Gadgets for Good How Computer In

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					Gadgets for Good
How Computer Innovation
Can Help Save Lives
in Low-Income Countries.


                                  Neal Lesh
              Harvard School of Public Health
   ~40 million HIV infected people         ~3 million died in 2004
~9 years on ave. to live w/o treatment ~6 million need treatment
      >75% unaware of status            ~700,000 receiving treatment




                                 ARVs




     Samuel Morin, 2001, Haiti                  One year later,
                                                after treatment
Clinical staff using Partners in
Health‘s EMR in Belladere, Haiti.
                Roadmap
 me
 the world
 international public health
 computers
 me
                My Background
• Computer science experience:
  – 1991-1997: PhD in A.I. at U. Washington
  – 1997-1998: postdoc at U. Rochester
  – 1998-2004: research at MERL

• Areas of work:
 – planning        – story sharing    – collaboration
 – optimization    – data mining      – engagement
 – indoor          – inference        – probabilistic
   navigation        intention          reasoning
 – information     – intelligent      – data
   visualization     tutoring           exploration
                      Currently
• Full-time student, masters of public health (MPH)
   – taking classing, field trip to India, starting some
     research projects.


• Goals for this talk:
   – Give you flavor of the field
   – Generate excitement
   – Get invited back in a couple years
                  Warning!
• Many approximations:
    ―There is a tendency for all knowledge, like all
    ignorance, to deviate from the truth in an
    opportunistic direction.‖—Gunnar Myrdal.
• Neglecting lots, e.g.
  – disadvantaged people in rich countries
• Glossing over a lot of complexity
• Assuming you know about what I did 1 year ago
                  How are we doing?
~six billion people
       World Population Growth
Population and year    Time to add a billion
1 billion in 1804          1,001,804 years
2 billion in 1927                123 years
3 billion in 1960                 33 years
4 billion in 1974                 14 years
5 billion in 1987                 13 years
6 billion in 1999                 12 years
7 billion in 2012                 13 years
8 billion in 2026                 14 years
8.9 billion in 2050              26+ years
How are we doing?
                     How are we doing?
one billion people
in rich countries




                                             five billion people in
                                 middle- or low-income countries
         Poverty as a Risk Factor
         for surviving the Titanic.
             70
             60
             50
% survived




             40
             30
             20
             10
             0
                  1st         2nd          3rd
                        class of service
Poverty as a Risk Factor
for dying young.

                                             Malawi       U.S.
                   Life expectancy
                                             38 yrs.    77 yrs.
                   at birth
                   Prob. of dying before 5
                   years old.
                                             18.3%        .8%
                   Prob. of dying before               13% die before
                   40 year old.
                                             49.8%         60 yr.

                   HIV rate among 18-49
                   year olds (2001)
                                              15%         .6%
                   GDP per capita
                                             $585      $35,991
                Roadmap
me
the world
 international public health
 computers
 me
          Unit of measurement
• Need to quantify population health
   – measure success
   – allocate resources


• Measure health by counting deaths?
                       Canada      Mexico

Deaths per 1000
per year (2003 est.)       7.61        4.97

  (answer: Mexicans are younger than Canadians)
               Life Years Lost
• Select a target/ideal length of life.
   – e.g., 80 years for men, 82 for women



• For each death, calculate life years (LY) lost
  relative to target length.
   – E.g. death of a 40 year old woman =
     82 – 40 = 42 LY lost
How many Life Years lost?
• Tsunami:
  deaths X LY per death (my guess) =
  300,000 x 65 =
  19,500,000 LY lost

• Malawi:
  population X death rate X LY per death =
  12,000,000 X .024 X 42 =
  12,096,000 LY lost

• Sub-Saharan Africa:
  650,000,000 X .018 X 34 =
  397,800,000 LY lost =
  20 tsunami‘s worth of LY lost per year
  DALYs: Disability Adjusted LY
• Assign weights to                 cause of lost DALYs
  health states:                1   Lower respiratory      6.4%
  – E.g. ―Give 1000 people          infection
    a year of healthy life or   2   Perinatal conditions   6.2%
    2000 people a year of       3   HIV/AIDS               6.1%
    paralyzed life?‖            4   Unipolar depression 4.4%
• Assign weights years          5   Diarrhoea              4.2%
  – E.g. 25th year worth        6   Ischaemic heart        3.8%
    more than 5th or 65th       7   Cerebrovascular        3.1%
• Discount future years         8   Road traffic           2.8%
  – E.g. 3% per year            9   Malaria                2.7%
                                10 Tuberculosis            2.4%
                What can we do?

                                 Phys/Human
       Income                      Capital




E.g. being pushed                   E.g. hard to learn
into poverty by                     when ill, or if
medical expenses
                    Demography      working because
                    and Health      parent is ill.
Reducing Child Mortality
HIV Prevention
               Roadmap
me
the world
international public health
 computers
 me
  Information & Communication
• Had another revolution in the
  last 10-15 years:
  – ease of communication
  – availability of information
  – tracking of objects


• Many opportunities to
  address fatal information
  deficits in healthcare.
                    But...




Information Kiosk            Less than $5 on her
                             healthcare, annually
           Information Deficits
              for Medication
• Tele-medicine          • What‘s in stock, expirations
• Electronic medical     • Healthcare workers
  records (EMR)            – medical expertise
• Decision support         – patient‘s medical history
• Intelligent tutoring   • Population/policy
• Sensor networks          – Needs assessment
                           – What‘s working
• Data mining and
  visualization          • Individual
                           – When to seek care
• Connectivity for
  low-income regions
                    Tele-health
• Addresses information deficits due to
  – unfortunate distribution of medical expertise
  – burden of travel
• Many options
  –   doctor to patient, never meet
  –   doctor to patient, meet occasionally
  –   doctor to doctor
  –   doctor to data repository (HealthNet)
• Technical challenges
  – sensors for health data
  – max. use of bandwidth
  – user interface
      Electronic Medical Records
• Info. management in med. care:
  –   patient history at point-of-service
  –   drug inventory, and prediction
  –   decision support
  –   monitoring and evaluation


• Challenges for computerization:
  – expense
  – electricity & connectivity
  – expertise
                                         Nurses in India, using
                                       EMR by Dimagi and AIIMS.
                    Ca:sh
(Community Access to Sustainable Health)

• Handhelds for nurses
• Targets antenatal care,
  immunization, disease
  management
• 80,000 records since
  February 2002
• 25¢ per patient per year
• Now using desktops &
  car batteries in clinics.
• By Dimagi, AIIMS
• Encode standard protocols
                                   Symptoms
  to guide health workers
• Working on HIV protocols                         □
                                             fever 
                                  RR > 40/50 or
• First target: filter out easy   chest indrawing □
  ―no change needed‖ cases                diarrhea □
                                         abd. pain □
• Information periodically                    rash □
  uploaded                                  next 
• Led by Marc Mitchell,
  Hilarie Cranmer
        Need Research?

"The task before us is very urgent, so we
must slow down.‖




Analogy: 10/90 gap in medical research
             Behavior Change
• Information deficits in
  caretakers of children:
  – keep children away from smoke
  – don‘t withhold food from children
    w/ diarrhea
  – don‘t rub dirt into umbilical cord


• Possible tools:
  – interactive tutoring/testing
  – games, animation
  – virtual reality
           Cost Effectiveness
• Behavior change system
  – laptops, PDA, phones,
    projectors, VR goggles, etc.   • Impact
  – operated by one person          lower child mortality
                                      – $333 per life
• Cost                                – ~$10 per DALY
  – $1000 per year for equipment       reduced fertility
                                      – World Bank says
  – $4000 per year operational           $150 per DALY is
                                   better health & wealth
                                         cost effective
• Reach
  – present to 10 people per day
  – 200 presentations saves a
    child‘s life
Passive Surveillance
                  Crisis Mapping
 • Field personnel register location of
     – physical resources (e.g., medicine)
     – activities (NGO‘s)
     – situations (people, disease)


 • Upload to GIS system to improve
     – coordination of responders
     – cooperation between NGO‘s

“It's such an obvious idea that
no one has done it. Go figure.”
          Related Challenges
• Predicting path of
  fleeing refugees



• Population counting
  for refugee camps
               Connectivity
• Vehicle-mounted hubs (Pentland)

• Boosting 802.11b (Brewer,
  Pentland)
  – many hardware/power issues
  – unconventional networking
  – specialized protocols


• DVDs by Postal service (Wang)
Parting thoughts              the
                             answer

  • Easy pickings for exciting ideas

  • Must work with people in field

  • Funding etc. a challenge

  • My next years: visit many sites
    and field-test variety of ideas.
                  Inspiration
• “We’re going to be a millionaire of a different sort.
We’re going to try to affect the lives of a million
people.” - Vikram Kumar, CEO of Dimagi.

• The new abolitionist: someone working to
eliminate extreme poverty this century.
               Thanks!
To keep in touch, email me at
neal@equalarea.com
Age distribution
                 Surveillance
• Def: ongoing & standardized data collection
• Crucial for:
   – Resource allocation
   – Evaluation
   – Outbreak detection

• Currently inadequate:
   – Often rely on studies & models
   – Push for ―evidence-based medicine‖
          Road traffic safety




Road traffic injuries
expected to move to 3rd
leading cause of DALY‘s
by 2020.
           Medical Records
          & Decision Support
• Many of the world‘s poor:
  – never see physician
  – not reached by standard treatment
    protocols, e.g., case management
    for diarrhea or measles
  – have no continuity of care


• Computerization improves:
  – patient info. at point-of-service
  – decision support, latest protocols
  – collection of data
  Life-threatening shortages of...
• Human expertise
  – 2 physicians per 100000 Malawians

• Information
  – recently ‗found‘ 250,000,000 cases of malaria

• Efficiency
  – many drugs expire in rural clinics

• Coordination/communication
  – tremendous overlap of activity in humanitarian efforts
       How and when
to introduce technologies?
Shortage         Example                      Tools
 Human        2 physicians per          telemedicine
              100,000 Malawians         HealthNet
Expertise                               decision support
                                        intelligent training
                                       systems
Information   recently ‗found‘ 250      passive sensing
              million cases of malaria  standardized records
                                        pattern detection in
                                       health data
Efficiency    drugs expiring in clinics  drug inventory in EMR
                                         path prediction of
                                        fleeing refugees

 Comm. &      importance of email       better connectivity

  Coord.
                                   Phys/Human
     Income                          Capital




                  Demography
                  and Health


Applied computer science to make
new tools for healthcare efforts
  Leading causes of DALYS
  Cause                    %Total DALYS (2002)
1 Lower respiratory infections    6.4%
2 Perinatal conditions            6.2%
3 HIV/AIDS                        6.1%
4 Unipolar depressive disorders   4.4%
5 Diarrhoeal diseases             4.2%
6 Ischaemic heart disease         3.8%
7 Cerebrovascular disease         3.1%
8 Road traffic accidents          2.8%
9 Malaria                         2.7%
10 Tuberculosis                   2.4%