Handout 6 computer networks healthcare
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Handout # 6: Computer Networks & Healthcare. SII 199 – Computer Networks and Society. Professor Yashar Ganjali Department of Computer Science University of Toronto [email protected] http://www.cs.toronto.edu/~yganjali. Announcements. Assignment # 1

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Handout # 6: Computer Networks & Healthcare

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Handout 6 computer networks healthcare

Handout # 6:Computer Networks & Healthcare

SII 199 – Computer Networks and Society

Professor Yashar Ganjali

Department of Computer Science

University of Toronto

[email protected]

http://www.cs.toronto.edu/~yganjali


Announcements

Announcements

  • Assignment # 1

    • Submission deadline: 5PM on Friday Oct. 5th

    • E-mail your solutions to me; or

    • Slide them under my office door

      • BA5238

  • Volunteer for lecture notes?

University of Toronto – Fall 2012


The story so far

The Story So Far …

  • Introduction to computer networks

    • Internet vs. mail

  • The science of networks

    • Characteristics, graphs, scale-free networks, …

  • This week: Computer networks and healthcare

University of Toronto – Fall 2012


Outline

Outline

  • Motivational example

  • Information flow

    • Data collection

    • Universal access

    • Public awareness

  • Networks and awareness

  • How networking technology helps with healthcare

  • Detour: sensor networks

  • Science of networks

    • Epidemic prediction/control

  • Big Idea …

University of Toronto – Fall 2012


Traditional uses of networks in healthcare

Traditional Uses of Networks in Healthcare

  • Communication

    • Phone

    • Video

    • Teleconferencing

  • Data transfer

    • Fax

    • E-mail

University of Toronto – Fall 2012


Motivational example wifi for rural connectivity

Motivational Example: WiFi for Rural Connectivity

  • Very low cost

    • Why?

  • Unlicensed spectrum (mostly!)

    • What is spectrum?

  • Incremental deployment

    • Limited capital

    • Start small, grow over time

University of Toronto – Fall 2012


Handout 6 computer networks healthcare

New World Record – 382 Kms

Pico El Aguila, Venezuela

Elev: 4200 meters

University of Toronto – Fall 2012


Airjaldi rural wifi isp

Routers used: (a) Linksys WRT54GL, (b) PC Engines Wrap Boards,

Costs: (a) $50, (b) $140

AirJaldi Rural WiFi ISP

  • North India

  • Tibetan Community

  • WiLD links + APs

  • Links are 10–40 km long

  • Achieve 4–5 Mb/s per link

  • VoIP + Internet

  • 10,000 users

Hybrid: closed mesh for backhaul

University of Toronto – Fall 2012


Rural telemedicine

Rural Telemedicine

  • Aravind Eye Hospitals

    • Tamil Nadu, India

    • 5 hospitals

    • But too far for most to walk

  • Need:

    • 15M blind in India

    • 70% of blindness treatable

    • 7% in rural areas get care

  • Goals:

    • 50 rural vision centers

    • Diagnosis and prevention

University of Toronto – Fall 2012


Handout 6 computer networks healthcare

University of Toronto – Fall 2012


Real impact

Real Impact

  • Over 130,000 patients so far

  • Centers are cash-flow positive

  • Over 20,000 patients have recovered sight

  • Growing to 50 centers covering 2.5M people

  • Hoping to replicate in Lumbini, Nepal

University of Toronto – Fall 2012


Remote diagnosis

Remote Diagnosis

  • Use network (the Internet) as a medium to help with diagnosis

  • Not a perfect tool

    • Can lead to incorrect diagnosis

    • Might work in some situations

  • Internet can also help with follow up and consulting sessions that do not require physical presence

    • Even in more advanced regions

University of Toronto – Fall 2012


Outline1

Outline

  • Motivational example

  • Information flow

    • Data collection

    • Universal access

    • Public awareness

  • Networks and awareness

  • How networking technology helps with healthcare

  • Detour: sensor networks

  • Science of networks

    • Epidemic prediction/control

  • Big Idea …

University of Toronto – Fall 2012


Medical information flow

Medical Information Flow

  • Many sources of information

    • Patient history

    • Lab records

      • Electrocardiogram (EGG or EKG)

      • CAT scan

      • Magnetic resonance imaging (MRI)

      • Ultrasound

      • Digital X rays

    • Doctor’s diagnosis, prescription, …

  • Traditionally

    • Go back to the same doctor, or

    • Transfer the data

Computer networks can help here.

University of Toronto – Fall 2012


Electronic health record ehr system

Electronic Health Record (EHR) System

  • Collect all information related to a patient in digital format

  • Universal access

    • Doctor’s can access this data from anywhere

    • More information  better decisions

  • Less space to store

  • Faster access

    • Quick sharing/transfer

  • Reduced possibility of some errors

    • Easy to access and verify

  • Great resource for research

    • Data is extremely valuable in medical research

University of Toronto – Fall 2012


Possible concerns

Possible Concerns

  • Requires many resources

    • EHR system

      • Setup

      • Maintenance

    • Network

    • Doctor time to collect data

  • Might introduce new types of errors

    • Example?

  • Privacy issues

    • Who has access?

    • Hackers, …

University of Toronto – Fall 2012


Awareness

Awareness

  • Networks can help with raising awareness in healthcare

  • Many resources available on the Web

    • Information for specialists:

      • Medical journals, papers

      • PubMed, …

    • Information for all

      • Symptoms, available treatments, side effects, …

      • WebMD, BabyCenter, …

  • We have great search engines: Google, Bing, …

  • Online forums and support groups

    • No need to be physically close

    • Low cost (time and money)

One should be careful about these resources. Not all are trustworthy.

University of Toronto – Fall 2012


Outline2

Outline

  • Motivational example

  • Information flow

    • Data collection

    • Universal access

    • Public awareness

  • Networks and awareness

  • How networking technology helps with healthcare

  • Detour: sensor networks

  • Science of networks

    • Epidemic prediction/control

  • Big Idea …

University of Toronto – Fall 2012


How technology made this possible

How Technology Made This Possible?

  • Large and reliable storage

    • Store high volumes of data at very low cost

    • Small probability of error (or loss)

  • High speed networks

    • Make access possible

    • To store and retrieve

    • No need to store locally

  • Large scale information management systems

    • It is not just a pile of data

University of Toronto – Fall 2012


Technology helps with data collection

Technology Helps with Data Collection

  • Many medical devices today collect data in digital format

    • Easy to transfer and analyze

    • Can be kept in an EHR system

  • We can also collect data using non-traditional devices

    • Sensor networks

University of Toronto – Fall 2012


What are sensor networks

What Are Sensor Networks

  • Tiny electronic devices

  • Equipped with a sensor to collect

    • Temperature, humidity, …

  • Use a wireless network to transfer data to a base-station

    • Usually in an ad hoc manner

  • Used to collect various forms of data with applications in

    • Wildlife, environment, military, healthcare, …

University of Toronto – Fall 2012


Sending data to the base station

Sending Data to the Base Station

  • What if topology changes constantly?

    • Quickly moving nodes

    • Highly dynamic environment

    • ...

  • We might not find a path to send data to the base station

University of Toronto – Fall 2012


Volcano routing scheme vrs

Volcano Routing Scheme (VRS)

  • Lava flows towards the sea (low altitude)

  • Local balancing of load

  • Obstacles do not stop lava

  • No explicit route discovery

  • Reordering layers doesn’t disrupt the flow

University of Toronto – Fall 2012


Volcano routing scheme

Volcano Routing Scheme

University of Toronto – Fall 2012


Multi flow volcano routing

Multi-Flow Volcano Routing

University of Toronto – Fall 2012


Science of networks epidemics

Science of Networks: Epidemics

  • We can use the science of networks to predicting and control epidemics

  • Propagation of viruses similar to …

    • Diffusion of information in social network

  • In random networks

    • Either the entire network is infected, or

    • It dies out

  • Depends on spreading rate

    • Above a threshold all nodes will be infected

    • Below that threshold  spread will die out

  • In scale-free networks however

    • No epidemic threshold

    • Steady state of small persistence rate

University of Toronto – Fall 2012


Outline3

Outline

  • Motivational example

  • Information flow

    • Data collection

    • Universal access

    • Public awareness

  • Networks and awareness

  • How networking technology helps with healthcare

  • Detour: sensor networks

  • Science of networks

    • Epidemic prediction/control

  • Big Idea …

University of Toronto – Fall 2012


The big idea

The Big Idea …

University of Toronto – Fall 2012


Discussion

Discussion

  • Extremely valuable dataset

    • What is the incentive of people to help?

    • Can we create similar incentives in other situations?

  • How reliable is the results gained from this system?

    • Can doctors rely on the results?

    • Do we need extra checks?

  • Can we integrate a system like this with today’s online social networks?

    • Facebook maybe?

    • What are the pros and cons?

University of Toronto – Fall 2012


Summary and discussion

Summary and Discussion

  • Computer networks are extremely useful in healthcare

    • Help with information flow

    • Data collection

    • Data management

  • Assuming extremely fast networks, high capacity storage, …

    • What other areas can you think of?

    • What are the technologies we need to work on today?

University of Toronto – Fall 2012


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