Monday, June 16, 2008
Thursday, June 12, 2008
QR Codes for Health Information Exchange
Yesterday night's skype meeting with Gordon Gow brought forth the idea of using QR codes to code and decode health information in relation to the next research in many aspects, a research him and I plan to start shortly; i.e. the RTBP. Refer to his blog for a note on QR Codes and application for mobile phones for emergency managers - Barcodes meet cellphones: intriguing possibilities.
The Real-Time Biosurveillance Program (RTBP) is a research that envisions pilot testing mobile phones for collecting health-related information and applying AutonLab's suite of statistical data mining algorithms for fetching anomalies in the health datasets. The Healthcare-Workers will be provided with mobile phones and a Java application, a rendition of openROSA suit of applications. Indian Institute of Technology - Madras will be developing the mobile applications.
The health information will be mostly patient counts with similar symptoms. We discussed the possibility of using QR codes during transport and storage of information:
1) the Healthcare-Worker recorded data on the mobile handhelds can be encoded as a QR Code prior to transmitting the information to central repository (database). Since the QR Codes use the Reed-Solomon error correction method the misinterpretation of health information during transport and storage is further reduced
2) Given, that a QR codes are already encoded in binary form, the possibility of increasing the speed of the statistical data mining algorithms are worth testing
3) encoding communicable diseases and other known diseases with symptoms in QR Code form printed as hard copy for Healthcare Workers to use for entering information by simply scanning the QR code with the mobile phone camera instead of typing the lengthy string with the possibility of misspelling
4) In the event the Java application residing on the handheld fails the Healthcare Workers can use the hard copy QR code version to scan predefined health information strings to record the information on the handheld, then use Email, MMS or SMS to transit the information over any technology that allows the standard Email, SMS, MMS applications, making it easier for the database to also decipher and parse the information before storing in the relevant attributes (fields)
5) The Healthcare Workers can store the patient information in QR Code form as a hardcopy as a backup. Since it is in a human unreadable form the possibility of an unauthorized random individual reading the confidential patient information is null
These are thoughts that came up during our discussion and look forward to testing the concepts with the RTBP.
The Real-Time Biosurveillance Program (RTBP) is a research that envisions pilot testing mobile phones for collecting health-related information and applying AutonLab's suite of statistical data mining algorithms for fetching anomalies in the health datasets. The Healthcare-Workers will be provided with mobile phones and a Java application, a rendition of openROSA suit of applications. Indian Institute of Technology - Madras will be developing the mobile applications.
The health information will be mostly patient counts with similar symptoms. We discussed the possibility of using QR codes during transport and storage of information:
1) the Healthcare-Worker recorded data on the mobile handhelds can be encoded as a QR Code prior to transmitting the information to central repository (database). Since the QR Codes use the Reed-Solomon error correction method the misinterpretation of health information during transport and storage is further reduced
2) Given, that a QR codes are already encoded in binary form, the possibility of increasing the speed of the statistical data mining algorithms are worth testing
3) encoding communicable diseases and other known diseases with symptoms in QR Code form printed as hard copy for Healthcare Workers to use for entering information by simply scanning the QR code with the mobile phone camera instead of typing the lengthy string with the possibility of misspelling
4) In the event the Java application residing on the handheld fails the Healthcare Workers can use the hard copy QR code version to scan predefined health information strings to record the information on the handheld, then use Email, MMS or SMS to transit the information over any technology that allows the standard Email, SMS, MMS applications, making it easier for the database to also decipher and parse the information before storing in the relevant attributes (fields)
5) The Healthcare Workers can store the patient information in QR Code form as a hardcopy as a backup. Since it is in a human unreadable form the possibility of an unauthorized random individual reading the confidential patient information is null
These are thoughts that came up during our discussion and look forward to testing the concepts with the RTBP.
Wednesday, June 11, 2008
Attempt to Classify Early Warning Systems
Over the past 2 months, since my work completed as the researcher/project manager evaluating a last-mile hazard information dissemination research, a contract received through LIRNEasia, I've been taking a stab at classifying early warning systems (EWS). The research so far does not reveal a concrete abstraction for this classification effort. Most of the work done are domain specific, thus financial specialist trying to classify financial EWS, engineers trying to classify engineering EWS, so on and so forth.
The question that pops in my mind is "how does one distinguish between two similar EWS designs for the same purpose; i.e. pick the best?" or "how does one enumerate the capability and capacity of a given EWS?" or "how does one decompose an existing design to depict the possibility of adding on to extend the value to service other risks?" Current thinking is to design systems to the decision makers liking. Also there is no regard for including response systems in the design. As I see a EWS designed without taking the "customer attribute"; i.e. the response system in to consideration, is like throwing darts in to an empty space; i.e. no target.
The works so far leads me to believe that three main parameters that can classify any EWS, whether it be natural (as in the animal kingdom), engineered, social, or economic, are by understanding them through observer-controller (predictor corrector) systems, complexity theory, and Markov processes. These three primary fields give us the tools to define the operational orientations, capabilities of the design, and the expected capacity in real conditions.
I am testing the above mentioned framework on four examples: community-based last-mile hazard warning system (I was personally involved in), debt crisis financial EWS, Dam failure (safety) EWS, and a EWS based on the Traceability of Agriculture markets. The classification scheme inclusive of the enumeration theories are working in my favor. Although the exact simulated values for the system design capabilities and expect capacity are yet to be determined.
I would greatly appreciate anyone working in the same arena or has any interest in discussing the aspects of solving this classification problem to share their opinions with me through dialog.
The question that pops in my mind is "how does one distinguish between two similar EWS designs for the same purpose; i.e. pick the best?" or "how does one enumerate the capability and capacity of a given EWS?" or "how does one decompose an existing design to depict the possibility of adding on to extend the value to service other risks?" Current thinking is to design systems to the decision makers liking. Also there is no regard for including response systems in the design. As I see a EWS designed without taking the "customer attribute"; i.e. the response system in to consideration, is like throwing darts in to an empty space; i.e. no target.
The works so far leads me to believe that three main parameters that can classify any EWS, whether it be natural (as in the animal kingdom), engineered, social, or economic, are by understanding them through observer-controller (predictor corrector) systems, complexity theory, and Markov processes. These three primary fields give us the tools to define the operational orientations, capabilities of the design, and the expected capacity in real conditions.
I am testing the above mentioned framework on four examples: community-based last-mile hazard warning system (I was personally involved in), debt crisis financial EWS, Dam failure (safety) EWS, and a EWS based on the Traceability of Agriculture markets. The classification scheme inclusive of the enumeration theories are working in my favor. Although the exact simulated values for the system design capabilities and expect capacity are yet to be determined.
I would greatly appreciate anyone working in the same arena or has any interest in discussing the aspects of solving this classification problem to share their opinions with me through dialog.
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