This analysis of the e–health technology landscape (henceforth referred to as ICT or simply technology) relies on information obtained from the Center for Health Market Innovations’ (CHMI) database.CHMI, launched in July 2010 and updated daily, systematically collects information on programmes and policies – implemented by a wide variety of public and private actors – that have the potential to improve health systems in low–and middle–income countries, where private providers tend to predominate and household out–of–pocket spending is a major source of health financing. These programmes use innovative delivery and financing mechanisms to improve access to health services, as well as their quality and affordability, for the poor. Examples include private clinical social franchises, vouchers for safe deliveries, high–volume/low–cost maternity hospitals with cross–subsidies for poor patients and government accreditation for private drug shops. Not all CHMI–documented programmes rely on innovative information technologies, but many do.

Traditionally, clinics, hospitals, and public health programmes run by the government or by non-governmental organizations are outside the scope of CHMI and are excluded from this institution’s database. Since CHMI focuses on programmes that work primarily with private providers, large–scale government e–health infrastructures, such as national electronic medical record systems, were not included in this analysis. Similarly, the database screens out programmes that serve mainly high–income populations and focuses on programmes targeting the poor or people in a range of income brackets.

CHMI data were obtained through systematic searches for innovative health programmes led by partners in 16 countries chosen for their thriving private sectors: Bangladesh, Bolivia, Brazil, Cambodia, Ecuador, India, Indonesia, Kenya, Pakistan, Peru, the Philippines, Rwanda, South Africa, Uganda, the United Republic of Tanzania and Viet Nam. Direct searches were supplemented by literature reviews and self–reported information obtained from the programmes themselves. As a result, the data were limited to the information captured by contributions to CHMI’s database and do not include the full universe of all relevant programmes. Furthermore, CHMI’s focus on private care delivery and its relationships with partner organizations in specific countries may have resulted in data collection biases. More information on the data collection methods can be found on CHMI’s web site

At the time of this study, the database included 657 programmes, 176 of which were identified as “technology–enabled”, that is, as deliberately using ICT to improve health. The programmes that passed this screening were those driven by technology as a core function, such as health insurance programmes whose client interactions take place entirely through smart–card technologies, or programmes that use technology as part of a broader health strategy, such as clinics offering comprehensive primary care but using cell phones for patient follow–up. Programmes not considered technology–enabled included those that use no form of ICT or do not report it as a key element that enhances their work. Based on this definition, a programme using an X–ray machine alone would not be classified as technology–enabled; however, if the X–ray machine were part of a remote diagnostic service using telemedicine, the programme would be classified as technology-enabled.

Two taxonomies were developed to categorize technology–enabled CHMI programmes: the type of technology used (Box 1) and the purpose of the technology (Box 2). All 176 programmes were coded in accordance with these taxonomies, which were then combined with comparable data collected on all programmes, such as geographic location, health focus (e.g. human immunodeficiency virus [HIV] infection and acquired immunodeficiency syndrome [AIDS], primary care, family planning) and source of funding, to identify emerging patterns in the technology landscape. Further insights were drawn from 20 qualitative interviews with a subset of programme implementers and from anecdotal self–reports on impact obtained from certain programmes.

Our analysis captures the number of programmes using technology, not the scale of technology use, because this information is incomplete. Nevertheless, the limited information on scale does indicate a wide range in programme size. For example, several programmes report reaching hundreds of patients or users,whereas others claim to have reached thousands or more. A case in point is India’s government–sponsored rashtriya swasthya bima yojana health insurance, which claims to have distributed over 27 million active smart cards to its subscribers.