Going Wireless: Cloud Computing & mHealth

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)

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

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

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.

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)

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).

”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?

Requirements for reliable server hosting

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?

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).

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

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

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)

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!

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

Low-end – SMS-based applications Sheet to help compose SMS message: “TEST. 8. 0. 0. 8. 7. 3. 4”

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

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

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!

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

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!

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?

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)

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

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

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