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

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1 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

2 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

3 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

4 Directly observed therapy in Haiti PIH photo

5 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

6 Systematic review of evaluation studies Surveyed 2043 articles and reports Used 45 in final analysis Completed summer 2009 Blaya, Fraser, Holt, Health Affairs 2010, 29;2:

7 Summary of the Key Studies ehealth Category Qualitative Quantitative Descriptive Studies Controlled Studies Electronic Health Record (EHR) Laboratory Information Management Systems (LIMS) Pharmacy Information Systems Patient Registration or Scheduling Systems Monitoring, Evaluation and Patient Tracking Systems Clinical Decision Support Systems (CDSS) Patient Reminder Systems Research or Data Collection Systems TOTAL

8 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

9 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

10 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?

11 Some examples Supporting HIV and MDR-TB care

12 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?

13 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

14 Key processes in HIV care Case finding and VCT Patient numbers? 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

15 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 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

16 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 Partners In Health Regenstrief Institute Medical reseach council SA

17 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

18 Rwinkwavu Infectious Disease clinic

19 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

20 Clinical Alerts (Rwinkwavu, Rwanda)

21 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

22 Physician looking up ARV patients Photo Rockefeller Foundation

23 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< 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: / jamia

24 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

25 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 ; 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 ; p=0 04) Loss to follow up rate 6% intervention, 11% control P=0.094

26 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:

27 Other important studies SMS for Life: a pilot project to improve antimalarial 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)

28 echasqui, Lima

29 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

30 echasqui System, Peru Transportation - Mototaxi Mean time 6 months Blaya J et al, Int J Tuberc Lung Dis Aug;14(8):

31 Outcomes of Interest Turn-around-times (TAT) Proportion of Late DST Results (Lab TAT > 60 days) Number of communication errors JA Blaya

32 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).

33 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 ) 87% less errors for cultures (2.0% vs. 15.1%, P <0.001, OR 0.13, 95%CI ), Preventing missing results through online viewing accounted for at least 72% of all errors.

34 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 JA Blaya 34/21

35 Some success and failure factors

36 Impact of health care investments Impact Operating room Teaching Hospital 70 Medical training New hospital Supply chain CHWs Nurse training Mobile clinics Vaccines Investment

37 Impact 100 Impact of Ehealth Investment? Power, Hardware, Networking, Staffing, Software, Training, Leadership 10 0 Investment

38 Impact 100 Impact of Ehealth Investment? Power, Hardware, Networking, Staffing, Software, Training, Leadership 10 0 Investment

39 Impact 100 Impact of Ehealth Investment? Power, Hardware, Networking, Staffing, Software, Training, Leadership 10 0 Investment

40 Impact 100 Impact of Ehealth Investment? Power, Hardware, Networking, Staffing, Software, Training, Leadership 10 0 Investment

41 The importance of local data use MOH Dist Clinic Village CHW

42 The importance of local data use Avoid systems that just suck! MOH District Clinic Village CHW

43 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.

44 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

45 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?

46 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

47 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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