Handout # 6: Computer Networks & Healthcare

1. Handout # 6:Computer Networks & Healthcare SII 199 – Computer Networks and Society Professor Yashar Ganjali Department of Computer Science University of Toronto yganjali@cs.toronto.edu http://www.cs.toronto.edu/~yganjali

2. 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

3. 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

4. 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

5. Traditional Uses of Networks in Healthcare • Communication • Phone • Video • Teleconferencing • … • Data transfer • Fax • E-mail University of Toronto – Fall 2012

6. 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

7. New World Record – 382 Kms Pico El Aguila, Venezuela Elev: 4200 meters University of Toronto – Fall 2012

8. 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

9. 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

10. University of Toronto – Fall 2012

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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

24. Volcano Routing Scheme University of Toronto – Fall 2012

25. Multi-Flow Volcano Routing University of Toronto – Fall 2012

26. 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

27. 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

28. The Big Idea … University of Toronto – Fall 2012

29. 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

30. 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