the millennium global village-network: helping to …...nov. – jan ave. feb. – april ave. %...

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Andrew S. Kanter a,b,c , Patricia Mechael a , Matt Berg a Columbia International eHealth Laboratory (CIEL) a Millennium Villages Project, Earth Institute, Columbia University, New York, USA b Department of Biomedical Informatics, Columbia University, New York, NY, USA c Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA The Millennium Global Village-Network: Helping to Achieve the MDGs by Improving Quality, Access and Efficiency 1

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Page 1: The Millennium Global Village-Network: Helping to …...Nov. – Jan Ave. Feb. – April Ave. % Change % of patients treated < 5 50.0% 24.4% -51.3% >= 5 35.4% 12.3% -65.4% %

Andrew S. Kantera,b,c, Patricia Mechaela, Matt Berga

Columbia International eHealth Laboratory (CIEL)

a Millennium Villages Project, Earth Institute, Columbia University, New York, USA b Department of Biomedical Informatics, Columbia University, New York, NY, USA

c Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA

The Millennium Global Village-Network:

Helping to Achieve the MDGs by

Improving Quality, Access and Efficiency

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Agenda

Millennium Villages Project Overview

The Problem

MGV-Net Solution: OpenMRS/ChildCount+

Terminology/Enterprise Architecture

OASIS II Evaluation and Malaria in Uganda

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Millennium Villages Project

Partnership between the Earth Institute Columbia University, UNDP, Millennium Promise and national governments.

Primarily operates in 10 countries in 14 agro-ecological zones in Sub-Saharan Africa

Integrated development at $120 pc/y

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Millennium Villages

Koraro, Ethiopia

Bonsaaso, Ghana

Dertu, Kenya

Sauri, Kenya

Gumalira, Malawi

Mwandama, Malawi

Tiby, Mali

Timbuktu, Mali

Ikaram, Nigeria

Pampaida, Nigeria

Mayange, Rwanda

Potou, Senegal

Mbola, Tanzania

Ruhiira, Uganda 4

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Role of Health Information

Three of eight MDGs are health-related

MVP establishes one clinic per 5000 people

1 CHW for 100 HH

Improve care

Assess impact and effectiveness of program

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Comparable and Timely Data

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

11 12 1 2 3

Uganda

Rwanda

Nov. – Jan Ave. Feb. – April Ave.

% Change

% of patients treated

< 5 50.0% 24.4% -51.3% >= 5 35.4% 12.3% -65.4%

% suspected < 5 73.7% 73.5% 0.4% >= 5 58.3% 53.3% -8.5%

% suspected, treated

< 5 67.2% 32.3% -51.0% >= 5 60.2% 22.6% -62.4%

Coartem 377 165 -56.3%

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Computers/Servers/Mobile Phones

Telecommunications Infrastructure

mHealth Applications and mLearning

Community Health Care Worker Training

FIGURE 1. eHealth M&E Logical Framework

Emergency Medical Services protocol

Non-emergent referral system

Telemedicine system

Electronic Medical Records

Capacity Building

Improve disease prevention

Leverage Achievement of MDGs Goal 4: Reduce Child Mortality Goal 5: Improve Maternal Health Goal 6: Combat HIV/AIDS, Malaria, other diseases

INPUTS OUTPUTS OUTCOMES IMPACT

Disease Surveillance and Early Warning

Community Mobilization, Wellbeing, and Empowerment

Improve treatment compliance

Supply Chain Management

Improve point-of care decision making and service delivery

Basic Voice and Text Communication

Data warehouse

Reduce Error Rates from Data Entry

Reduce time to access care

Behavior Change

Reduce Isolation/ Improve Psychosocial Outcomes

Improve access and quality of health care

Improve coordination and administration of health service delivery

Improve Intra-facility Communication

Improve Monitoring and Evaluation

Raise knowledge and awareness about diseases

Reduce administrative and point-of-care costs

Monitor and target specific MDG indicators

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Millennium Global Village Network

9

Courtesy of Andrew Kanter. Used with permission.

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Vision: MGV-Net Information System

Bottom-up

Patient-centered

Locally-owned/maintained

Integrated/interoperable

Provide local value

Global reach

Sustainable

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MGV-Net

OpenMRS

ChildCount+

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CHW Decision Support and Info System

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Looking up Patients…

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Courtesy of Andrew Kanter. Used with permission.

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Prior Encounters…

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Courtesy of Andrew Kanter. Used with permission.

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View Prior Encounters

15Courtesy of Andrew Kanter. Used with permission.

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English

French

Same Data in multiple languages

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Courtesy of Andrew Kanter. Used with permission.

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Courtesy of Andrew Kanter. Used with permission.

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Terminology Service Bureau- 50,000 concepts

18Courtesy of Andrew Kanter. Used with permission.

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Common Data

Dictionary

Kenya

Rwanda

Malawi

Uganda

Tanzania

India

Nigeria

Ghana

Nepal Philippines

Indonesia

Mali

Senegal

Afghanistan

Peru

Chile

Nicaragua

. . .

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OASIS II Study-Evaluating Impact

The proposed research systematically evaluates the impact of all MGV-Net components (e.g. ChildCount+; OpenMRS) on various aspects of the healthcare delivery system including:

service coverage;

quality of services;

early detection, referral, and treatment of danger signs

morbidity and mortality

management of resources (human, financial, etc) 20

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OASIS II Study Design

Mixed methods

Retrospective Case-Control- exposure to on-time CHW visits

Repeated Measures with & without MGV-Net

Action Research Component

Repeated Measures of Paper vs. SMS

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Key measure of success

Pre-intervention

Patients treated who were not tested or tested negative

90%

Patients treated who tested negative

48%

Patients treated who tested positive

10%

Malaria in Uganda- Action Research

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Key measure of success

Pre-intervention

Post-intervention

Patients treated who were not tested or tested negative

90% 32%

Patients treated who tested negative

48% 12% in Q1 18% in Q4

Patients treated who tested positive

10% 30% in Q1 58% in Q4

Malaria in Uganda- Action Research

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Challenges and Lessons Learned

Human capacity

Clinician engagement

Reliable electricity, computers, connectivity

Need interoperability

Need maturity model

Gov’t policies

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Shared Terminology and Resources

www.maternalconceptlab.com

www.openmrs.org

www.childcount.org

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MIT OpenCourseWarehttp://ocw.mit.edu

HST.S14 Health Information Systems to Improve Quality of Care in Resource-Poor SettingsSpring 2012 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.