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/


If Ebola Spreads to Canada

While reading the news about the public health agency of Canada taking all possible steps to prevent the spread of Ebola to Canada, with a glass of Ontario wine in my hands, I for a brief moment thought, what if ………

Picture credit DFID @ Flikr (Image altered and text added) - If Ebola spreads to Canada

So let me set the context right. I am not an infectious disease expert, though my post on cutaneous signs of Ebola virus infection got more attention that it deserved. I am not an epidemiologist either to comment authoritatively on what healthmap is doing. To me it is the social media version of what John Snow did two centuries back to identify the epicentre of the cholera outbreak and established epidemiology as a speciality.

So if Ebola spreads to Canada, How do we identify the epicentre and take preventive measures? Turn to healthmaps and see where it originated and take measures to contain? Healthmaps will get that information from Google news and similar services. We have half a dozen major Health Information Exchange (HIE) initiatives in the country and would probably have accurate records of where each case presented with the characteristic symptoms. But we would look up to healthmaps and google since we cannot use HIE data for research!
I am not a health policy expert neither am I an HIE architecture expert. But to me, if we have to realize the benefits of the ever increasing number of HIE initiatives, we have to find a way to use the wealth of the information there for population health. If we get it right, privacy is not even a concern.

HIE, built to abolish silos, paradoxically created larger silos, because of fragmented systems. The utopian population health requires a glue to bring these silos together. We got it wrong the first time, with data-centric HIS that offered little clinical workflow support and were (inadvertently) rejected by doctors. (We always have the doctors to blame as the universal slow technology adopters. BTW India’s mission to Mars discovered that all doctors in the planet originated from Mars!). We are sure to get it wrong again if we don’t change the data-centric HIE models.

HIE should be versatile, structureless and scalable enough to support disparate clinical use cases. The only option that comes to my mind is RDF.

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Apple HealthKit: Health Information Exchange for Apps

Last year, one of my colleagues proposed an add-on for a popular fitness app as a course project. There has been a whopping increase in the number of health and fitness apps, monitoring a variety of parameters from blood sugar to the number of strides you take. As my colleague pointed out, all the necessary features are never there in any single application. How do you write an add-on for an existing app? Apps do not talk to each other much like their big brothers: health information systems.
English: Mediated Reality running on Apple iPhone
(Photo credit: Wikipedia)

Apple is trying to solve just that, with its new HealthKit framework. HealthKit allows apps that provide health and fitness services to share their data with the new Health app in the cloud and with each other. A user’s health information is stored in a centralized and secure location and the user decides which data should be shared with your app. I have a couple of friends currently working on population pharmacokinetics. HealthKit may be an ideal framework for sharing pharmacokinetic data too.

Though HealthKit is lingering over a last minute bug that slows down apps that use this framework, it may be a novel and effective tool for app designers. However whether the technology will bring the prophesied ‘Healthcare revolution’ remains to be seen. The new generation apps are going to make life difficult for physicians, already inundated with lots of data. Hope the proverbial ‘Apple a day’ does not happen!

Please share to read about Apple Watch

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Intelligent Federated Clinical Viewers IFCV

I have blogged about federated search clinical viewers before. Essentially such viewers query source health information systems in real time and provide the user with a consolidated view. There is no data repository, ensuring data integrity and data privacy. Though the system can be slow because of the real-time search, there are many successful regional implementations of this type.

There is an obvious disadvantage here. Intelligence cannot be built into such viewers. Since there is no server side data storage, there is no scope for server side processing. The data comes together only in the rendered view. Some  mixed systems that have a data-repository provide some crude warning flags that are not-real time. But clinically useful alerts are beyond the capabilities of federated systems. So how do we build intelligent federated clinical viewers?
Intelligent Federated Clinical Viewer #IFCV

As HTML5 specifications mature, intelligence can be built into clinical viewers by client side processing and storage. Though local database storage implemented by Safari is too insecure at this stage for this purpose, it might become viable in the future. Some useful functionality can be built with the Session storage functionality that has been expanded to store up to 4MB of data depending on browser implementations. This is much more than the conventional cookie storage. The Sessions object persist only till the window is active and is reasonably secure. I have listed some typical use cases below.

A typical pharmacy module of a clinical viewer brings together all the medications the patient is on from all source hospitals. A client side script could identify drug interactions by analysing the view. This is beyond the local EHR system as disparate systems do not talk to each other.

If all the active drugs can be stored locally during the pharmacy module view, contraindications such as steroids in hyperglcemia can be alerted when the lab module is accessed. These intelligent alerts could be clinically invaluable.

The utopian dream of cross-communicating EHRs are still a long way in the future. Regional federated clinical viewers are going to rule the roost for some time. So intelligent federated clinical viewers may be worth a consideration. I don't know whether this is already in the pipeline for vendors such as Mulesoft, Medseek or Mirth. If not, a link here (and a mail) will be highly appreciated when they do.

Twitter hashtag for this topic: #IFCV

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LesionMapper: Pictographic lesion encoder for Dermatology

An electronic medical record example
An electronic medical record example (Photo credit: Wikipedia)
Grading systems and novel methods of symptom coding is becoming more and more important with the growth of telehealth and electronic health records. It is probable that in dermatology too, a significant number of consultations will move online soon.

Visual Analogue Scale (VAS) is a commonly used tool for measuring subjective sensations such as itching. There is evidence showing that visual analogue scales have superior metrical characteristics than discrete scales, thus a wider range of statistical methods can be applied to the measurements.

Couple of months back, I attended a thesis defense in McMaster in which an innovative web based tool called Pain-QuILTTM for visual self-report of pain was presented. The technique of iconography - pictorial material relating to or illustrating a subject - was employed to represent pain using a flash based web-interface. Pain-QuILTTM tracks quality, intensity, location and temporal characteristics of the pain. Quality is represented by different icons, intensity is represented by a visual analogue scale of 1 -10, location by the position of icon on the body image and temporal characteristics by the time stamp. The clinical feasibility of Pain-QuILTTM has been successfully validated and published (1).
Pain-QuILTTM is a property of McMaster University and is subject to McMaster University's terms of use. It can be accessed here

The iconographic symptom encoding could be applied easily to dermatology as well. Dermatology lesions are primarily visual and dermatological diagnosis to a great extend is based on the type, distribution, intensity and temporal characteristics of the skin lesions. However the representation may be challenging because of the diverse nature of lesions.

Recently I came across fabric.js a javascript library for image manipulation based on HTML5 canvas. Fabric.js was much more versatile and powerful than I expected. I could prototype  LesionMapperTM (that is what I want to call it), in less than 24 hours. The type of lesions are symbolized by representative clinical pictures instead of icons, intensity is represented by the opacity/translucency of the image and the location and distribution by the position and size of the lesion respectively, on the body image. The images can be dragged, enlarged or rotated.

Febrile neutrophilic dermatosis
Febrile neutrophilic dermatosis (Photo credit: Wikipedia)
The icing on the cake is the ability of fabric.js to rasterize the image into a JSON that can be stored easily in a database. LesionMapper allows you to save the LesionMap for 6 months and can be rendered back by providing the ID. If you want to include this as an Electronic Health Record system plugin, do give me a shout by clicking on Contact Me on the menu above.

Take a look at LesionMapper(TM) here!

Ref:
1Lalloo, Chitra et al. "Pain-QuILT: Clinical Feasibility of a Web-Based Visual Pain Assessment Tool in Adults With Chronic Pain." Journal of medical Internet research 16.5 (2014). [JMIR]

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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:


Read more »

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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