Applied Bimatics - An Informatics & eHealth Blog

I am a clinician with a passion for informatics. This blog is about my eHealth journey exploring interoperability in Electronic Medical Records (EMR/EHR), Patient Safety, Pharmacovigilance, Data Analytics, Clinical Research and Bioinformatics in a clinical context. Comparing Canadian, Indian and Middle Eastern healthcare systems and services. Join our open facebook group here: https://www.facebook.com/groups/clinical.bioinformaticians/


GIT for doctors (Part 3) - stash, branch, merge, rebase and tag

To continue with our git story: Read the full series on GIT for doctors here

If you think you have made a mistake, you can “stash” the changes. Your file will be returned to the previous state. You have the option of returning to the stash if needed, but this is beyond our scope at present.


Now let us consider another scenario: You have two differential diagnosis for your patient and you want to investigate the patient for both conditions. You may decide to keep two versions of the same case sheet to continue the work up on both differentials. In Git you can create a “branch” for this situation. You can work on branches independently. The main branch or the trunk is called “master” by convention. You can give any name for the other branches.


Embiodea
Embiodea (Photo credit: beapen)
If you decide to consult your colleague, he/she may want to continue working on their copy without changing the “master” file in your possession. So they can work on their “branches” too. Later on if you want to add your differential branch or your colleague’s branch into the master file, you can choose “merge”.

You can create branches of branches. If you want to merge several such branches into the “master”, you have to paste all branches together into a single branch. This is called “rebase”.

If you have to submit a master chart for audit, you can “tag” the chart, so that you can give a name to the current state. “Tagging” for software is generally done when you decide to release a version to the end user.

In the next part, I will discuss how to collaborate with your friends. If you are not on github.com as yet, do register for an account now. If you want to follow someone, let me suggest yours truly: https://github.com/dermatologist

Read the full series on GIT for doctors here

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Psoriasis support : eHealth gaming tools for patient engagement

Psoriasis manum
Psoriasis manum (Photo credit: Wikipedia)

Here is the IFPA  survey to compare 17 different strategies and activities that can be used to advance psoriasis education, advocacy and awareness. Preliminary results of the survey will be presented on World Psoriasis Day and the final results will be announced at the 4th World Psoriasis & Psoriatic Arthritis Conference in Stockholm July 8-11, 2015.

I have listed below some of my random ideas on eHealth tools for patient engagement in psoriasis:


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Resource Description Framework (RDF) and Population Informatics

English: A PICTURE OF A RDF
A PICTURE OF A RDF (Photo credit: Wikipedia)
I have been an RDF fan even before I used it for dermbase. I promptly signed the Yosmite Manifesto and blogged about it last year. After gaining more experience in the regional health information exchange initiative(s), I still feel that RDF is important, but in a different way.

Most federated regional clinical viewers query host databases, convert the results into an intermediary format (mostly xml or HL7), apply filters and then provide a consolidated view in the browser and mobile as html embellished with jQuery. Though this seems not-so-scalable technology, it works remarkably well in a regional context. Federated clinical viewers also attempt to create data warehouses on top of the Clinical Viewer. Such data warehouses have enormous potential in population informatics and RDF could be an ideal framework for this purpose.

RDF is a proven technology that is schema agnostic. However in this context the biggest advantage of RDF is its data-atomic nature that enables each data element to be queried, changed, or deleted independent of any other data element. RDF blank nodes can be used to effectively anonymize the data. From a data analytics perspective representation in the RDF format makes data amenable for “reasoning” to discover new knowledge.

Genomic data analytics has revolutionized pre-clinical research. Growing popularity of Health Information Technology (HIT) and Health Information Exchange (HIE) has not yet resulted in a similar impact on population health. There are some fundamental differences between genomic and clinical data.

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About Me

As a Dermatologist and Informatician my research mainly involves application of bioinformatics techniques and tools in dermatological conditions. However my research interests are varied and I have publications in areas ranging from artificial intelligence, sequence analysis, systems biology, ontology development, microarray analysis, immunology, computational biology and clinical dermatology. I am also interested in eHealth, Health Informatics and Health Policy.

Address

Bell Raj Eapen
Hamilton, ON
Canada