Healthcare involves the collaboration of various medical organizations, institutions, and providers, all of whom manage their own data under different storage and retrieval systems, and though practical and efficient within the target system, there is a lack of uniformity in structure across other systems (Masud et al. 2012). Moreover, there are often variations in data compiled from the same resources. This lack of synergy between the various data storage systems, and the types of information stored, preclude the sharing of data among the various healthcare professionals, institutions, and organizations. E-health systems, on the other hand, allow the storage and retrieval of heterogeneous multimedia content data under a single global platform, facilitating interdisciplinary sharing of data. E-health technologies can be grouped into three main categories: (1) management of data, (2) clinical decision support, and (3) management of healthcare from a distance (Black et al. 2011). Successful implementation of any of these e-health technologies relies on the evaluation of a multitude of factors at each step of the execution process, in particular, socio-technical and economic factors. Government support is also important. In 2004, the United States President’s Information Technology Advisory Committee proposed a restructuring of the current healthcare information system to lower cost, reduce medical errors, and improve quality of care (Geissbühler 2012). The cornerstones of this proposal included electronic data storage and retrieval systems, clinical decision-support applications, computerised physician order entry, and inter-organizational data exchange. These technologies support e-health at each level of the hierarchy of the healthcare system: clinical, institutional, organizational, national, and global. Also in 2004, the CSIRO (Commonwealth Scientific and Industrial Research Organisation) and the Queensland Government together established the Australian e-Health Research Centre (AEHRC) for the purpose of establishing a national e-health system (Hansen et al. 2011). The aim of AEHRC is to improve delivery of healthcare, increase patient satisfaction, and reward healthcare workers. The main technological focus is on medical data, new clinical models, advanced medical imaging, and the development of medical skills. Queensland now has a tele-cardiac rehabilitation program, and advanced medical imagining technologies are being used throughout the Australian healthcare system.
Impact of e-Health in clinical systems
The use of e-health in the clinical setting is gaining widespread acceptance in developed countries, although there are wide variations in the scope and extent of their use. In the EU, Denmark, the Netherlands, Sweden, and the United Kingdom use e-health systems to store most of their clinical data, while in the United States, less than 20% of primary patient data are store electronically (Piette 2012). Cebul et al. (2011) compared the quality of care in 27, 207 patients with diabetes at 46 clinics in Cleveland, Ohio, and found that, regardless of the type of insurance held by the patient, practices that used electronic health records (EHRs) were able to deliver better quality of diabetes care than practices using standard paper records, suggesting that the use of EHRs can help provide higher quality of diabetes care. In New Zealand, the documentation of patients at risk of cardiovascular disease is poor, despite national recommendation guidelines for the management of CVD (Wells et al. 2008). The authors conducted a short study of e-health decision support in the clinical setting and found a four-fold increase in the recording of patient risk assessment data, suggesting e-health should be adopted in routine primary care practice to improve patient quality of care.
In addition to what is rapidly becoming conventional e-data storage and retrieval, Ibaida, Khalia, and Al-Shammary (2010) suggest that e-cardiology applications be combined with data hiding and watermarking techniques, to embed confidential patient information into the recorded ECG signals of the patient, since ECG data is large enough to host other information without compromising the primary data. The embedded information could then be retrieved separately. Furthermore, these data would be secure as it would be difficult for hackers to indentify their exact location.
In Kenya, clinical laboratory order rates for CD4+ lymphocyte counts were significantly higher at clinics that relied on electronic data systems for automatic reminders, compared to control clinics (53% versus 38%) (Were et al. 2011). However, sporadic cuts in power supplies weakened the infrastructure and increased the risk of reliance on e-health delivery systems. Thus, unless provisions are implemented to address peripheral technical issues, there can be small hope for major advances in e-health in countries facing this type of technical challenge. For this very reason, Tawfik, Anya, and Nagar (2012) warn against the implementation of e-health across boundaries without first understating the differences in conceptual framework or the socio-technological challenges against which different clinicians must construct their medical decisions. The authors detail how the different work settings of primary care providers in the UK, the UAE, and Nigeria impact clinical decisions, thus any collaboration across borders should proceed with caution.
Scott, Ndumbe and Wooton (2005) conduced a study in Cameroon, where medical residents from Yaounde I University are sent out in the field to complete their training, often to remote and rural areas where medical resources are limited and far removed from their medical school environment. A significant number of residents (65%) relied on their mobile phones to contact a medical colleague for advice, and most wished they could have been able to reach a mentor before reaching a medical diagnosis, suggesting that e-health can be a cost-effective solution to help meet the healthcare needs of people at the fringes of the healthcare system. However, to be truly effective, an e-health system such as this should be developed in cooperation with all major stakeholders to maximize data access and continuity of care.
Care at a distance
Rural populations have limited access to healthcare, little faith in the healthcare system, and often need to travel far to seek medical care (Lipsky et al. 2003 in Maserat 2008). Cancer patients have a particularly difficult time in accessing medical care as rural hospitals lack the resources to manage the disease (Maserat 2008). WHO studied the issue and concluded that telemedicine represented the best approach in addressing the healthcare needs in remote populations, and defined telemedicine as “ the delivery of healthcare services, where distance is the critical factor, by all healthcare professionals using information and communication technologies for the exchange of valid information for diagnosis, treatment and prevention of disease and injuries, research and evaluation, and for the continuing education of healthcare providers, all in the interest of advancing the health of individuals and their communities” (WHO 1998 in Maserat).Telemedicine includes a variety of applications including tele-consultation and tele-diagnosis, with specialty applications like tele-radiology and tele-oncology. Oncology is a complex discipline that relies on the multi-disciplinary orchestration of care, and as such, a variety of e-health applications would be necessary for tele-oncology to succeed, including multi-media data storage and retrieval platforms (Olver 2003 in Maserat). Most such applications would have to be patient-centered and thus designed to deliver clinical oncology services at a distance. Figure 11 (Maserat) illustrates the flow of tele-oncology management with patient home care positioned at the apex of care.
Care at a distance shows promise for a variety of other chronic diseases. In the Netherlands, van Os-Medendorp et al. (2012) evaluated the effect of e-health in follow up care of patients with atopic dermatitis, after initial face-to-face diagnosis and treatment at the clinic, and found e-health intervention to be just as effective as traditional follow up interventions in treating the disease and improving quality of life. Moreover, patients who were treated via e-health saved an estimated 73% in health costs during the first year of treatment. Another chronic disease that requires constant monitoring for its management with frequent clinical visits is ulcerative colitis, thus compliance tends to be poor. Elkjaer et al. (2010) conducted a study in Denmark and Ireland in 333 patients with ulcerative colitis randomized to receive either e-health or conventional intervention and found higher rates of compliance in the e-health group. Adherence to treatment protocol in the e-health group was 31% higher in Denmark, and 44% in Ireland compared to controls. Furthermore, health costs were lower and IBD knowledge and quality of life improved in the e-health group. The management and control of chronic diseases is an even more critical problem in developing countries, where human and medical resources are limited; therefore, any cost-effective methods warrant serious consideration. Marcelo et al. (2011) compared the use of e-health against face-to-face TB diagnosis in the Philippines and Pakistan and found that when culture was used as the gold standard, e-health was more accurate in the diagnosis of TB, suggesting the potential of e-health in the management of TB. E-health technology can also be used to personalize healthcare while at the same time saving valuable resources. Kastania et al. (2010) developed the e-Herophilus platform to improve and expand e-cardiology in Greece. Their aim was to provide standardized e-homecare for any cardiology patient anywhere in Greece, via 24-hour monitoring of 12-lead ECG, because early intervention can translate into considerable savings to the national healthcare service.
Impact of e-Health in institutional and organizational systems
Amarasingham et al. (2009) studied the effect of e-health technologies on the outcome of healthcare in 67,233 patients (age>50 years) in 41 urban hospitals in Texas and found that e-data resulted in 15% lower incidence of hospital-associated fatalities; and e-clinical decision support, in 16% fewer medical complications. Moreover, e-clinical decision support was associated with overall lower hospital admission costs (-$538, P < .05). Thus, hospitals that used e-data and e-clinical decision support had lower mortality rates, fewer medical complications, and lower costs than hospitals relying on traditional systems. These results agree with those in a Canadian study by Webster (2010), who found that hospitals that use e-health to manage their data see significant savings, fewer medical errors, and improved healthcare. For example, South Okanagan General Hospital in Oliver, BC, the highest-rated hospital in Canada, has been able to prevent at least one medically significant error every day since the hospital began using an electronic health record system. However, hospitals in Canada are behind the United States in their adoption of e-health systems, and there is also a wide technological gap between small and large hospitals, and amongst provinces, that represent a significant barrier in the establishment of a national integrated e-health system.
One of the most successful and creative applications of e-health technology is the virtual e-hospital of the Balkans (Latifi 2008). The idea of a virtual hospital began with Kosova in 2000, which wanted to rebuild its broken healthcare system after the war. The International Virtual e-Hospital (IVEH) now includes other countries in the Balkans and is funded by the Bureau of Education and Cultural Exchange of the State department of USA, and is being run by the Arizona Telemedicine Program and University of Arizona Health Science Center Library in collaboration with the Alaska Telemedicine program in Anchorage. The aim of the program is to provide medical education support in the Balkans, and has enrolled 48 physicians, nurses, and engineers from Kosova, Montenegro, Macedonia and Albania for an extensive and intensive program in telemedicine, e-health, electronic data management, as well as traditional medical trauma and surgical training. The program is proving such a success that IVEH now plans to expand its program beyond the Balkans to the Amazon, then to Africa.
Impact of e-Health in multi-national programmes
E-health systems are being designed to promote collaboration at all levels of the healthcare system—between individual practitioners, institutions, and organizations; and at a national and international level. A good example at the international level is the ePORTUGUÊSe initiative of the World Health Organization (WHO), an e-health model that includes all Portuguese-speaking countries. Another example is a scheme devised by the Rockefeller Foundation, whereby countries lacking the resources to sustain an e-health program are invited to form a single e-health association in affiliation with major world health organizations, including the ISfTeH and the International Medical Informatics Association (Proceedings 2003). The main focus of this multi-national e-health organization is the sharing of public health data, and the establishment of national e-health policies. There have also been a number of highly successful programmes established in Africa; most notably, the Drug Resources Enhancement against AIDS and Malnutrition (DREAM) project (Nucita et al. 2009) that began in Mozambique and spread across the sub-Sahara and into west Africa, the multi-national Open-source Medical Record System (OpenMRS) program (Seebregts 2010) launched in Kenya, and the Zambia Electronic Perinatal Record System (ZEPRS) in Zambia (Chi 2011).
E-health Programmes in the African Region
The African Region suffers the greatest burden of disease; it also lacks many of the sociotechnical resources to protect itself against these diseases. Conventional healthcare systems in the African Region operate within weak infrastructures and cannot sustain continuity of patient care; therefore, global health organizations have stepped in to implement a number of e-health systems to improve health care and delivery, manage medical records, and support clinical decisions.
In 2002, the Community of Sant'Egidio to fight AIDS in sub-Saharan Africa launched a programme in Mozambique called Drug Resources Enhancement against AIDS and Malnutrition (DREAM) (Nucita et al. 2009). DREAM programes have now been established in ten other countries in the African Region: Mozambique, Malawi, Tanzania, Kenya, the Republic of Guinea, Guinea Bissau, Cameroon, Congo RDC, Angola and Nigeria. The majority of programmes are centered in Mozambique and Malawi (Fig. 12, Nucita). Nigeria has established two DREAM programmes in Abuja and Iwaro Oka. What gives the DREAM project its strength is that it has been incorporated into the national health system of each country, and that it is highly focused on the management of HIV/AIDS. As of 2009, there were 73,000 HIV/AIDS patients enrolled in the system. This data bank serves not just for intervention at the individual patient level, but also represent a valuable resource in the research field, allowing for the advancement of HIV/AIDS therapeutics in sub-Saharan Africa.
There are 33 million people with HIV/AIDS in the world, and 60% live in sub-Saharan Africa (WHO in Nucita), thus the DREAM projects play a critical role in the prevention and eradication of the global burden of HIV/AIDS. There is no cure for HIV/AIDS, although in the West antiretroviral drugs have been able to reduce mortality rates by 90%. The drugs are expensive and a scarce and valuable resource in the sub-Saharan Africa region, and those infected must be use the drugs for their entire life. Moreover, HIV/AIDS is a complex disease and must be closely monitored, and the issue becomes even more complicated with the existence of co-morbidities, like malaria and malnutrition. Thus, the management of patient data is of critical import; but unfortunately this largely relies on patient compliance, which is far from ideal. Perhaps there is a role here for tele-health interventions. But the largest problem is the lack of qualified healthcare providers in sub-Saharan Africa, as well as other stakeholders of the healthcare system, like laboratory technician, biologists, and ICT specialists. Nevertheless, the DREAM project is a big step in the right direction.
In 2004, the Indiana University School of Medicine developed an electronic data system to record HIV/AIDS patient records in western Kenya (Seebregts 2010, Mohammed-Rajput 2011). The Open-source Medical Record System (OpenMRS) was developed using a dedicated Wiki site and e-mail server; it was released in 2006, and by 2011 the data of over 300,000 patients had been recorded. OpenMRS is an open-source platform that is free, easy to use, and supported by a large global network. It now serves as a platform to support the management of HIV/AIDS for hundreds of thousands of patients in over 40 countries. OpenMRS also supports medical care and research. Table 4 (Mohammed-Rajput 2011) summarises the perceived benefits of OpenMRS in Africa and Table 5 (Mohammed-Rajput 2011) tabulates the various applications of OpenMRS. In total, 165225 patients are enrolled in OpenMRS in Africa, with by far the greatest majority of patients coming from South Africa (n=99000), followed by Kenya (n=45,500), even though the program was originally designed for, and released, in Kenya, reflecting the HIV/AIDS crisis in South Africa on the one hand, and the success of HIV/AIDS interventions in Kenya on the other hand.
Reason/Benefit of Implementation
Reporting to Ministry of Health
Reporting to Funders
Table 5. African countries enrolled in OpenMRS: Users roles & Tasks performed
Clinical providers. Data managers. Data assistants. System managers
Patient care, data export and reporting, data entry, system maintenance
Administrator, nurse, supervisor
Administration, consultations, patient registration
Registration, data entry, reporting
Registration, data entry, reporting
Registration, data entry, reporting
Registration, data entry, reporting
System developer, data entry
data entry, reporting
Data entry, data manager, clinician
data entry, reporting
Developer, data manager
Forms development, data entry, reporting
Registration, nurse, vitals clerk, clinician, pharmacist, administrator
Data entry, data managers, physicians, researchers
Data entry, data export, reporting
System developers, data manager, data assistant. Data entry, review, reporting, clinicians
Data entry, data quality and accuracy, clinical summaries, laboratory alerts
One of the most recent and successful programs is the Zambia Electronic Perinatal Record System (ZEPRS) project that was designed, developed, and implemented by the Bill and Melinda Gates Foundation (Chi 2011). ZEPRS is used to manage prenatal healthcare for pregnant women and postnatal healthcare for their infants. In less than three years, 155,552 pregnant women in the Lukasa public health sector have had their data recorded in ZEPRS. The data included HIV test results for 96% (n=111,108) of the women, of whom 22% tested positive, representing a valuable epidemiological pool of data. The extent of newborn data is also unprecedented for this region and has great potential value in research. The program owes its success in great part to the support of the Zambian Ministry of Health.
Adekolu-John E.O.(1988). A study of vital and health statistics of the Kainji Lake Area
of Nigeria. African Journal of Medical Science 17(3), pp.149-56.
Agundu P.U. (2003). Financial statistics for public health dispensary decisions in Nigeria:
insights on standard presentation typologies. J Hosp Mark Public
Akom, E.E. (2008). Atlas on Regional Integration in West Africa, Population Series.
Communicable Diseases. Centre de Coopération Internationale
en Santé et Développement (CCISD). ECOWAS-SWAC/OECD.
Amarasingham, R., Plantinga, L., Diener-West, M., Gaskin, D.J., & Powe, N.R. (2009)
Clinical information technologies and inpatient outcomes: a multiple hospital
study, Archives of Internal Medicine.,169(2), pp. 108–14.
Ayeni O. (1976) The importance of morbidity statistics in the evaluation of
public health in Africa. Jimlar Mutane, 1(2), pp.193-7.
Black, A.D., Car, J., Pagliari, C., Anandan, C., Cresswell, K., Bokun, T., et al. (2011)
The impact of eHealth on the quality and safety of health care: a systematic
overview. PLoS Med, 8(1), e1000387.
Bukachi F, & Pakenham-Walsh N. (2007) Information technology for health in
developing countries. Chest., 132(5), pp.1624-30.
Cebul, R.D., Love, T.E., Jain, A.K., Hebert, C.J. (2011) Electronic health records and
quality of diabetes care, New England Journal of Medicine, 365(9), pp. 825–33.
Chi BH, Vwalika B, Killam WP, Wamalume C, Giganti MJ, Mbewe R, et al.
Implementation of the Zambia electronic perinatal record system for
comprehensive prenatal and delivery care. Int J Gynaecol Obstet. 2011;113:131–6
Elkjaer M, Shuhaibar M, Burisch J, Bailey Y, Scherfig H, Laugesen B, Avnstrøm S,
Langholz E, O'Morain C, Lynge E, Munkholm P. (2010) E-health empowers
patients with ulcerative colitis: a randomised controlled trial of the web-
guided 'Constant-care' approach. Gut 59(12), pp.1652-61
Geissbühler, A. (2012) eHealth: easing the transitions in healthcare, Swiss Med
Hansen DP, Gurney P, Morgan G, Barraclough B. (2011) The Australian e-
Health Research Centre: enabling the health care information and communication
technology revolution. Medical Journal of Australia 21,194(4), pp. S5-7.
Ibaida A, Khalil I, Al-Shammary D. (2010) Embedding patients confidential data in ECG
signal for healthcare information systems. Conference Proeedingsc IEEE Engineering in
Medicine and Biology Society 2010, pp. 3891-4.
Kastania AN, Loudos G, Sgouros NP, Constantinou I, Vavatsioula M, Boudoulas H,
Kossida S. (2010) e-Herophilus: 24-hour personalised telecardiology services.
International Journal of Electronic Healthcare 5(4), pp. 340-54.
Latifi, R. (2008) International virtual e-hospital: the Balkans journey, Studies in Health
Technology and Informatics, 131, pp.3-20.
Lopez A.D., Mathers C.D., Ezzati M., Jamison D.T., & Murray C.J. (2006). Global and
regional burden of disease and risk factors, 2001: systematic analysis of
population health data. Lancet, 367(9524), pp.1747-57.
Marcelo A, Fatmi Z, Firaza PN, Shaikh S, Dandan AJ, Irfan M, Bari V, Scott RE. (2011)
An online method for diagnosis of difficult TB cases for developing countries.
Studies in Health Technology and Information, 164, pp.168-73.
Maserat E (2008) Information communication technology: new approach for rural cancer
care improvement. Asian Pacific Journal of Cancer Prevention 9(4), pp. 811-4.
Masud, M., Hossain, M.S., & Alamri A. (2012) Data interoperability and multimedia
content management in e-health systems, IEEE Trans Information Technology
Biomed. [Epub ahead of print].
Mathers CD, Boerma T, Ma Fat D. (2009). Global and regional causes of death.
British Medical Bulletin, 92, pp. 7-32.
Mathers C.D., & Loncar D. (2006). Projections of global mortality and burden of disease
from 2002 to 2030. PLoS Med., 3(11), e442.
Mohammed-Rajput NA, Smith DC, Mamlin B, Biondich P, Doebbeling BN. (2011)
OpenMRS, A Global Medical Records System Collaborative: Factors Influencing
Successful Implementation, AMIA Annual Symposium Proceedings, 2011, pp.
Murray C.J. & Lopez A.D. (1997). Mortality by cause for eight regions of the world:
Global Burden of Disease Study. Lancet 349(9061), 1269-76.
Okonjo-Iweala N., & Osafo-Kwaako P. (2007). Improving health statistics in Africa.
Piette, J.D., Lun, K.C., Moura, L.A. Jr., Fraser, H.S., Mechael, P.N., Powell, J., Khoja,
S.R.(2012) Impacts of e-health on the outcomes of care in low- and middle-
income countries: where do we go from here? Bulletin of the World Health
Organanization, 90(5), pp. 365–372.
Proceedings of the Fourth Working Conference of the International Medical
Informatics Association Working Group on Health Information Systems. April
2002. Heidelberg, Germany. (2003) International Journal of Medical Informatics,
Scott, R.E., Ndumbe, P., and Wooton, R. (2005) An e-health needs assessment of medical
residents in Cameroon. Journal of Telemedicine and Telecare 11 Suppl 2:S78-80.
Seebregts CJ, Mamlin BW, Biondich PG, Fraser HS, Wolfe BA, Jazayeri D, et al. (2010)
Human factors for capacity building: lessons learned from the OpenMRS
implementers network, Yearb Med Inform, 2010, pp. 13–20.
Setel P.W., Macfarlane S.B., Szreter S., Mikkelsen L., Jha P., Stout S., & AbouZahr C.,
(2007). A scandal of invisibility: making everyone count by counting everyone.
Monitoring of Vital Events. Lancet. 370(9598):1569-77.
Tawfik H, Anya O, Nagar AK. (2012) Understanding Clinical Work Practices for Cross-
Boundary Decision Support in e-Health, IEEE Tranactions on Information
Technology in Biomedicine 16(4), pp.530-41.
van Os-Medendorp H, Koffijberg H, Eland-de Kok PC, van der Zalm A, de Bruin-Weller
MS, Pasmans SG, Ros WJ, Thio HB, Knol MJ, Bruijnzeel-Koomen CA. (2012)
E-health in caring for patients with atopic dermatitis: a randomized controlled
cost-effectiveness study of internet-guided monitoring and online self-
management training. British Journal of Dermatology 166(5), pp.1060-8.
Webster, P.C. (2010) Canadian hospitals make uneven strides in utilization of electronic
health records, CMAJ 82(11): E487–E488
Wells S, Furness S, Rafter N, Horn E, Whittaker R, Stewart A, Moodabe K, Roseman P,
Selak V, Bramley D, et al. (2008) Integrated electronic decision support increases
cardiovascular disease risk assessment four fold in routine primary care practice.
European Journal of Cardiovascular Prevention and Rehabilitation 15(2),
Were, M.C., Shen, C., Tierney, W.M., Mamlin, J.J., Biondich, P.G., Li, X., et al. ) 2011)
Evaluation of computer-generated reminders to improve CD4 laboratory
monitoring in sub-Saharan Africa: a prospective comparative study. Journal of
the American Medical Informatics Association,18, pp. 150–5.
WHO. ePORTUGUÊSe. Access at:
WHO (2008). The Global Burden of Disease. 2004 Update. World Health Organization.
WHO (2011). World Health Organization. World Health Statistics. Access at: