Going Wireless: Cloud Computing & mHealth
Terje Aksel Sanner
Topic overview
• Use of mobile and web technologies for health information
• Security and confidentiality of ‘wireless’ health data
• New needs for human resources and IT-infrastructure
Four processes of technology change in HMIS
From paper to computer and mobile phone (digitization)
From stand-alone to networked systems (integration)
From registers to electronic health records and quantified self (big data)
From offline to online (web-based HMIS)
From stand-alone computers to web-based services
Software services increasingly available online• Gmail, yahoo, googledocs, dropbox, facebook, DHIS2
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.
Online Deployment Web browser only requirementData not lost in case of disk crash
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”
• 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
Only one installation of the software and database + regular backups
But where is the data?
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? User training remains the most expensive budget item
Lack of regulatory and policy environment regarding governance of health data
Lack of exit strategy with vendor – control over data / subscriptions
mHealth solutionsAggregate Data
Clinical Use
Patient Data
Program “tracking”
Medical Sensors
Smartphones
Routine reporting
Treatment Support
Voice consultationDiagnostic tool
Medical Sensors
SMS-reminders
Low-end Phones
Heerden el al,. “Point of Care in Your Pocket: A Research Agenda for the Field of M-Health” (2012)
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
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 during manual collation and transfer of data
Feedback and access to locally relevant data on mobile
mHealth: empowering health workers?
Integrated GPS for disease surveillance or for task force surveillance?
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 demandInsurance schemes (Universal Health Coverage)
Mother and child tracking for follow-up
Various mHealth initiatives tracking TB, HIV etc.
Valuable data for pharmaceutical companies
Implications Integration with Civil Registration &
Vital Statistics (CRVS) becomes important
Need for robust Unique ID scheme
JavaSMS Android PC/laptop/tabletBrowser
Community
VillagersCommunity
Health Workers
Clinics
Districts
Hospitals
Extending reach through mobiles
mobile solutionsfor different contexts and budget
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