a systematic review of ehealth systems in developing countries and practical examples ·...

A systematic review of eHealth

systems in developing countries and

practical examples

Hamish SF Fraser MBChB, MRCP, MSc

Director of Informatics and Telemedicine,

Partners In Health

Assistant Professor,

Division of Global Health Equity, Brigham and Womens

Hospital and Harvard Medical School

Summary

• Motivation for EMR systems in developing

countries

• Systematic review of Global eHealth

• Key studies addressing questions in

delivery of care for HIV and MDR-TB

• Some lessons learned and next steps

Partners In Health Model of Care

• Access to health care for all people

• Creation of long-term development by partnering

with local people and communities

• Use of community health workers to grow a local

and sustainable work force

• Addressing the effects of poverty including poor

nutrition, water, and housing

• Drawing on the resources of the world’s elite

medical and academic institutions and on the

lived experience of the world’s poorest and

sickest communities

Directly observed therapy in Haiti

PIH photo

Status of Global eHealth

• Rapid development over the last 3 years

– Bellagio meeting on e-Health in July 2008

• Driven by the coincidence of:

– need for better Global Health Delivery

– increased resources for health system

strengthening such as the Global Fund, PEPFAR

– more effective, robust, low-cost technologies

– massive growth of mobile phone use and

“mHealth”

Systematic review of

evaluation studies

Blaya, Fraser, Holt, Health Affairs 2010, 29;2: 244-251

• Surveyed 2043 articles and reports • Used 45 in final analysis• Completed summer 2009

Summary of the Key Studies

eHealth Category Qualitative Quantitative

Descriptive

Studies

Controlled

Studies

Electronic Health Record (EHR) 5 1 5

Laboratory Information Management Systems (LIMS) 0 1 2

Pharmacy Information Systems 4 2 3

Patient Registration or Scheduling Systems 1 0 2

Monitoring, Evaluation and Patient Tracking Systems 0 2 4

Clinical Decision Support Systems (CDSS) 1 0 3

Patient Reminder Systems 0 1 3

Research or Data Collection Systems 5 1 11

TOTAL 15 8 32

Findings of the Review

Key functions supported by “initial” evidence:

• Tracking patients through treatment initiation, monitoring adherence, and detecting those at risk for loss to follow-up

• Decreasing time to create administrative reports

• Tools to label or register samples and patients

• Collection of clinical or research data using PDAs

• Reduction in errors in laboratory and medication data

• Reminding patients of health care actions

Ongoing reviews

• Role of eHealth and mHealth in HIV care

• Review of broader Global eHealth

literature

• Small number of more rigorous studies

have been published in last two years

• Growing interest in mHealth

Building the evidence base for

decision makers

• How do we answer the question “why

should we invest in eHealth rather than

medical staff, clinics, drugs or training?”

• When does eHealth become important or

essential rather than an option?

• What are the best ways to deliver, support

and sustain eHealth?

• What is not useful or not ready?

Some examples Supporting HIV

and MDR-TB care

Original challenge:

Can care for HIV and MDR-TB be delivered:

1. In settings with limited or absent infrastructure?

2. To thousands or tens of thousands of patients?

3. Over long periods of time?

4. With outcomes equivalent to treatment in the developed world?

5. At a “manageable” cost?

Key processes in HIV care

Case finding and VCT

Registration in pre-ART care

Monitoring clinical & lab status

Starting on ART

Drug supply management

Ensuring adherence to Rx

Monitoring side effects and

opportunistic infections





Starting on ART





Patient

numbers?





Starting on ART





Home based care,

AMPATH mHealth

Registry/EMR

CD4 alert and tracking

Registry, EMR system

Inventory / dispensary

and shipping systems

CHW, mHealth/SMS

Paper flowsheets,

EMR alerts, CHW

OpenMRS:

a modular, open source, EMR platform

• Developed as a collaboration of PIH, the Regenstrief

Institute and South African MRC

• Uses concept dictionary for data storage

• Modular design simplifies adding new functions and linking

to other systems

• Released with open source license (April 2007)

• Core of paid programmers with growing community support

• Clinical use in over 40 developing countries

• Secure logins and auditing of access and data changes

• www.openmrs.org

Partners In Health Regenstrief InstituteMedical reseach council SA

OpenMRS at PIH

sites in Rwanda

• Currently used for 24 PIH –

supported MOH health centers

• HIV, TB, primary care and heart failure care

• 20,000 patients tracked (approx)

• Rwandan data officers, data managers, and

programmers

• Many sites have their own server and maintain a

synchronized copy of the entire database

• Using laptop servers and cellular GPRS network

Rwinkwavu

Infectious

Disease clinic

Evaluating access to CD4 counts

• We evaluated whether the ID physicians

had access to the latest CD4 count for

their patients in Rwinkwavu, Rwanda

• The physicians record their belief of the

correct CD4 on the follow-up form based

on paper lab result forms

• We checked if CD4 was current before

and after a new lab component was added

to the EMR to ensure up to date results

Clinical Alerts (Rwinkwavu, Rwanda)

Results – Access to CD4 counts

• The proportion of CD4 counts conducted

within the past 60 days but unknown to the

clinician at the time of consultation was:

• 24.7% in the pre-intervention period

• 16.7% in the post intervention period

• 32.4% reduction in CD4 loss (p=.002)

• We are now extending direct clinician

access to the EMR

Amoroso et al, Stud Health Technol Inform. 2010;160:337-41

:

Physician looking up ARV patients

Photo Rockefeller Foundation

Impact of OpenMRS patient

summaries at AMPATH

• The OpenMRS EMR system at AMPATH in Western

Kenya was used to generate printed patient summaries

including reminders for ordering repeat CD4 counts

• The computerized reminder system identified 717

encounters (21%) with overdue CD4 counts

• In the intervention clinic with computer-generated

reminders, CD4 order rates were significantly higher

compared to the control clinic:

53% vs 38%, OR =1.80, CI 1.34 to 2.42, p<0.0001

• Order rates in intervention clinic were even higher (63%)

in cases where the summary was actually printed.

Were MC, et al. J Am Med Inform Assoc (2011).doi:10.1136/ jamia.2010.005520

HIV Treatment Adherence

Reminders (Lester)

• RCT of weekly SMS reminders for ART adherence

in Kenya with follow-up by phone if no response

• Outcomes:

– self-reported ART adherence (>95% of prescribed doses

in the past 30 days at both 6 and 12 month follow-up

visits)

– Plasma HIV-1 viral RNA load suppression

Lester et al, Lancet Vol 376 November 27, 2010

SMS reminders: outcome

• Adherence to ART was reported in 168 of 273

(61.5%) patients receiving the SMS intervention

compared with 132 of 265 (49.8%) in the control

group ( [RR] 0·81, 95% CI 0·69–0·94; p=0·006)

• Suppressed viral loads were reported in 156 of

273 patients in the SMS group and 128 of 265 in

the control group,

• (RR for virologic failure 0·84, 95% CI 0·71–

0·99; p=0·04)

• Loss to follow up rate 6% intervention, 11%

control P=0.094

HIV Treatment Adherence

Reminders (Pop-Eleches)

• RCT of four SMS reminder interventions

• 431 adults randomized to control of one of 4

interventions

• Short or long SMS reminders daily or weekly

• MEMS pharmacy monitor used for adherence

• Outcome: adherence >90% for 48 weeks in 53%

with weekly reminder, and 40% in controls

• Short and weekly SMS were much better

• Loss to follow-up rates: 10 – 22% no change

Pop-Eleches et al, AIDS 2011, 25: 825 - 834

Other important studies

• SMS for Life: a pilot project to improve anti-

malarial drug supply management in rural

Tanzania using standard technology.

(Barrington et al)

• Evaluation of computerized health management

Information system for primary health care in

rural India (Krishnan et al)

• The effect of mobile phone text-message

reminders on Kenyan health workers’ adherence

to malaria treatment guidelines: a cluster

randomised trial. (Zurovac, et al Lancet 2011)

eChasqui, Lima

Example: MDR-TB in Lima, Peru

• Highest incidence of TB in South America

• 40,000 patients treated with DOTS per year

• > 3% have MDR-TB

DOTS = directly observed therapy

short course

eChasqui System, Peru

Mean time6 months

Transportation - Mototaxi

Blaya J et al, Int J Tuberc Lung Dis. 2010 Aug;14(8):1009-15

JA Blaya

Outcomes of Interest

• Turn-around-times (TAT)

• Proportion of Late DST Results (Lab TAT > 60 days)

• Number of communication errors

eChasqui Results (delays)

• Intervention health centers took significantly less

time to receive:

- DST results (median 11 vs. 17 days, p<0.001)

- culture results (5 vs. 8 days, p<0.001)

- 47% fewer DSTs took over 60 days to arrive

(p=0.12).

• No change in time to start or modify treatment

• Patients in intervention health centers had a 20%

reduction in time to culture conversion (p=0.047).

eChasqui results (errors)

• Results reported in intervention HCs compared

to control HCs had respectively:

• 82% less errors for drug susceptibility tests

(2.1% vs. 11.9%, P < 0.001, OR 0.17, 95% CI

0.09–0.31)

• 87% less errors for cultures (2.0% vs. 15.1%, P

<0.001, OR 0.13, 95%CI 0.07–0.24),

• Preventing missing results through online

viewing accounted for at least 72% of all errors.

JA Blaya 34/21

DST Lab TATRandomized Trial

ResultsIntervention HCs havesignificantly lower

1. DST Lab TAT (p<0.001) - 11 vs 17 days (median)

17.7%

7.9%

60

Some success and failure factors

Impact of health care investments

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Impact

Vaccines

Medical training

New hospital

Supply chain

CHWs

Nurse training

Teaching Hospital

Operating room

Investment

Mobile clinics

Impact of Ehealth Investment?

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Impact

Power,

Leadership

Software,Staffing,

Hardware,

Training,

Investment

Networking,

MOH Village

CHWDist Clinic

The importance of local data use

MOH District Clinic

The importance of local data use

Village

CHW

Avoid systems that just suck!

Call to Action on Global eHealth Evaluation

Consensus Statement of the WHO Global eHealth

Evaluation Meeting,

Bellagio, September 2011

“To improve health and reduce health inequalities, rigorous

evaluation of eHealth is necessary to generate evidence

and promote the appropriate integration and use of

technologies.”

Bellagio Call to Action

• Develop repository of tools, studies,

frameworks, teaching materials

• Refine frameworks – GEP-HI, PRISM,

KDS, etc.

• Create training course in developing

countries

• Advocate to funders require evaluation in

eHealth projects

• Follow up meeting in SF last week

Observations

• Large investment in eHealth to date -

$Billions!!

• Particular need to monitor day to day

performance and activities:

– down time, forms entered, usage, data quality

and completeness

• Measuring impact of eHealth, what are the

alternatives ans control groups?

Conclusion

• The evidence base is improving…slowly

• There is still much to do - even the most

rigorous studies have many unknowns

• Important questions of how role of e/mHealth

differs in resource poor environments

Collaborators and Funders • Partners In Health

• Regenstrief institute

• Medical Research Council, South Africa

• World Health Organization

• US Centers for Disease Control

• Brigham and Women hospital

• Harvard Medical School

• University of KwaZulu-Natal

• Millennium Villages Project

• International Development Research

Centre, Ottawa

• Rockefeller Foundation

• Google Inc

a systematic review of ehealth systems in developing countries and practical examples ·...

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