1. mHealth

2. 2 Aggregate Clinical Use Patient Centered Program tracking Medical Sensors Diagnostic tool Smartphone Routine reporting SMS-reminders Treatment Support Voice consultation Low-end Phone

3. Use case  Types of mobile application & data bearer  Plaintext SMS  Structured SMS  SIM-apps  “GPRS-apps” (Java J2ME)  Mobile Browser – offline/online  Voice!  Interactive voice response (IVR) Paper is still a viable option in many contexts!

4. Sheet to help compose SMS message: TEST. 8. 0. 0. 8. 7. 3. 4

5. Aggregate data: routine reporting of health data from facilities/communities Robust Available Not so prone to theft sometimes privately owned Long standby time on one charge (e.g. with small solar panel)charge Local service /maintenance competence Local mobile phone literacy Mobile coverage [ where there is no road, no power, no fixed line phone] Low End Mobile Phones

6. mHealth & HMIS goals Timeliness Assist local decision making based on accurate data on time NB: Not all solutions have to be measurable in terms of improved health service quality. Cost effective HMIS is also important

7. How can mobiles improve HMIS? Data Quality - Validation rules on phone On the spot data capture and transfer Save time and reduce mistakes caused by manual collation and transfer of data mHealth application areas  Routine data (HMIS)  Notifiable Diseases (IDSR)  Individual “Tracking” => aggregate  Stock-outs  Individual health monitoring  Reminders  Etc.

8. Types of mHealth data Name based/program tracking (ANC, HIV, TB) or aggregate data (ISDR & routine HMIS) CHALLENGES  Security of identifiable patient data  Complexity of work routine (not easy to capture on a small screen – or any screen)  mHealth - Additional burden or Helpful tool?

9. mHealth; empowering health workers or job surveillance? Integrate with GIS/GPS – for disease surveillance or can be used for task force surveillance and control [Example: daily reporting Punjab] Some managers would love to have a camera-drone following their health workers 24-7!

10. Missing Feedback in HMIS Supervision feedback only when there are errors, mistakes, shortcomings Supervision is often irregular and non-supportive and requires time & resources Mobile “Feedback” (access to processed data)  Progress over time  Comparisons to other organization units [vertical/horizontal]  HMIS metadata – completness, timeliness %  Push or Pull?

11. What’s in it for the end users? Save money and time spent on travel [maybe!] More time for service provision [ideally…] Closed User Group (CUG) agreement with mobile operator = free communication with colleagues! Processed data ”Feedback” Phone Credit top-up/ reimbursements/bonus

12. Pilotitis in Uganda

13. Problems with mHealth Pilots Additional burden for health workers Donor short attention span - unsustainable What works as a pilot does not necessarily scale Pilots may focus on technical feasibility while ignoring larger organizational and political mechanisms (e.g. health worker unions) Hard to evaluate and-compare across mHelath projects

14. Partners in mHealth “Ecosystem of actors”: Ministry of Health, NGOs, researchers, Programme Donors &… Mobile Operators  Network coverage  Closed User Group Agreement  Social responsibility or New revenue streams? BUT mHealth Initiative may get stuck with one operator! Win-Win-Win?