Going Wireless; Clouds and MobilesHealth Information Systems Strengthening

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Going Wireless

– Security and confidentiality issues around health data (shift from paper -> stand alone computers -> web-based services)

– Challenges of human resources– Global priorities with implications for IHIAs

Civil registration and vital events systems

Universal Health Coverage

Insurance schemes

Pay for performance schemes (P4P)

”Cloud computing”

mHealth

Evolution of HISP / DHIS

HISP since 1994– Essential dataset / Hierarchy of standards – Decentralization– Local information use, bottom-up HIS development

Now, more diverse– Many agendas: NGOs, international orgs. – Partnerships– Top-down and bottom-up

Technology has changed: from offline to online and mobile

From stand-alone to web-based

Software services increasingly available as online services rather than installed applications on computers– Gmail, yahoo, googledocs, dropbox, facebook

Access to data from any computer (that is online)

This has implications, also for HIS/IHIA

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Stand-alone HIS deployment

Software installed on each computer (e.g. Sierra Leone) or mobile (e.g. Punjab)– Hard to manage across many users– How to maintain the data definitions, share data,

get access to data etc?– Reinstall deleted software, upgrades, bug-fixes,

etc.

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Distributed stand-alone implementation in Sierra Leone

Each district (13) had their own instance of DHIS2

Each district run a Local Area Network (LAN), where several people within the district could access the same database, running on a Linux (district) server

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Challenges of stand-alone implementation Maintaining servers on LANs distributed around the country is

challenging and costly Power supply interruptions frequent Workstation problems can be dealt with by local IT companies, but DHIS

on the server requires more specialized competence Even with hardware working 100%, keeping the entire HMIS metadata

in synch between so many systems over time is an uphill battle => comparability loss

Software: virus and mal-ware infections, bad security practices (USB-sticks)

Each of these factors point to the non-sustainability of

distributed architecture and the resulting pressure to technically centralize

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Online Deploymento Web browser only requiremento Computer can be reset to fix problemso No data lost in case of disk crash

Great promises of ”cloud computing”

Only one installation of the software and database + backups– All changes instantly apply to all users

– No need to travel around the country to update and synchronize software and database

– All users can get access to peer data for comparison analysis

– Capacity to maintain the server can be centralized and professionalized

– External experts can be given access to help solve issues

But where will the data reside?

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WARNING: Technical expertise to maintain the server is crucial and needed from day 1 – all users depend on the server running

Requirements for reliable hosting

Modes of online deploymentMOH hosted Govt. hosted (not

MOH)Privately hosted

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, 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

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In c

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Three examples

Kenya hosting privately abroad

Rwanda hosting within MoH

Ghana hosting at a national private ISP

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Kenya“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”

“Cloud computing" in this context meant a third part commercial Linux hosting company with its primary site in London, UK.

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Since Sep 2011 used in all districts (~250)

Online using mobile Internet (USB modems)

Reporting rates are around 92% (forms submitted/forms expected)

Rwanda

Smaller than Kenya. 11 million living within a land area of 25,000 square kilometers. Approximately 550 registered health facilities spread across 30 districts

Original plan in mid-2011 was to follow the Kenya example. MOH e-Health Coordinator intervened - data had to be stored within the country!

Revised plan to host DHIS in the National Data Centre, along with other eHealth systems

With training and configuration of the system complete, the NDC wasn't ready ..

DHIS2 was set up within the MOH as a temporary solution

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Ghana

Scale similar to Kenya ... 170 districts and 4000 facilities

New server specifically for running DHIS

Like Kenya, there were perceived difficulties in locating server within MOH

Decision was made to physically host the server with a local Accra Internet Service Provider

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Learnings from the deployment cases

Despite challenges, the online deployments are viewed as successful by national stakeholders

Reporting rates are high, users are active, data is visible in ways it wasn't before

Handover of full control of the servers to the country teams remains an outstanding concern in all cases (Rwanda is furthest along this path)

The more distributed model of Sierra Leone is very hard to sustain over time

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Managing risks Data is held by government in trust on behalf of citizens. Ghana and Kenya both prefers internal hosting. Outsourced

hosting is pragmatic. Centralized data storage has increased dependencies - mobile

operators, ISPs, hosting providers, technical support (HISP) Ghana and Rwanda risk hardware failure Kenya risk in terms of governance and sovereignty Outsourcing hardware to the “cloud" can obviate the need for

internal technical competence and infrastructure, but generates requirements for new IS management capacity

The storage of patient data raises security compliance challenges on extra territorial servers! [or any server…]

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Some Concerns

Are tradeoffs and total-cost-of-ownership issues understood when promoting eHealth and mHealth initiatives?

Regulatory and policy environment regarding governance of health data is far behind the technological possibilities. Does it matter if HMIS data is hosted in London or Chicago?

Important to have a viable exit strategy with vendors – generally means maintain control of the data (e.g. avoid premium charges or subscriptions required to access own data)

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Better data quality?

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Individual based data in public health?

Universal Health Coverage (UHC)

Quality of health services?

Some concepts relevant to UHC

Access: health services that people might need are available, of good quality, and close to them

Coverage of interventions: people who need an intervention actually receive it

Effective coverage: people who need health intervention obtain them in a timely manner and at a level of quality necessary to obtain the desired effect

Obstacles to obtaining effective coverage: physical access, affordability, acceptability

Universal Health Coverage: people receive the services they need without incurring financial hardship

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Individual data – increasing importance

Universal health coverage (everybody counts)

Insurance schemes

Pregnant mother and child tracking

Various mHealth initiatives (programme tracking)

Implications– Civil Registration & Vital Statistics (CRVS)

becomes increasingly important– Need for CRVS to speak with HIS/IHIA

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CRVS: some key functions

Add, change, count, inquire data and events relating to individuals

Check routine data for errors and correctness Provide data export for external systems Process and tabulate vital events Integrate CR with other data sources

(surveys, sample registration, etc.) Create birth, death, cause of death, live birth

statistics

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CRVS integration

Unique identifiers Patient access to their own health data Pay for performance schemes (P4P)o Avoid high-risk patientso Some insurance companies will not pay for new practices (reduce errors) o Physicians and hospitals can bill for additional services that are needed

when patients are injured by mistakes

Citizen empowerment, doctor feedback Privacy, Security New data collection tools & routines

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Recap

Web-based HIS has many challenges as well as opportunities

Application domain is becoming incresaingly complex, requiring multisectoral information

Institutional barriers still remain, for example in CRVS

Legal frameworks are not in place in most developing countries to protect indvidual data. The demand, however, continues to increase.

Possibilities offered by technologies far outpace progress in legal frameworks

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mHealth

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mHealth

Public HealthPublic Health

Clinical UseClinical Use

Patient CenteredPatient Centered

Program trackingProgram tracking

Medical SensorsMedical Sensors

Diagnostic toolDiagnostic tool

SmartphoneSmartphone

Routine reportingRoutine reporting

SMS-remindersSMS-reminders

Treatment SupportTreatment Support

Voice consultationVoice consultation

Low-end PhoneLow-end Phone

Use case Types of mobile application & data bearer

Plaintext SMS Structured SMS SIM-apps GPRS-apps (Java J2ME) Mobile Browser – offline/online

Paper is still a viable option in many contexts!

Aggregate data: routine reporting of health data from facilities/communities

RobustDomesticatedNot so prone to theft, preferably privately owned Long standby time on one charge (e.g. with small solar

panel)Local service /maintenance competenceLocal mobile phone literacy Mobile coverage [ where there is no road, no power, no

fixed line phone]

Low End Mobile PhonesLow End Mobile Phones

mHealth & HMIS goals

Timeliness & Data Quality

Assist local decision making based on accurate data on time

NB: Not all improvements have to be measurable in terms of improved health services. Cost effective service provision and HMIS functions also important

How can mobiles improve HMIS?Data Quality - Validation rules

On the spot data capture and transfer

Save time and reduce mistakes caused by manual aggregation of data

mHealth application areas Routine data (HMIS) Notifiable Diseases (IDSR) Individual “Tracking” => aggregate

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?

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!

Missing Feedback in HMIS?What do we know about supervision?

Feedback and reward from local community are significant to health worker motivation and performance

Supervision feedback only when there are errors, mistakes, shortcomings

Supervision is 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?

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”

Integrate with mobile banking?

Phone Credit top-up/ reimbursements/bonus

The plague of ‘pilotitis’ in HIS

HIS in developing countries – `pilotitis’ abound HMIS – requires ‘all or nothing’? Systems do not sustain or scale, and we keep

repeating the same mistakes – Why? We operate in isolation, do not share experiences,

and are unable to link pilot work with national HIS needs

Pilotitis in Mhealth, Uganda

Problem of 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 mHelath projects

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?