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Analysis of the factors influencing healthcare professionals’ adoption of mobile electronic medical record (EMR) using the unified theory of acceptance and use of technology (UTAUT) in a tertiary…

Overview of attention for article published in BMC Medical Informatics and Decision Making, January 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

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1 news outlet
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5 X users

Citations

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214 Dimensions

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611 Mendeley
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Title
Analysis of the factors influencing healthcare professionals’ adoption of mobile electronic medical record (EMR) using the unified theory of acceptance and use of technology (UTAUT) in a tertiary hospital
Published in
BMC Medical Informatics and Decision Making, January 2016
DOI 10.1186/s12911-016-0249-8
Pubmed ID
Authors

Seok Kim, Kee-Hyuck Lee, Hee Hwang, Sooyoung Yoo

Abstract

Although the factors that affect the end-user's intention to use a new system and technology have been researched, the previous studies have been theoretical and do not verify the factors that affected the adoption of a new system. Thus, this study aimed to confirm the factors that influence users' intentions to utilize a mobile electronic health records (EMR) system using both a questionnaire survey and a log file analysis that represented the real use of the system. After observing the operation of a mobile EMR system in a tertiary university hospital for seven months, we performed an offline survey regarding the user acceptance of the system based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technology Acceptance Model (TAM). We surveyed 942 healthcare professionals over two weeks and performed a structural equation modeling (SEM) analysis to identify the intention to use the system among the participants. Next, we compared the results of the SEM analysis with the results of the analyses of the actual log files for two years to identify further insights into the factors that affected the intention of use. For these analyses, we used SAS 9.0 and AMOS 21. Of the 942 surveyed end-users, 48.3 % (23.2 % doctors and 68.3 % nurses) responded. After eliminating six subjects who completed the survey insincerely, we conducted the SEM analyses on the data from 449 subjects (65 doctors and 385 nurses). The newly suggested model satisfied the standards of model fitness, and the intention to use it was especially high due to the influences of Performance Expectancy on Attitude and Attitude. Based on the actual usage log analyses, both the doctors and nurses used the menus to view the inpatient lists, alerts, and patients' clinical data with high frequency. Specifically, the doctors frequently retrieved laboratory results, and the nurses frequently retrieved nursing notes and used the menu to assume the responsibilities of nursing work. In this study, the end-users' intentions to use the mobile EMR system were particularly influenced by Performance Expectancy and Attitude. In reality, the usage log revealed high-frequency use of the functions to improve the continuity of care and work efficiency. These results indicate the influence of the factor of performance expectancy on the intention to use the mobile EMR system. Consequently, we suggest that when determining the implementation of mobile EMR systems, the functions that are related to workflow with ability to increase performance should be considered first.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 611 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Malaysia 2 <1%
United Kingdom 2 <1%
Colombia 1 <1%
Unknown 606 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 121 20%
Student > Ph. D. Student 89 15%
Student > Bachelor 41 7%
Lecturer 38 6%
Researcher 35 6%
Other 98 16%
Unknown 189 31%
Readers by discipline Count As %
Computer Science 92 15%
Business, Management and Accounting 75 12%
Nursing and Health Professions 67 11%
Medicine and Dentistry 50 8%
Engineering 29 5%
Other 89 15%
Unknown 209 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 September 2022.
All research outputs
#2,721,342
of 23,577,761 outputs
Outputs from BMC Medical Informatics and Decision Making
#197
of 2,027 outputs
Outputs of similar age
#48,761
of 399,889 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#4
of 37 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,027 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done particularly well, scoring higher than 90% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 399,889 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.