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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
Samuel Morin, 2001, Haiti One year later, after treatment
~40 million HIV infected people ~3 million died in 2004
~700,000 receiving treatment
~6 million need treatment
>75% unaware of status
~9 years on ave. to live w/o treatment
ARVs
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
– optimization
– indoor navigation
– story sharing
– data mining
– inference intention
– collaboration
– engagement
– probabilistic reasoning
– information visualization– intelligent tutoring – data 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
• 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
Warning!
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 peoplein rich countries
five billion people in middle- or low-income countries
Poverty as a Risk Factorfor surviving the Titanic.
0
10
20
30
40
50
60
70
1st 2nd 3rd
class of service
% s
urvi
ved
Poverty as a Risk Factorfor dying young.
Malawi U.S.Life expectancy
at birth38 yrs. 77 yrs.
Prob. of dying before 5 years old.
18.3% .8%
Prob. of dying before 40 year old.
49.8% 13% die before 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 health states:– E.g. “Give 1000 people
a year of healthy life or 2000 people a year of paralyzed life?”
• Assign weights years– E.g. 25th year worth
more than 5th or 65th
• Discount future years– E.g. 3% per year
cause of lost DALYs
1 Lower respiratory infection
6.4%
2 Perinatal conditions 6.2%
3 HIV/AIDS 6.1%
4 Unipolar depression 4.4%
5 Diarrhoea 4.2%
6 Ischaemic heart 3.8%
7 Cerebrovascular 3.1%
8 Road traffic 2.8%
9 Malaria 2.7%
10 Tuberculosis 2.4%
Demography and Health
Phys/HumanCapitalIncome
What can we do?
E.g. being pushed into poverty by medical expenses
E.g. hard to learn when ill, or if working because 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
• What’s in stock, expirations• Healthcare workers
– medical expertise– patient’s medical history
• Population/policy– Needs assessment– What’s working
• Individual– When to seek care
• Tele-medicine• Electronic medical
records (EMR)• Decision support• Intelligent tutoring• Sensor networks• Data mining and
visualization• 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 to guide health workers
• Working on HIV protocols• First target: filter out easy
“no change needed” cases• Information periodically
uploaded• Led by Marc Mitchell,
Hilarie Cranmer
fever □
RR > 40/50 or
chest indrawing □
diarrhea □
abd. pain □
rash □
Symptoms
next
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.– operated by one person
• Cost– $1000 per year for equipment– $4000 per year operational
• Reach– present to 10 people per day– 200 presentations saves a child’s
life
• Impact– $333 per life– ~$10 per DALY– World Bank says
$150 per DALY is cost effective
lower child mortality
reduced fertility
better health & wealth
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
• 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.
the answer
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.
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
• 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
Life-threatening shortages of...
How and when to introduce technologies?
Shortage Example Tools
Human Expertise
2 physicians per 100,000 Malawians
telemedicine HealthNet decision support intelligent training systems
Information recently ‘found’ 250 million cases of malaria
passive sensing standardized records pattern detection in health data
Efficiency drugs expiring in clinics drug inventory in EMR path prediction of fleeing refugees
Comm. & Coord.
importance of email better connectivity
Demography and Health
Phys/HumanCapitalIncome
Applied computer science to make new tools for healthcare efforts
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%
%Total DALYS (2002)
Leading causes of DALYS
Cause
