Semantic Web shopping list (courtesy of Jabin White)

The Semantic Web will both require and consume metadata of the type shown in the right-hand, XML-labeled column. On today’s HTML-based web, Jabin’s shopping list is tagged so that it can be displayed as a list, but not much else. The future’s semantic shopping list defines the list as relating to a trip to the grocery store, and adds a category for each list item, allowing a computer to do much more with it than simply rendering it in list format. With the right “app,” Jabin’s semantic list could be sorted to match the aisle-by-aisle layout as he walks into a grocery store. Or it could warn him that his ratio of grain to veggie will blow his diet. 

Of course, still to come to enable a full realization of the Semantic Web is a controlled vocabulary (or taxonomy, or ontology) to serve as the authority file—the “single source of truth”—for these semantic categories and concepts, so that systems can understand that “veggie” is the same thing as “vegetable,” and that “grain” in this context doesn’t refer to sand or bullet weights. You can imagine the challenges of language variation and disambiguation as we move from grocery shopping to more complex domains such as in the scientific, technical, and medical world. 

In the medical domain, the National Library  of Medicine’s UMLS metathesaurus serves as just such an authority file. UMLS maps—to a single unique concept ID—the many different terminologies used in health care (to define insurance reimbursement and billing codes, disease diagnoses and treatments, drug information, medical literature concepts, and so on; UMLS maps 100+ terminologies). Silverchair’s Cortex biomedical taxonomy is connected to UMLS through this unique ID, but our team of taxonomy and subject matter experts work continually to make sure that Cortex and its companion Equivalents Server include the new concepts and evolving language that we encounter daily in the medical literature and through user interactions with our applications. We add new concepts and equivalents every working day, but they often don’t make it into the UMLS mapping for months or even years—an eternity in a fast-changing field like medicine. 

But even with its drawbacks, UMLS gives medicine a significant leg up in terms of semantic infrastructure. At Silverchair we are betting that the promise of the Semantic Web will be realized for medicine well before most other domains.

Unlike some taxonomies that have a more “academic” (read: stodgy) approach to what is considered a synonym, we put real-world equivalents in our thesaurus because we want it to work for real-world users. Many users of our health care information sites are pressed for time and are looking for an answer to a specific question. They shouldn’t have to think very hard about how to structure a query so that a search engine can understand it. It’s our job to be knowledgeable about both their language and their lingo. At Silverchair, we believe the searcher is never wrong (our version of “the customer is always right”).

Bob Wachter, with whom we’re privileged to work on two sites sponsored by the Agency for Healthcare Research and Quality (PSNet and WebM&M), recently wrote a humorous blog post about the way his hospital colleagues at UCSF (and other hospitals) commonly turn the nouns of their everyday work life into verbs as a shorthand way of communicating. For example, a resident might report that she “heparinized” her patient, or that a patient ready for discharge has been “housed and spoused,” meaning it had been determined that the patient had somewhere to go and someone to care for him. In addition, he reports the creation of new terms based on healthcare IT functionality, based, for example, on the way buttons are named in an EHR (“I done-ed it”).

That post is a fun reminder of the many ways medical lingo—and language—evolve, and the importance of attentive, systematic approaches to managing and supporting the information needs of those who invent the common parlance of their specialty in the course of doing their work (we hope, while using the sites we develop for them).

Location of Jupiter in Palm Beach County, Florida (image via Wikipedia)

The names of clinical trials are often long and unwieldy, as they try to describe the focus and mission of the trial in their name—for example, a clinical trial studying drug treatment of high cholesterol is “Arterial Biology for the Investigation of the Treatment Effects of Reducing Cholesterol 6–HDL and LDL Treatment Strategies.” Because of these long names, trials are more commonly known by their acronyms—in this case, “ARBITER 6–HALTS” trial—and no doubt their full names are being crafted to result in a catchy or apropos—or hopeful—acronym. For example, the acronym for the trial studying the effect of the drug Vytorin on cholesterol levels is “IMPROVE-IT.” (See this blogpost for some humorous trial names and acronyms.)

One of my pet peeves is the incorrect use of the word “acronym” to mean any abbreviation for a term. Actually an abbreviation is also an acronym only when the abbreviation spells a word or is a combination of letters that people can pronounce as a word. So yes—abbreviations of clinical trials are acronyms, and ah, there’s the rub for commonly used full-text nonsemantic search engines. A full-text search engine treats them like any other word.

So yikes—a PubMed search for “JUPITER” (the acronym for the trial “Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin”) delivers the first two results correctly, but the third result appears because the name of the institution that issued the paper is in Jupiter, Florida! OK so yes—the PubMed search box tries to help you by suggesting “Jupiter trial” (98 results) … but it also suggests “Jupiter study” (257 results). People—the JUPITER trial and the JUPITER study are exactly the same thing to any searcher wanting to know about JUPITER. The number of results should be the same for both searches. And nobody searching PubMed for JUPITER wants to know more about Jupiter, Florida. Trust me.

We can do better. At Silverchair, our Cortex taxonomy contains a list of clinical trials and the accompanying thesaurus includes their acronyms, so when our tagging and retrieval systems encounter those concepts, we’re able to separate them from their normal English language counterparts and tag them correctly.  Yet another benefit of an automated tagging system supported by a robust and up-to-date medical thesaurus. It understands medical information and the health care professionals who depend on it so that we can give them results, not guesses.

I’m sure you’ve experienced Google Suggest in action: as you type into the search box, Google offers suggestions that change dynamically as you type each letter of your query. The suggestions are sometimes spookily on target but many times flat-out inappropriate.

You’ll find many examples of Google Suggest inappropriateness documented online. But the Slate contest took a different angle, challenging readers to explore the different suggestions made in response to a “less intelligent” Google query versus a “more intelligent” one.” The winners are in:

The winning entry … follows Google Suggest into the realm of moral inquiry. It doesn’t neatly divide into “less intelligent” and “more intelligent,” but it’s the best example I received of how one word can make all the difference. [Is it wrong to…] involves love affairs, God, and younger men. [Is it ethical to…] puts us on the plane of animal research, privacy concerns, and cooking the books.

Putting aside the entertainment and cultural value of Google Suggest, how does it work? Like most things Google, those details are vague:

Our algorithms use a wide range of information to predict the queries users are most likely to want to see. For example, Google Suggest uses data about the overall popularity of various searches to help rank the refinements it offers.

On the Silverchair SCM web content management platform, we also use autosuggest to aid searchers. But there’s no mystery about how it works. Once three characters have been typed into the search box, our search engine starts matching the query against the index of semantic tags that have been applied to that specific content set from our Cortex biomedical taxonomy. Suggestions become more precise with each query character typed, and because we are matching against only those semantic tags applied to the content, the search results set is always targeted and relevant. Our search engine also checks each query against a database of taxonomy equivalents—synonyms, abbreviations, jargon—to normalize the search query and expand it to cover all possible matches.

Silverchair search autosuggest

Because the content in the products Silverchair builds is tagged so granularly, we can often suggest a more precise term than many searchers start with. Our goals for autosuggest are to save time for users, speed them to the most relevant possible query, and return the most precise answer to their question. Try autosuggest for yourself on the AccessSurgery site we built for McGraw-Hill. Search autosuggest is just one of the many ways a robust taxonomy can promote content discovery.

(I wonder how many of you are running off to play with Google Suggest—a perfect Friday afternoon time-waster…)

Google Suggest

Image via Wikipedia

The NIH has rolled out their new RePORT (Research Portfolio Online Reporting Tool) web site for information on funding, grants, and NIH research. As someone who works on government grants and contracts, I’m happy with this new level of transparency and clarity as to what topics (and who!) is being funded. It is a big upgrade from the incumbent system, which was hard to navigate and understand.

The most useful area of the site to me is the categorical spending section. It really gives you an idea of NIH’s funding priorities—it offers over 200 categories of funding.

However, it still has ample room for improvement. Currently it is an alphabetical list that contains items that are hard to compare. Here are some example categories that are not equivalent in scope:

• Allergic Rhinitis (Hay Fever)
• American Indians / Alaska Natives
• Burden of Illness
• Cancer
• Cardiovascular
• Clinical Trials
• Conditions Affecting Unborn Children
• Gene Therapy
• Gene Therapy Clinical Trials
• Genetic Testing
• Genetics

Some are very specific (hay fever), some are broad (cancer), some are ambiguous (cardiovascular), some take a completely different approach than the dominant disease/condition approach (American Indians/Alaska Natives), and some seem to be repetitive.

With a bit of work, this information could be turned from its current flat list expression into a multilevel taxonomy that allows users to slice it up in the ways that appeal to them (conditions or target populations, for example). Silverchair does this for the Agency for Healthcare Research and Quality on their PSNet patient safety clearinghouse. A small amount of classification work can go a long way in creating valuable new features—NIH has proven that with their RePORT upgrade, but I’d like to see them go farther.

I’d be happy to help out with the NIH site, but I’m not sure what category that would be funded under…

My Silverchair colleagues who attended (Elizabeth Willingham and Pam Harley) agreed with the sentiment, expressed by virtually every participant we spoke with, that this novel conference was fun, energizing, and useful. No sneaking off to skip sessions—one’s active engagement was required throughout. The opportunity to work through a series of ideas by means of small group discussions among fellow STM experts was quite effective, and while some of the resulting product strategies may be overly ambitious, ideas developed by each of the teams are worthy of serious evaluation and development in our real-world organizations.

Unless we all fell victims to mass hypnosis (or acute groupthink), it was instructive to observe several definitive trends that emerged contemporaneously from the various teams. First off, taxonomy strategies featured heavily in many of the proposed solutions, either as a key enabling infrastructure or an end-product (or both, as in the case of our faux-publisher, Van der Prophett NV). Second, the development of robust social communication tools (and defined well beyond Web 2.0 buzzwords) were essential to most of the groups. “Content-enabled Social Media Network” (I think that coinage belongs to Mike Beveridge at AACR, who was on our team) seems to capture the concepts best, and each team had a dimension of this notion in its offering.

It struck me as fascinating that there was such a consensus of vision, and a set of conceptually compelling products, very little of which is likely to be executed (well, and seriously) anytime soon by the major publishing organizations. It’s more probable that startups from outside the traditional industry will make some of these concepts into successful, market-redirecting services. Why is the deep thinking being done in the absence of “low technology/high authority” (thanks Kent Anderson, NEJM) executives who could marshall the necessary resources (and make the attendant difficult structural decisions) to execute these plans?

• Medical Terminology: A Short Course
• Quick Medical Terminology
• Medical Terminology: The Basics
• Medical Terminology Simplified
• Medical Terminology for Dummies

Anyone else sensing a theme? Considering that the Unified Medical Language System (UMLS) has more than 2,000,000 terms, I’m not surprised simplicity is in demand.

For publishers, taking measures to make medical terminology (and life) easier for health care professionals has a direct payback [re-read list, above]. Consider it mission critical!

Map created by Robert Lewis Stevenson in Treasure Island (image from Wikipedia)

When good authors write, they choose the terminology they want to describe the topics they are addressing and use that terminology consistently throughout the text. This, of course, is good for readers in terms of internal clarity and consistency.

But this authoring strategy is distinctly disadvantageous to discovery (search) and integration (linking) in modern web applications. Why? Because every time an author makes a terminology choice they EXCLUDE other equivalent options. These excluded options could include terminology that other authors have chosen or are the preferred terminology of their potential readers. I’m not blaming the authors, of course—their writing would be nonsense if they included all equivalent choices in their text.

So how do you deal with these missing options? Thesauri to the rescue! Every web search, linking, and categorization system should employ some form of thesaurus behind the scenes. And in specialized areas like medicine, you’ll need a specialized thesaurus rather than a basic broad one. This thesaurus should include synonyms, acronyms, abbreviations, and jargon, and should be based on real-world authoring and searching behavior (rather than academic nit-picking).

In essence, a thesaurus expands the author’s original text into much richer data for automated searching and linking algorithms. Let’s look at an example:

ACTUAL TEXT: Chemoreceptors in the carotid bodies and medulla are activated by hypoxemia, acute hypercapnia, and acidemia.

EXPANDED TEXT¹: Chemoreceptors in the carotid bodies (carotid glomus, glomera carotica, glomus caroticum, glomus caroticus) and medulla (adrenal medulla, medulla oblongata, glandula suprarenalis, suprarenal medulla, adm, metepencephalon, medullary, myelencephalon) are activated by hypoxemia (hypoxaemia, arterial hypoxemia), acute hypercapnia (blood carbon dioxide increased, blood co2 increased, carbon dioxide retention, carbon dioxide, increased level, hypercapnemia, hypercapnaemia, hypercarbia, pco2 increased on arterial blood gas, elevated pco2, retention carbon dioxide, serum carbon dioxide increased), and acidemia (acidaemia).

The actual text as written was 15 words. The expanded text was 71 words, or approximately 4.7 times longer. Humans read the first sentence, and machines read the second.

No matter how a user searches for this text (“hypercapnia” vs. “hypercarbia,” for example) they will match this text with a good thesaurus.

Are readers finding what they want on your web site this easily?

¹Thesaurus Source: Silverchair’s Cortex taxonomy—with references to SNOMED, Read Codes, MeSH, Digital Anatomist, NCI Thesaurus, NeuroNames Brain Hierarchy, MedDRA, WHO Adverse Reaction Terminology, OMIM, DXplain, CRISP Thesaurus, Clinical Problem Statements, and COSTART.

The Tower of Babel by Pieter Brueghel the Elder (1563) (image from Wikipedia)

Anyone in the mix or even periphery of health care reform—and certainly consumers—can’t help but be overwhelmed by the terminology involved in the discussion, whether it’s the language of the politicians and the various competing stakeholders and interest groups or the filtering layer the media applies when trying to interpret what’s happening politically. In a July 22nd interview on The Diane Rehm Show, NPR health policy correspondent Julie Rovner, who authored the critically acclaimed reference book Health Care Policy and Politics A to Z, spoke to the complexity of the nitty-gritty of health care reform and the public’s understandable confusion and difficulty making connections between the political arguments and the substance of the reform. She indicated that the problem existed during the Clinton administration as well, and one 2002 definition of health care reform defines it only with regard to that political effort: “The ‘Clinton Plan’, proposed by President(s) Bill & Hillary Clinton in 1993, to revamp and address inequities in US health care.” (Sidebar: Interesting that this definition offers the possibility that it was proposed by not just one President Clinton, but two!)

Anybody even remotely aware of current events knows that the definition of health care reform has been updated to refer to the effort under way by the Obama administration to “reform our system by expanding coverage, improving quality, lowering costs, honoring patient choice and holding insurance companies accountable.” If only it could really be as simple as Obama’s mantra. Instead, politicians and stakeholders define the terms in their own way and then disagree over the definitions and hierarchical relationships of pretty much every issue. See, for example, a sophisticated taxonomic approach to the definition of “socialized medicine” by Princeton economist Uwe E. Reinhardt.

As Julie Rovner said in her July 22nd radio interview, “It’s all about language and semantics …”

So what would happen if there were more clarity around the language used in discussions of health care reform? Will we ever see a taxonomy of health care reform? Unlikely in this political effort, but I like the sensible argument and impassioned vision for the use of taxonomies in all arenas where there is both too much data and a need for more data made by Lucy Bernholz, founder and president of Blueprint Research & Design, Inc., a strategy consulting firm for philanthropic institutions and individuals. I disagree with her when she says taxonomy is “the wonkiest buzzword ever”—wonkish for sure! Buzzword: I don’t think so. Taxonomy is here to stay. But she’s spot on with everything else, including the tie-in to health care reform: “And we may one day be able to see how much money from philanthropy, political contributions, corporate sponsors, and the public sector is going into research on various health care reform proposals—but not without functioning taxonomies that can be connected across data centers.”

Anyone trying to make the case for taxonomizing data in STM publishing organizations should read Bernholz’s whole post. Unlike the Babel of health care reform, her message is crystal clear: “All of it is about sense-making. And all of it requires taxonomies.” If taxonomies are critical to the giving away of money, think what they mean to the making of money.