Embed
Email

Blood_Glucose_Portal

Document Sample

Shared by: cuiliqing
Categories
Tags
Stats
views:
0
posted:
11/10/2011
language:
English
pages:
14
Blood Glucose Portal



Xinformatics Blue Team

Overview



• Use Case - sumitra

• Modeling - fred

• Design - hithika

• Back-End - scott

• Front-End - evan

• Demo - evan

• Summary - evan/sumitra

Use Case Outline

Use Case Name : Collaborative Health Care Tracker



Goal : Patient and physician develop plan and track progress

towards healthy living style with the help of a collaborative

health information system



Primary Actors :

• Patient

• Physician



Secondary Actors:

• Physician's record system

• Patient's personal health records

• Nutritionist's record system etc

Basic Flow



1. Doctor uses system to retrieve health records of the patient

and nutritionist's reports

2. Performs tests and inserts results into the record system

3. If results indicate pre-diabetes symptoms (for example),

doctor sets goals for the patients and annotates

recommendations

4. Patient takes periodic measurements of weight and blood

glucose, enters this into his personal record and compares

this with limits imposed by the doctor

5. Patient returns for a check-up after a month having

succeeded in meeting goals encoded in system. Symptoms

no longer persist.

Model



conceptual model • The patient:

o contributes their own data

(PHR)

o contributes to their PHR as well

as the nutrition record

• The doctor:

o updates the EMR

o prescribes a treatment plan

o diagnoses the patient

• The EMR:

o is updated by the doctor

o is the central repository for the

patient's medical information

Model

logical model









• This details the primary as well as the foreign keys and their relationships in

this portal

• EMR is dependent on all other factors in the portal

Design



• Blood glucose information is displayed on a plot with time on

the X-axis and blood glucose in mg/dL on the Y-axis

• The graph is composed of two components - Unhealthy

Areas and Data Point Collection

• The unhealthy areas are colored in a light red to differentiate

them from the ‘normal’ area as defined by the doctor

• Data points have two attributes: a direction attribute and a

goodness attribute

• Direction is determined by the data point’s value relative to

the boundaries

Design (contd)



• The goodness of an attribute is defined by its relationship to

the previous data point and as a function of the direction

• Intuitively, if the data point is closer to the center of the good

blood glucose region, then the data point will turn green

• If it moves in the opposite direction, it turns red and neutral

points and will always appear green

• The averaging feature help smooth out anomalous data

• The use of a simple triangle as a symbol to indicate desired

direction also follows these same principles of making

information easily identifiable, easily readable

• These are some of the areas where information uncertainty,

semiotics, cognition, and architectures were taken into

consideration

Back-End



• Java HTTP servlets running on Tomcat.

• Distributed nature makes traditional databases

more difficult to use and introduce uncertainty in

schema.

• Data kept on disk in NetCDF.

• Transfer between Front-End and Back-End done in

JSON.

NectCDF and Why.



• Standard for array-based scientific data.

• Open Standard.

• Binary.

• Self-Describing and Machine-Independent.

o Reduce Information uncertainty.

o Allow for data to be portable.

• Add annotations to individual data points.

Front-End Presentation



• Optimizing two dimensions:

o Understanding of information content (max)

o Time spent interpreting information (min)

• Data points

o Triangles indicate direction (square = neutral)

o Color indicates direction of movement in addition to

change in Y-axis

• Smoothing via averaging

o Reduces noise in the data and presents a more long-term

view of how the information changes

• Annotations

o Doctors and patients can annotate data points

o Doctors identify healthy boundaries annotated on the

graph by light red regions

Demonstration

Summary



• In health sciences, reducing information uncertainty should

be a high priority goal

• Growing obesity and diabetes epidemic are candidates

where the application of informatics could make large

improvements

o Information needs to be readily available and understood

o Online tools can provide useful feedback at higher

frequency than traditional offline methods

• Informatics over the web introduces additional challenges

o Security and authenticity of information

o Different interpretations of standards by browsers can

introduce information uncertainty

Questions?



Other docs by cuiliqing
11.1 Exploring Area and Perimeter
Views: 0  |  Downloads: 0
Volusia County
Views: 2  |  Downloads: 0
choosing_topics_and_y10
Views: 0  |  Downloads: 0
CLE Credit - rscrpubs.com
Views: 2  |  Downloads: 0
Meeting Minutes September 8 Final
Views: 0  |  Downloads: 0
nov2411
Views: 3  |  Downloads: 0
EKG Spreadsheet - Geocities.ws
Views: 0  |  Downloads: 0
Gift from Christ to the Church
Views: 0  |  Downloads: 0
By registering with docstoc.com you agree to our
privacy policy

You are almost ready to download!

You are almost ready to download!