue expansion rather than for cost reduction. While offering incremental improvements in patient outcomes, therefore creating theoretical value for society, these innovations rarely deliver incremental reductions in short- or long-term costs at the health system level.

For example, content-based, performance-enhancing, additive, sustaining innovations tend to add layers of complexity to the health care system—which in turn require additional administration to manage. The net result is employment growth in excess of outcome improvement, leading to productivity losses. This gap leads to continuously increasing overall expenditures in turn passed along to payers and consumers.

Nonetheless, high-cost innovations are incentivized across health care stakeholders (exhibit 2). From the supply side of innovation, for academic researchers, “breakthrough” and “groundbreaking” innovations constitute the basis for career advancement via funding and tenure. This is despite stakeholders’ frequent inability to generalize early successes to become cost-effective in the clinical setting. As previously discussed, the increasing influence of private entities in setting the medical research agenda is also likely to stimulate innovation benefitting single stakeholders rather than the system.

Exhibit 2: Incentives promoting low-value innovation

Source: Authors’ analysis adapted from Hofmann BM. Too much technology. BMJ. 2015 Feb 16.

From the demand side of innovation (providers and health systems), a combined allure (to provide “cutting-edge” patient care), imperative (to leave “no stone unturned” in patient care), and profit-motive (to amplify fee-for-service reimbursements) spur participation in a “technological arms-race.” The status quo thus remains as Clay Christensen has written: “Our major health care institutions…together overshoot the level of care actually needed or used by the vast majority of patients.”

Christensen’s observations have been validated during the COVID-19 epidemic, as treatment of the disease requires predominantly century-old technology. By continually adopting innovation that routinely overshoots the needs of most patients, layer by layer, health care institutions are accruing costs that quickly become the burden of society writ large.

Recommendations To Reduce The Costs Of Health Care Innovation

Henry Aaron wrote in 2002 that “…the forces that have driven up costs are, if anything, intensifying. The staggering fecundity of biomedical research is increasing…[and] always raises expenditures.” With NHEs spiraling ever-higher, urgency to “bend the cost curve” is mounting. Yet, since much biomedical innovation targets the “flat of the [productivity] curve,” alternative forms of innovation are necessary.

The shortcomings in net productivity revealed by the COVID-19 pandemic highlight the urgent need for redesign of health care delivery in this country, and reevaluation of the innovation needed to support it. Specifically, efforts supporting process redesign are critical to promote cost-reducing, substitutive innovations that can inaugurate new and disruptive business models.

Process redesign rarely involves novel gizmos, so much as rejiggering the wiring of, and connections between, existing gadgets. It targets operational changes capable of streamlining workflows, rather than technical advancements that complicate them. As described above, precisely these sorts of “frugal innovations” have led to productivity improvements yielding lower costs in other high-technology industries, such as the computing industry.

Shrank and colleagues recently estimated that nearly one-third of NHEs—almost $1 trillion—were due to preventable waste. Four of the six categories of waste enumerated by the authors—failure in care delivery, failure in care coordination, low-value care, and administrative complexity—represent ripe targets for process innovation, accounting for $610 billion in waste annually, according to Shrank.

Health systems adopting process redesign methods such as continuous improvement and value-based management have exhibited outcome enhancement and expense reduction simultaneously. Internal processes addressed have included supply chain reconfiguration, operational redesign, outlier reconciliation, and resource standardization.

Despite the potential of process innovation, focus on this area (often bundled into “health services” or “quality improvement” research) occupies only a minute fraction of wallet- or mind-share in the biomedical research landscape, accounting for 0.3 percent of research dollars in medicine. This may be due to a variety of barriers beyond minimal funding. One set of barriers is academic, relating to negative perceptions around rigor and a lack of outlets in which to publish quality improvement research. To achieve health care cost containment over the long term, this dimension of innovation must be destigmatized relative to more traditional manners of innovation by the funders and institutions determining the conditions of the research ecosystem.

Another set of barriers is financial: Innovations yielding cost reduction are less “reimbursable” than are innovations fashioned for revenue expansion. This is especially the case in a fee-for-service system where reimbursement is tethered to cost, which creates perverse incentives for health care institutions to overlook cost increases. However, institutions investing in low-cost innovation will be well-positioned in a rapidly approaching future of value-based care—in which the solvency of health care institutions will rely upon their ability to provide economically efficient care.

Innovating For Cost Control Necessitates Frugality Over Novelty

Restraining US NHEs represents a critical step toward health promotion. Innovation for innovation’s sake—that is content-based, incrementally effective, additive, and sustaining—is unlikely to constrain continually expanding NHEs.

In contrast, process innovation offers opportunities to reduce costs while maintaining high standards of patient care. As COVID-19 stress-tests health care systems across the world, the importance of cost control and productivity amplification for patient care has become apparent.

As such, frugality, rather than novelty, may hold the key to health care cost containment. Redesigning the innovation agenda to stem the tide of ever-rising NHEs is an essential strategy to promote widespread access to care—as well as high-value preventive care—in this country. In the words of investors across Silicon Valley: Cost-reducing innovation is no longer a “nice-to-have,” but a “need-to-have” for the future of health and overall well-being this country.

So Do We Need A New Way of Disseminating Scientific Information?  Can Curation Help?

We had high hopes for Science 2.0, in particular the smashing of data and knowledge silos. However the digital age along with 2.0 platforms seemed to excaccerbate this somehow. We still are critically short on analysis!

Old Science 1.0 is still the backbone of all scientific discourse, built on the massive amount of experimental and review literature. However this literature was in analog format, and we moved to a more accesible digital open access format for both publications as well as raw data. However as there was a structure for 1.0, like the Dewey decimal system and indexing, 2.0 made science more accesible and easier to search due to the newer digital formats. Yet both needed an organizing structure; for 1.0 that was the scientific method of data and literature organization with libraries as the indexers. In 2.0 this relied on an army mostly of volunteers who did not have much in the way of incentivization to co-curate and organize the findings and massive literature.

The Intenet and the Web is rapidly adopting a new “Web 3.0” format, with decentralized networks, enhanced virtual experiences, and greater interconnection between people. Here we start the discussion what will the move from Science 2.0, where dissemination of scientific findings was revolutionized and piggybacking on Web 2.0 or social media, to a Science 3.0 format. And what will it involve or what paradigms will be turned upside down?

We have discussed this in other posts such as

Will Web 3.0 Do Away With Science 2.0? Is Science Falling Behind?

and

Curation Methodology – Digital Communication Technology to mitigate Published Information Explosion and Obsolescence in Medicine and Life Sciences

For years the pharmaceutical industry has toyed with the idea of making innovation networks and innovation hubs

It has been the main focus of whole conferences

Tales from the Translational Frontier – Four Unique Approaches to Turning Novel Biology into Investable Innovations @BIOConvention #BIO2018

However it still seems these strategies have not worked

Is it because we did not have an Execution plan? Or we did not understand the lead measures for success?

Other Related Articles on this Open Access Scientific Journal Include:

Old Industrial Revolution Paradigm of Education Needs to End: How Scientific Curation Can Transform Education

Analysis of Utilizing LPBI Group’s Scientific Curation Platform as an Educational Tool: New Paradigm for Student Engagement

Global Alliance for Genomics and Health Issues Guidelines for Data Siloing and Sharing

Multiple Major Scientific Journals Will Fully Adopt Open Access Under Plan S

eScientific Publishing a Case in Point: Evolution of Platform Architecture Methodologies and of Intellectual Property Development (Content Creation by Curation) Business Model 

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Will Web 3.0 Do Away With Science 2.0? Is Science Falling Behind?

Posted in Artificial Intelligence Applications in Health Care, BioIT: BioInformatics, Blockchain Transactions System, Curation, Curation methodology, De novo synthesis, Discovery process, Historical relevance, IP Development by LPBI Group Team, IP Development by LPBI Group Team & Other Organizations, Key Opinion Leaders in eScientific Publishing - Interviews with, LPBI Group, e-Scientific Media, DFP, R&D-M3DP, R&D-Drug Discovery, US Patents: SOPs and Team Management, Open Access Journals, REAL TIME Conference Coverage Twitter's Hashtags and Handles per Presentation/session, Scientific Publishing, tagged #science2_0, #science3_0, communication of scientific findings, Curation of Scientific Content, Curation of Scientific Findings, LinkedIn, NFT, science communication, Twitter, web 2.0, web 3.0 on April 1, 2022| 2 Comments »

Will Web 3.0 Do Away With Science 2.0? Is Science Falling Behind?

Curator: Stephen J. Williams, Ph.D.

UPDATED 4/06/2022

A while back (actually many moons ago) I had put on two posts on this site:

Scientific Curation Fostering Expert Networks and Open Innovation: Lessons from Clive Thompson and others

Twitter is Becoming a Powerful Tool in Science and Medicine

Each of these posts were on the importance of scientific curation of findings within the realm of social media and the Web 2.0; a sub-environment known throughout the scientific communities as Science 2.0, in which expert networks collaborated together to produce massive new corpus of knowledge by sharing their views, insights on peer reviewed scientific findings. And through this new media, this process of curation would, in itself generate new ideas and new directions for research and discovery.

The platform sort of looked like the image below:

 

This system lied above a platform of the original Science 1.0, made up of all the scientific journals, books, and traditional literature:

Previous image source: PeerJ.com

To index the massive and voluminous research and papers beyond the old Dewey Decimal system, a process of curation was mandatory. The dissemination of this was a natural for the new social media however the cost had to be spread out among numerous players. Journals, faced with the high costs of subscriptions and their only way to access this new media as an outlet was to become Open Access, a movement first sparked by journals like PLOS and PeerJ but then begrudingly adopted throughout the landscape. But with any movement or new adoption one gets the Good the Bad and the Ugly (as described in my cited, above, Clive Thompson article). The bad side of Open Access Journals were

1. costs are still assumed by the individual researcher not by the journals
2. the arise of the numerous Predatory Journals

 

Even PeerJ, in their column celebrating an anniversary of a year’s worth of Open Access success stories, lamented the key issues still facing Open Access in practice

• which included the cost and the rise of predatory journals.

In essence, Open Access and Science 2.0 sprung full force BEFORE anyone thought of a way to defray the costs

 

Can Web 3.0 Finally Offer a Way to Right the Issues Facing High Costs of Scientific Publishing?

What is Web 3.0?

From Wikipedia: https://en.wikipedia.org/wiki/Web3

Web 1.0 and Web 2.0 refer to eras in the history of the Internet as it evolved through various technologies and formats. Web 1.0 refers roughly to the period from 1991 to 2004, where most websites were static webpages, and the vast majority of users were consumers, not producers, of content.^[6][7] Web 2.0 is based around the idea of “the web as platform”,^[8] and centers on user-created content uploaded to social-networking services, blogs, and wikis, among other services.^[9] Web 2.0 is generally considered to have begun around 2004, and continues to the current day.^[8][10][4]

Terminology[edit]

The term “Web3”, specifically “Web 3.0”, was coined by Ethereum co-founder Gavin Wood in 2014.^[1] In 2020 and 2021, the idea of Web3 gained popularity^{[citation needed]}. Particular interest spiked towards the end of 2021, largely due to interest from cryptocurrency enthusiasts and investments from high-profile technologists and companies.^[4][5] Executives from venture capital firm Andreessen Horowitz travelled to Washington, D.C. in October 2021 to lobby for the idea as a potential solution to questions about Internet regulation with which policymakers have been grappling.^[11]

Web3 is distinct from Tim Berners-Lee‘s 1999 concept for a semantic web, which has also been called “Web 3.0”.^[12] Some writers referring to the decentralized concept usually known as “Web3” have used the terminology “Web 3.0”, leading to some confusion between the two concepts.^[2][3] Furthermore, some visions of Web3 also incorporate ideas relating to the semantic web.^[13][14]

Concept[edit]

Web3 revolves around the idea of decentralization, which proponents often contrast with Web 2.0, wherein large amounts of the web’s data and content are centralized in the fairly small group of companies often referred to as Big Tech.^[4]

Specific visions for Web3 differ, but all are heavily based in blockchain technologies, such as various cryptocurrencies and non-fungible tokens (NFTs).^[4] Bloomberg described Web3 as an idea that “would build financial assets, in the form of tokens, into the inner workings of almost anything you do online”.^[15] Some visions are based around the concepts of decentralized autonomous organizations (DAOs).^[16] Decentralized finance (DeFi) is another key concept; in it, users exchange currency without bank or government involvement.^[4] Self-sovereign identity allows users to identify themselves without relying on an authentication system such as OAuth, in which a trusted party has to be reached in order to assess identity.^[17]

Reception[edit]

Technologists and journalists have described Web3 as a possible solution to concerns about the over-centralization of the web in a few “Big Tech” companies.^[4][11] Some have expressed the notion that Web3 could improve data security, scalability, and privacy beyond what is currently possible with Web 2.0 platforms.^[14] Bloomberg states that sceptics say the idea “is a long way from proving its use beyond niche applications, many of them tools aimed at crypto traders”.^[15] The New York Times reported that several investors are betting $27 billion that Web3 “is the future of the internet”.^[18][19]

Some companies, including Reddit and Discord, have explored incorporating Web3 technologies into their platforms in late 2021.^[4][20] After heavy user backlash, Discord later announced they had no plans to integrate such technologies.^[21] The company’s CEO, Jason Citron, tweeted a screenshot suggesting it might be exploring integrating Web3 into their platform. This led some to cancel their paid subscriptions over their distaste for NFTs, and others expressed concerns that such a change might increase the amount of scams and spam they had already experienced on crypto-related Discord servers.^[20] Two days later, Citron tweeted that the company had no plans to integrate Web3 technologies into their platform, and said that it was an internal-only concept that had been developed in a company-wide hackathon.^[21]

Some legal scholars quoted by The Conversation have expressed concerns over the difficulty of regulating a decentralized web, which they reported might make it more difficult to prevent cybercrime, online harassment, hate speech, and the dissemination of child abuse images.^[13] But, the news website also states that, “[decentralized web] represents the cyber-libertarian views and hopes of the past that the internet can empower ordinary people by breaking down existing power structures.” Some other critics of Web3 see the concept as a part of a cryptocurrency bubble, or as an extension of blockchain-based trends that they see as overhyped or harmful, particularly NFTs.^[20] Some critics have raised concerns about the environmental impact of cryptocurrencies and NFTs. Others have expressed beliefs that Web3 and the associated technologies are a pyramid scheme.^[5]

Kevin Werbach, author of The Blockchain and the New Architecture of Trust,^[22] said that “many so-called ‘web3’ solutions are not as decentralized as they seem, while others have yet to show they are scalable, secure and accessible enough for the mass market”, adding that this “may change, but it’s not a given that all these limitations will be overcome”.^[23]

David Gerard, author of Attack of the 50 Foot Blockchain,^[24] told The Register that “web3 is a marketing buzzword with no technical meaning. It’s a melange of cryptocurrencies, smart contracts with nigh-magical abilities, and NFTs just because they think they can sell some monkeys to morons”.^[25]

Below is an article from MarketWatch.com Distributed Ledger series about the different forms and cryptocurrencies involved

From Marketwatch: https://www.marketwatch.com/story/bitcoin-is-so-2021-heres-why-some-institutions-are-set-to-bypass-the-no-1-crypto-and-invest-in-ethereum-other-blockchains-next-year-11639690654?mod=home-page

by Frances Yue, Editor of Distributed Ledger, Marketwatch.com

Clayton Gardner, co-CEO of crypto investment management firm Titan, told Distributed Ledger that as crypto embraces broader adoption, he expects more institutions to bypass bitcoin and invest in other blockchains, such as Ethereum, Avalanche, and Terra in 2022. which all boast smart-contract features.

Bitcoin traditionally did not support complex smart contracts, which are computer programs stored on blockchains, though a major upgrade in November might have unlocked more potential.

“Bitcoin was originally seen as a macro speculative asset by many funds and for many it still is,” Gardner said. “If anything solidifies its use case, it’s a store of value. It’s not really used as originally intended, perhaps from a medium of exchange perspective.”

For institutions that are looking for blockchains that can “produce utility and some intrinsic value over time,” they might consider some other smart contract blockchains that have been driving the growth of decentralized finance and web 3.0, the third generation of the Internet, according to Gardner. 

“Bitcoin is still one of the most secure blockchains, but I think layer-one, layer-two blockchains beyond Bitcoin, will handle the majority of transactions and activities from NFT (nonfungible tokens) to DeFi,“ Gardner said. “So I think institutions see that and insofar as they want to put capital to work in the coming months, I think that could be where they just pump the capital.”

Decentralized social media？ 

The price of Decentralized Social, or DeSo, a cryptocurrency powering a blockchain that supports decentralized social media applications, surged roughly 74% to about $164 from $94, after Deso was listed at Coinbase Pro on Monday, before it fell to about $95, according to CoinGecko.

In the eyes of Nader Al-Naji, head of the DeSo foundation, decentralized social media has the potential to be “a lot bigger” than decentralized finance.

“Today there are only a few companies that control most of what we see online,” Al-Naji told Distributed Ledger in an interview. But DeSo is “creating a lot of new ways for creators to make money,” Al-Naji said.

“If you find a creator when they’re small, or an influencer, you can invest in that, and then if they become bigger and more popular, you make money and they make and they get capital early on to produce their creative work,” according to AI-Naji.

BitClout, the first application that was created by AI-Naji and his team on the DeSo blockchain, had initially drawn controversy, as some found that they had profiles on the platform without their consent, while the application’s users were buying and selling tokens representing their identities. Such tokens are called “creator coins.”

AI-Naji responded to the controversy saying that DeSo now supports more than 200 social-media applications including Bitclout. “I think that if you don’t like those features, you now have the freedom to use any app you want. Some apps don’t have that functionality at all.”

 

But Before I get to the “selling monkeys to morons” quote,

I want to talk about

THE GOOD, THE BAD, AND THE UGLY

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

THE GOOD

My foray into Science 2.0 and then pondering what the movement into a Science 3.0 led me to an article by Dr. Vladimir Teif, who studies gene regulation and the nucleosome, as well as creating a worldwide group of scientists who discuss matters on chromatin and gene regulation in a journal club type format.

For more information on this Fragile Nucleosome journal club see https://generegulation.org/fragile-nucleosome/.

Fragile Nucleosome is an international community of scientists interested in chromatin and gene regulation. Fragile Nucleosome is active in several spaces: one is the Discord server where several hundred scientists chat informally on scientific matters. You can join the Fragile Nucleosome Discord server. Another activity of the group is the organization of weekly virtual seminars on Zoom. Our webinars are usually conducted on Wednesdays 9am Pacific time (5pm UK, 6pm Central Europe). Most previous seminars have been recorded and can be viewed at our YouTube channel. The schedule of upcoming webinars is shown below. Our third activity is the organization of weekly journal clubs detailed at a separate page (Fragile Nucleosome Journal Club).

 

His lab site is at https://generegulation.org/ but had published a paper describing what he felt what the #science2_0 to #science3_0 transition would look like (see his blog page on this at https://generegulation.org/open-science/).

This concept of science 3.0 he had coined back in 2009.  As Dr Teif had mentioned

So essentially I first introduced this word Science 3.0 in 2009, and since then we did a lot to implement this in practice. The Twitter account @generegulation is also one of examples

 

This is curious as we still have an ill defined concept of what #science3_0 would look like but it is a good read nonetheless.

His paper,  entitled “Science 3.0: Corrections to the Science 2.0 paradigm” is on the Cornell preprint server at https://arxiv.org/abs/1301.2522 

 

Abstract

Science 3.0: Corrections to the Science 2.0 paradigm

Vladimir B. Teif

The concept of Science 2.0 was introduced almost a decade ago to describe the new generation of online-based tools for researchers allowing easier data sharing, collaboration and publishing. Although technically sound, the concept still does not work as expected. Here we provide a systematic line of arguments to modify the concept of Science 2.0, making it more consistent with the spirit and traditions of science and Internet. Our first correction to the Science 2.0 paradigm concerns the open-access publication models charging fees to the authors. As discussed elsewhere, we show that the monopoly of such publishing models increases biases and inequalities in the representation of scientific ideas based on the author’s income. Our second correction concerns post-publication comments online, which are all essentially non-anonymous in the current Science 2.0 paradigm. We conclude that scientific post-publication discussions require special anonymization systems. We further analyze the reasons of the failure of the current post-publication peer-review models and suggest what needs to be changed in Science 3.0 to convert Internet into a large journal club. [bold face added]

In this paper it is important to note the transition of a science 1.0, which involved hard copy journal publications usually only accessible in libraries to a more digital 2.0 format where data, papers, and ideas could be easily shared among networks of scientists.

As Dr. Teif states, the term “Science 2.0” had been coined back in 2009, and several influential journals including Science, Nature and Scientific American endorsed this term and suggested scientists to move online and their discussions online.  However, even at present there are thousands on this science 2.0 platform, Dr Teif notes the number of scientists subscribed to many Science 2.0 networking groups such as on LinkedIn and ResearchGate have seemingly saturated over the years, with little new members in recent times. 

The consensus is that science 2.0 networking is:

1. good because it multiplies the efforts of many scientists, including experts and adds to the scientific discourse unavailable on a 1.0 format
2. that online data sharing is good because it assists in the process of discovery (can see this evident with preprint servers, bio-curated databases, Github projects)
3. open-access publishing is beneficial because free access to professional articles and open-access will be the only publishing format in the future (although this is highly debatable as many journals are holding on to a type of “hybrid open access format” which is not truly open access
4. only sharing of unfinished works and critiques or opinions is good because it creates visibility for scientists where they can receive credit for their expert commentary

There are a few concerns on Science 3.0 Dr. Teif articulates:

A.  Science 3.0 Still Needs Peer Review

Peer review of scientific findings will always be an imperative in the dissemination of well-done, properly controlled scientific discovery.  As Science 2.0 relies on an army of scientific volunteers, the peer review process also involves an army of scientific experts who give their time to safeguard the credibility of science, by ensuring that findings are reliable and data is presented fairly and properly.  It has been very evident, in this time of pandemic and the rapid increase of volumes of preprint server papers on Sars-COV2, that peer review is critical.  Many of these papers on such preprint servers were later either retracted or failed a stringent peer review process.

Now many journals of the 1.0 format do not generally reward their peer reviewers other than the self credit that researchers use on their curriculum vitaes.  Some journals, like the MDPI journal family, do issues peer reviewer credits which can be used to defray the high publication costs of open access (one area that many scientists lament about the open access movement; where the burden of publication cost lies on the individual researcher).

An issue which is highlighted is the potential for INFORMATION NOISE regarding the ability to self publish on Science 2.0 platforms.

 

The NEW BREED was born in 4/2012

An ongoing effort on this platform, https://pharmaceuticalintelligence.com/, is to establish a scientific methodology for curating scientific findings where one the goals is to assist to quell the information noise that can result from the massive amounts of new informatics and data occurring in the biomedical literature. 

B.  Open Access Publishing Model leads to biases and inequalities in the idea selection

The open access publishing model has been compared to the model applied by the advertising industry years ago and publishers then considered the journal articles as “advertisements”.  However NOTHING could be further from the truth.  In advertising the publishers claim the companies not the consumer pays for the ads.  However in scientific open access publishing, although the consumer (libraries) do not pay for access the burden of BOTH the cost of doing the research and publishing the findings is now put on the individual researcher.  Some of these publishing costs can be as high as $4000 USD per article, which is very high for most researchers.  However many universities try to refund the publishers if they do open access publishing so it still costs the consumer and the individual researcher, limiting the cost savings to either.  

However, this sets up a situation in which young researchers, who in general are not well funded, are struggling with the publication costs, and this sets up a bias or inequitable system which rewards the well funded older researchers and bigger academic labs.

C. Post publication comments and discussion require online hubs and anonymization systems

Many recent publications stress the importance of a post-publication review process or system yet, although many big journals like Nature and Science have their own blogs and commentary systems, these are rarely used.  In fact they show that there are just 1 comment per 100 views of a journal article on these systems.  In the traditional journals editors are the referees of comments and have the ability to censure comments or discourse.  The article laments that comments should be easy to do on journals, like how easy it is to make comments on other social sites, however scientists are not offering their comments or opinions on the matter. 

In a personal experience, 

a well written commentary goes through editors which usually reject a comment like they were rejecting an original research article.  Thus many scientists, I believe, after fashioning a well researched and referenced reply, do not get the light of day if not in the editor’s interests.  

Therefore the need for anonymity is greatly needed and the lack of this may be the hindrance why scientific discourse is so limited on these types of Science 2.0 platforms.  Platforms that have success in this arena include anonymous platforms like Wikipedia or certain closed LinkedIn professional platforms but more open platforms like Google Knowledge has been a failure.

A great example on this platform was a very spirited conversation on LinkedIn on genomics, tumor heterogeneity and personalized medicine which we curated from the LinkedIn discussion (unfortunately LinkedIn has closed many groups) seen here:

Issues in Personalized Medicine: Discussions of Intratumor Heterogeneity from the Oncology Pharma forum on LinkedIn

 

 

Issues in Personalized Medicine: Discussions of Intratumor Heterogeneity from the Oncology Pharma forum on LinkedIn

 

In this discussion, it was surprising that over a weekend so many scientists from all over the world contributed to a great discussion on the topic of tumor heterogeneity.

But many feel such discussions would be safer if they were anonymized.  However then researchers do not get any credit for their opinions or commentaries.

A Major problem is how to take the intangible and make them into tangible assets which would both promote the discourse as well as reward those who take their time to improve scientific discussion.

This is where something like NFTs or a decentralized network may become important!

See

https://pharmaceuticalintelligence.com/portfolio-of-ip-assets/

 

UPDATED 5/09/2022

Below is an online @TwitterSpace Discussion we had with some young scientists who are just starting out and gave their thoughts on what SCIENCE 3.0 and the future of dissemination of science might look like, in light of this new Meta Verse.  However we have to define each of these terms in light of Science and not just the Internet as merely a decentralized marketplace for commonly held goods.

This online discussion was tweeted out and got a fair amount of impressions (60) as well as interactors (50).

Set a reminder for my upcoming Space! https://t.co/7mOpScZfGN @Pharma_BI @PSMTempleU #science3_0 @science2_0

— Stephen J Williams (@StephenJWillia2) April 28, 2022

 For the recording on both Twitter as well as on an audio format please see below

<blockquote class=”twitter-tweet”><p lang=”en” dir=”ltr”>Set a reminder for my upcoming Space! <a href=”https://t.co/7mOpScZfGN”>https://t.co/7mOpScZfGN</a&gt; <a href=”https://twitter.com/Pharma_BI?ref_src=twsrc%5Etfw”>@Pharma_BI</a&gt; <a href=”https://twitter.com/PSMTempleU?ref_src=twsrc%5Etfw”>@PSMTempleU</a&gt; <a href=”https://twitter.com/hashtag/science3_0?src=hash&amp;ref_src=twsrc%5Etfw”>#science3_0</a&gt; <a href=”https://twitter.com/science2_0?ref_src=twsrc%5Etfw”>@science2_0</a></p>&mdash; Stephen J Williams (@StephenJWillia2) <a href=”https://twitter.com/StephenJWillia2/status/1519776668176502792?ref_src=twsrc%5Etfw”>April 28, 2022</a></blockquote> <script async src=”https://platform.twitter.com/widgets.js&#8221; charset=”utf-8″></script>

 

 

To introduce this discussion first a few startoff material which will fram this discourse

 

The Intenet and the Web is rapidly adopting a new “Web 3.0” format, with decentralized networks, enhanced virtual experiences, and greater interconnection between people. Here we start the discussion what will the move from Science 2.0, where dissemination of scientific findings was revolutionized and piggybacking on Web 2.0 or social media, to a Science 3.0 format. And what will it involve or what paradigms will be turned upside down?

Old Science 1.0 is still the backbone of all scientific discourse, built on the massive amount of experimental and review literature. However this literature was in analog format, and we moved to a more accesible digital open access format for both publications as well as raw data. However as there was a structure for 1.0, like the Dewey decimal system and indexing, 2.0 made science more accesible and easier to search due to the newer digital formats. Yet both needed an organizing structure; for 1.0 that was the scientific method of data and literature organization with libraries as the indexers. In 2.0 this relied on an army mostly of volunteers who did not have much in the way of incentivization to co-curate and organize the findings and massive literature.

Each version of Science has their caveats: their benefits as well as deficiencies. This curation and the ongoing discussion is meant to solidy the basis for the new format, along with definitions and determination of structure.

We had high hopes for Science 2.0, in particular the smashing of data and knowledge silos. However the digital age along with 2.0 platforms seemed to excaccerbate this somehow. We still are critically short on analysis!

 

We really need people and organizations to get on top of this new Web 3.0 or metaverse so the similar issues do not get in the way: namely we need to create an organizing structure (maybe as knowledgebases), we need INCENTIVIZED co-curators, and we need ANALYSIS… lots of it!!

Are these new technologies the cure or is it just another headache?

 

There were a few overarching themes whether one was talking about AI, NLP, Virtual Reality, or other new technologies with respect to this new meta verse and a concensus of Decentralized, Incentivized, and Integrated was commonly expressed among the attendees

The Following are some slides from representative Presentations

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Other article of note on this topic on this Open Access Scientific Journal Include:

Electronic Scientific AGORA: Comment Exchanges by Global Scientists on Articles published in the Open Access Journal @pharmaceuticalintelligence.com – Four Case Studies

eScientific Publishing a Case in Point: Evolution of Platform Architecture Methodologies and of Intellectual Property Development (Content Creation by Curation) Business Model 

e-Scientific Publishing: The Competitive Advantage of a Powerhouse for Curation of Scientific Findings and Methodology Development for e-Scientific Publishing – LPBI Group, A Case in Point

@PharmaceuticalIntelligence.com –  A Case Study on the LEADER in Curation of Scientific Findings

Real Time Coverage @BIOConvention #BIO2019: Falling in Love with Science: Championing Science for Everyone, Everywhere

Old Industrial Revolution Paradigm of Education Needs to End: How Scientific Curation Can Transform Education

 

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Crowdsourcing Difficult-to-Collect Epidemiological Data in Pandemics: Lessons from Ebola to the current COVID-19 Pandemic

Posted in Biomarkers: Inflammation, coronavirus, Coronavirus Gene Expression, COVID-19, Curation, Curation methodology, Economic Impact of Coronavirus Pandemic, Evidence-based decision-making, Evolution of Biology Through Culture, Immune-Mediation (independent immunopathology: lung and reticuloendothelial system), number of asymptomatic infections, Population Health Management, Population Health Management, Genetics & Pharmaceutical, SAR-Cov-2 a vasculotropic (blood vessels) RNA Virus, SARS-CoV-2, SARS-COV2 Hijacking the Complement and Coagulation Systems, Seasonality & Environmental Factors in Resurgence, Serology tests for coronavirus antibodies, Treatment Protocols for COVID-19, Vaccinology, Virus Infective Acute Respiratory Syndrome: SARS-CoV, tagged China, coronavirus, Ebola virus, Epidemiology, Pandemic, Population Health Management, SARS-CoV-2 on May 15, 2020| Leave a Comment »

Crowdsourcing Difficult-to-Collect Epidemiological Data in Pandemics: Lessons from Ebola to the current COVID-19 Pandemic

 

Curator: Stephen J. Williams, Ph.D.

 

At the onset of the COVID-19 pandemic, epidemiological data from the origin of the Sars-Cov2 outbreak, notably from the Wuhan region in China, was sparse.  In fact, official individual patient data rarely become available early on in an outbreak, when that data is needed most. Epidemiological data was just emerging from China as countries like Italy, Spain, and the United States started to experience a rapid emergence of the outbreak in their respective countries.  China, made of 31 geographical provinces, is a vast and complex country, with both large urban and rural areas.

Geographic Regions of China. Source: https://www.unicef.cn/en/figure-11-geographic-regions-china

 

 

 

As a result of this geographical diversity and differences in healthcare coverage across the country, epidemiological data can be challenging.  For instance, cancer incidence data for regions and whole country is difficult to calculate as there are not many regional cancer data collection efforts, contrasted with the cancer statistics collected in the United States, which is meticulously collected by cancer registries in each region, state and municipality.  Therefore, countries like China must depend on hospital record data and autopsy reports in order to back-extrapolate cancer incidence data.  This is the case in some developed countries like Italy where cancer registry is administered by a local government and may not be as extensive (for example in the Napoli region of Italy).

 

 

 

 

 

 

Population density China by province. Source https://www.unicef.cn/en/figure-13-population-density-province-2017

 

 

 

Epidemiologists, in areas in which data collection may be challenging, are relying on alternate means of data collection such as using devices connected to the internet-of-things such as mobile devices, or in some cases, social media is becoming useful to obtain health related data.  Such as effort to acquire pharmacovigilance data, patient engagement, and oral chemotherapeutic adherence using the social media site Twitter has been discussed in earlier posts: (see below)

Twitter is Becoming a Powerful Tool in Science and Medicine at https://pharmaceuticalintelligence.com/2014/11/06/twitter-is-becoming-a-powerful-tool-in-science-and-medicine/

 

 

 

 

 

Now epidemiologists are finding crowd-sourced data from social media and social networks becoming useful in collecting COVID-19 related data in those countries where health data collection efforts may be sub-optimal.  In a recent paper in The Lancet Digital Health [1], authors Kaiyuan Sun, Jenny Chen, and Cecile Viboud present data from the COVID-19 outbreak in China using information collected over social network sites as well as public news outlets and find strong correlations with later-released government statistics, showing the usefulness in such social and crowd-sourcing strategies to collect pertinent time-sensitive data.  In particular, the authors aim was to investigate this strategy of data collection to reduce the time delays between infection and detection, isolation and reporting of cases.

The paper is summarized below:

Kaiyuan Sun, PhD Jenny Chen, BScn Cécile Viboud, PhD . (2020).  Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study.  The Lancet: Digital Health; Volume 2, Issue 4, E201-E208.

Summary

Background

As the outbreak of coronavirus disease 2019 (COVID-19) progresses, epidemiological data are needed to guide situational awareness and intervention strategies. Here we describe efforts to compile and disseminate epidemiological information on COVID-19 from news media and social networks.

Methods

In this population-level observational study, we searched DXY.cn, a health-care-oriented social network that is currently streaming news reports on COVID-19 from local and national Chinese health agencies. We compiled a list of individual patients with COVID-19 and daily province-level case counts between Jan 13 and Jan 31, 2020, in China. We also compiled a list of internationally exported cases of COVID-19 from global news media sources (Kyodo News, The Straits Times, and CNN), national governments, and health authorities. We assessed trends in the epidemiology of COVID-19 and studied the outbreak progression across China, assessing delays between symptom onset, seeking care at a hospital or clinic, and reporting, before and after Jan 18, 2020, as awareness of the outbreak increased. All data were made publicly available in real time.

Findings

We collected data for 507 patients with COVID-19 reported between Jan 13 and Jan 31, 2020, including 364 from mainland China and 143 from outside of China. 281 (55%) patients were male and the median age was 46 years (IQR 35–60). Few patients (13 [3%]) were younger than 15 years and the age profile of Chinese patients adjusted for baseline demographics confirmed a deficit of infections among children. Across the analysed period, delays between symptom onset and seeking care at a hospital or clinic were longer in Hubei province than in other provinces in mainland China and internationally. In mainland China, these delays decreased from 5 days before Jan 18, 2020, to 2 days thereafter until Jan 31, 2020 (p=0·0009). Although our sample captures only 507 (5·2%) of 9826 patients with COVID-19 reported by official sources during the analysed period, our data align with an official report published by Chinese authorities on Jan 28, 2020.

Interpretation

News reports and social media can help reconstruct the progression of an outbreak and provide detailed patient-level data in the context of a health emergency. The availability of a central physician-oriented social network facilitated the compilation of publicly available COVID-19 data in China. As the outbreak progresses, social media and news reports will probably capture a diminishing fraction of COVID-19 cases globally due to reporting fatigue and overwhelmed health-care systems. In the early stages of an outbreak, availability of public datasets is important to encourage analytical efforts by independent teams and provide robust evidence to guide interventions.

A Few notes on Methodology:

• The authors used crowd-sourced reports from DXY.cn, a social network for Chinese physicians, health-care professionals, pharmacies and health-care facilities. This online platform provides real time coverage of the COVID-19 outbreak in China
• More data was curated from news media, television and includes time-stamped information on COVID-19 cases
• These reports are publicly available, de-identified patient data
• No patient consent was needed and no ethics approval was required
• Data was collected between January 20, 2020 and January 31,2020
• Sex, age, province of identification, travel history, dates of symptom development was collected
• Additional data was collected for other international sites of the pandemic including Cambodia, Canada, France, Germany, Hong Kong, India, Italy, Japan, Malaysia, Nepal, Russia, Singapore, UK, and USA
• All patients in database had laboratory confirmation of infection

 

Results

• 507 patient data was collected with 153 visited and 152 resident of Wuhan
• Reported cases were skewed toward males however the overall population curve is skewed toward males in China
• Most cases (26%) were from Beijing (urban area) while an equal amount were from rural areas combined (Shaanzi and Yunnan)
• Age distribution of COVID cases were skewed toward older age groups with median age of 45 HOWEVER there were surprisingly a statistically high amount of cases less than 5 years of age
• Outbreak progression based on the crowd-sourced patient line was consistent with the data published by the China Center for Disease Control
• Median reporting delay in the authors crowd-sourcing data was 5 days
• Crowd-sourced data was able to detect apparent rapid growth of newly reported cases during the collection period in several provinces outside of Hubei province, which is consistent with local government data

The following graphs show age distribution for China in 2017 and predicted for 2050.

Source: https://www.indexmundi.com/china/age_structure.html

projected age distribution China 2050. Source https://chinapower.csis.org/aging-problem/

 

 

 

 

 

 

 

 

 

 

 

 

The authors have previously used this curation of news methodology to analyze the Ebola outbreak[2].

A further use of the crowd-sourced database was availability of travel histories for patients returning from Wuhan and onset of symptoms, allowing for estimation of incubation periods.

The following published literature has also used these datasets:

Backer JA, Klinkenberg D, Wallinga J: Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20-28 January 2020. Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin 2020, 25(5).

Lauer SA, Grantz KH, Bi Q, Jones FK, Zheng Q, Meredith HR, Azman AS, Reich NG, Lessler J: The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of internal medicine 2020, 172(9):577-582.

Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, Ren R, Leung KSM, Lau EHY, Wong JY et al: Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. The New England journal of medicine 2020, 382(13):1199-1207.

Dataset is available on the Laboratory for the Modeling of Biological and Socio-technical systems website of Northeastern University at https://www.mobs-lab.org/.

References

1. Sun K, Chen J, Viboud C: Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study. The Lancet Digital health 2020, 2(4):e201-e208.
2. Cleaton JM, Viboud C, Simonsen L, Hurtado AM, Chowell G: Characterizing Ebola Transmission Patterns Based on Internet News Reports. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 2016, 62(1):24-31.

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