Mher Beglaryan1, Varduhi Petrosyan2, Edward Bunker3. 1. Turpanjian School of Public Health, American University of Armenia, 40 Marshal Baghramian, Yerevan 0019, Armenia. Electronic address: author@mher.pro. 2. Turpanjian School of Public Health, American University of Armenia, 40 Marshal Baghramian, Yerevan 0019, Armenia. Electronic address: vpetrosi@aua.am. 3. Division of Health Sciences Informatics, Johns Hopkins University School of Medicine, 2024 East Monument St. S 1-200, Baltimore, MD 21205, USA. Electronic address: ebunker@jhu.edu.
Abstract
BACKGROUND AND PURPOSE: In health care, information technologies (IT) hold a promise to harness an ever-increasing flow of health related information and bring significant benefits including improved quality of care, efficiency, and cost containment. One of the main tools for collecting and utilizing health data is the Electronic Health Record (EHR). EHRs implementation can face numerous barriers to acceptance including attitudes and perceptions of potential users, required effort attributed to their implementation and usage, and resistance to change. Various theories explicate different aspects of technology deployment, implementation, and acceptance. One of the common theories is the Technology Acceptance Model (TAM), which helps to study the implementation of different healthcare IT applications. The objectives of this study are: to understand the barriers of EHR implementation from the perspective of physicians; to identify major determinants of physicians' acceptance of technology; and develop a model that explains better how EHRs (and technologies in general) are accepted by physicians. METHODS: The proposed model derives from a cross-sectional survey of physicians selected through multi-stage cluster sampling from the hospitals of Yerevan, Armenia. The study team designed the survey instrument based on a literature review on barriers of EHR implementation. The analysis employed exploratory structural equation modeling (ESEM) with a robust weighted least squares (WLSMV) estimator for categorical indicators. The analysis progressed in two steps: appraisal of the measurement model and testing of the structural model. RESULTS: The derived model identifies the following factors as direct determinants of behavioral intention to use a novel technology: projected collective usefulness; personal innovativeness; patient influence; and resistance to change. Other factors (e.g., organizational change, professional relationships, administrative monitoring, organizational support and computer anxiety) exert their effects through projected collective usefulness, perceived usefulness, and perceived ease of use. The model reconciles individual-oriented and environment-oriented theoretical approaches and proposes a Tripolar Model of Technology Acceptance (TMTA), bringing together three key pillars of the healthcare: patients, practitioners, and provider organizations. The proposed TMTA explains 85% of variance of behavioral intention to use technology. CONCLUSIONS: The current study draws from the barriers of EHR implementation and identifies major determinants of technology acceptance among physicians. The study proposes TMTA as affording stronger explanative and predictive abilities for the health care system. TMTA paves a long overlooked gap in TAM and its descendants, which, in organizational settings, might distort construal of technology acceptance. It also explicates with greater depth the interdependence of different participants of the healthcare and complex interactions between healthcare and technologies.
BACKGROUND AND PURPOSE: In health care, information technologies (IT) hold a promise to harness an ever-increasing flow of health related information and bring significant benefits including improved quality of care, efficiency, and cost containment. One of the main tools for collecting and utilizing health data is the Electronic Health Record (EHR). EHRs implementation can face numerous barriers to acceptance including attitudes and perceptions of potential users, required effort attributed to their implementation and usage, and resistance to change. Various theories explicate different aspects of technology deployment, implementation, and acceptance. One of the common theories is the Technology Acceptance Model (TAM), which helps to study the implementation of different healthcare IT applications. The objectives of this study are: to understand the barriers of EHR implementation from the perspective of physicians; to identify major determinants of physicians' acceptance of technology; and develop a model that explains better how EHRs (and technologies in general) are accepted by physicians. METHODS: The proposed model derives from a cross-sectional survey of physicians selected through multi-stage cluster sampling from the hospitals of Yerevan, Armenia. The study team designed the survey instrument based on a literature review on barriers of EHR implementation. The analysis employed exploratory structural equation modeling (ESEM) with a robust weighted least squares (WLSMV) estimator for categorical indicators. The analysis progressed in two steps: appraisal of the measurement model and testing of the structural model. RESULTS: The derived model identifies the following factors as direct determinants of behavioral intention to use a novel technology: projected collective usefulness; personal innovativeness; patient influence; and resistance to change. Other factors (e.g., organizational change, professional relationships, administrative monitoring, organizational support and computer anxiety) exert their effects through projected collective usefulness, perceived usefulness, and perceived ease of use. The model reconciles individual-oriented and environment-oriented theoretical approaches and proposes a Tripolar Model of Technology Acceptance (TMTA), bringing together three key pillars of the healthcare: patients, practitioners, and provider organizations. The proposed TMTA explains 85% of variance of behavioral intention to use technology. CONCLUSIONS: The current study draws from the barriers of EHR implementation and identifies major determinants of technology acceptance among physicians. The study proposes TMTA as affording stronger explanative and predictive abilities for the health care system. TMTA paves a long overlooked gap in TAM and its descendants, which, in organizational settings, might distort construal of technology acceptance. It also explicates with greater depth the interdependence of different participants of the healthcare and complex interactions between healthcare and technologies.
Keywords:
Electronic health record; Health information technology; Organizational behavior; Socio-technical factors; Structural equation modeling; Technology acceptance model
Authors: Mark Erik Larsen; Sofian Berrouiguet; Romain Billot; Jorge Lopez-Castroman; Isabelle Jaussent; Michel Walter; Philippe Lenca; Enrique Baca-García; Philippe Courtet Journal: JMIR Ment Health Date: 2019-05-07