A systematic review of ehealth systems in developing countries and practical examples

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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

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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

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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

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Directly observed therapy in Haiti

PIH photo

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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”

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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

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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

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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

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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

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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?

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Some examples Supporting HIV

and MDR-TB care

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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?

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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

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Key processes in HIV care

Starting on ART

Patient numbers?

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Key processes in HIV care

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

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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 Institute Medical reseach council SA

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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

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Rwinkwavu Infectious

Disease clinic

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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

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Clinical Alerts (Rwinkwavu, Rwanda)

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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 :

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Physician looking up ARV patients

Photo Rockefeller Foundation

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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

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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

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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

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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

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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)

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eChasqui, Lima

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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

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eChasqui System, Peru

Mean time 6 months Transportation - Mototaxi

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

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JA Blaya

Outcomes of Interest

• Turn-around-times (TAT)

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

• Number of communication errors

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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).

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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.

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JA Blaya 34/21

DST Lab TAT

Randomized Trial

Results

Intervention HCs have significantly lower

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

17.7%

7.9%

60

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Some success and failure factors

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Impact of health care investments

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

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Impact of Ehealth Investment?

0 10 20 30 40 50 60 70 80 90 100

Impact

Power,

Leadership Software, Staffing,

Hardware,

Training,

Investment

Networking,

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Impact of Ehealth Investment?

0 10 20 30 40 50 60 70 80 90 100

Impact

Power,

Hardware,

Training,

Investment

Networking,

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Impact of Ehealth Investment?

0 10 20 30 40 50 60 70 80 90 100

Impact

Power,

Hardware,

Training,

Investment

Networking,

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Impact of Ehealth Investment?

0 10 20 30 40 50 60 70 80 90 100

Impact

Power,

Hardware,

Training,

Investment

Networking,

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MOH ^Village

Dist Clinic CHW

The importance of local data use

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MOH ^District ^Clinic

The importance of local data use

Village CHW

Avoid systems that just suck!

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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.”

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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

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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?

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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

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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

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