Phenoscape is proud to announce the immediate availabiity of Phenex 1.0, the first public release of our platform-independent desktop application for annotating character-by-taxon matrices with ontology terms.  Phenex has been in development and available in beta form for over a year, while we used it to curate more than 50 publications for inclusion in the Phenoscape Knowledgebase.

The 1.0 release of Phenex includes features allowing it to be used for work with any taxonomic group. It can be configured to load any user-specified OBO ontologies, and users can specify the sets of ontology terms available within each type of entry field.  Also, this release uses the latest version of the NeXML schema for its file format, storing ontology annotations in an embedded RDFa-compliant format.

We look forward to working with anyone interested in making use of Phenex with their own data. Phenex is open source software, released under the MIT license.

LAVA: Laboratory for Advanced Visualization in Archaeology
The LAVA project centers around web based tools for investigating archaeological data sets.  In its initial phase (2009) I designed a 3D tool which would visualize spatial data within an archaeological excavation.  The design of the tool is based upon the Munsel colour wheel and uses multiple regions, arranges spatially, to visualise archaeological databases.  The tool is currently being developed with Dr. Stefan Sinclair at McMaster University.  My overall aim with LAVA is to create a suit of tools that will work with existing web-based collections and which will allow users to create new collections of born-digital materials.

LAVA Tool for archaeological visualisations

This is the research blog for Dr. Megan Meredith-Lobay.  You can find information about my research interests and ongoing projects, as well as publications, teaching-related content, and links to other information about Scottish archaeology, digital humanities, and archaeological informatics.

If you recognize the stapler, you know the movie.  “Office Space”—Possibly the best movie ever made. Ever worked for a boss like Lumbergh? Here’s a smart bit of dialog for your Friday.

Peter Gibbons: I work in a small cubicle. I uh, I don’t like my job, and, uh, I don’t think I’m gonna go anymore.

Joanna: You’re just not gonna go?

Peter Gibbons: Yeah.

Joanna: Won’t you get fired?

Peter Gibbons: I don’t know, but I really don’t like it, and, uh, I’m not gonna go.

Joanna: So you’re gonna quit?

Peter Gibbons: Nuh-uh. Not really. Uh… I’m just gonna stop going.

Joanna: When did you decide all that?

Peter Gibbons: About an hour ago.

Joanna: Oh, really? About an hour ago… so you’re gonna get another job?

Peter Gibbons: I don’t think I’d like another job.

Joanna: Well, what are you going to do about money and bills and…

Peter Gibbons: You know, I’ve never really liked paying bills. I don’t think I’m gonna do that, either.

One more tidbit:

Peter Gibbons: Well, I generally come in at least fifteen minutes late, ah, I use the side door – that way

Lumbergh can’t see me, heh heh – and, uh, after that I just sorta space out for about an hour.

Bob Porter: Da-uh? Space out?

Peter Gibbons: Yeah, I just stare at my desk; but it looks like I’m working. I do that for probably another hour after lunch, too. I’d say in a given week I probably only do about fifteen minutes of real, actual, work.

I like to think of Peter as my alter-ego.

When I’m playing me in a parallel universe, I’m reading about a surfer dude cum freelance physicist, Garrett Lisi. Even the title of his theory, “An exceptionally simple theory of everything,” seems oxymoronic. He surfs Hawaii and does physics things—physicates—in Tahoe. (I just invented that word; it’s the verb form of doing physics, physicates.)

Ignoring that I can’t surf, and know very little physics, I like to think that Garrett and I have a lot in common. I already know Peter Gibbons and I do. So, where does this take us?

It may be apparent that I look at healthcare IT and reform from a different perspective than most; I’m the guy who doesn’t mind yelling ‘fire’ in a crowded theater. The guy who will never be invited to speak at the AMA convention unless they need a heretic to burn for the evening entertainment. I can live with that.

Like Garrett, I too see an exceptionally simple theory in everything, especially when it comes to improving the business of healthcare. It’s not rocket surgery, but then, it was never meant to be–before someone writes, I know it should be scientists.  It’s process, change management, leadership and foresight.

Sometimes I like to look at the problem from a different dementia—Word didn’t have a problem with my usage of that word.  I look at healthcare and ask myself three questions:

1. How did they ever get so siloed?

2. How did they ever get so so big without a cohesive IT strategy?

3. Is it possible to reverse both of those AND improve the business.

I am convinced the answer is yes.

Here are a few more thoughts just to Emerilize the discussion—to kick it up a notch.  Not only do I think the national EHR market is ripe for the taking by a big three like Microsoft, Google, and Oracle, I’ll go so far to suggest that when the dust settles in 5-7 years, the N-HIN, the National Health Information Network, will be a regulated combination of a handful of those firms.

As for the other firms offering or planning to offer PHRs, permit me to suggest the following scenario.  Let’s say I am in charge of Google’s so far somewhat nonexistent healthcare line of business.  One of my goals would be to have more users of my PHR than any other firm.

Why does this model make sense?  Two ways, both of which come from the cable/telco business model.  Rule number one, content is king.  In cable, it is shows like HBO and Discovery.  In healthcare it is data; patient data, effectiveness data, disease data.

Reason number two, the cable/telco model values the businesses based on the number of assets.  What are the assets?  Subscribers.  You and me.  Each body adds somewhere between five and ten thousand dollars to the valuation model of a Comcast or Verizon.  Downstream, some valuation will be placed on each PHR subscriber.

So, back to the example of me running Google’s healthcare offering—if you don’t like Google as an example, insert your favorite firm.  If I’m Google, am I troubled by the fact that other firms are building their own solutions?  No, and here’s why.  The difficult part of the business model is adding users, adding subscribers.  Why not let a bunch of firms do the business development work for me, do the dirty work to get the users, and then just devour those firms?  Once I own them, I convert them to my platform.  Do I then get some ‘ownership’ or right to use the data?  That would certainly be the business goal.

One million users valued at five thousand dollars adds five billion in valuation.  Ten million adds fifty billion.  Ten billion is about 2.5% of the US market.  Do I stop at the border?  Of course not.

By the way, while all this is going on, Google, MS, or whoever will also be creating standards and be building or buying up EHR firms.

eResource Annotations could help with

• making better choices: which resource is best?
• which is available?
• reduce curation
• help with service discovery

Approach: link bioinformatics resources using semantic descriptors generated from text mining….head terms for services can be used to assign services to types..e.g. applications, data sources etc.

Typical researcher spends 12.4 hours a week searching for information. Why not use Google? ‘Cause Google is not semantic.

Go PubMed – Filter PubMed contents against all the terms in the Gene Ontology. If you use simple categorisation for information retrieval potentially increase search burden due to compartmentalisation. However works the other way round too…useful filtering.

Showing some examples of faceted browsing of PubMed content and systematic drilldown into search results. Not easy to blog, but literature exploration in this way is always fascinating. Examples include the analysis of research trends, networks of colaborators etc..new tool in Go PubMed also allows the discovery of indirect links or inferred links.

Have developed a similar system for the web: Go Web (works on the top yahoo search results).

Remarks on Ontology Generation: have developed a plugin for OBO Edit…search for term and plugin makes suggestions for terms that might be included in new ontologies. Points out terms in existing ontologies. Also helps with the generation of definitions for terms…wow this is extremely useful in SO many ways….

Now let’s talk about drugs and targets….

Try and mine for gene mentions in text…find a gene term and then use context to decide what it is we are talking about. Once gene has found look for statistically significant co-occurences. The results have been made available in GoGene. Again can do bibliometric trend analysis – genes are ranked by community interest.

From drugs to genes..what is the link between a gene and a drug using context profiles: what are the disease terms related to a given drug…then to genes.

Gotta stop blogging…enjoying this talk far too much…….

My take on this is probably far-afield from the mainstream. I think the Dark-Side, firms like Google, Microsoft, and Oracle look at the confusion and lack of planning in terms of what the final EHR/PHR platform will look like and they simply drool. With hundreds of EHR vendors and RHIOs and RECs and standards groups all operating independently, all aiming at an undefined target, which group is best positioned to solve this platform problem, the Dark Side, or the Blind Side?

The Dark Side’s plans are underway and visible through their PHRs. Like the tip of an iceberg, I bet that most of what they are doing to own this space is presently unseen. Practice Fusion, if their product attracts enough customers will be devoured, or they will be ignored. RECs, RHIOs, Meaningful Use, Certification, a lack of standards, and no network are large red flags from the government saying “we don’t know where we’re going, but we’re making real good time.”

Here’s a reply I drafted at the request of Brian Ahier to his blog, http://radar.oreilly.com/2009/11/getting-personal-with-health-t.html

The Dark Side knows exactly where they’re going. They don’t need a network; they have one. The Internet. There are those who argue HIPAA and security. HIPAA and security can be more readily handled on a network that’s been up and running for twenty years and was built by the military than they would be under anything developed off the cuff under Washington’s leadership.

Now for the Deathstar issue–ownership of the data. The question is are ownership and possession one in the same? I bet they will not be. I’d also bet that five years from now somehow that Dark Side will have at least access to it. I can’t prove any of this, but I’d love to sit in on the strategic planning committees of the Dark Side. I bet some or all of this is underway. The Blind Side may be blind-sided.

(Notes frm the presentation as it happens)

Knowledge is not uni or bivariate, but we think of it as such: this leads to information loss.

Naming things: showing example of a trivial name, an IUPAC systematic name and an InChI and points out that these have different information content.

Points out scaling problem: drug discovery is multivariate and happens in a space of approx 10¹⁶ molecules (all molecules that are feasible and thought to be drug-like). Information loss occurs as you traverse this space backwards and forwards.

Now talks about molecular information in RDF: http://rdf.openmolecules.net for the provision of derefernceable URIs for molecules….and plugging the Chemistry Development Kit (CDK) as a means for cnverting between multiple representations of a molecule. Now moves on to Bioclipse as an integrating tool that allows chemical data transformations and the tracking of vwhy these transformations occur (version-controllable scripts to drive Bioclipse).

RDF extension to bioclipse: local RDF storage, read/write RDF, run SPARQL queries and extract RDF from XHTTML/RDFa.

Now shows an example of the expression of the CDK data model using ontologies but no details. Brief mention of his recent descriptor ontology.

(Again, these are notes as the talk happens)

This has to do with the Siemens Project Theseus Medico – Semantic Medical Image Understanding (towards flexible and scalable access to medical images)

Different images from many different sources: e.g. X-ray, MRI etc…use this and combine with treatment plans, patient data etc and integrate with external knowledge sources.

Example Clinical Query:” Show me theCT scans and records of patiens with a Lymph Node enlargement in the neck area” – at the moment query over several disjoint systems is required

Current Motivation: generic and flexible understanding of images is missing
Final Goal: Enhance medical image annotations by integrating clinical data with images
This talk: introduce a formal classification system for patients (ontological model)

Used Knowledge Sources:

• Ann-Arbor Staging System – particularly suitable for lymphoma patients
• RadLex
• Foundational Model of Anatomy
• Semantic Image Annotation

Requirements of the Ontological Model

• Capture the rationale of the Ann Arbor Staging system
• Integrate external ontologies
• Ontology must describe the patient record

Now showing an example axiomatisation for the counting and location of lymphatic occurences and discussses problems relating to extending existing ontologies….

Now talking about annotating patient records: typical problems are abbreviations, clinical codes, fragments of sentences etc…difficult for NLP people to deal with….

Now showing detailed patient example where application of their classification system led to reclassification of patient in terms of staging system.

(These are live-blogging notes from Alan’s keynote…so don’t expect any coherent text….use them as bullt points to follow the gist of the argument.)

The Science Commons:

• a project of the Creative Commons
• 6 people
• CC specializes CC to science
• information discovery and re-use
• establish legal clarity around data sharing and encourage automated attribution and provenance

Semantic Web for Biologist because it maximizes value o scientific work by removing repeat experimentation.

ImmPort Semantic Integration Feasibility Project

• Immport is an immunology database and analysis portal
• Goals:metaanalysis
• Question: how can ontology help data integration for data from many sources

Using semantics to help integrate sequence features of HLA with disorders
Challenges:

• Curation of sequence features
• Linking to disorders
• Associating allele sequences with peptide structures with nomenclature with secondary structure with human phenotype etc etc etc…

Talks about elements of representation

• pdb structures translated into ontology-bases respresentations
• canonical MHC molecule instances constructed from IMGT
• relate each residue in pdb to the canonical residue if exists
• use existing ontologies
• contact points between peptide and other chains computed using JMOL following IMGT. Represented as relation between residue instances.
• Structural features have fiat parts

Connecting Allele Names to Disease Names

• use papers as join factors: papers mention both disease and allele – noisy
• use regex and rewrites applied to titles and abstracts to fish out links between diseases and alleles

Correspondence of molecules with allele structures is difficult.

• use blast to fiind closest allele match between pdb and allele sequence
• every pdb and allele residue has URI
• relate matching molecules
• relate each allele residue to the canonical allele
• annotate various residoes with various coordinate systems

This creates massive map that can be navigated and queried. Example queries:

• What autoimmune diseases can de indexed against a given allele?
• What are the variant residues at a position?
• Classification of amino acids
• Show alleles perturned at contacts of 1AGB

Summary of Progress to Date:
Elements of Approach in Place: Structure, Variation, transfer of annotation via alignment, information extraction from literature etc…

Nuts and Bolts:

• Primary source
• Local copy of souce
• Scripts transforms to RDF
• Exports RDF Bundles
• Get selected RDF Bundles and load into triple store

• Parsers generate in memory structures (python, java)
• Template files are instructions to fomat these into owl
• Modeling is iteratively refined by editiing templates
• RDF loaded into Neurocommons, some amount of reasoning

RDFHerd package management for data

neurocommons.org/bundles

Can we reduce the burden of data integration?

• Too many people are doing data integration – wasting effort
• Use web as platform
• Too many ontologies…here’s the social pressure again

Challenges

• have lawyers bless every bit of data integration
• reasoning over triple stores
• SPARQL over HTTP
• Understand and exploit ontology and reasoning
• Grow a software ecosystem like Firefox

I tweeted the following two tweets on 02009.11.16…

• Jaguar supercomputer hits 1.759 petaflops http://bit.ly/3eInDv

  A supercomputer known as Jaguar has finally bested IBM’s Roadrunner supercomputer in the biannual TOP500 list, but researchers have already begun looking into exascale supercomputers that consist of 100 million cores and run 1,000 times faster than Jaguar.

• Yottabytes of data via PopSci.com “National Security Agency’s
  Surveillance Data Could Fill Two States by 2015″ http://ow.ly/CRGE

  The NSA estimates it will have enough data by 2015 to fill a million datacenters spread across the equivalent combined area of Delaware and Rhode Island. The NSA wants to store yottabytes of data, and one yottabyte comes to 1,000,000,000,000,000 GB.

Researchers at Sweden’s Norrköping Visualization Center  (link in Swedish) have developed a “virtual autopsy” experience using a multitouch surface computer and various patient images.  Using CT and MR imagery data procured posthumously, the program recreates the body in three-dimensional space with multiple layers than can be applied or removed with the swipe of a hand.

The utility of this non-invasive approach is, according to the team, multifactorial.  First, the perceivable educational benefits for teaching anatomy are obvious (although, can you really replace the cadaveric experience?).  Additionally, the system could be used by forensic pathologists.  They highlight that the whole process can be completed in minutes as opposed to the hour for a traditional autopsy and also that 3-D imagery would be more understandable and less “gruesome” for juries to see.

Watch the video below for more information and to watch how slick it is in action.

As of now, there is only one working prototype, but the group is planning on starting production in the near future.

It seems very intuitive and cool for sure; but as far as practicality is concerned, we all know there’s no substitute for the real thing…  Now, if only they could combine this with smell-o-vision for the real experience!

There are three or four basic rules those of us who write should use, unfortunately I do not know them. For those of my ramblings that seem long, it’s only because I have not had the time that is required to make them shorter. This I fear is one of those. I write to find out what I am thinking; if and why you read remains uncertain. All of us learned to write in elementary school—most then moved on to greater things—I remained trapped with the notion that being able to spell words more than one way may one day be regarded as a talent.

I found it is not a bad idea to get in the habit of writing down my thoughts–it saves me from having to verbally rake others with them. Some of my thoughts require little or no thought from those who read them, for the very simple reason, they made no such equivalent demand upon me when I wrote them. My goal in writing, other than to entertain myself is to create a somewhat humorous context to facilitate thinking. As one who enjoys the written word I understand that no urge is equal to the urge to edit someone else’s thoughts, as several of you have done with mine. It sometimes feels as though the best I can hope for in formulating a series of ideas about a topic is to borrow well from experts, those people whose have already made all the mistakes that can be made in a very narrow field. The need to write and share my opinions requires constantly trying to prove my opinion to an audience who may not be friendly, which is why silence may be better–silence is often the most difficult opinion to refute. Unfortunately, trapped inside every consultant is the urge to write; sometimes that urge is best left trapped inside.

Much of the project management office consulting I do comes from having listened respectfully to very good advice, and then going away and doing the exact opposite. In general there appears to be a lack of strategy concerning EHR, making it like trying to jump a chasm in two leaps—it can’t be done. Without knowing what outcome you want to achieve, any path will take you there. This isn’t because the people in charge don’t see the solution—it is because most people have no familiarity with the scope and magnitude of the problem.

Large information technology projects like EHR are often dominated by two types of people: those who understand what they do not manage, and those who manage what they do not understand. If we are being honest, the end product of project management is making it more and more difficult for people to work effectively. It’s sort of like why we don’t allow horses do medical procedures—it would probably take way too much training. I think that many EHR projects are ineffective because those leading the charge attempt to rely upon reason for answers, thinking, “If we know one then we know two since one and one are two”.

To make the EHR efforts more effective, I humbly suggest we need to learn much more about what constitutes the “and”.

EHR technology makes it easier to do a lot of things, but some of the things it makes easier ought not to be done. The only reason to have an EHR system is to to solve specific business problems within the organization. Getting EHR to do want you want it to is ninety percent mental–the other fifty percent involves voodoo. If you don’t make mistakes during the process, you’re not working hard enough on the problem—and that’s a big mistake. Need I say more? Any complex system that works almost always comes from a simple system that works. The corollary is also true, if the current paper and manual records system didn’t deliver best practices, how can the more evolved ones be expected yield best practices?  EHR alone won’t make you better, it will just make you automated.

Success is a much more likely outcome when one builds upon success. Most EHRs have enough technology to handle anything that comes up, unless a provider forgets that the EHR is just a tool.  It took human error to create the problems we have with our health records processing.  Why then are we so quick to think that technology will fix them?

Misery not only loves company, it insists on it. That is why having a competent project management office (PMO) plays such a dominant role in the success or failure of the EHR. When the circumstances turn extraordinary, as they are in today’s economy, extraordinary measures are required. Plan, take time to deliberate, and when the time for action has arrived, stop thinking and get after it. The important thing to remember in deciding what action to take is not to search for new data points but to discover new ways to think about the ones you have. The direction of am EHR strategy may have limits, but perhaps it says more about the limits of imagination and common sense instead of the limits of what is possible. And remember this basic rule, when assessing common sense and imagination, always round up.

I’m not always disgruntled about that which I write, but I’m often far from gruntled. As graduate student I aspired to a stable job, I craved factual certainty and the respect of my peers—so I became a consultant. I soon learned that this is like wanting to be a vegetarian so you can work with animals. The only job I was fit for was consulting. This notion rested on my belief that I was not suited to work nine to five, and that consulting wasn’t quite like working. One of the nice things about consulting is that putting forth absurd ideas is not always a handicap. The good news is that consultants, when addressing things outside of their expertise are just as dumb as the next guy. I’ve always believed that being honest with my clients is the best policy—does that mean that if I chose to be dishonest I would be using second best policy? Oscar Wilde said, “If you want to tell people the truth, make them laugh, otherwise they’ll kill you.” That’s my hope with these little musings. Remember, we’re all in this alone.

The preceding was a pilfering of quotations.

From the Colloquia Archive at IUPUI, School of Informatics:

Creating and organizing a movie is a complex process. As Marcie Begleiter demonstrates, storyboarding is an essential process to the visualization of media and new media. She works at getting the audience more involved than I expected from a lecturer. She has fine art, liberal arts, film, storyboarding, and communication background. She also talks a bit about how she had her fortunes turn from painting sets to storyboarding and film training early in her career. Opportunity met need with her basic skill set.

Her experience with multidiscipline helped her many times in her career, including her current job in developing an art design school curriculum. Its simple, the story telling that comes from boards has its roots in drawing, spoken word, and songs. She delves into forcing students to describe a movie with no sound and describe what you are seeing in filmic language. She also points out that light travels faster than sound, and we are in part, firstly driven by the image.

Her slides start with the history of storyboards. It was hard to discover because most studios track the costs of a movie and not the art direction of the film from storyboards. They even begin to show the beginnings of mise-en-scene of how the director is going to create the feel of the movie. These early storyboards also served as early location scouting to find the proper places to shoot. Some do quick sketches of highlight, shadow, and mid-tone to show the shots of the film. Others draw more of a “visual metaphor.” The pictures are even less detailed in some but are showing what is to be emphasized in the frame with the objects scaled. The scene in Mildred Pierce shows how the camera is directing the focus in the frame from the ocean to Mildred. It is also a nonverbal queue that happens so fast in transition but greatly aids in storytelling. This is something that can be pre-planned so the direction can be maintained and not lost when in actual production.

She also shows more of a keyframe versus a storyboard, a very detailed shot with a lot of depth and feeling versus a thumbnail sketch. She explains how the eyes are led through the frame. She describes also, Alfred Hitchcock, and the simplicity that show a more logistical point of view. The frames are largely devoid of small details and show more movement in frame which was key to Hitchcock’s success as the characters act in the frames but the viewers also feel they are in the movie themselves. She points out a wider shot of a man hanging at the Statue of Liberty and it shows that attention of negative space; it sucks in the viewers to also feeling like they are less of a character and more of an actor. She really helps to show how varied storyboard are because they are a reflection of the director, less of a 2D representation of frames and more of a capturing of 4D ideas.

This past summer, various news sources reported on Cell Scope, the portable Cell Phone Microscope.  It was a well-developed and supported, sophisticated device that at least seemed like it could be very helpful in underserved areas for such applications as searching peripheral smears for malarial parasites. 

Well, if you just cannot wait for Cell Scope to come to market, or if you are on more of a shoe-string budget, researchers at UCLA have fabricated a cell phone microscope of their own, of the DIY variety.

The engineer behind this project, Aydogan Ozcan told the New York Times that the device was created out of $10 worth of off-the-shelf parts (based on the picture, it seems as if they have some pretty amazing shelves) and actually involves no lens!  Instead, the set-up uses the cell phone’s camera sensor to read light scatter created by shining an LED light through a blood sample (I believe, much like routine automated hemocytometry) and the rig then reports the acquired information wirelessly to a computer which is able to interpret the information and report out certain information about the sample from the white cell count to the presence of malarial parasites.

Not surprisingly, this kit set-up is also targeted at indigent and undeserved areas for basic blood interpretation including utilization in the diagnosis of malaria.  Again, no word on price or release date, or more importantly for the DIY-er: instructions; I’m still waiting…

[New York Times via Gizmodo]

Data analysis is tremendously useful in helping to make sense out of the mountains of data that everyone accumulates everyday.  I use data analysis on a regular basis in my own life and professional experiences.  One particularly challenging project recently has been in managing my contract work.  I perform work for a variety of companies, all of whom pay different amounts and at different times.  Additionally, I have to make sure to keep track of my expenses incurred during the completion of the work that I do.  To keep track of all of these different metrics, I use a database system that tracks companies for whom I work, jobs I complete for those companies, and payments I receive for those jobs, and expenses I incur.  The process falls into four rough steps:

1) Find or Design a Useful Way to Organize Your Data

I use an Access database that contains information about my work that I designed myself.  Three are three tables: one containing data about the companies for which I work, one containing data about the jobs I complete for those companies (and incurred expenses), and one containing data about the payments I (hopefully!) receive from those companies.  In the ‘jobs’ table, for example, each tuple (or row, or record) contains information about a specific job: where it is, when it must be done, and how much I am paid for it.  This is linked to the company for which I am doing the work, and eventually to a payment record, using the relational database model.

2) Collect Quality Data Using The Tool

This is a challenging step for me, as I work entirely for myself and do not have a support staff.  I have to make sure that I collect on a regular basis the data for my work.  In other words, when I get a new assignment, I have to make sure that I record the details about that assignment.  When I get paid, that needs to go in as well.  This can be challenging because I don’t have the time later to go back and organize the data, so I have to make sure that the data is entered in a “clean,” consistent format each and every time.  Without this, my data analysis (the next step) is difficult or impossible.

3) Analyze the Collected Data

Once the data is collected, I try to analyze the data to determine what is happening and what I need to do.  This is another challenging step, because I am so busy with finding and completing the work first, and then managing the workflow using my data collection tools.  This third step often gets lost in the shuffle.  However, one advantage to using a tool like a relational database such as Access is that I can generate stock “reports” that give me useful information, and I do not have to redesign them every time.  This gives me the ability to look at profit and loss, or productivity over a specific period, relatively quickly.

4) Take Action in Reliance Upon the Analyzed Data

Data is great, but it is ultimately useless in the absence of action taken in reliance upon the business intelligence I have derived from my data analysis.  I learned this last year when I realized that I focused on obtaining large projects that often required a great deal of time and travel.  When I looked closely, I realized that seeking out this work precluded the “low-hanging fruit” of assignments located near me, or those that would take far less time to complete.  I realized that I was making less money doing the obvious thing (taking high-value assignments) than I would have if I had done the opposite.

As you can see, these four steps are critical in developing intelligence about what works and what doesn’t.  It is critical to find and develop an organized data collection system and structure; to collect data and ensure that it is clean and consistent (in form and format); to analyze that collected data looking for patterns; and, finally, to take action on that analysis.

The way most doctors and health care professionals do their jobs has hardly changed over the past thirty to forty years. Contrast this with the enormous changes in, say, transport, manufacturing and telecommunications!

But hang on to your stethoscopes! Despite the fact that some doctors still have their heads buried firmly in the sand, the winds of change are blowing and most doctors are now using electronic communication technologies, if not enthusiastically, then at least regularly. The combination of technological change, the demands of business and the rise of consumerism are causing radical changes in the way healthcare is practiced around the world. Health Informatics experts are poised to revolutionize health practices by implementing the enormous changes needed in the health system, that have already occurred in other industries. These professionals typically have backgrounds in either healthcare, such as nurses and doctors, or information technology, and then receive cross-training so as to be able to work across both areas in the newly emerging electronic health systems of today and tomorrow.

The changes in healthcare will be the 21st century’s equivalent of the public health initiatives, job, of sanitation and nutrition which revolutionized health care in the twentieth,, job, job, century. Integration of online technologies will see doctors and patients working together on electronic health records with patients having much more say in their treatments. The, job,, job, development of widely available broadband networks and, job, video mail will bring electronic health into everyone’s home. Patients and doctors will work collaboratively, job, on the internet as parters with the agreed mutual objective of health improvement.

Look at how fast the average adolescent can send messages on their phone – gone are the days when a telephone was just an audio device. The way we interact with communication systems is radically changing the way we behave and think in ways that are impossible to predict. And the computer literate children of today – the millenials and succeeding generations – will drive these, job, changes. How many doctors want to interact with patients using instant messaging? Not many today, but the doctors of the millennial generation will probably think nothing of this approach. And these sorts of systems will be developed by experts who have been trained in health informatics, and who understand how to apply information technologies of all sorts to change and improve the way that we deliver patient care.

Knowledge has never been as important – and as accessible – as it is today.

Technology, and in particular, Internet technology, is transforming the academic medical landscape. A large number of institutions are moving to digital-only radiography and full electronic medical records. I no longer write any notes on paper – all my clinical work is electronically recorded. Residents now come to rounds armed with a vast array of reference information stored in hand-held personal digital assistants. The iPod is now a platform for lectures presented either as “podcasts” and “videocasts” and is also used as a mobile x-ray image viewer. Continuing medical education is increasingly available through the Internet. The digital revolution has greatly altered how academic health systems pursue education, research, and clinical care, and this is spreading through the rest of the health system.

The provision of clinical care is changing rapidly as health informatics technologies become increasingly used and accepted, with a move away from episodic care to concentrating on continuity of care, especially for patients with chronic disease who will create the greatest disease burden in the future. Care is gradually moving away from a focus on the service provider to that of the informed patient and from an individual approach to treatment to a team approach. Increasingly, less focus is placed on treating the illness and more is placed on wellness promotion and illness prevention: the model of the”Information Age care” first described by Dr Tom Ferguson MD. To move to this future of information age healthcare, the availability and use of information must be strengthened to facilitate changes in health service delivery, and a much greater focus must be placed on developing and refining the information technology infrastructure, and on training experts in health informatics who can create and develop the electronic clinical environments needed by both patients and doctors.

This is all occurring at a time of difficulty in our economy, but America is known for its capacity to thrive on challenges, and to rapidly change its industrial practices in the face of adversity. The health system needs large numbers of experts in health informatics, and training programs are being rapidly, job, expanded. The University of California Davis Health Informatics graduate program, for instance, has doubled the number of Masters Health Informatics students in one year, and has enrolled 76 new students in a fully online Health Informatics certificate program within the last three months. The Obama Administration is putting billions of dollars into health informatics implementation and training with funds from the American Recovery and Reinvestment Act and increasing numbers of jobs in health informatics are already appearing. The jobs website, CareerBuilder has just marked health informatics as it’s number one emerging industry job opportunity, and is highlighting a number of jobs in areas as diverse as telemedicine, nursing information officers, clinical information technology liaisons, programmers, analysts, data integration experts and health service managers.

So, as healthcare continues to change and become more electronically enabled, watch out for this whole new generation of professionals trained in Health Informatics, and how they will facilitate the changes in healthcare, and eventually contribute to improving the health of all of us.

I like reading the informative stuff at Edge.org. The other day Edge.org had the headline “The Age of the Informavore: A Talk With Frank Schirrmacher.”

Edge.org defined informavore as follows: “The term informavore characterizes an organism that consumes information. It is meant to be a description of human behavior in modern information society, in comparison to omnivore, as a description of humans consuming food.”

For the last few years I’ve written a lot about 21st century Informatics*. In a nutshell, informatics feeds the informavores.

* 21st century Informatics is data/information processing supported by a cyber-infrastructure of high-performance computing systems.

Edge.org::The Age of the Informavore