An adoption model describing clinician’s acceptance of automated diagnostic system for tuberculosis

被引：8

作者：

Panicker R.O. ^{[1
]}

Soman B. ^{[2
]}

Gangadharan K.V. ^{[3
]}

Sobhana N.V. ^{[4
]}

机构：

[1] Department of Information Technology, College of Engineering, Trikaripur

[2] AMCHSS, Sree Chitra Tirunal Institute of Medical Sciences and Technology, Trivandrum

[3] Department of Mechanical Engineering, National Institute of Technology Karnataka, Mangalore

[4] Department of Computer Science and Engineering, Rajiv Gandhi Institute of Technology, Kottayam

来源：

Health and Technology | 2016年 / 6卷 / 4期

关键词：

Computerized medical diagnosing; technology acceptance; tuberculosis; UTAUT model;

D O I：

10.1007/s12553-016-0136-4

中图分类号：

学科分类号：

摘要：

Computerised medical diagnosing systems are very important to all healthcare professionals, especially clinicians who help in clinical decision-making in complex situations. The acceptance of automated or computerised medical diagnosing system for Tuberculosis (TB) among clinicians is very essential for its effective implementation and usage. This paper proposes a framework that aims to examine factors that influence clinician’s acceptance and use of computerised TB detection system. An extended Unified Theory of Acceptance and Use of Technology (UTAUT) model is adopted in the healthcare context of a developing country for this purpose. The proposed framework is expected to help researchers and clinicians to assess the uptake of modern technology by health care professionals and the tool could be used in other healthcare contexts also. This paper also reviewed previous research adopting UTAUT model, for identifying the constructs promoting the adoption of technology acceptance in health care context. © 2016, IUPESM and Springer-Verlag Berlin Heidelberg.

引用

页码：247 / 257

页数：10

共 50 条

[11]

Schaper L.K., Pervan G.P., ICT and OTs: a model of information and communication technology acceptance and utilisation by occupational therapists, Int J Med Inform. 7 6 S, pp. S212-S221, (2007)

[12]

Shibl R., Lawley M., Debuse J., Factors influencing decision support system acceptance, Decis Support Syst, 54, pp. 953-961, (2013)

[13]

Panicker R.O., Soman B., Saini G., Rajan J., A review of automatic methods based on image processing techniques for tuberculosis detection from microscopic sputum smear images, J Med Syst, 40, (2016)

[14]

Shojania K.G., Burton E.C., McDonald K.M.M.M., Goldman L., Changes in rates of autopsy-detected diagnostic errors over time: a systematic review, JAMA, 289, 21, pp. 2849-2856, (2003)

[15]

Sambasivan M., Esmaeilzadeh P., Kumar N., Nezakati H., Intention to adopt clinical decision support systems in a developing country: effect of Physician’s perceived professional autonomy, involvement and belief: a cross-sectional study, BMC Med Inform Dec Making, 12, (2012)

[16]

Tapley A., Switz N., Reber C., Davis J.L., Miller C., Matovu J.B., Worodria W., Huang L., Fletcher D.A., Cattamanchi A., Mobile digital fluorescence microscopy for diagnosis of tuberculosis, J Clin Microbiol, 51, 6, pp. 1774-1778, (2013)

[17]

Costa M.G.F., Filho C.C.F., Junior K.A., Levy P.C., Xavier C.M., Fujimoto L.B., A sputum smear microscopy image database for automatic bacilli detection in conventional microscopy, ConfProc IEEE Eng Med Biol Soc, pp. 2841-2844, (2014)

[18]

Osibote O.A., Dendere R., Krishnan S., Douglas T.S., Automated focusing in bright-field microscopy for tuberculosis detection, J Microsc, 240, 2, pp. 155-163, (2010)

[19]

Veropoulos K., Campbell C., Learmonth G., Knight B., Simpson J., The automated identification of tubercle bacilli using image processing and neural computing techniques, Proceedings of the 8th International Conference on Artificial Neural Networks

[20]

Sweden, 2, pp. 797-802, (1998)

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