A Systematic Review of the Technology Acceptance Model in Health Informatics

Rahimi, Bahlol; Nadri, Hamed; Afshar, Hadi Lotfnezhad; Timpka, Toomas

doi:10.1055/s-0038-1668091

Cited by 429 publications

(366 citation statements)

References 137 publications

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“…This result is consistent with the studies by Briz-Ponce and García-Peñalvo 32 and Schomakers et al 44 Therefore, many health programs and services can be implemented on this technology because it is easy to learn and use. According to review by Rahimi et al, 26 facilitating condition is one of the most common factors in most technology settings, especially mobile technology for all user groups. So, the facilitating condition factor in this study is confirmed for mHealth technology and student user groups and is compatible with the study by Rahimi et al 26 In addition, there was a significant relationship between behavioral intention and use which is consistent with the results of many studies.…”

Section: Discussionmentioning

confidence: 99%

“…18 MHealth has been used to treat many diseases as a health intervention just like a new treatment, and studies have also talked about its effectiveness in monitoring and tracking patients, especially chronic patients, as the use of healthrelated capabilities plays an effective and beneficial role in the therapeutic process of patients with asthma, acquired immune deficiency syndrome (AIDS), the monitoring of elderly peoples, and many other diseases. [19][20][21][22][23][24] In health care services, where distance is a major factor, the use of mHealth by health care providers to exchange valid information for the diagnosis and treatment of diseases and prevention of injury, research and evaluation, and providing continuing education are inevitable in advancing the goals of the health system. 25 In other hand, Rahimi et al identified three main (ICT) application areas for the technology acceptance model (TAM) in health services: telemedicine, electronic health records, and mobile applications.…”

Section: Introductionmentioning

confidence: 99%

“…25 In other hand, Rahimi et al identified three main (ICT) application areas for the technology acceptance model (TAM) in health services: telemedicine, electronic health records, and mobile applications. 26 But despite all these applications, like other technologies in the implementation and usage phase, this technology faces a variety of human problems and attitudes. In addition, studying the acceptance factors from the perspective of the final user and identifying the views, expectations and demands of mHealth technology will play an important role in implementing this technology successfully.…”

Section: Introductionmentioning

confidence: 99%

“…The best known and important of these are the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) that can be administered by the users of a technology. 26,30 In addition, Venkatesh et al compared eight models in association with core constructs, beliefs, moderators, and percentage of explained variance including innovation diffusion theory (IDT), social cognitive theory (SCT), theory of reasoned action (TRA), theory of planned behavior (TPB), decomposed-TPB (DTPB), TAM, TAM2, combined-TAM-TPB, and UTAUT. 31 UTAUT was able to account for 70% of the variance (adjusted R 2 ) in usage intention, a substantial improvement over any of the original eight models and their extensions.…”

Section: Introductionmentioning

confidence: 99%

“…There have been many studies on mobile technology adoption factors in health care, but most have focused on consumers and health care professionals, and few studies have been conducted on the adoption of mobile technology among college students. 26 Briz-Ponce and García-Peñalvo 32 conducted a study aimed at examining acceptance factors using the TAM model among medical students and professionals. The TAM model explains that 46.7% of behavioral intention to use mobile devise or "apps" for learning.…”

Section: Introductionmentioning

confidence: 99%

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Effective Factors in Adoption of Mobile Health Applications between Medical Sciences Students Using the UTAUT Model

et al. 2019

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Background Students with complex health care services process face constant challenges with regard to health education. The mobile devices are an important tool that can install various applications for using information such as clinical guidelines, drug resources, clinical calculations, and the latest scientific evidence without any time and place limitations. And this happens only when students accept and use it. Objective The purpose of this article is to identify the factors influencing students in their intention to use mobile health (mHealth) by using Unified Theory of Acceptance and Use of Technology (UTAUT) model. Methods A standard questionnaire was used to collect the data from nearly 302 Lorestan University of medical science students including nutrition and public health, paramedicine, nursing and midwifery, pharmacy, dentistry, and medical schools. The data were processed using LISREL (Scientific Software International, Inc., Lincolnwood, Illinois) and SPSS (IBM Corp., Armonk, New York) softwares and the statistical analysis technique was based on structural equation modeling (SEM). Result A total of 300 questionnaires including valid responses were used in this study. The results showed that mediator of age did not affect the predictors of intention to use mHealth, and the level of education and gender directly affected the intention to use. In addition, effort expectancy, facilitating condition, and behavioral intention directly and indirectly have effect on use, whereas the result revealed no significant relationship between two important processes of performance expectancy and social influence with students' behavioral intention to use the mHealth. Conclusions The present study provides valuable information on mobile health acceptance factors for widespread use of this device among students of universities of medical sciences as a base infrastructure for a variety of information about health services and learning. Review and comparison of results with other studies showed that mHealth acceptance factors were different from other end users (elderly, patients, and health professionals).

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effective Factors in Adoption of Mobile Health Applications between Medical Sciences Students Using the UTAUT Model

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Technologies Based on Reusable Wind Turbine Blades

Thakur,

Kumar

2024

Wind Energy Storage and Conversion

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Model‐Informed Precision Dosing Using Machine Learning for Levothyroxine in General Practice: Development, Validation and Clinical Simulation Trial

Janssen Daalen,

Doesburg,

Hunik

et al. 2024

Clin Pharma and Therapeutics

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Levothyroxine is one of the most prescribed drugs in the western world. Dosing is challenging due to high‐interindividual differences in effective dosage and the narrow therapeutic window. Model‐informed precision dosing (MIPD) using machine learning could assist general practitioners (GPs), but no such models exist for primary care. Furthermore, introduction of decision‐support algorithms in healthcare is limited due to the substantial gap between developers and clinicians' perspectives. We report the development, validation, and a clinical simulation trial of the first MIPD application for primary care. Stable maintenance dosage of levothyroxine was the model target. The multiclass model generates predictions for individual patients, for different dosing classes. Random forest was trained and tested on a national primary care database (n = 19,004) with a final weighted AUC across dosing options of 0.71, even in subclinical hypothyroidism. TSH, fT4, weight, and age were most predictive. To assess the safety, feasibility, and clinical impact of MIPD for levothyroxine, we performed clinical simulation studies in GPs and compared MIPD to traditional prescription. Fifty‐one GPs selected starting dosages for 20 primary hypothyroidism cases without and then with MIPD 2 weeks later. Overdosage and underdosage were defined as higher and lower than 12.5 μg relative to stable maintenance dosage. MIPD decreased overdosage in number (30.5 to 23.9%, P < 0.01) and magnitude (median 50 to 37.5 μg, P < 0.01) and increased optimal starting dosages (18.3 to 30.2%, P < 0.01). GPs considered lab results more often with MIPD and most would use the model frequently. This study demonstrates the clinical relevance, safety, and effectiveness of MIPD for levothyroxine in primary care.

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A Systematic Review of the Technology Acceptance Model in Health Informatics

Cited by 429 publications

References 137 publications

Effective Factors in Adoption of Mobile Health Applications between Medical Sciences Students Using the UTAUT Model

Effective Factors in Adoption of Mobile Health Applications between Medical Sciences Students Using the UTAUT Model

Technologies Based on Reusable Wind Turbine Blades

Model‐Informed Precision Dosing Using Machine Learning for Levothyroxine in General Practice: Development, Validation and Clinical Simulation Trial

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