Siru Liu1, Thomas J Reese2, Kensaku Kawamoto2, Guilherme Del Fiol2, Charlene Weir2. 1. Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA. siru.liu@utah.edu. 2. Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.
Abstract
BACKGROUND: Studies that examine the adoption of clinical decision support (CDS) by healthcare providers have generally lacked a theoretical underpinning. The Unified Theory of Acceptance and Use of Technology (UTAUT) model may provide such a theory-based explanation; however, it is unknown if the model can be applied to the CDS literature. OBJECTIVE: Our overall goal was to develop a taxonomy based on UTAUT constructs that could reliably characterize CDS interventions. METHODS: We used a two-step process: (1) identified randomized controlled trials meeting comparative effectiveness criteria, e.g., evaluating the impact of CDS interventions with and without specific features or implementation strategies; (2) iteratively developed and validated a taxonomy for characterizing differential CDS features or implementation strategies using three raters. RESULTS: Twenty-five studies with 48 comparison arms were identified. We applied three constructs from the UTAUT model and added motivational control to characterize CDS interventions. Inter-rater reliability was as follows for model constructs: performance expectancy (κ = 0.79), effort expectancy (κ = 0.85), social influence (κ = 0.71), and motivational control (κ = 0.87). CONCLUSION: We found that constructs from the UTAUT model and motivational control can reliably characterize features and associated implementation strategies. Our next step is to examine the quantitative relationships between constructs and CDS adoption.
BACKGROUND: Studies that examine the adoption of clinical decision support (CDS) by healthcare providers have generally lacked a theoretical underpinning. The Unified Theory of Acceptance and Use of Technology (UTAUT) model may provide such a theory-based explanation; however, it is unknown if the model can be applied to the CDS literature. OBJECTIVE: Our overall goal was to develop a taxonomy based on UTAUT constructs that could reliably characterize CDS interventions. METHODS: We used a two-step process: (1) identified randomized controlled trials meeting comparative effectiveness criteria, e.g., evaluating the impact of CDS interventions with and without specific features or implementation strategies; (2) iteratively developed and validated a taxonomy for characterizing differential CDS features or implementation strategies using three raters. RESULTS: Twenty-five studies with 48 comparison arms were identified. We applied three constructs from the UTAUT model and added motivational control to characterize CDS interventions. Inter-rater reliability was as follows for model constructs: performance expectancy (κ = 0.79), effort expectancy (κ = 0.85), social influence (κ = 0.71), and motivational control (κ = 0.87). CONCLUSION: We found that constructs from the UTAUT model and motivational control can reliably characterize features and associated implementation strategies. Our next step is to examine the quantitative relationships between constructs and CDS adoption.
Entities:
Keywords:
Clinical decision support; Taxonomy; Unified Theory of Acceptance and Use of Technology
Authors: Jerome A Osheroff; Jonathan M Teich; Blackford Middleton; Elaine B Steen; Adam Wright; Don E Detmer Journal: J Am Med Inform Assoc Date: 2007-01-09 Impact factor: 4.497
Authors: Lorenzo Moja; Koren H Kwag; Theodore Lytras; Lorenzo Bertizzolo; Linn Brandt; Valentina Pecoraro; Giulio Rigon; Alberto Vaona; Francesca Ruggiero; Massimo Mangia; Alfonso Iorio; Ilkka Kunnamo; Stefanos Bonovas Journal: Am J Public Health Date: 2014-10-16 Impact factor: 9.308
Authors: Daniella Meeker; Jeffrey A Linder; Craig R Fox; Mark W Friedberg; Stephen D Persell; Noah J Goldstein; Tara K Knight; Joel W Hay; Jason N Doctor Journal: JAMA Date: 2016-02-09 Impact factor: 56.272
Authors: Kristen Miller; Muge Capan; Danielle Weldon; Yaman Noaiseh; Rebecca Kowalski; Rachel Kraft; Sanford Schwartz; William S Weintraub; Ryan Arnold Journal: Int J Med Inform Date: 2018-05-21 Impact factor: 4.046
Authors: Stijn Van de Velde; Annemie Heselmans; Nicolas Delvaux; Linn Brandt; Luis Marco-Ruiz; David Spitaels; Hanne Cloetens; Tiina Kortteisto; Pavel Roshanov; Ilkka Kunnamo; Bert Aertgeerts; Per Olav Vandvik; Signe Flottorp Journal: Implement Sci Date: 2018-08-20 Impact factor: 7.327
Authors: Siru Liu; Kensaku Kawamoto; Guilherme Del Fiol; Charlene Weir; Daniel C Malone; Thomas J Reese; Keaton Morgan; David ElHalta; Samir Abdelrahman Journal: J Am Med Inform Assoc Date: 2022-04-13 Impact factor: 4.497
Authors: Adama M Keita; Ben J Brintz; Ashraful I Khan; Md Taufiqul Islam; Zahid Hasan Khan; Youssouf Keita; Jennifer Hwang; Eric J Nelson; Firdausi Qadri; Samba Sow; Daniel T Leung; Melissa H Watt Journal: Am J Trop Med Hyg Date: 2022-07-13 Impact factor: 3.707
Authors: Francesc Saigí-Rubió; Josep Vidal-Alaball; Joan Torrent-Sellens; Ana Jiménez-Zarco; Francesc López Segui; Marta Carrasco Hernandez; Xavier Alzaga Reig; Josep Maria Bonet Simó; Mercedes Abizanda González; Jordi Piera-Jimenez; Oscar Solans Journal: J Med Internet Res Date: 2021-05-31 Impact factor: 5.428