1. Going Wireless: Cloud Computing & mHealth

2. Four (uneven) processes of technology change in the context of HMIS
From paper to computer and mobile phone (digitization) From stand-alone to networked systems (interoperability) From registers to electronic patient records and quantified self / Big Data (granularity) From offline to online (web-based HMIS)

3. Topic overview Security and confidentiality of ‘wireless’ health data
Challenges of human resources and IT-infrastructure
Use of mobile and web technologies for health information

5. From stand-alone computers to web-based services
Software services increasingly available online
Gmail, yahoo, googledocs, dropbox, facebook
Access to data from any online device
This has implications for HMIS and health information systems in general

6. Stand-alone HIS deployment
Hard to manage across many users
Difficult to manage data definitions and share access to data
Reinstall deleted software, upgrades, bug-fixes, etc.

7. Challenges with stand-alone implementations
Maintaining servers around the country is difficult and costly
Erratic power supply cause downtime and IT damage
Computer hardware can be fixed locally, but software products and mobile applications, such as DHIS2, require special competence
Keeping HMIS metadata in synch between servers is difficult => comparability loss
Software: virus and mal-ware infections - bad security (USB-sticks)

8. Online Deployment Web browser only requirement
Data not lost in case of disk crash
Online Deployment
Manya et al., “National Roll Out of District Health Information Software (DHIS 2) in Kenya, 2011–Central Server and Cloud Based Infrastructure.” (2012).

9. ”Cloud computing” But where is the data?
Only one installation of the software and database + regular backups
All changes instantly apply to all users
No need to travel to update and synchronize software and database
Users may get access to peer data for comparison analysis
Technical capacity to maintain the server can be centralized
External experts can be given access to help solve technical issues
But where is the data?

10. Requirements for reliable server hosting

11. Modes of online HMIS deployment
MOH hosted
Govt. hosted (not MOH)
Privately hosted
(in country)
(abroad)
Direct ownership. Data security?
National soverginity
Need Capacity to keep it robust and secure
Within country. Better capacity than MoH? Cheaper. National soverginity
Bureaucracy between departments, slow planning cycles
More robust. 24/7 support. In country. Cheaper
Elasticity
Running much the same infrastructure as MoH. Might outsource
More robust. 24/7 support. Cheaper. Elasticity. Minimal investment up front
Other laws apply?

12. Kenya Cloud Computing Example
“Due to poor Internet connectivity and inadequate capacity of the servers at the Ministry of Health headquarters, a reliable central server using cloud computing was set up”
Since Sep 2011 used in all districts (~250)
Online using mobile Internet (USB modems)
Reporting rates are around 92% (forms submitted/forms expected)
Manya et al., “National Roll Out of District Health Information Software (DHIS 2) in Kenya, 2011–Central Server and Cloud Based Infrastructure.” (2012).

13. Managing risks Data is held by governments on behalf of citizens
Centralized data storage may increase dependencies
mobile operators, ISPs, hosting providers, IT- support
Storage of patient data raises security challenges and concerns

14. Some concerns
Are total-cost-of-ownership well understood? Regulatory and policy environment regarding governance of health data Viable exit strategy with vendor – control over data

15. mHealth solutions Low-end Phones Smartphones Medical Sensors
Aggregate Data
Patient Data
Routine reporting
Program “tracking”
Clinical Use
SMS-reminders
Medical Sensors
Voice consultation
Diagnostic tool
Treatment Support
Low-end Phones
Smartphones
Medical Sensors
Heerden el al,. “Point of Care in Your Pocket: A Research Agenda for the Field of M-Health” (2012)

16. Types of mobile applications
Plaintext SMS
Structured SMS
SIM-apps
Mobile Apps using Mobile Data
Mobile Browser – HTML5
Voice Calls!
Interactive voice response (IVR)
Paper is still a viable option in many contexts and for many use cases!

17. Some mHealth application areas
Routine data (HMIS)
Notifiable Diseases (IDSR)
Individual “Tracking” => aggregate
Stock-outs
Individual health monitoring
Reminders
Chronic disease monitoring
Etc.
CHALLENGES
Security of patient data
Complexity of work practice
not easy to capture on a small screen

18. Low-end – SMS-based applications
Sheet to help compose SMS message: “TEST ”

19. Uganda: eMTCT - SMS Weekly Reporting
Goal: Elimination of mother to child transmition of HIV Rolling out to 2,400 Option B+ implementing service outlets
pmtct
a.400.
b.359.
c.50.
d.98.
e.10.
f.50.
g.0.
h.n.
i.y

20. 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) 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

21. mHealth & HMIS
Timeliness Assist decision making based on accurate data on time Expand Reach (community?) NB: Not all solutions have to be measurable in terms of improved health service quality Cost effective HMIS is also important!

22. 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 Feedback and access to locally relevant data on mobile

23. mHealth: empowering health workers?
Integrate with GPS – for disease surveillance or for task force surveillance and control
Some managers would love to have a camera following their health workers 24-7!

24. Feedback usually only when there are errors, mistakes
Direct Supervision is often irregular 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 data?

25. mHealth ‘pilotitis’ Donors short attention span
What works as a pilot does not necessarily scale
Focus on technical feasibility while ignoring organizational and political factors
Hard to evaluate and compare mHealth projects
Heerden el al,. “Point of Care in Your Pocket: A Research Agenda for the Field of M-Health” (2012)
Labrique et al., “H_pe for mHealth: More ‘y’ or ‘o’ on the Horizon?” (2013)

26. Individual data in increasing demand
Insurance schemes (Universal Health Coverage)
Mother and child tracking for follow-up
Various mHealth initiatives (programme tracking (TB/HIV)
Implications
Integration with Civil Registration & Vital Statistics (CRVS) becomes increasingly important
Need for robust Unique ID scheme

27. Extending reach through mobiles
Community
Villagers
Health Workers
Clinics
Districts
Hospitals
mobile solutions
for different contexts and budget
Java
SMS
Android
PC/laptop/tablet
Browser

28. DHIS2 as a platform with mobile innovations
As DHIS2 grows, the core must remain stable, but allow for innovation
WEB API and web app support
DHIS2
Analytics
Maps
Mobile
Data entry
Org units
Aggregation
Interoperability