going wireless: cloud computing & mhealth...kenya cloud computing example due to poor internet...

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