Adoption of Mobile Technology by Public Healthcare Doctors: A Developing Country Perspective

Adoption of Mobile Technology by Public Healthcare Doctors: A Developing Country Perspective

Nesaar Banderker (University of Cape Town, South Africa) and Jean-Paul Van Belle (University of Cape Town, South Africa)
DOI: 10.4018/978-1-60566-030-1.ch022
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

Doctors working in the South African public healthcare sector are faced with the unique resource constraints prevalent in a developing country. Mobile information and communication technologies (ICTs) hold the promise of improving the quality of healthcare, but this potential can only be unlocked if individuals decide to adopt the new technologies. Understanding the factors that influence the doctor’s adoption of a technology is therefore vital. This chapter reports on an investigation into the factors influencing the adoption of mobile devices by doctors in the public healthcare sector in the Western Cape, South Africa. The research methodology was shaped by qualitative enquiry and described through thematic analysis. The authors confirmed the key adoption factors identified in prior research: job relevance, usefulness, perceived user resources and device characteristics. However, some additional adoption factors were uncovered in this research, namely patient influence, support structures from national government and hospital administration, and unease in respect of malpractice legal suits.
Chapter Preview
Top

Background

Healthcare in South Africa

Healthcare is a key component of South African society, socially and economically (Chiasson et al 2004). Total healthcare spending in South Africa is 8.7% of GDP which is substantially above the norm of 5% recommended for developing countries by the WHO (Chetty, 2007). The public healthcare budget alone totalled R47.8 billion (approximately US$ 6.7 billion) in 2006, representing 4.27% of GDP. This represents a substantial growth compared with 1995 when it stood at only 1.84% of GDP. However, despite these efforts by the new democratically elected government, huge inequalities remain. The budget of the private sector, which services less than 8 million people, exceeds that of the public sector servicing 38 million (Chetty, 2007). Much of this remains a legacy of the pre-1994 apartheid era inequalities institutionalised through labour laws and highly unequal provision of services for different racial groups (Department of Health, 2004). There is still a movement of skilled resources from areas of poverty and low socio-economic development to more wealthy areas. Doctors who have recently qualified and completed their compulsory two years working for the public healthcare sector are either moving into private practice or leaving South Africa to work in other countries (Padarath et al 2003). This results in a scarcity of skilled clinical resources in the public healthcare sector.

Although doctors working in the public healthcare sector are highly skilled, their available time is preciously sliced to try and diagnose and care for as many patients in a day as is possible. 80 to 85% of the South African population has access to public healthcare facilities only (Chetty, 2007). A doctor in the public day hospital environment is expected to diagnose, treat and manage about 40 patients during an 8–hour shift. This means that the doctor spends an average of only 12 minutes with each patient. Doctors in public healthcare also have to work extremely long shifts of sometimes more than 30 hours. Due to this extreme pressure, doctors can easily make an incorrect diagnosis or prescribe the incorrect patient management routine. Patients attending these public hospitals have to wait in long queues to be seen by the doctor. When they finally see the doctor, the visit is rushed. A full examination of the patient is not always possible and this could result in inadequate care of the patient.

ICTs offer tremendous potential in supporting the public healthcare function in the South African society. Although administrative healthcare information systems have been implemented, the shift to systems that support the clinical work performed by healthcare professionals directly has been slow to take off (Andersen 1997). Better ICT support would, in turn, enable doctors to facilitate the provision of high quality, better informed and cost-effective public healthcare to all the citizens of South Africa.

Key Terms in this Chapter

Perceived usefulness/Job relevance: A new technology needs to be useful to its user. Usefulness of a technology can be measured indirectly by checking whether it causes a real (or perceived) increase in the adopter’s productivity by being relevant to the adopter.

Result demonstrability: The degree to which the results or benefits of using the innovation are apparent i.e. how tangible or apparent these benefits are to the adopter. The technology should visibly improve the quality or effectiveness of the adopters’ work or processes e.g. the decision making process or the job output quality.

Perceived usefulness/Job relevance: A new technology needs to be useful to its user. Usefulness of a technology can be measured indirectly by checking whether it causes a real (or perceived) increase in the adopter’s productivity by being relevant to the adopter.

Result demonstrability: The degree to which the results or benefits of using the innovation are apparent i.e. how tangible or apparent these benefits are to the adopter. The technology should visibly improve the quality or effectiveness of the adopters’ work or processes e.g. the decision making process or the job output quality.

Task/technology fit: The alignment of a technology with current work practices. It was thought that doctors would more likely adopt a new technology if it aligned closely with their current work practices. The task/technology fit is in itself a complex, composite theoretical construct which has to be broken down into a number of sub-constructs or dimensions if one wishes to measure it empirically and a number of task/technology fit models have been proposed in the literature.

Image: This refers to the degree to which the proposed adoption of an innovation is likely to enhance the status or image of the adopter in his/her social environment. Many people are heavily influence by their perceptions of the impression they make, or the status they have obtained within their social or professional circles. E.g. doctors may perceive the use of a mobile technology device as enhancing their professional status within their working environment.

Perceived user resources: The extent to which an individual believes that they have the personal and organisational support to use the device.

Perceived user resources: The extent to which an individual believes that they have the personal and organisational support to use the device.

Subjective norm: An adoption factor which looks at the influence exerted by the social environment of the adopter i.e. other people which the adopter may perceive as important. It is really the person’s perception of social normative pressures and relevant others’ beliefs whether the adopter should adopt or not. These people can be professional peers, colleagues, subordinates, parents, people of authority etc.

Subjective norm: An adoption factor which looks at the influence exerted by the social environment of the adopter i.e. other people which the adopter may perceive as important. It is really the person’s perception of social normative pressures and relevant others’ beliefs whether the adopter should adopt or not. These people can be professional peers, colleagues, subordinates, parents, people of authority etc.

Adoption Model: A model that postulates a number of factors driving or influencing the adoption decision of individuals or organisations, e.g as used in the context of the adoption of a particular technology. Much research has been done around the empirical testing and validation of models proposed by various researchers. Most of the hypothesized adoption factors are typically not directly measurable or observable but are theoretical constructs themselves e.g. usability, user satisfaction. Their influences (or relationships) can be direct or indirect through intermediate factors (such as intention to adopt) and many models indicate that some factors may mediate the relationships between factors rather than exert a direct influence. Some models are multi-stage models. Some of the most widely research adoption models in the field of technology adoption are the TAM (Technology Adoption Model), TAM2 (an extended version) and the UTAUT (Unified Theory of Acceptance and Usage of Technology).

Adoption Model: A model that postulates a number of factors driving or influencing the adoption decision of individuals or organisations, e.g as used in the context of the adoption of a particular technology. Much research has been done around the empirical testing and validation of models proposed by various researchers. Most of the hypothesized adoption factors are typically not directly measurable or observable but are theoretical constructs themselves e.g. usability, user satisfaction. Their influences (or relationships) can be direct or indirect through intermediate factors (such as intention to adopt) and many models indicate that some factors may mediate the relationships between factors rather than exert a direct influence. Some models are multi-stage models. Some of the most widely research adoption models in the field of technology adoption are the TAM (Technology Adoption Model), TAM2 (an extended version) and the UTAUT (Unified Theory of Acceptance and Usage of Technology).

Task/technology fit: The alignment of a technology with current work practices. It was thought that doctors would more likely adopt a new technology if it aligned closely with their current work practices. The task/technology fit is in itself a complex, composite theoretical construct which has to be broken down into a number of sub-constructs or dimensions if one wishes to measure it empirically and a number of task/technology fit models have been proposed in the literature.

Image: This refers to the degree to which the proposed adoption of an innovation is likely to enhance the status or image of the adopter in his/her social environment. Many people are heavily influence by their perceptions of the impression they make, or the status they have obtained within their social or professional circles. E.g. doctors may perceive the use of a mobile technology device as enhancing their professional status within their working environment.

Complete Chapter List

Search this Book:
Reset