SlideShare uma empresa Scribd logo
1 de 30
Rapid Mobile Phone-based Surveys (RAMP)
      for Evidence-based Emergency Response

                     ALNAP 28th Annual Meeting,
                   5-7 March 2013, Washington, D.C.



                       Scott Chaplowe, Senior M&E Officer, IFRC
                          Rose Donna, Director, Datadyne.org

       Jason Peat, Senior Officer Public Health, IFRC
       Amanda Mcclelland, Emergency Health Officer, IFRC
       Joel Selanikio, CEO DataDyne Group
       Mac Otten

www.ifrc.org
Saving lives, changing minds.
Presentation Overview
   Application of mobile technology (RAMP) to address specific
   challenges in data collection during emergency operations.


   1) Introduce RAMP
   2) How RAMP works
   3) Emergency contexts
   4) Key considerations




www.ifrc.org
Saving lives, changing minds.
What is RAMP?

   RAMP (Rapid Mobile Phone-based Surveys) is a survey
   methodology utilizing mobile phones to help RCRC National
   Societies, governments, NGOs and other partners efficiently
   conduct quality surveys that:
          Reduced time
          Reduced cost
          Improved quality assurance
          Limited external technical assistance


www.ifrc.org
Saving lives, changing minds.
RAMP Background (www.ifrc.org/ramp)
   1. Developed by IFRC in partnership with WHO, CDC, and
      other partners.

   2. Initial focus = malaria program household surveys
          Four pilots in Africa 2011-2012 (Kenya, Namibia and Nigeria),

   3. Refine and developed trio of user guides:
         1. Designing a RAMP survey
         2. Implementing a RAMP survey
         3. Training a RAMP survey team

   4. Scale-up to other program areas – increase survey
      functionality – use of SMS

www.ifrc.org
Saving lives, changing minds.
RAMP takes advantage of 2 technologies
                                1. Mobile phone to collect data
                                (Low-cost, standard mobile phones, as well as Android,
                                Symbian, Blackberry, SMS, and iPhone)



                                                                   2. Web-based software
                                                                   application
                                                                   Enables mobile phones
                                                                   to become a data
                                                                   collection platform




www.ifrc.org
Saving lives, changing minds.
How does RAMP work?
                                                     2. Data collection
                                                         on phone



    1. Develop survey on
       website




                                                                  3. Transmit
                                                                      data
                                4. Collate/analyze
      5. Data Reports
                                data on computer

www.ifrc.org
Saving lives, changing minds.
Connectivity
               Internet                                 Internet
               Required                               Not Required
     • Create/edit surveys                        •   Collect data

     • View/export data
     • Create reports


                                Can be cellular, wifi, cable
www.ifrc.org
Saving lives, changing minds.
Data monitoring and analysis


  Preliminary analysis available
   before data collection is
   complete




www.ifrc.org
Saving lives, changing minds.
Timely Reporting
     Survey bulletins/updates                Full survey reports




www.ifrc.org
Saving lives, changing minds.
Digital Data Collection – Changing the way we work

         The “old”                                The “new”

        Paper questionnaires filled out in      Mobile and internet-based
         the field                                technologies used to reduce time
                                                  for data collection to reporting
        Data entered into a computer at
         a central location                      Enables rapid reporting of results,
                                                  decision making, and action
        Data analysis and reporting often
         takes months to complete                Empowers local ownership of
                                                  evaluation and research
        Local capacity is often under-
         utilized and there is a
         dependence on external experts



www.ifrc.org
Saving lives, changing minds.
Anything that can be put on a form

                                Vaccination coverage
                                   Surveillance
                         Supply chain management
                                Household surveys
                                   Clinic surveys
                            Supervisory checklists
www.ifrc.org
Saving lives, changing minds.
RAMP Potential in Emergencies?
  Beginning to explore the potential of RAMP in emergency context:
         Site assessment – needs, damage
         Community assessment – needs, damage
         Beneficiary registration
         Distribution of emergency (and non-emergency) items
         Baseline/endline data collection (monitoring and impact study)
         Repeated surveys to track time trends for key indicators
         Beneficiary communication – (broadcast Terra)
         Beneficiary/community monitoring
         Disaster preparedness – EWS monitoring

www.ifrc.org
Saving lives, changing minds.
SMS Disease Surveillance Systems
   Piloting in community based disease surveillance

   Sierra Leone – 400 community volunteers distributing ORS.

   Referred only 5% of cases of AWD they saw in community = only
    5% of cases were potentially recorded in normal MoH system.

   RAMP allows real time communication and data gathering
    suitable for this context.

   Problems with integration and harmonization of data between
    community and MoH.

   But SMS proved real time information to assist program
    prioritization in outbreak scenarios.

www.ifrc.org
Saving lives, changing minds.
SMS Considerations
  Simplified questions rather than full surveys

  Coding syntax with 2 to 7 key variables as best practice

  Quantity of messages handled depend on networks, and whether
   staggered or simultaneous reporting.

  Paper form can be used to facilitate data entry to SMS

  Quality assurance auto feedback

  Reminder SMS to field person to report data at a set time

  Thank you SMS to confirm receipt of data.

  Ability to send airtime to the mobile account if someone reports from
   a common central account.
www.ifrc.org
Saving lives, changing minds.
Benefits?




www.ifrc.org
Saving lives, changing minds.
Benefits – decision making


  Data rapidly available for
   decision-making

  Maintain data control

  Scalable for studies of varying
   sizes

  Shared, electronic database to compare across contexts and with
   partners to build a body of evidence related to impact

www.ifrc.org
Saving lives, changing minds.
Benefits - management
   Cost effective

   Do not have to reinvent the wheel –
    Adaptable RAMP toolkit

   Consultants not required

   No software licensing or subscriptions

   Multiple languages (depending on
    program)

   Export data for custom analysis using
    any statistical analysis package

   Additional SMART phone features
www.ifrc.org
Saving lives, changing minds.
Benefits - management

 Online library of survey
  forms

 Collect and aggregate data
  form multiple areas and
  partners

 Ease of creating and
  changing analyses/reports

 Efficient reporting and
  dissemination


www.ifrc.org
Saving lives, changing minds.
Benefits - Fieldworkers
 • Build local capacity for M&E

 • Standard and familiar mobile
   phones

 • No more paper to collect,
   transport or return

 • Automated data submission
   (assuming network)


www.ifrc.org
Saving lives, changing minds.
Benefits - Quality Assurance
   Immediate QA:
      Real-time error analysis and field
       correction
      Utilize skip patterns, custom logic and
       validation

   Remote QA:
      Enables monitoring of survey team work rate, productivity and quality
      Monitor times/location of data collection (time/date data stamps)
      Provide feedback remotely
   Efficient data management reduces “paper” mistakes
      Easier to back-up forms/data
      Reduced error of repetitive data entry and re-entry
      Easier to change and update forms

www.ifrc.org
Saving lives, changing minds.
Reality Check!

   Not suitable for very long questionnaires

   No “magic bullet” –work is still in the details!

   Things to improve – i.e. offline form generation

   Technology is a moving target – (hardware and software)
         Challenges resource development/training
         (But also means improvements and reduced costs)

www.ifrc.org
Saving lives, changing minds.
Questions to Consider

        What applications do you see for mobile data collection
         in the humanitarian sector?

        What has worked well?

        What hasn’t worked well?




www.ifrc.org
Saving lives, changing minds.
www.ifrc.org/ramp
  Package of field-friendly User Guides:
       1.    Volume 1: Designing a RAMP survey: technical considerations
       2.    Volume 2: Implementing a RAMP survey: practical field guide
       3.    Volume 3: Training a RAMP survey team: guide for trainers


  Living archive of additional resources:
         Example database and STATA files for data cleaning and analysis of a
          sample malaria survey
         Latest up-to-date malaria questionnaires and STATA files for data
          cleaning and analysis
         Country reports and results bulletins, information, useful links


www.ifrc.org
Saving lives, changing minds.
www.ifrc.org
Saving lives, changing minds.
The following slides are extra and can be
  referred to if needed, (but unlikely).




www.ifrc.org
Saving lives, changing minds.
Cost of a IFRC RAMP HH survey for
                   Malaria programming (average)
                                Description               Cost (US $)

Training (4 or 5 days) including two facilitators       10,623

Field survey, including transportation, daily allowances
                                                         12,415
and accommodation
Mobile phones, accessories and air time                 3,806

Survey administration                                   2,243

Total in-country expenditure (US $)                     US $ 29,087

www.ifrc.org
Saving lives, changing minds.
When might a RAMP survey be suitable? Flexibility
              Items that can be adjusted                          Comments

     Adjust precision                             ±10%, 5%, 3%, etc.

     Adjust indicator type (denominator of        -   Person all ages
     indicator) including mixtures of indicator   -   Children <5 years old
     types                                        -   Pregnant women
                                                  -   Households
                                                  -   Schools
     Adjust number of domains                     - 1 domain with 30 clusters
                                                  - 2 domains with 30 clusters to
                                                    compare statistically
                                                  - 10 domains with 30 clusters each to
                                                    compare
     Adjust overall sample size                   -   200 to 5000 households



www.ifrc.org
Saving lives, changing minds.
How is the RAMP method different from MIS/DHS?
                      MIS/DHS                                      RAMP
Complex design, uses external consultants       Simple design, external consultants not
to design survey                                needed
Listing of all households is done in all        Divides clusters into manageable-sized
clusters; expensive, often taking several       segments using standard survey methods;
days in each cluster                            takes <1/2 day

Simple random sampling of households            Simple random sampling of households
(from the cluster list)                         (from the final segment list)
Real-time data cleaning not possible            Real-time data cleaning during the survey

Real-time data analysis not possible; results   Real-time data analysis and results/draft
take several months                             report finished within 3 days of last
                                                interview
Data analysis done by third-party               Organization performs analysis, building
consultants                                     capacity and maintaining control of data


www.ifrc.org
Saving lives, changing minds.
Mobile application

Record field data, even
without network coverage




www.ifrc.org
Saving lives, changing minds.
RAMP
    Based on standard survey sampling methodology

    Web-based platform for survey design, data storage,
     analysis, reporting and data export.

    Field-based data entry through mobile phone application.

    Questionnaires downloaded to standard mobile phones

    Web-based dataset that can export “real-time” for rapid
     analysis and reporting

www.ifrc.org
Saving lives, changing minds.

Mais conteúdo relacionado

Semelhante a Rapid mobile phone based surveys (Scott Chaplowe, IFRC)

Uber - Building Intelligent Applications, Experimental ML with Uber’s Data Sc...
Uber - Building Intelligent Applications, Experimental ML with Uber’s Data Sc...Uber - Building Intelligent Applications, Experimental ML with Uber’s Data Sc...
Uber - Building Intelligent Applications, Experimental ML with Uber’s Data Sc...
Karthik Murugesan
 
Building Intelligent Applications, Experimental ML with Uber’s Data Science W...
Building Intelligent Applications, Experimental ML with Uber’s Data Science W...Building Intelligent Applications, Experimental ML with Uber’s Data Science W...
Building Intelligent Applications, Experimental ML with Uber’s Data Science W...
Databricks
 
Artificial Intelligence_Strategy.pptx
Artificial Intelligence_Strategy.pptxArtificial Intelligence_Strategy.pptx
Artificial Intelligence_Strategy.pptx
SureshMaddi1
 
SplunkLive! Denver - Nov 2012 - Interac
SplunkLive! Denver - Nov 2012 - InteracSplunkLive! Denver - Nov 2012 - Interac
SplunkLive! Denver - Nov 2012 - Interac
Splunk
 

Semelhante a Rapid mobile phone based surveys (Scott Chaplowe, IFRC) (20)

You Want to Mobilize Your Workforce… Now What?
You Want to Mobilize Your Workforce… Now What?You Want to Mobilize Your Workforce… Now What?
You Want to Mobilize Your Workforce… Now What?
 
Offline Surveys: Seamlessly Collect Data Anywhere
Offline Surveys: Seamlessly Collect Data AnywhereOffline Surveys: Seamlessly Collect Data Anywhere
Offline Surveys: Seamlessly Collect Data Anywhere
 
Real Time Business Platform by Ivan Novick from Pivotal
Real Time Business Platform by Ivan Novick from PivotalReal Time Business Platform by Ivan Novick from Pivotal
Real Time Business Platform by Ivan Novick from Pivotal
 
Data Ingestion At Scale (CNECCS 2017)
Data Ingestion At Scale (CNECCS 2017)Data Ingestion At Scale (CNECCS 2017)
Data Ingestion At Scale (CNECCS 2017)
 
National malaria control programme
National malaria control programmeNational malaria control programme
National malaria control programme
 
Getting Started with Splunk Breakout Session
Getting Started with Splunk Breakout SessionGetting Started with Splunk Breakout Session
Getting Started with Splunk Breakout Session
 
The Critical Role of Spatial Data in Today's Data Ecosystem
The Critical Role of Spatial Data in Today's Data EcosystemThe Critical Role of Spatial Data in Today's Data Ecosystem
The Critical Role of Spatial Data in Today's Data Ecosystem
 
Uber - Building Intelligent Applications, Experimental ML with Uber’s Data Sc...
Uber - Building Intelligent Applications, Experimental ML with Uber’s Data Sc...Uber - Building Intelligent Applications, Experimental ML with Uber’s Data Sc...
Uber - Building Intelligent Applications, Experimental ML with Uber’s Data Sc...
 
Building Intelligent Applications, Experimental ML with Uber’s Data Science W...
Building Intelligent Applications, Experimental ML with Uber’s Data Science W...Building Intelligent Applications, Experimental ML with Uber’s Data Science W...
Building Intelligent Applications, Experimental ML with Uber’s Data Science W...
 
Healthcare trends and information management strategy
Healthcare trends and information management strategyHealthcare trends and information management strategy
Healthcare trends and information management strategy
 
Artificial Intelligence_Strategy.pptx
Artificial Intelligence_Strategy.pptxArtificial Intelligence_Strategy.pptx
Artificial Intelligence_Strategy.pptx
 
Data collection
Data collectionData collection
Data collection
 
Mobilizing Electronic Healthcare Records
Mobilizing Electronic Healthcare RecordsMobilizing Electronic Healthcare Records
Mobilizing Electronic Healthcare Records
 
Mobile Testing Trends
Mobile Testing TrendsMobile Testing Trends
Mobile Testing Trends
 
The Essentials of Mobile App Performance Testing and Monitoring
The Essentials of Mobile App Performance Testing and MonitoringThe Essentials of Mobile App Performance Testing and Monitoring
The Essentials of Mobile App Performance Testing and Monitoring
 
Appistry WGDAS Presentation
Appistry WGDAS PresentationAppistry WGDAS Presentation
Appistry WGDAS Presentation
 
ICT Trends WorldWide
ICT Trends WorldWide ICT Trends WorldWide
ICT Trends WorldWide
 
Making it fit: How survey technology proviers are responding to the challenge...
Making it fit: How survey technology proviers are responding to the challenge...Making it fit: How survey technology proviers are responding to the challenge...
Making it fit: How survey technology proviers are responding to the challenge...
 
SplunkLive! Denver - Nov 2012 - Interac
SplunkLive! Denver - Nov 2012 - InteracSplunkLive! Denver - Nov 2012 - Interac
SplunkLive! Denver - Nov 2012 - Interac
 
IBM Aspera In Life Sciences
IBM Aspera In Life SciencesIBM Aspera In Life Sciences
IBM Aspera In Life Sciences
 

Mais de ALNAP

Disaster risk management in nepal
Disaster risk management in nepalDisaster risk management in nepal
Disaster risk management in nepal
ALNAP
 
Comisión Nacional de Prevención de Riesgos y Atención de Emergencias
Comisión Nacional de Prevención de Riesgos y Atención de EmergenciasComisión Nacional de Prevención de Riesgos y Atención de Emergencias
Comisión Nacional de Prevención de Riesgos y Atención de Emergencias
ALNAP
 
Government Forum for Government Response - an overview
Government Forum for Government Response - an overviewGovernment Forum for Government Response - an overview
Government Forum for Government Response - an overview
ALNAP
 
Monitoring and evaluation: The Caribbean Disaster Emergency Management Agency...
Monitoring and evaluation: The Caribbean Disaster Emergency Management Agency...Monitoring and evaluation: The Caribbean Disaster Emergency Management Agency...
Monitoring and evaluation: The Caribbean Disaster Emergency Management Agency...
ALNAP
 
Jamaican government experience and learning on disaster response
Jamaican government experience and learning on disaster responseJamaican government experience and learning on disaster response
Jamaican government experience and learning on disaster response
ALNAP
 
Disaster Management Initiatives in India
Disaster Management Initiatives in IndiaDisaster Management Initiatives in India
Disaster Management Initiatives in India
ALNAP
 
Disaster Response dialogue
Disaster Response dialogueDisaster Response dialogue
Disaster Response dialogue
ALNAP
 
International assistance for major disasters in Indonesia
International assistance for major disasters in IndonesiaInternational assistance for major disasters in Indonesia
International assistance for major disasters in Indonesia
ALNAP
 
Simulation a tool to strengthen capabilities in India
Simulation  a tool to strengthen capabilities in IndiaSimulation  a tool to strengthen capabilities in India
Simulation a tool to strengthen capabilities in India
ALNAP
 
Cracks in the machine: is the humanitarian system fit for purpose? (Peter Wal...
Cracks in the machine: is the humanitarian system fit for purpose? (Peter Wal...Cracks in the machine: is the humanitarian system fit for purpose? (Peter Wal...
Cracks in the machine: is the humanitarian system fit for purpose? (Peter Wal...
ALNAP
 
Data, evidence and access to information
Data, evidence and access to informationData, evidence and access to information
Data, evidence and access to information
ALNAP
 
Whast goes up must come down: challenges of getting evidence back to the ground
Whast goes up must come down: challenges of getting evidence back to the groundWhast goes up must come down: challenges of getting evidence back to the ground
Whast goes up must come down: challenges of getting evidence back to the ground
ALNAP
 
Response analysis in food security crises: a 'road map'
Response analysis in food security crises: a 'road map'Response analysis in food security crises: a 'road map'
Response analysis in food security crises: a 'road map'
ALNAP
 

Mais de ALNAP (20)

Gf john's presentation
Gf john's presentationGf john's presentation
Gf john's presentation
 
From best practice to best fit: changing to a more flexible approach to human...
From best practice to best fit: changing to a more flexible approach to human...From best practice to best fit: changing to a more flexible approach to human...
From best practice to best fit: changing to a more flexible approach to human...
 
ALNAP PPT FOR MONTREUX XIII | 'From best practice to best fit'
ALNAP PPT FOR MONTREUX XIII  |  'From best practice to best fit'ALNAP PPT FOR MONTREUX XIII  |  'From best practice to best fit'
ALNAP PPT FOR MONTREUX XIII | 'From best practice to best fit'
 
ALNAP PPT FOR OFDA | 50 years: From best practice to best fit
ALNAP PPT FOR OFDA | 50 years: From best practice to best fitALNAP PPT FOR OFDA | 50 years: From best practice to best fit
ALNAP PPT FOR OFDA | 50 years: From best practice to best fit
 
Strengthening humanitarian leadership teams: Rethinking leadership?
Strengthening humanitarian leadership teams: Rethinking leadership?Strengthening humanitarian leadership teams: Rethinking leadership?
Strengthening humanitarian leadership teams: Rethinking leadership?
 
A networked response? 2013 presentation
A networked response? 2013 presentationA networked response? 2013 presentation
A networked response? 2013 presentation
 
Disaster risk management in nepal
Disaster risk management in nepalDisaster risk management in nepal
Disaster risk management in nepal
 
Comisión Nacional de Prevención de Riesgos y Atención de Emergencias
Comisión Nacional de Prevención de Riesgos y Atención de EmergenciasComisión Nacional de Prevención de Riesgos y Atención de Emergencias
Comisión Nacional de Prevención de Riesgos y Atención de Emergencias
 
Government Forum for Government Response - an overview
Government Forum for Government Response - an overviewGovernment Forum for Government Response - an overview
Government Forum for Government Response - an overview
 
Monitoring and evaluation: The Caribbean Disaster Emergency Management Agency...
Monitoring and evaluation: The Caribbean Disaster Emergency Management Agency...Monitoring and evaluation: The Caribbean Disaster Emergency Management Agency...
Monitoring and evaluation: The Caribbean Disaster Emergency Management Agency...
 
Jamaican government experience and learning on disaster response
Jamaican government experience and learning on disaster responseJamaican government experience and learning on disaster response
Jamaican government experience and learning on disaster response
 
Disaster Management Initiatives in India
Disaster Management Initiatives in IndiaDisaster Management Initiatives in India
Disaster Management Initiatives in India
 
Disaster Response dialogue
Disaster Response dialogueDisaster Response dialogue
Disaster Response dialogue
 
International assistance for major disasters in Indonesia
International assistance for major disasters in IndonesiaInternational assistance for major disasters in Indonesia
International assistance for major disasters in Indonesia
 
Simulation a tool to strengthen capabilities in India
Simulation  a tool to strengthen capabilities in IndiaSimulation  a tool to strengthen capabilities in India
Simulation a tool to strengthen capabilities in India
 
Humanitarian leadership: who's in charge here?
Humanitarian leadership: who's in charge here?Humanitarian leadership: who's in charge here?
Humanitarian leadership: who's in charge here?
 
Cracks in the machine: is the humanitarian system fit for purpose? (Peter Wal...
Cracks in the machine: is the humanitarian system fit for purpose? (Peter Wal...Cracks in the machine: is the humanitarian system fit for purpose? (Peter Wal...
Cracks in the machine: is the humanitarian system fit for purpose? (Peter Wal...
 
Data, evidence and access to information
Data, evidence and access to informationData, evidence and access to information
Data, evidence and access to information
 
Whast goes up must come down: challenges of getting evidence back to the ground
Whast goes up must come down: challenges of getting evidence back to the groundWhast goes up must come down: challenges of getting evidence back to the ground
Whast goes up must come down: challenges of getting evidence back to the ground
 
Response analysis in food security crises: a 'road map'
Response analysis in food security crises: a 'road map'Response analysis in food security crises: a 'road map'
Response analysis in food security crises: a 'road map'
 

Rapid mobile phone based surveys (Scott Chaplowe, IFRC)

  • 1. Rapid Mobile Phone-based Surveys (RAMP) for Evidence-based Emergency Response ALNAP 28th Annual Meeting, 5-7 March 2013, Washington, D.C. Scott Chaplowe, Senior M&E Officer, IFRC Rose Donna, Director, Datadyne.org Jason Peat, Senior Officer Public Health, IFRC Amanda Mcclelland, Emergency Health Officer, IFRC Joel Selanikio, CEO DataDyne Group Mac Otten www.ifrc.org Saving lives, changing minds.
  • 2. Presentation Overview Application of mobile technology (RAMP) to address specific challenges in data collection during emergency operations. 1) Introduce RAMP 2) How RAMP works 3) Emergency contexts 4) Key considerations www.ifrc.org Saving lives, changing minds.
  • 3. What is RAMP? RAMP (Rapid Mobile Phone-based Surveys) is a survey methodology utilizing mobile phones to help RCRC National Societies, governments, NGOs and other partners efficiently conduct quality surveys that:  Reduced time  Reduced cost  Improved quality assurance  Limited external technical assistance www.ifrc.org Saving lives, changing minds.
  • 4. RAMP Background (www.ifrc.org/ramp) 1. Developed by IFRC in partnership with WHO, CDC, and other partners. 2. Initial focus = malaria program household surveys  Four pilots in Africa 2011-2012 (Kenya, Namibia and Nigeria), 3. Refine and developed trio of user guides: 1. Designing a RAMP survey 2. Implementing a RAMP survey 3. Training a RAMP survey team 4. Scale-up to other program areas – increase survey functionality – use of SMS www.ifrc.org Saving lives, changing minds.
  • 5. RAMP takes advantage of 2 technologies 1. Mobile phone to collect data (Low-cost, standard mobile phones, as well as Android, Symbian, Blackberry, SMS, and iPhone) 2. Web-based software application Enables mobile phones to become a data collection platform www.ifrc.org Saving lives, changing minds.
  • 6. How does RAMP work? 2. Data collection on phone 1. Develop survey on website 3. Transmit data 4. Collate/analyze 5. Data Reports data on computer www.ifrc.org Saving lives, changing minds.
  • 7. Connectivity Internet Internet Required Not Required • Create/edit surveys • Collect data • View/export data • Create reports Can be cellular, wifi, cable www.ifrc.org Saving lives, changing minds.
  • 8. Data monitoring and analysis  Preliminary analysis available before data collection is complete www.ifrc.org Saving lives, changing minds.
  • 9. Timely Reporting Survey bulletins/updates Full survey reports www.ifrc.org Saving lives, changing minds.
  • 10. Digital Data Collection – Changing the way we work The “old” The “new”  Paper questionnaires filled out in  Mobile and internet-based the field technologies used to reduce time for data collection to reporting  Data entered into a computer at a central location  Enables rapid reporting of results, decision making, and action  Data analysis and reporting often takes months to complete  Empowers local ownership of evaluation and research  Local capacity is often under- utilized and there is a dependence on external experts www.ifrc.org Saving lives, changing minds.
  • 11. Anything that can be put on a form Vaccination coverage Surveillance Supply chain management Household surveys Clinic surveys Supervisory checklists www.ifrc.org Saving lives, changing minds.
  • 12. RAMP Potential in Emergencies? Beginning to explore the potential of RAMP in emergency context:  Site assessment – needs, damage  Community assessment – needs, damage  Beneficiary registration  Distribution of emergency (and non-emergency) items  Baseline/endline data collection (monitoring and impact study)  Repeated surveys to track time trends for key indicators  Beneficiary communication – (broadcast Terra)  Beneficiary/community monitoring  Disaster preparedness – EWS monitoring www.ifrc.org Saving lives, changing minds.
  • 13. SMS Disease Surveillance Systems  Piloting in community based disease surveillance  Sierra Leone – 400 community volunteers distributing ORS.  Referred only 5% of cases of AWD they saw in community = only 5% of cases were potentially recorded in normal MoH system.  RAMP allows real time communication and data gathering suitable for this context.  Problems with integration and harmonization of data between community and MoH.  But SMS proved real time information to assist program prioritization in outbreak scenarios. www.ifrc.org Saving lives, changing minds.
  • 14. SMS Considerations  Simplified questions rather than full surveys  Coding syntax with 2 to 7 key variables as best practice  Quantity of messages handled depend on networks, and whether staggered or simultaneous reporting.  Paper form can be used to facilitate data entry to SMS  Quality assurance auto feedback  Reminder SMS to field person to report data at a set time  Thank you SMS to confirm receipt of data.  Ability to send airtime to the mobile account if someone reports from a common central account. www.ifrc.org Saving lives, changing minds.
  • 16. Benefits – decision making  Data rapidly available for decision-making  Maintain data control  Scalable for studies of varying sizes  Shared, electronic database to compare across contexts and with partners to build a body of evidence related to impact www.ifrc.org Saving lives, changing minds.
  • 17. Benefits - management  Cost effective  Do not have to reinvent the wheel – Adaptable RAMP toolkit  Consultants not required  No software licensing or subscriptions  Multiple languages (depending on program)  Export data for custom analysis using any statistical analysis package  Additional SMART phone features www.ifrc.org Saving lives, changing minds.
  • 18. Benefits - management  Online library of survey forms  Collect and aggregate data form multiple areas and partners  Ease of creating and changing analyses/reports  Efficient reporting and dissemination www.ifrc.org Saving lives, changing minds.
  • 19. Benefits - Fieldworkers • Build local capacity for M&E • Standard and familiar mobile phones • No more paper to collect, transport or return • Automated data submission (assuming network) www.ifrc.org Saving lives, changing minds.
  • 20. Benefits - Quality Assurance  Immediate QA:  Real-time error analysis and field correction  Utilize skip patterns, custom logic and validation  Remote QA:  Enables monitoring of survey team work rate, productivity and quality  Monitor times/location of data collection (time/date data stamps)  Provide feedback remotely  Efficient data management reduces “paper” mistakes  Easier to back-up forms/data  Reduced error of repetitive data entry and re-entry  Easier to change and update forms www.ifrc.org Saving lives, changing minds.
  • 21. Reality Check!  Not suitable for very long questionnaires  No “magic bullet” –work is still in the details!  Things to improve – i.e. offline form generation  Technology is a moving target – (hardware and software)  Challenges resource development/training  (But also means improvements and reduced costs) www.ifrc.org Saving lives, changing minds.
  • 22. Questions to Consider  What applications do you see for mobile data collection in the humanitarian sector?  What has worked well?  What hasn’t worked well? www.ifrc.org Saving lives, changing minds.
  • 23. www.ifrc.org/ramp Package of field-friendly User Guides: 1. Volume 1: Designing a RAMP survey: technical considerations 2. Volume 2: Implementing a RAMP survey: practical field guide 3. Volume 3: Training a RAMP survey team: guide for trainers Living archive of additional resources:  Example database and STATA files for data cleaning and analysis of a sample malaria survey  Latest up-to-date malaria questionnaires and STATA files for data cleaning and analysis  Country reports and results bulletins, information, useful links www.ifrc.org Saving lives, changing minds.
  • 25. The following slides are extra and can be referred to if needed, (but unlikely). www.ifrc.org Saving lives, changing minds.
  • 26. Cost of a IFRC RAMP HH survey for Malaria programming (average) Description Cost (US $) Training (4 or 5 days) including two facilitators 10,623 Field survey, including transportation, daily allowances 12,415 and accommodation Mobile phones, accessories and air time 3,806 Survey administration 2,243 Total in-country expenditure (US $) US $ 29,087 www.ifrc.org Saving lives, changing minds.
  • 27. When might a RAMP survey be suitable? Flexibility Items that can be adjusted Comments Adjust precision ±10%, 5%, 3%, etc. Adjust indicator type (denominator of - Person all ages indicator) including mixtures of indicator - Children <5 years old types - Pregnant women - Households - Schools Adjust number of domains - 1 domain with 30 clusters - 2 domains with 30 clusters to compare statistically - 10 domains with 30 clusters each to compare Adjust overall sample size - 200 to 5000 households www.ifrc.org Saving lives, changing minds.
  • 28. How is the RAMP method different from MIS/DHS? MIS/DHS RAMP Complex design, uses external consultants Simple design, external consultants not to design survey needed Listing of all households is done in all Divides clusters into manageable-sized clusters; expensive, often taking several segments using standard survey methods; days in each cluster takes <1/2 day Simple random sampling of households Simple random sampling of households (from the cluster list) (from the final segment list) Real-time data cleaning not possible Real-time data cleaning during the survey Real-time data analysis not possible; results Real-time data analysis and results/draft take several months report finished within 3 days of last interview Data analysis done by third-party Organization performs analysis, building consultants capacity and maintaining control of data www.ifrc.org Saving lives, changing minds.
  • 29. Mobile application Record field data, even without network coverage www.ifrc.org Saving lives, changing minds.
  • 30. RAMP  Based on standard survey sampling methodology  Web-based platform for survey design, data storage, analysis, reporting and data export.  Field-based data entry through mobile phone application.  Questionnaires downloaded to standard mobile phones  Web-based dataset that can export “real-time” for rapid analysis and reporting www.ifrc.org Saving lives, changing minds.

Notas do Editor

  1. To decrease dramatically the time and effort needed to complete data collectionEnables timely reportingResults are rapidly available for decision-making: emergency &amp; development programming.
  2. Epi Info is public domain statistical software for epidemiology developed by Centers for Disease Control and Prevention (CDC) in Atlanta, Georgia (USA).The mobile phone software used for RAMP is EpiSurveyor, created by the not-for-profit organization Datadyne.MIS= Malaria Indicator SurveyRBM= Roll Back MalariaThe Red Cross National Societies at headquarters and branch levels played a leading role in the surveys, and Red Cross volunteers were recruited and trained to collect the data in the field survey. There are many public health problems in Africa that could have been chosen to pilot the surveys. However, malaria was selected to test the RAMP tools. The pilot surveys in Africa established conclusively that National Societies can be a core partner in leading a RAMP survey, with community-based volunteers able to collect data using mobile phones, and the results being available within days of the last interviews in the survey. Lessons learnt from the pilot surveys have been used to refine the RAMP survey methodology and tools, and to provide sample materials in the RAMP toolkit.
  3. Smart phones increasingly as cheaper
  4. RAMP deceases the time between data collection and the production of the survey resultsThe results can be available within days of the last interviews
  5. Traditionally, the paper questionnaires used in the field are sent to a central location where the data are entered into a computer.
  6. Quality assurance: SMS program can automatically feedback on mistakes, i.e. type “I” instead of “1” or “O” instead of “0” automatically generates a correction request to sender. You cant do any of the three last points with RAMP yet !!
  7. Trees!
  8. Reduced time = more timely decision making and action. Real-time dataset exported for rapid analysis and reporting purposesMore timely with changes/adjustments to survey tool
  9. Reduced monetary &amp; environmental costs Paper usage, data entry, transportation and associated costs (i.e. change a form)Additional SMART phone features i.e. GPS, pictures, videoMobile phones are widely-available and understood technology, (jumps digital divides in developing countries).
  10. Paper and data entry
  11. Not suitable for very long questionnaires with a large quantity of skip patternsNo “magic bullet” – the work is still in the details Survey design, enumerator training, data collection and analysis, and effective reporting and dissemination.Things to improve – i.e. offline form generation (i.e. on long airline flights)
  12. - Examples might include: surveys to estimate the percentage of households that were visited by community-based volunteers to discuss the care and repair of mosquito netssurveys to estimate the percentage of households that are receiving clean watersurveys to estimate the percentage of six year old female children that are attending school
  13. MIS=Malaria Indicator SurveyDHS=Demographic and Health Survey
  14. To decrease dramatically the time and effort needed to complete data collectionEnables timely reportingResults are rapidly available for decision-making: emergency &amp; development programming.