Using diffusion of innovation theory to understand the factors impacting patient acceptance and use of consumer e-health innovations: a case study in a primary care clinic

摘要

Background Consumer e-Health is a potential solution to the problems of accessibility, quality and costs of delivering public healthcare services to patients. Although consumer e-Health has proliferated in recent years, it remains unclear if patients are willing and able to accept and use this new and rapidly developing technology. Therefore, the aim of this research is to study the factors influencing patients’ acceptance and usage of consumer e-health innovations. Methods A simple but typical consumer e-health innovation – an e-appointment scheduling service – was developed and implemented in a primary health care clinic in a regional town in Australia. A longitudinal case study was undertaken for 29?months after system implementation. The major factors influencing patients’ acceptance and use of the e-appointment service were examined through the theoretical lens of Rogers’ innovation diffusion theory. Data were collected from the computer log records of 25,616 patients who visited the medical centre in the entire study period, and from in-depth interviews with 125 patients. Results The study results show that the overall adoption rate of the e-appointment service increased slowly from 1.5% at 3?months after implementation, to 4% at 29?months, which means only the ‘innovators’ had used this new service. The majority of patients did not adopt this innovation. The factors contributing to the low the adoption rate were: (1) insufficient communication about the e-appointment service to the patients, (2) lack of value of the e-appointment service for the majority of patients who could easily make phone call-based appointment, and limitation of the functionality of the e-appointment service, (3) incompatibility of the new service with the patients’ preference for oral communication with receptionists, and (4) the limitation of the characteristics of the patients, including their low level of Internet literacy, lack of access to a computer or the Internet at home, and a lack of experience with online health services. All of which are closely associated with the low socio-economic status of the study population. Conclusion The findings point to a need for health care providers to consider and address the identified factors before implementing more complicated consumer e-health innovations.