The acceptance of a computerised decision-support system in primary care: A preliminary investigation

Abstract

Within the framework of technology acceptance modelling (Davis 1993), this study investigated the acceptance of a computerised decision-support system in primary care. Thirty general practitioners (GP) completed a questionnaire that detailed potential advantages of the system. A majority (70%) of GPs intended to use the system with a 2-min increase in consultation times (for proportion of GPs intending to use, CI 0.95 = [0.54; 0.85]) and eight advantages of the system were predictors of intention to use (R L 2 = 0.51, p 5 0.05). However, a majority (77%) did not intend to use the system with a 5-min increase in consultation time (CI 0.95 = [0.12; 0.42]). Furthermore, a majority of 90% preferred the system to be used by non-physicians (CI 0.95 = [0.78; 0.98]). These results confirm relationships between acceptance factors in a new domain, but most importantly they indicate the need to consider the balance of perceived advantages, or benefits, and disadvantages, or costs, of a new system in technology acceptance modelling. Implications for the design of a prototype system and further research are discussed.

A consumer perspective on moving images: The design and adoption of public audiovisual heritage services in the Netherlands

“…Barriers such as cost should be included in future adoption models. Hence, similar to Van Schaik's (2004) conclusions, the results regarding payment also suggest that user acceptance research should consider a balance between benefits (e.g., usefulness) and barriers (e.g., costs).…”

The Technology Acceptance Model: Its past and its future in health care

“…Few have done this kind of beliefs elicitation work with respect to health IT [70, 72, 76, 96, 102], and none to our knowledge have revised TAM based on such work 3. Yet guides on how to carry out such work are readily available [42, 142, 146].…”

Reasons For Physicians Not Adopting Clinical Decision Support Systems: Critical Analysis

“…These models offer a potential explanation for how expectations of performance, effort, social influences, and facilitating conditions are determinants of user acceptance and technology usage [12]. Using the TAM, Van Schaik et al [13] evaluated a gastroenterology referral CDSS. The system assisted primary care providers by suggesting an appropriate subspecialty referral (medical vs surgical), prioritizing urgency, and offering real-time booking [13].…”

“…Using the TAM, Van Schaik et al [13] evaluated a gastroenterology referral CDSS. The system assisted primary care providers by suggesting an appropriate subspecialty referral (medical vs surgical), prioritizing urgency, and offering real-time booking [13]. They found that physicians rated acceptance based on the potential merits of the system rather than their experience with the computer system [13].…”

“…The system assisted primary care providers by suggesting an appropriate subspecialty referral (medical vs surgical), prioritizing urgency, and offering real-time booking [13]. They found that physicians rated acceptance based on the potential merits of the system rather than their experience with the computer system [13]. This finding was concordant with Venkatesh et al’s [12] proposal of the UTAUT model, in which they demonstrated that performance expectancy is the strongest predictor of user acceptance of technology.…”