Archive for the ‘information ecosystem’ Category

QOTD: Working out scientific insights on paper, Lavoisier case study

July 12th, 2017

…language does do much of our thinking for us, even in the sciences, and rather than being an unfortunate contamination, its influence has been productive historically, helping individual thinkers generate concepts and theories that can then be put to the test. The case made here for the constitutive power of figures [of speech] per se supports the general point made by F.L. Holmes in a lecture addressed to the History of Science Society in 1987. A distinguished historian of medicine and chemistry, Holmes based his study of Antoine Lavoisier on the French chemist’s laboratory notebooks. He later examined drafts of Lavoisier’s published papers and discovered that Lavoisier wrote many versions of his papers and in the course of careful revisions gradually worked out the positions he eventually made public (Holmes, 221). Holmes, whose goal as a historian is to reconstruct the careful pathways and fine structure of scientific insights, concluded from his study of Lavoisier’s drafts

We cannot always tell whether a thought that led him to modify a passage, recast an argument, or develop an alternative interpretation occurred while he was still engaged in writing what he subsequently altered, or immediately afterward, or after some interval during which he occupied himself with something else; but the timing is, I believe, less significant than the fact that the new developments were consequences of the effort to express ideas and marshall supporting information on paper (225).

– page xi of Rhetorical Figures in Science by Jeanne Fahnestock, Oxford University Press, 1999.

She is quoting Frederich L. Holmes. 1987. Scientific writing and scientific discovery. Isis 78:220-235. DOI:10.1086/354391

As Moore summarizes,

Lavoisier wrote at least six drafts of the paper over a period of at least six months. However, his theory of respiration did not appear until the fifth draft. Clearly, Lavoisier’s writing helped him refine and understand his ideas.

Moore, Randy. Language—A Force that Shapes Science. Journal of College Science Teaching 28.6 (1999): 366. http://www.jstor.org/stable/42990615
(which I quoted in
a review I wrote recently)

Fahnestock adds:
“…Holmes’s general point [is that] there are subtle interactions ‘between writing, thought, and operations in creative scientific activity’ (226).”

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David Liebovitz: Achieving Care transformation by Infusing Electronic Health Records with Wisdom

May 1st, 2017

Today I am at the Health Data Analytics summit. The title of the keynote talk is Achieving Care transformation by Infusing Electronic Health Records with Wisdom. It’s a delight to hear from a medical informaticist: David M. Liebovitz (publications in Google Scholar), MD, FACP, Chief Medical Information Officer, The University of Chicago. He graduated from University of Illinois in electrical engineering, making this a timely talk as the engineering-focused Carle Illinois College of Medicine gets going.

David Liebovitz started with a discussion of the data problems — problem lists, medication lists, family history, rules, results, notes — which will be familiar to anyone using EHRs or working with EHR data. He draws attention also to the human problems — both in terms of provider “readiness” (e.g. their vision for population-level health) as well as about “current expectations”. (An example of such an expectation is a “main clinician satisfier” he closed with: U Chicago is about to turn on outbound faxing from the EHR!) He mentioned also the importance of resilience.

He mentioned customizing systems as a risk when the vendor makes upstream changes (this is not unique to healthcare but a threat to innovation and experimentation with information systems in other industries.) Still, in managing the EHR, there is continual optimization, scored based on a number of factors. He mentioned:

  • Safety
  • Quality/patient experience
  • Regulatory/legal
  • Financial
  • Usability/productivity
  • Availability of alternative solutions

As well as weighting for old requests.

He emphasized the complexity of healthcare in several ways:

complexity of drug purchasing

An image from “Prices That Are Too High”, Chapter 5, The Healthcare Imperative: Lowering Costs and Improving Outcomes: Workshop Series Summary (2010)

  • Icosystem’s diagram of the complexity of the healthcare system
Complexity of the healthcare system

Icosystem – complexity of the healthcare system

  • Another complexity is the modest impact of medical care compared to other factors
    • such as the impact of socioeconomic and political context on equity in health and well-being (see the WHO image below).
    • For instance, there is a large impact of health behaviors, which “happen in larger social contexts.” (See the Relative Contribution of Multiple Determinants to Health, August 21, 2014, Health Policy Briefs)

Given this complexity, David Liebovitz stresses that we need to start with the right model, “simultaneously improving population health, improving the patient experience of care, and reducing per capita cost”. (See Stiefel M, Nolan K. A Guide to Measuring the Triple Aim: Population Health, Experience of Care, and Per Capita Cost. IHI Innovation Series white paper. Cambridge, Massachusetts: Institute for Healthcare Improvement; 2012).

triple aims to measure healthcare improvement

Table 1 from Stiefel M, Nolan K. A Guide to Measuring the Triple Aim: Population Health, Experience of Care, and Per Capita Cost. IHI Innovation Series white paper. Cambridge, Massachusetts: Institute for Healthcare Improvement; 2012.

Given the modest impact of medical care, and of data, he suggests that we should choose the right outcomes.

David Liebovitz says that “not enough attention has been paid to usability”; I completely agree and suggest that information scientists, human factors engineeers, and cognitive ergonomists help mainstream medical informaticists fill this gap. He put up Jakob Nielsen’s 10 usability heuristics for user interface design A vivid example is whether a patient’s resuscitation preferences are shown (which seems to depend on the particular EHR screen): the system doesn’t highlight where we are in the system. For providers, he says user control and freedom are very important. He suggests that there are only a few key tasks. A provider should be able to do ANY of these things wherever they are in the chart:

  • put a note
  • order something
  • send a message

Similarly, EHR should support recognition (“how do I admit a patient again?”) rather than requiring recall.

Meanwhile, on the decision support side he highlights the (well-known) problems around interruptions by saying that speed is everything and changing direction is much easier than stopping. Here he draws on some of his own work, describing what he calls a “diagnostic process aware workflow”

David Liebovitz. Next steps for electronic health records to improve the diagnostic process. Diagnosis 2015 2(2) 111-116. doi:10.1515/dx-2014-0070

Can we predict X better? Yes, he says (for instance pointing to Table 3 of “Can machine-learning improve cardiovascular risk prediction using routine clinical data?” and its machine learning analysis of over 300,000 patients, based on variables chosen from previous guidelines and expert-informed selection–generating further support for aspects such as aloneness, access to resources, socio-economic status). But what’s really needed, he says, is to:

  • Predict the best next medical step, iteratively
  • Predict the best next lifestyle step, iteratively
  • (And what to do about genes and epigenetic measures?)

He shows an image of “All of our planes in the air” from flightaware, drawing the analogy that we want to work on “optimal patient trajectories” — predicting what are the “turbulent events” to avoid”. This is not without challenges. He points to three:

He closes suggesting that we:

  • Finish the basics
  • Address key slices of the spectrum
  • Descriptive/prescriptive
  • Begin the prescriptive journey: impact one trajectory at a time.

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QOTD: Scholarly communication online, circa 1996

December 2nd, 2015

Here is a glimpse into scholarly communication 20 years ago, from a paper about Alzforum, the Alzheimer Research Forum website. “In July of 1996, the website made its debut at the International Conference on Alzheimer’s Disease and Related Disorders in Osaka, Japan.”1

Having established a foothold in cyberspace, the challenge for Alzforum was and continues to be to define new types of scientific publishing that take advantage of the speed and wide distribution of the Web and to curate and add value to information available from other public sources. This is a perennial challenge, thanks to the rapid advances in biomedical resources on the Web.

This uphill struggle, however, seems less strenuous when we compare the current situation with the “old days.” Recall that in 1996, PubMed did not exist! (PubMed was launched in June of 1997.) Medical institutions had access to Medline, but in order for Alzforum to produce its Papers of the Week listings, the editor had to ask the Countway Medical Library at Harvard Medical School to provide weekly text files listing newly indexed AD papers. The Alzforum hired a curator to paraphrase each abstract so that this information could be posted without violating journal copyrights. These documents were manually edited, sent out in a weekly email to the advisors for comments, and compiled into a static HTML page. Looking back, we can see that the entire process seems as antiquated as the hand-copying of manuscripts in the Middle Ages.

(emphasis mine)

From pages 459-460 of “Alzheimer Research Forum: a knowledge base and e-community for AD research”2

  1. page 458, Kinoshita, June, and Gabrielle Strobel. “Alzheimer Research Forum: a knowledge base and e-community for AD research.” in Alzheimer: 100 Years and Beyond, Mathias Jucker, Konrad Beyreuther, Christian Haass, Roger M. Nitsch, Yves Christen, eds. Berlin Heidelberg:Springer-Verlag, 2006: 457-463. []
  2. Kinoshita, June, and Gabrielle Strobel. “Alzheimer Research Forum: a knowledge base and e-community for AD research.” in Alzheimer: 100 Years and Beyond, Mathias Jucker, Konrad Beyreuther, Christian Haass, Roger M. Nitsch, Yves Christen, eds. Berlin Heidelberg:Springer-Verlag, 2006: 457-463. []

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Evidence Informatics

January 20th, 2015

I sent off my revised abstract to ECA Lisbon 2015, the European Conference on Argumentation. Evidence informatics, in 75 words:

Reasoning and decision-making are common throughout human activity. Increasingly, human reasoning is mediated by information technology, either to support collective action at a distance, or to support individual decision-making and sense-making.

We will describe the nascent field of “evidence informatics”, which considers how to structure reasoning and evidence. Comparing and contrasting evidence support tools in different disciplines will help determine reusable underlying principles, shared between fields such as legal informatics, evidence-based policy, and cognitive ergonomics.

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Genre defined, a quote from John Swales

October 21st, 2014

A genre comprises a class of communicative events, the members of which share some set of communicative purposes. These purposes are recognized by the expert members of the parent discourse community and thereby constitute the rationale for the genre. This rationale shapes the schematic structure of the discourse and influences and constrains choice of content and style. Communicative purpose is both a privileged criterion and one that operates to keep the scope of a genre as here conceived narrowly focused on comparable rhetorical action. In addition to purpose, exemplars of a genre exhibit various patterns of similarity in terms of structure, style, content and intended audience. If all high probability expectations are realized, the exemplar will be viewed as prototypical by the parent discourse community. The genre names inherited and produced by discourse communities and imported by others constitute valuable ethnographic communication, but typically need further validation.1

  1. Genre defined, from John M. Swales, page 58, Chapter 3 “The concept of genre” in Genre Analysis: English in academic and research settings. Cambridge University Press 1990. Reprinted with other selections in
    The Discourse Studies Reader: Main currents in theory and analysis (see pages 305-316). []

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Linked Science 2014 paper: Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base

October 19th, 2014

Today I’m presenting a talk in the ISWC 2014 Workshop on Linked Science 2014—Making Sense Out of Data (LISC2014). The LISC2014 paper is joint work with Paolo Ciccarese, Tim Clark and Richard D. Boyce. Our goal is to make the evidence in a scientific knowledge base easier to access and audit — to make the knowledge base easier to maintain as scientific knowledge and drug safety regulations change. We are modeling evidence (data, methods, materials) from biomedical communications in the medication safety domain (drug-drug interactions).

The new architecture for the drug-drug interaction knowledge base is based on:

This is part of a 4-year National Library of Medicine project, “Addressing gaps in clinically useful evidence on drug-drug interactions” (1R01LM011838-01)

Abstract of our paper, “Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base.”:

Semantic web technologies can support the rapid and transparent validation of scientific claims by interconnecting the assumptions and evidence used to support or challenge assertions. One important application domain is medication safety, where more efficient acquisition, representation, and synthesis of evidence about potential drug-drug interactions is needed. Exposure to potential drug-drug interactions (PDDIs), defined as two or more drugs for which an interaction is known to be possible, is a significant source of preventable drug-related harm. The combination of poor quality evidence on PDDIs, and a general lack of PDDI knowledge by prescribers, results in many thousands of preventable medication errors each year. While many sources of PDDI evidence exist to help improve prescriber knowledge, they are not concordant in their coverage, accuracy, and agreement. The goal of this project is to research and develop core components of a new model that supports more efficient acquisition, representation, and synthesis of evidence about potential drug-drug interactions. Two Semantic Web models—the Micropublications Ontology and the Open Annotation Data Model—have great potential to provide linkages from PDDI assertions to their supporting evidence: statements in source documents that mention data, materials, and methods. In this paper, we describe the context and goals of our work, propose competency questions for a dynamic PDDI evidence base, outline our new knowledge representation model for PDDIs, and discuss the challenges and potential of our approach.

Citation: Schneider, Jodi, Paolo Ciccarese, Tim Clark, and Richard D. Boyce. “Using the Micropublications ontology and the Open Annotation Data Model to represent evidence within a drug-drug interaction knowledge base.” Linked Science 2014 at ISWC 2014.

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Rating the evidence, citation by citation?

September 4th, 2014

Publishers from HighWire Press are experimenting with a plugin called SocialCite. This is intended to rate the evidence, citation by citation. Like this:

SocialCite at PNAS, HighWire Press from  http://www.pnas.org/content/108/14/5488.full#ref-list-1

SocialCite at PNAS, HighWire Press from http://www.pnas.org/content/108/14/5488.full#ref-list-1:

So far a few publishers (including PNAS) have implemented it as a pilot. Apparently the Journal of Bone and Joint Surgery is apparently leading this effort, I’d be really interested in speaking with them further:

Find out more about SocialCite from their website or the slidedeck from their debut at the HighwirePress meeting.

I’m *very* curious to hear what peopel think of this — it really surprised me.

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Altmetrics can help surface quality content: Jason Priem on the Decoupled Journal as the achievable future of scholarly communication

November 4th, 2012

Jason Priem has a wonderful slidedeck on how to smoothly transition from today’s practices in scientific communication to the future. Here is my reading of the argument given in Jason’s slides:

Communicating science is a central and essential part of doing science, and we have always used the best technology available.
Yet currently, there are several problems with journals, the primary form of scholarly communication.

Journal publication is

  • Slow
  • Closed
  • Hard to innovate
  • and has

  • Restrictive format: function follows form
  • Inconsistent quality control

These problems are fixable, if we realize that journals serve four traditional functions:

  1. Registration
  2. Archiving
  3. Dissemination
  4. Certification

By decoupling these functions, into an a la carte publishing menu, we can fix the scholarly communication system. Decoupled scholarly outlets already exist. Jason mentions some outlets (I would say these mainly serve registration functions, maybe also dissemination ones):

  • ArXiv
  • Math Overflow
  • SSRN
  • Faculty of 1000 Research
  • the blag-o-sphere

Jason doesn’t mention here — but we could add to this list — systems for data publishing, e-science workflow, and open notebook science; these may fulfil registration and archiving functions. Also, among existing archiving systems, we could add the journal archiving functions of LOCKSS is the main player I’m familiar with.

To help with the certification functions, we have altmetrics tools like Impact Story (Jason’s Sloan Founded project with Heather Piwowar).

Jason’s argument well worth reading in full; it’s a well-articulated argument for decoupling journal functions, with some detailed descriptions of altmetrics. The core argument is very solid, and of wide interest: Unlike previous articulations for “pre-publication peer review”, this argument will make sense to everyone who believes in big data, I think. There are other formats: video of the talk1 and a draft article called “Decoupling the scholarly journal”2.


Briefly noted in some of my earlier tweets.

  1. Thanks to Siegfriend Handschuh, who suggested the video of Jason giving this talk at Purdue. []
  2. by Jason Priem and Bradley M. Hemminger, under review for the Frontiers in Computational Neuroscience special issue “Beyond open access: visions for open evaluation of scientific papers by post-publication peer review” []

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Google Docs ‘research’ tab

May 19th, 2012

Increasingly, I’m using Google Docs with collaborators. Yesterday, one of them pointed out the new “Research” search tab within Google Docs. (Tools->Research). I’m a bit surprised that your searches don’t show up on your collaborators’ screen. I’m particularly surprised that sharing searches doesn’t seem possible.

Google Docs' new 'Research' tab promotes search within Google Docs.

Apparently, it is pretty new. More at the Google Docs blog.

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Error reporting: it’s easier in Kindle

May 9th, 2012

One thing I can say about Kindle: error reporting is easier.

You report problems in context, by selecting the offending text. No need to explain where - just what the problem is.

Feedback receipt is confirmed, along with the next steps for how it will be used.

By contrast, to report problems to academic publishers, you often must fill out an elaborate form (e.g. Springer or Elsevier). Digging up contact information often requires going to another page (e.g. ACM.). Some make you *both* go to another page to leave feedback and then fill out a form (e.g. EBSCO). Do any academic publishers keep the context of what journal article or book chapter you’re reporting a problem with? (If so, I’ve never noticed!)

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