Theoretical Model of Determinants Factors of Health Information Technology (HIT) Acceptance

Sevani, Nina; Marpaung, Budi

doi:10.1109/isriti.2018.8864471

Cited by 2 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…UTAUT is also considered to be a general model, and by itself, it is thus insufficient to investigate the unique features of doctors’ responses, making it important to develop and improve it to determine those factors that explain the behaviours of doctors more specifically in terms of the use of health technologies such as CDSS (Sambasivan, Esmaeilzadeh, Kumar, and Nezakati, 2012). Sevani and Marpaung (2018) stated that recognition and understanding of the factors of HIS acceptance are essential for successful implementation of IT; they argued that, in order to implement IT in developing countries, UTAUT should thus be extended, particularly to provide an appropriate model to help to implement HIS.…”

Section: Model Developmentmentioning

confidence: 99%

Acceptance of clinical decision support systems in Saudi healthcare organisations

Aljarboa¹,

Miah²

2021

Information Development

View full text Add to dashboard Cite

Applications of clinical decision support systems (CDSS) have become essential for physicians seeking to make appropriate decisions. The implementation of CDSS, however, is complicated if the factors affecting physician’s acceptance are not recognised. This study aims to explore the various factors that may influence the acceptance of CDSS in Saudi Arabia. A qualitative method was used to collect data from interviews with 54 GPs, with interviews conducted in three stages. The study then integrated the Unified Theory of Acceptance and Use of Technology (UTAUT) and Task-Technology Fit (TTF) models to communicate the findings. It is suggested that all factors of both UTAUT and TTF influence acceptance of CDSS by GPs, with the sole exception of the social influence factor. Some additional factors were also discovered by means of in-depth interviews, including accessibility, patient satisfaction, informativeness (increased knowledge), connectedness (informing patients), communication and shared knowledge, privacy and security, and perceived risk (functional performance risk and time risk). The study thus offers a new insight of the factors influencing GPs’ acceptance of CDSS.

show abstract

Section: Model Developmentmentioning

confidence: 99%

Acceptance of clinical decision support systems in Saudi healthcare organisations

Aljarboa¹,

Miah²

2021

Information Development

View full text Add to dashboard Cite

show abstract

AI-driven innovations for enhancing mental health care in Tanzania: opportunities and challenges

Mwogosi,

Mambile,

Shao

et al. 2024

MHSI

View full text Add to dashboard Cite

Purpose This study aims to explore how artificial intelligence (AI) can enhance mental health care in Tanzania, focusing on its potential to enhance mental health services and address challenges in a low-resource setting. Design/methodology/approach A qualitative case study approach was used, with data collected through semi-structured interviews and focus group discussions involving key stakeholders in mental health and AI, including policymakers, technical experts, health-care providers and patient advocacy groups. Thematic analysis was used to identify key themes related to the opportunities and barriers to AI integration in mental health care. Findings This study identified several benefits of AI in mental health care, including improved diagnostic accuracy, personalised treatment and the potential for real-time monitoring of patients. However, significant barriers to AI adoption remain, such as infrastructure limitations, data privacy concerns and the need for training and resources to effectively integrate AI into mental health services. Originality/value This study contributes to the growing literature on AI in health care by focusing on its application in mental health care in Tanzania, a low-resource setting. The research provides valuable insights into how AI can bridge gaps in mental health service delivery, particularly in underserved regions, while highlighting the challenges that must be addressed for successful implementation.

show abstract

Theoretical Model of Determinants Factors of Health Information Technology (HIT) Acceptance

Cited by 2 publications

References 24 publications

Acceptance of clinical decision support systems in Saudi healthcare organisations

Acceptance of clinical decision support systems in Saudi healthcare organisations

AI-driven innovations for enhancing mental health care in Tanzania: opportunities and challenges

Contact Info

Product

Resources

About