Impacts of e-health on the outcomes of care in low- and middle-income countries: where do we go from here?
John D Piette a, KC Lun b, Lincoln A Moura c, Hamish SF Fraser d, Patricia N Mechael e, John Powell f & Shariq R Khoja g
a. Veteran Affairs Ann Arbor Center for Clinical Management Research, Health Services Research and Development Center of Excellence, PO Box 130170, Ann Arbor, MI, 48113-0170, United States of America (USA).
b. School of Computing, National University of Singapore, Singapore.
c. Assis Moura eHealth, São Paulo, Brazil.
d. Harvard Medical School, Boston, USA.
e. Earth Institute, Columbia University, New York, USA.
f. Division of Health Sciences, University of Warwick, Coventry, England.
g. eHealth Resource Centre, The Aga Khan University, Nairobi, Kenya.
Correspondence to John D Piette (e-mail: firstname.lastname@example.org).
(Submitted: 18 November 2011 – Revised version received: 27 January 2012 – Accepted: 31 January 2012.)
Bulletin of the World Health Organization 2012;90:365-372. doi: 10.2471/BLT.11.099069
Difficulties in achieving health targets, such as the Millennium Development Goals, and growing consumer demand have forced health planners to look for innovative ways to improve the outcomes of health-care and public-health initiatives while controlling service costs. Health systems must address diverse population needs, provide high-quality services even in remote and resource-poor environments, and improve training and support for health-care workers. Services that can be scaled up and are reliable (despite any infrastructural deficits) and cost-effective are in high demand worldwide, especially in low- and middle-income countries. E-health systems have the potential to support these objectives in ways that are both economically viable and sustainable.
E-health tools are designed to improve health surveillance, health-system management, health education and clinical decision-making, and to support behavioural changes related to public-health priorities and disease management.1 Some systematic evidence of the benefits of e-health in general,2–4 and of specific areas of e-health, such as decision-support systems for clinicians5,6 or patient-targeted text messaging,7–10 already exists. The objectives of the current review were to highlight gaps in our knowledge of the benefits of e-health and identify areas of potentially useful future research on e-health. There were three main topics of interest: outcomes among patients with chronic health conditions, the cost-effectiveness of various e-health approaches, and the impact of e-health in low- and middle-income countries.
We focused on evidence for the impact of e-health in three areas identified by prior reviews: (i) systems facilitating clinical practice; (ii) institutional systems, and (iii) systems facilitating care at a distance.3,4
Systems facilitating clinical practice include electronic medical record systems, picture archiving and communication systems for managing digital medical images, and laboratory information systems that automate laboratory workflow and reporting. Institutional systems include systems for health information and management, early disease warning and disaster management. These systems aggregate data from health facilities and patients to create community-wide views of disease trends and clinical activity.11,12 Systems facilitating care at a distance include the use of a short message service (SMS) or other text messaging to improve outcomes through patient reminders; between-visit monitoring and/or health education; videoconferencing facilities for live consultations and asynchronous communication between clinicians, and automated telephone calls with recorded messages (sometimes called interactive voice response calls).
Multiple systematic reviews have been conducted on some of these e-health approaches, whereas the rest are barely covered in the peer-reviewed literature. To provide a rapid updated summary of the evidence for decision-makers, we conducted a scoping review by gathering information through targeted scans of scientific databases, reviews of reference lists and conversations with other experts.13 Emphasis was given to projects that provided insights on the impact of e-health on the outcomes of chronic disease management and the scalability of e-health tools and/or data relevant to low- and middle-income countries. Throughout the review we highlight priorities for future research.
Systems facilitating clinical practice
In developed countries, usage of electronic medical-record systems varies widely. For example, such systems are used for nearly all primary care patients in Denmark, the Netherlands, Sweden and the United Kingdom of Great Britain and Northern Ireland, but for less than 20% of such patients in the United States.14,15 In low- and middle-income countries, electronic medical record systems, such as Dream, OpenMRS, Baobab Health (in Malawi) and the ZEPRS antenatal system (in Zambia), are available in some larger specialist hospitals but are rarely available in smaller health centres.16–19 The use of picture archiving and communication systems in low- and middle-income countries is, however, increasing rapidly.20–22
Impact on outcomes and health-care costs
A recent review of reviews of decision-support systems found that, although 52 (57%) of 91 unique studies demonstrated improved practitioner performance, only 25 (30%) of the 82 in which patient outcomes were assessed showed benefits to patient outcomes.5 In a series of meta-analyses, the prompting of clinicians via electronic medical record systems was found to increase the number of guideline-recommended preventive care services that were performed by a mean of 13%.6 In Kenya, order rates for overdue CD4+ lymphocyte counts were 53% (or even 63% if summaries that could not be printed were excluded) in a clinic that used computer-generated reminders produced in an electronic medical record system, but only 38% in a control clinic that had no such system.23 Although in a systematic review published in 1997 most studies of picture archiving and communication systems were found to cite the benefits of such systems, evidence of improvements in health outcomes, efficiency or costs was nil.24 There also appeared to be no convincing proof that digital X-ray images were at least as good as conventional X-ray films in terms of diagnostic accuracy.24 Information on the impact of laboratory information systems on outcomes is also fragmentary, although the use of such systems within the National Peruvian Tuberculosis Programme was associated with significant reductions in reporting errors and delays.25 A 2008 study involving over 5000 health-care organizations in the United States showed that, while hospitals were implementing laboratory information systems at a steady rate, many lacked fully integrated administration and clinical application modules or fail-safe strategies for handling downtime events.26
Very little has been published on the costs of implementing and maintaining electronic medical record systems and decision support systems in low- and middle-income countries. The adoption of specification standards may drive down implementation costs as buyers choose or build systems with compatible components, rather than being limited to proprietary systems.27 The use of picture archiving and communication systems may lead to reductions in hospital stays and increased clinical efficiency, which, in turn, may also reduce costs.28 The web-based laboratory information system used for tuberculosis care in Peru, eChasqui, was estimated to cost only 1% of the entire budget of the National Peruvian Tuberculosis Programme.29 Electronic picture-archiving and communication systems could be particularly cost-effective in low- and middle-income countries, where access to film and chemicals is often difficult. The second opinions made possible via easily shared electronic images could also improve patient outcomes. However, in areas with intermittent power supplies and unreliable infrastructure, relying on servers and computers for radiographic images involves considerable risks. The scale-up of electronic systems for picture archiving and communication to be implemented across regions or nations requires the resolution of many practical problems, such as widespread staff training and the provision of adequate network bandwidth.30 To be useful, the large-scale implementation of both picture archiving and communication systems and of laboratory information systems in low- and middle-income countries calls for effective off-site data backup.
Examples of community information systems used in low- and middle-income countries include the District Health Information System in Malawi, Rwanda and South Africa, which collects data on routine health-care events from clinics. Other examples include the TRACnet system in Rwanda, which aggregates data on the care of patients infected with human immunodeficiency virus (HIV) from large numbers of clinics, and the Monitoring, Evaluation, and Surveillance Interface in Haiti, which performs a similar function.31 The Mekong Basin Disease Surveillance system, which covers various regions of six countries, tracks malaria, dengue, cholera and other diseases. The Sistema Integrado de Gestão e Atenção à Saúde [Integrated System for Health Management and Care], which operates in the Brazilian city of São Paulo, is an example of a large health information and management system that also manages patient flow.32,33 One health information system for clinical care, public health reporting and drug supply management covers the whole of Belize.32 Few low- and middle-income countries have adequate operability between their community-level systems of health information and other information systems,32,33 although this forms part of the planned e-health architecture in countries like Rwanda.12
The Bill & Melinda Gates Foundation commissioned a survey of efforts to deploy systems for health information and management in low- and middle-income countries.32 A case study from Brazil showed that, by adopting standards and defining a proper architecture, it is possible to scale up such systems to a multi-institutional level.32,33 More recently, the Sistema Integrado began integrating with three accredited laboratory systems in São Paulo city using standards such as HL7 (Health Level Seven) and LOINC® (Logical Observation Identifiers Names and Codes).34
Impact on outcomes and costs
The District Health Information System used in Malawi, Rwanda and South Africa may have limited impact on outcomes in settings where data quality is poor. Unfortunately, many such settings exist in low- and middle-income countries because of a general paucity of effective data-collection tools and training for data collectors in health facilities.35–37 More data exist regarding the potential impact of the District Health Information System on efficiency and costs than on the system’s potential impact on the outcomes of care. Preliminary results indicate that, following the implementation of the Sistema Integrado, optimization of resource use and patient flow led to a 35% increase in the productivity of outpatient services in São Paulo city.32 After the same system was implemented in the Brazilian city of Campinas, health officials there saw a 30% increase in patient visits without the need for any additional human resources.32 The Programa Mãe Paulistana [São Paulo Mothers’ Programme], which is largely based on the Sistema Integrado, has managed the health information on more than 440 000 pregnant women and 460 000 babies in São Paulo city. Since this programme’s inception in 2006, the proportion of pregnant women in the programme area who complete all six scheduled prenatal visits has increased from 10% to 80%, transmission of syphilis and maternal deaths from hypertension have decreased, and the percentage of children visited by a health-care worker within 15 days of birth has increased from 15% to 82%.38 However, since many other administrative and demographic changes have taken place in the programme area over this period, further studies are required to define the health benefits specifically attributable to the Programa Mãe Paulistana. Moreover, the data collected so far have focused primarily on productivity and treatment quality, and more research on the impact of the institutional systems on health outcomes and the overall cost of care is needed.
Systems facilitating care at a distance
In a recent World Health Organization Global mHealth Survey, 60% of high-income countries and 30% of low- and middle-income countries reported some use of SMS messages or other mobile health communication tools for improving treatment compliance.39 The related programmes in low- and middle-income countries address a variety of priority health concerns, including H1N1 influenza virus infection, HIV infection, vaccination, reproductive health and management of chronic illness.39,40 Large-scale implementations of live “telehealth” programmes include the Ontario Telemedicine Network in Canada, Kaiser Permanente programmes in Kaiser Permanente in the United States, and programmes offered throughout Mexico via the federal Centro Nacional de Excelencia Tecnológica en Salud [National Centre for Excellence in Technology in Health].41–43 Large-scale implementations of programmes that use a “store and forward” approach to accommodate intermittent telephone connectivity include the Swinfen Trust and iPath models, both of which are in use in several low- and middle-income countries.44,45 The COSMOS model is being implemented in parts of Chile for the care of patients with type 2 diabetes via “interactive voice response” calls, and similar calls are being made via the CarePartner model for the management of non-communicable diseases in parts of Honduras, Mexico and the United States.46–48
Impact on outcomes and health-care costs
Literature reviews indicate that SMS messages and other tools for communicating with patients between medical visits can improve health behaviours and physiological outcomes.7–10 In a review of seven intervention studies, including four randomized trials, text messaging showed significant promise for improving adherence rates.7 Controlled evaluations of SMS-based appointment reminders implemented in several countries, including Australia, Brazil, China and the United Kingdom, gave mixed results.9 In a recent randomized trial, both text-message reminders and live telephone calls improved attendance rates for chronic disease follow-up among patients in Malaysia, with no statistically significant differences in impact between the two communication methods.49 In Kenya, text messages to health workers significantly improved their adherence to guidelines for malaria treatment.50
Studies on the effectiveness of SMS messages for health promotion have also shown improvements in the outcomes of care.51–53 In a trial of smoking cessation support that included 5800 participants, the percentage of participants who had quit smoking (verified biochemically) had more than doubled 6 months after a “txt2stop” intervention.52 The results of other controlled trials indicate that SMS messages can help increase weight loss, physical activity and sunscreen use, as well as improve other outcomes.54–56 Evidence for the long-term maintenance of such beneficial health behaviours is, however, lacking. Studies on the use of SMS messages in support of the self-management of diabetes show potential for improving health behaviours and physiological control.8–10 Asthma peak flow monitoring can also be improved by text-based interventions,57 but a study of the home monitoring of blood pressure by patients with hypertension showed that such interventions had no significant effect.58
Benefits, in terms of diagnostic accuracy, reduced waiting times, better referral management and greater satisfaction with services, have been observed in most studies on the use of “asynchronous telehealth”, in which clinically important digital samples (e.g. still images, video, audio or text files) and relevant data are collected in one location and transmitted for interpretation by health professionals working at a remote site.59 Although the evidence indicates that this approach leads to a reduction in the number of in-person visits, evidence of impact on other outcomes is lacking. In six of 14 trials of asynchronous telehealth interventions, declines in blood haemoglobin A1c concentration were moderately or substantially greater in the intervention group than in controls.60
Trials of “interactive voice response” calls that were part of the CarePartner programme in the United States and low- and middle-income countries demonstrated that such calls led to significantly better self-care and physiological outcomes, including better control of glycaemia and blood pressure.47,61 In a recent randomized trial of self-management among patients with poorly controlled hypertension in Honduras and Mexico, use of “interactive voice response” calls was associated with a decrease in systolic pressure (by a mean of a 8.8 mmHg) and improvements in the patients’ perceived health, depressive symptoms and medication-related problems.62 Such calls may be an important alternative to SMS messages among patients with low literacy or when the intervention to promote a behavioural change calls for more interactivity than is achievable with SMS.
In a meta-analysis of 21 randomized trials that included 5715 patients who had suffered heart failure, the cost per patient of treatment including remote monitoring was about 300–1000 euros less than the cost of more conventional treatment. These cost savings, combined with a modest gain of 0.06 of a quality-adjusted life-year per patient, indicated that remote patient monitoring was worthwhile.63 Telemedicine applications such as “teledermatology” have also shown promise as cost-saving services, with outcomes at least as good as those observed with conventional care.64
With the worldwide explosion in the use of mobile phones as well as growing internet access through mobile data services, low- and middle-income countries increasingly have the opportunity to benefit from SMS-based services, live and asynchronous telemedicine, and interactive voice response calls. These services can address the major problems of access to care and the support of behavioural changes that will benefit health. Studies on the impact of such services on maternal and child health would be extremely valuable in the development of policies against some major causes of morbidity and mortality in low- and middle-income countries. Between-visit monitoring systems that result in much more frequent patient contact run the risk of increasing the use and cost of health services, as the often relatively crude data streams may lead clinicians to conduct telephone or in-person follow-ups for potentially minor or self-limiting conditions. Better data on both the positive and negative cost implications of mobile health services in low- and middle-income countries are badly needed.
Limitations of the review
The current report is based on an author-driven review of published studies as well as the authors’ experience with large-scale implementations that have not been described in the published literature. The authors have had considerable experience in e-health research and implementation projects and represent institutions in Africa, Asia, Europe and South and North America. Nevertheless, the current review was not systematic and the results probably under-represent innovative work that has not been described in the peer-reviewed literature or by professional organizations.
As is typical of scoping reviews, the quality of the source materials was not systematically evaluated using the tools employed by more exhaustive approaches to evidence synthesis.65 Peer-reviewed studies with negative findings are probably under-represented because such studies are less likely to be published. Although several of the largest implementation projects discussed above come from low- and middle-income countries, the scientific evidence for the impact of e-health continues to be dominated by studies in industrialized nations. Studies that evaluate the cost-effectiveness of e-health tools in low- and middle-income countries are particularly under-represented in the published literature. Rigorous trials to evaluate the impacts of such tools on outcomes and treatment costs in low- and middle-income countries should be a priority for future research.
Buntin et al. have indicated that 92% of recent articles on e-health reached generally positive conclusions.66 However, few studies clearly identify the features of interventions that are more likely to be effective. Patient communication via SMS messages, interactive voice response calls or other modalities is more likely to have an effect if the content is theoretically driven, and culture-specific factors may well influence uptake.8,67 Ultimately, independent development streams will need to be integrated to produce interoperable services that have standardized infrastructures and produce information that is useful to patients, clinicians and public health agencies.
Implications for large implementation projects
Deploying community-wide systems is much more complex than deploying smaller or short-term pilot projects, since, to avoid fragmentation, poor communication and poor interoperability, integration with existing systems becomes vital. Apart from the obvious need for a good infrastructure for general communications and information technology, systems intended to work in large settings need to embody an architecture that supports unique identifiers for patients, as well as open standards for data coding and exchange, to make it possible to integrate the novel systems with existing information systems and other new initiatives. Deploying an e-health tool in large settings requires a high degree of organizational skill and administrative systems for the ongoing training and supervision of health-care professionals where the tool is to be used. Since effective allocation of scant health resources is a major priority in low- and middle-income countries, evidence regarding the short-term impact of e-health systems on outcomes and costs (i.e. the returns on investment) is critical if decision-makers are to remain committed to the system’s support.
Directions for research
Large randomized trials, such as those by Free et al.52 and Zurovac et al.,50 provide strong evidence of the efficacy of e-health solutions and their potential impact on outcomes. However, highly controlled studies fail to answer questions about the reach of e-health in vulnerable communities or whether such systems can be adopted, scaled up and maintained outside the environments in which they were originally studied. New approaches to implementation science, emphasizing both qualitative and quantitative methods, community-based participatory research, and organizational theory can complement controlled trials and ensure that e-health systems are relevant and flexible enough to adapt to multiple environments.68 In particular, systems facilitating clinical care (e.g. electronic medical record systems) and institutional-level services (e.g. laboratory information systems) are difficult to evaluate, since appropriate designs require randomization at the facility level, which in turn requires involvement of multiple facilities and potentially thousands of patients. Traditional, large, multi-site trials are expensive and can take years to produce information. Investment in such studies should be carefully weighed against the funding of larger numbers of smaller and innovative (albeit less definitive) studies of solutions adapted to different cultures and environments. Also, “step-wedge” designs, in which software or improved functionality is gradually rolled out to new sites, can be more efficient.
Preliminary evidence shows that e-health systems can have a beneficial impact on the process of clinical care in low- and middle-income countries. However, more studies, particularly to examine the key information needs of health-care workers as well as the effects of e-health services on patient outcomes, are required in resource-poor settings. Research focused on large-scale implementation should address how an e-health architecture can help connect disparate health information systems, how interoperability can support coordination between multiple points of care, and how this coordination can improve health outcomes. Given the encouraging evidence regarding the benefits of mobile health tools, studies of their costs and impact on outcomes in low- and middle-income countries should be a priority.
John Piette is a Veterans Affairs Senior Research Career Scientist. The views expressed here do not necessarily represent the views of the US Department of Veterans Affairs.
- E-Health in the Eastern Mediterranean [Internet]. What is e-health? Cairo: World Health Organization, Regional Office for the Eastern Mediterranean; 2005. Available from: http://www.emro.who.int/his/ehealth/AboutEhealth.htm [accessed 1 February 2012].
- Blaya JA, Fraser HSF, Holt B. E-health technologies show promise in developing countries. Health Aff (Millwood) 2010; 29: 244-51 doi: 10.1377/hlthaff.2009.0894 pmid: 20348068.
- Black AD, Car J, Pagliari C, Anandan C, Cresswell K, Bokun T, et al., et al. The impact of eHealth on the quality and safety of health care: a systematic overview. PLoS Med 2011; 8: e1000387- doi: 10.1371/journal.pmed.1000387 pmid: 21267058.
- Oh H, Rizo C, Enkin M, Jadad A, Powell J, Pagliari C. What is eHealth (3): a systematic review of published definitions. J Med Internet Res 2005; 7: e1- doi: 10.2196/jmir.7.1.e1 pmid: 15829471.
- Jaspers MWM, Smeulers M, Vermeulen H, Peute LW. Effects of clinical decision-support systems on practitioner performance and patient outcomes: a synthesis of high-quality systematic review findings. J Am Med Inform Assoc 2011; 18: 327-34 doi: 10.1136/amiajnl-2011-000094 pmid: 21422100.
- Balas EA, Weingarten S, Garb CT, Blumenthal D, Boren SA, Brown GD. Improving preventive care by prompting physicians. Arch Intern Med 2000; 160: 301-8 doi: 10.1001/archinte.160.3.301 pmid: 10668831.
- Wei J, Hollin I, Kachnowski S. A review of the use of mobile phone text messaging in clinical and healthy behaviour interventions. J Telemed Telecare 2011; 17: 41-8 doi: 10.1258/jtt.2010.100322 pmid: 21097565.
- Cole-Lewis H, Kershaw T. Text messaging as a tool for behavior change in disease prevention and management. Epidemiol Rev 2010; 32: 56-69 doi: 10.1093/epirev/mxq004 pmid: 20354039.
- Krishna S, Boren SA, Balas EA. Healthcare via cell phones: a systematic review. Telemed J E Health 2009231-40 doi: 10.1093/epirev/mxq004 pmid: 20354039.
- Fjeldsoe BS, Marshall AL, Miller YD. Behavior change interventions delivered by mobile telephone short-message service. Am J Prev Med 2009; 36: 165-73 doi: 10.1016/j.amepre.2008.09.040 pmid: 19135907.
- Moura L, do Amaral MB, Lira A, Tachinardi U, Teixeira AC, Yamamoto J. Renewing information infrastructure at Hospital das Clinicas. Proc AMIA Symp 1998200-4.
- Capacity-based eHealth architecture roadmap. Part 1: Overview of national eHealth initiatives (Technical Report 14639). Geneva: International Standards Organization; 2011.
- Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol 2005; 8: 19-32 doi: 10.1080/1364557032000119616.
- DesRoches CM, Campbell EG, Rao SR, Donelan K, Ferris TG, Jha A, et al., et al. Electronic health records in ambulatory care – a national survey of physicians. N Engl J Med 2008; 359: 50-60 doi: 10.1056/NEJMsa0802005 pmid: 18565855.
- Jha AK, DesRoches CM, Campbell EG, Donelan K, Rao SR, Ferris TG, et al., et al. Use of electronic health records in U.S. hospitals. N Engl J Med 2009; 360: 1628-38 doi: 10.1056/NEJMsa0900592 pmid: 19321858.
- Nucita A, Bernava GM, Bartolo M, Masi FD, Giglio P, Peroni M, et al., et al. A global approach to the management of EMR (electronic medical records) of patients with HIV/AIDS in sub-Saharan Africa: the experience of DREAM software. BMC Med Inform Decis Mak 2009; 9: 42- doi: 10.1186/1472-6947-9-42 pmid: 19747371.
- Seebregts CJ, Mamlin BW, Biondich PG, Fraser HS, Wolfe BA, Jazayeri D, et al., et al. Human factors for capacity building: lessons learned from the OpenMRS implementers network. Yearb Med Inform 201013-20 pmid: 20938564.
- Douglas GP, Gadabu OJ, Joukes S, Mumba S, McKay MV, Ben-Smith A, et al., et al. Using touchscreen electronic medical record systems to support and monitor national scale-up of antiretroviral therapy in Malawi. PLoS Med 2010; 7: 711-20 doi: 10.1371/journal.pmed.1000319 pmid: 20711476.
- Chi BH, Vwalika B, Killam WP, Wamalume C, Giganti MJ, Mbewe R, et al., et al. Implementation of the Zambia electronic perinatal record system for comprehensive prenatal and delivery care. Int J Gynaecol Obstet 2011; 113: 131-6 doi: 10.1016/j.ijgo.2010.11.013 pmid: 21315347.
- Picture archiving and communication systems: a 2000–2008 study. Chicago: Dorenfest Institute for Health Information; 2010. Available from: http://www.himss.org/foundation/docs/PACS_ResearchWhitePaperFinal.pdf [accessed 1 February 2012].
- Sutton LN. PACS and diagnostic imaging service delivery – a UK perspective. Eur J Radiol 2011; 78: 243-9 doi: 10.1016/j.ejrad.2010.05.012 pmid: 21600402.
- Ralston MD, Coleman RM, Beaulieu DM, Scrutchfield K, Perkins T. Progress toward paperless radiology in the digital environment: planning, implementation, and benefits. J Digit Imaging 2004; 17: 134-43 doi: 10.1007/s10278-004-1002-x pmid: 15085445.
- Were MC, Shen C, Tierney WM, Mamlin JJ, Biondich PG, Li X, et al., et al. Evaluation of computer-generated reminders to improve CD4 laboratory monitoring in sub-Saharan Africa: a prospective comparative study. J Am Med Inform Assoc 2011; 18: 150-5 doi: 10.1136/jamia.2010.005520 pmid: 21252053.
- Anderson D, Flynn K. Picture archiving and communication systems: a systematic review of published studies of diagnostic accuracy, radiology work processes, outcomes of care and cost (Report No. 5). Boston: Management Decision and Research Center, Office of Research and Development, VA Medical Center; 1998. Available from: http://www.research.va.gov/resources/pubs/docs/pacs.pdf [accessed 1 February 2012].
- Blaya JA, Shin S, Contreras C, Yale G, Suarez C, Asencios L, et al., et al. Full impact of laboratory information system requires direct use by clinical staff: cluster randomized controlled trial. J Am Med Inform Assoc 2011; 18: 11-6 doi: 10.1136/jamia.2010.005280 pmid: 21113076.
- Harrison JP, McDowell GM. The role of laboratory information systems in healthcare quality improvement. Int J Health Care Qual Assur 2008; 21: 679-91 doi: 10.1108/09526860810910159 pmid: 19055276.
- Tagger B. An introduction and guide to successfully implementing a LIMS (laboratory information management system). Aberystwyth: University of Wales. Available from: http://www.cs.ucl.ac.uk/staff/B.Tagger/LimsPaper.pdf [accessed 1 February 2012].
- Becker SH, Arenson RL. Costs and benefits of picture archiving and communication systems. J Am Med Inform Assoc 1994; 1: 361-71 doi: 10.1136/jamia.1994.95153424 pmid: 7850560.
- Blaya JA, Shin SS, Yagui MJA, Yale G, Suarez CC, Asencios LL, et al., et al. A web-based laboratory information system to improve quality of care of tuberculosis patients in Peru: design, implementation, and evaluation methodology. BMC Med Inform Decis Mak 2007; 7: 33- doi: 10.1186/1472-6947-7-33 pmid: 17963522.
- Kalyanpur A, Singh J, Bedi R. Practical issues in picture archiving and communication system and networking. Indian J Radiol Imaging 2010; 20: 2-5 doi: 10.4103/0971-3026.59743 pmid: 20351983.
- Centers for Disease Control and Prevention. Launching a national surveillance system after an earthquake – Haiti 2010. Morb Mort Wkly Rep MMWR 2010; 59: 933-8 doi: 10.4103/0971-3026.59743 pmid: 20351983.
- Health information systems in developing countries; a landscape analysis. Palo Alto: Vital Wave Consulting; 2009 (Research Paper and Strategic Briefing). Available from: http://www.vitalwaveconsulting.com/publications/pdf/Vital%20Wave%20Consulting%20Gates%20Foundation%20HIS%20Analysis%202009.pdf [accessed 1 February 2012].
- Costa CG, Leão BF, Moura LA. São Paulo city health information system – a case report. Medinfo 2007; 2: 377-81.
- Leão BF. Electronic health record initiatives in South America. In: Proceedings of the 11th International HL7 Interoperability Conference – IHIC2010, 14−15 May 2010, Rio de Janeiro, Brazil.
- Mate KS, Bennett B, Mphatswe W, Barker P, Rollins N. Challenges for routine health system data management in a large public programme to prevent mother-to-child HIV transmission in South Africa. PLoS ONE 2009; 4: e5483- doi: 10.1371/journal.pone.0005483 pmid: 19434234.
- Garrib A, Stoops N, McKenzie A, Dlamini L, Govender T, Rohde J, et al., et al. An evaluation of the District Health Information System in rural South Africa. S Afr Med J 2008; 98: 549-52 pmid: 18785397.
- Svoronos T, Jillson IA, Nsabimana MM. TRACnet’s absorption into the Rwandan HIV/AIDS response. Int J Healthcare Technol Manage 2008; 9: .
- Programa Mãe Paulistana [Internet]. Washington: Center for Health Market Innovations; 2012. Available from: http://healthmarketinnovations.org/program/programa-m%C3%A3e-paulistana [accessed 1 February 2012].
- mHealth: new horizons for health through mobile technologies. Geneva: World Health Organization; 2011.
- Déglise C, Suggs LS, Odermatt P. Short message service (SMS) applications for disease prevention in developing countries. J Med Internet Res 2012; 14: e3- doi: 10.2196/jmir.1823 pmid: 22262730.
- Holmes M, Hart A. Profile: Ed Brown and the development of the Ontario Telemedicine Network. Healthc Q 2009; 12: 28-31 pmid: 20057227.
- Chen C, Garrido T, Chock D, Okawa G, Liang L. The Kaiser Permanente Electronic Health Record: transforming and streamlining modalities of care. Health Aff (Millwood) 2009; 28: 323-33 doi: 10.1377/hlthaff.28.2.323 pmid: 19275987.
- Influenza: medidas de promoción y prevención. Mexico City: Centro Nacional de Excelencia Tecnológica en Salud; 2012. Available from: http://www.cenetec.salud.gob.mx/ [accessed 1 February 2012].
- Swinfen R, Swinfen P. Low-cost telemedicine in the developing world. J Telemed Telecare 2002; 8: 63-5 doi: 10.1258/13576330260440899.
- Brauchli K, Oberli H, Hurwitz N, Kunze KD, Haroske G, Jundt G, et al., et al. Diagnostic telepathology: long-term experience of a single institution. Virchows Arch 2004; 444: 403-9 doi: 10.1007/s00428-004-0980-x pmid: 15021986.
- Bru J, Lange I, Blaya J, Piette J. mHealth for non-communicable disease care: experience from four countries. In: Proceedings of ISABEL 2011: 4th International Symposium on Applied Sciences in Biomedical and Communications Technologies, September 2011, Barcelona, Spain.
- Piette JD, Mendoza-Avelares MO, Ganser M, Mohamed M, Marinec N, Krishnan S. A preliminary study of a cloud-computing model for chronic illness self-care support in an underdeveloped country. Am J Prev Med 2011; 40: 629-32 doi: 10.1016/j.amepre.2011.02.014 pmid: 21565655.
- Piette JD, Beard A. Interactive voice response technology for chronic disease management. In: Noar S, Harrington N, eds. Interactive health communication technologies: promising strategies for health behavior change. New York: Rutledge; 2012. pp. 187-202.
- Liew S-M, Tong SF, Lee VKM, Ng CJ, Leong KC, Teng CL. Text messaging reminders to reduce non-attendance in chronic disease follow-up: a clinical trial. Br J Gen Pract 2009; 59: 916-20 doi: 10.3399/bjgp09X472250 pmid: 19712544.
- Zurovac D, Sudoi RK, Akhwale WS, Ndiritu M, Hamer DH, Rowe AK, et al., et al. The effect of mobile phone text-message reminders on Kenyan health workers’ adherence to malaria treatment guidelines: a cluster randomised trial. Lancet 2011; 378: 795-803 doi: 10.1016/S0140-6736(11)60783-6 pmid: 21820166.
- Haug S, Meyer C, Schorr G, Bauer S, John U. Continuous individual support of smoking cessation using text messaging: a pilot experimental study. Nicotine Tob Res 2009; 11: 915-23 doi: 10.1093/ntr/ntp084 pmid: 19542517.
- Free C, Knight R, Robertson S, Whittaker R, Edwards P, Zhou W, et al., et al. Smoking cessation support delivered via mobile phone text messaging (txt2stop): a single-blind, randomised trial. Lancet 2011; 378: 49-55 doi: 10.1016/S0140-6736(11)60701-0 pmid: 21722952.
- Whittaker R, Borland R, Bullen C, Lin RB, McRobbie H, Rodgers A. Mobile phone-based interventions for smoking cessation. Cochrane Database Syst Rev 2009CD006611- pmid: 19821377.
- Patrick K, Raab F, Adams MA, Dillon L, Zabinski M, Rock CL, et al., et al. A text message-based intervention for weight loss: randomized controlled trial. J Med Internet Res 2009; 11: e1- doi: 10.2196/jmir.1100 pmid: 19141433.
- Hurling R, Catt M, Boni MD, Fairley BW, Hurst T, Murray P, et al., et al. Using internet and mobile phone technology to deliver an automated physical activity program: randomized controlled trial. J Med Internet Res 2007; 9: e7- doi: 10.2196/jmir.9.2.e7 pmid: 17478409.
- Armstrong AW, Watson AJ, Makredes M, Frangos JE, Kimball AB, Kvedar JC. Text-message reminders to improve sunscreen use: a randomized, controlled trial using electronic monitoring. Arch Dermatol 2009; 145: 1230-6 doi: 10.1001/archdermatol.2009.269 pmid: 19917951.
- Ostojic V, Cvoriscec B, Ostojic SB, Reznikoff D, Stipic-Markovic A, Tudjman Z. Improving asthma control through telemedicine: a study of short-message service. Telemed J E Health 2005; 11: 28-35 doi: 10.1089/tmj.2005.11.28 pmid: 15785218.
- Márquez Contreras E, de la Figuera von Wichmann M, Gil Guillén V, Ylla-Catalá A, Figueras M, Balaña M, et al., et al. Eficacia de una intervención informativa a hipertensos mediante mensajes de alerta en el teléfono móvil (HTA-ALERT). Aten Primaria 2004; 34: 399-405 doi: 10.1157/13068215 pmid: 15546536.
- Deshpande A, Khoja S, Lorca J, McKibbon A, Rizo C, Husereau D, et al., et al. Asynchronous telehealth: a scoping review of analytic studies. Open Med 2009; 3: e69-91 pmid: 19946396.
- Jackson CL, Bolen S, Brancati FL, Batts-Turner ML, Gary TL. A systematic review of interactive computer-assisted technology in diabetes care. Interactive information technology in diabetes care. J Gen Intern Med 2006; 21: 105-10 pmid: 16390512.
- Piette JD, Weinberger M, McPhee SJ, Mah CA, Kraemer FB, Crapo LM. Do automated calls with nurse follow-up improve self-care and glycemic control among vulnerable patients with diabetes? Am J Med 2000; 108: 20-7 doi: 10.1016/S0002-9343(99)00298-3 pmid: 11059437.
- Piette JD, Datwani H, Gaudioso S, Foster SM, Westphal J, Perry W, et al., et al. Hypertension management using mobile technology and home blood pressure monitoring: results of a randomized trial in two low/middle income countries. Telemed J E Health .
- Klersy C, De Silvestri A, Gabutti G, Raisaro A, Curti M, Regoli F, et al., et al. Economic impact of remote patient monitoring: an integrated economic model derived from a meta-analysis of randomized controlled trials in heart failure. Eur J Heart Fail 2011; 13: 450-9 doi: 10.1093/eurjhf/hfq232 pmid: 21193439.
- Whited JD. Economic analysis of telemedicine and the teledermatology paradigm. Telemed J E Health 2010; 16: 223-8 doi: 10.1089/tmj.2009.0100 pmid: 20136558.
- Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, et al., et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ 2008; 336: 924-6 doi: 10.1136/bmj.39489.470347.AD pmid: 18436948.
- Buntin MB, Burke MF, Hoaglin MC, Blumenthal D. The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Aff (Millwood) 2011; 30: 464-71 doi: 10.1377/hlthaff.2011.0178 pmid: 21383365.
- Webb TL, Joseph J, Yardley L, Michie S. Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. J Med Internet Res 2010; 12: e4- doi: 10.2196/jmir.1376 pmid: 20164043.
- Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci 2009; 4: 50-64 doi: 10.1186/1748-5908-4-50 pmid: 19664226.