Examining the infusion of mobile technology by healthcare practitioners in a hospital setting

Connor, Yvonne O’; Reilly, Philip O'

doi:10.1007/s10796-016-9728-9

Cited by 24 publications

(16 citation statements)

References 78 publications

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“…healthcare physician tasks [2]. Task characteristics, in this study, are defined broadly as the actions taken by physicians and include flexibility, protocol adherence, and time criticality [2,18]. Due to the complex nature associated with the delivery of healthcare services, flexibility in workflows is clinically pertinent.…”

Section: Hypothesismentioning

confidence: 99%

“…mobile) characteristics refer to specific features, functionality, or usability of a technology that can affect its usage by target users [52]. It is argued that the implementation of any eHealth technology must live up to its fullest potential in real-world conditions and circumstance [18,53], therefore having real world value. Existing research argues that physicians may be reluctant to utilize some IT technologies because they may fear it will not perform reliably or possess insufficient functionality for users to perform tasks.…”

Section: Hypothesis 4-2mentioning

confidence: 99%

“…The lack of evaluation across the mHealth field, primarily in post-adoption stages of implementation, may be perceived as a major weakness of the mHealth domain [9,10]. Therefore, for mHealth to be truly valuable and have a positive impact on quality of healthcare delivery, the IT artefact must be utilized post-adoption [17,18]. Furthermore, Goodhue and Thompson [19] argue that the capabilities of the IT must match the tasks that the user must perform.…”

Section: Introductionmentioning

confidence: 99%

“…Serrano and Karahanna [20] argue that the role of the user in influencing successful task performance has not been explored in-depth within a healthcare setting. Indeed, there has been a dearth of research focused on exploring an mHealth specific evaluation framework [11,18] in a post-adoptive scenario, specifically in a hospital setting [21].…”

Section: Introductionmentioning

confidence: 99%

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MHealth and perceived quality of care delivery: a conceptual model and validation

O’Connor¹,

Andreev²,

O’Reilly³

2020

BMC Med Inform Decis Mak

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Background: The objective of this research is to examine, conceptualize, and empirically validate a model of mobile health (mHealth) impacts on physicians' perceived quality of care delivery (PQoC). Methods: Observational quasi-experimental one group posttest-only design was implemented through the empirical testing of the conceptual model with nine hypotheses related to the association of task and technology characteristics, self-efficacy, m-health utilization, task-technology fit (TTF), and their relationships with PQoC. Primary data was collected over a four-month period from acute care physicians in The Ottawa Hospital, Ontario, Canada. The self-reported data was collected by employing a survey and distributed through the internal hospital channels to physicians who adopted iPads for their daily activities. Results: Physicians' PQoC was found to be positively affected by the level of mHealth utilization and TTF, while the magnitude of the TTF direct effect was two times stronger than utilization. Additionally, self-efficacy has the highest direct and total effect on mHealth utilization; in the formation of TTF, technological characteristics dominate followed by task characteristics. Conclusion: To date, the impact of utilized mHealth on PQoC has neither been richly theorized nor explored in depth. We address this gap in existing literature. Realizing how an organization can improve TTF will lead to better PQoC.

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Section: Hypothesismentioning

confidence: 99%

Section: Hypothesis 4-2mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MHealth and perceived quality of care delivery: a conceptual model and validation

O’Connor¹,

Andreev²,

O’Reilly³

2020

BMC Med Inform Decis Mak

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“…With this advancement of information and communication technology (ICT) and the advent of 3G and 4G services by telecom companies, mobile technology has become an integral part of our everyday human life. Mobile services have been introduced into various areas like banking, commerce, government, and healthcare [1][2][3]. From this perspective, a bibliometric analysis recently performed by Hew [4] showed increased interest in the scientific world in mobile technology, and suggested avenues for the future research of mobile technologies.…”

Section: Introductionmentioning

confidence: 99%

Extended Technology Acceptance Model to Predict Mobile-Based Money Acceptance and Sustainability: A Multi-Analytical Structural Equation Modeling and Neural Network Approach

2019

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This research is a pioneering study into the adoption of mobile-based money services for financial inclusion and sustainability in developing countries like Togo. Owing to their differences from more usual mobile-based banking and payment services, such technology is being aggressively promoted by providers of network telecommunication companies. However, the factors influencing its sustainable acceptance remain largely unknown. This paper extends the original Technology Acceptance Model (TAM), by integrating self-efficacy (SEMM), technology anxiety (TAMM), and personal innovativeness (PIMM). The research model is assessed with survey data of 539 actual and prospective mobile money users employing structural equation modeling–artificial neural networks (SEM–ANN) approach. A feed-forward-back-propagation (FFBP) multi-layer perceptron (MLP) ANN with significant predictors obtained from SEM as the input units and the root mean square of errors (RMSE) indicated that the ANN method achieves high prediction accuracy. The results present conclusive evidence that perceived ease-of-use (PEMM) is the most significant factor affecting consumers’ attitudes to mobile-based money. While perceived usefulness (PUMM) and PIMM affect adoption decisions, their impact is much lower. Consumer attitudes and intentions were found to have a significant relationship with TAM. SEMM and TAMM; however, they showed mixed results. These findings will be useful to retain prevailing users and attract new ones.

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Digital Business Models in the Healthcare Industry

Hoppe

Häfner

Härting

2021

Handbook of Artificial Intelligence in Healthcare

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Examining the infusion of mobile technology by healthcare practitioners in a hospital setting

Cited by 24 publications

References 78 publications

MHealth and perceived quality of care delivery: a conceptual model and validation

MHealth and perceived quality of care delivery: a conceptual model and validation

Extended Technology Acceptance Model to Predict Mobile-Based Money Acceptance and Sustainability: A Multi-Analytical Structural Equation Modeling and Neural Network Approach

Digital Business Models in the Healthcare Industry

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