Factors that impact the adoption of clinical decision support systems (CDSS) in healthcare settings for evidence-based decision making

Abstract

Background: Clinical decision support systems (CDSS) are designed to promote evidence-based patient care and shared-decision making in healthcare settings. Despite these benefits, adoption and long-term use of the systems remain limited. There is a need to identify different factors that influence CDSS adoption in healthcare settings.Objective: The Unified Theory of Acceptance and Use of Technology (UTAUT) model was applied to determine different factors that influence the adoption of CDSS in healthcare settings.Methods. A cross-sectional online survey examining clinicians’ perceptions about CDSS implementation in hospital and primary care settings in Australia was undertaken from June - October 2019. Multivariate logistic regression was used to examine the association of UTAUT moderators (age, gender and experience) and care settings (hospital and primary care) with perceived benefits, barriers and facilitators to CDSS adoption.Results: Access to information required for evidence-based decision making, and improvements in quality and safety of patient care were the most common perceived benefits of CDSS. Mapping of different barriers and facilitators to the UTAUT model indicated that ease of use (effort expectancy), perceived benefit (performance expectancy) and a facilitating environment greatly influence the adoption of CDSS. Respondents indicated that systems providing a better fit between relevance, content and timeliness greatly facilitates uptake. Adoption of CDSS also depends on the ability of an organisation to create a facilitating environment that can help address the lack of users’ trust in these systems. The type of healthcare setting was found to be a significant predictor of lack of confidence in the content within CDSS, threat to professional autonomy, and time constraints as barriers to CDSS implementation. Therefore, setting dynamics as well as user-specific requirements need to be considered to improve the acceptability and use of CDSS. Conclusion: Our study has explored different factors that may help address implementation challenges for CDSS. By combining internal factors of users’ inclination and perceptions about the system’s perceived benefits, coupled with external factors of system design requirements, training and support, and stakeholders’ consultations, our findings highlight the need for a holistic implementation framework to enable effective CDSS adoption.

Using Theories, Models, and Frameworks to Inform Implementation Cycles of Computerized Clinical Decision Support Systems in Tertiary Health Care Settings: Scoping Review

“…These stakeholders may include informatics and health systems professionals, a range of clinical end users (such as nursing, allied health, and medical staff), and the provision of ongoing technical support [ 16 ]. Consequently, the sustainable ongoing adoption and appropriate use of CDSS requires targeted ongoing investment in upskilling staff, added labor, and the management of data outputs and related assets [ 24 , 25 ]. Theories and frameworks applicable to translational medicine, human factors, and implementation science were borne from the need to address this complexity, and their use in facilitating the uptake of digital innovations within health care remains relatively untapped [ 149 ].…”

“…A range of factors have been reported to hinder the adoption of CDSSs, including usability, clinical workflow disruption, constrained financing systems, and medico-legal concerns, among other wider organizational and contextual barriers [ 14 , 15 , 24 , 25 ]. The implementation process and the mechanisms for success or failure of CDSS uptake are likely multifactorial; however, the appropriate use of tools and processes to support the identification of these factors and subsequent adoption have typically lacked systematic planning and evaluation [ 24 , 26 ].…”