Moja L, Kwag KH, Lytras T, Bertizzolo L, Brandt L, Pecoraro V, et al. Effectiveness of computerized decision support systems linked to electronic health records: a systematic review and meta-analysis. Am J Public Health. 2014;104:e12-22.
Article Google Scholar
Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005;330:765.
Article Google Scholar
Osheroff JA, Teich JM, Middleton B, Steen EB, Wright A, Detmer DE. A roadmap for national action on clinical decision support. J Am Med Inform Assoc. 2007;14:141–5.
Article Google Scholar
Roshanov PS, Fernandes N, Wilczynski JM, Hemens BJ, You JJ, Handler SM, et al. Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials. BMJ. 2013;346:1–12.
Article Google Scholar
Van de Velde S, Kunnamo I, Roshanov P, Kortteisto T, Aertgeerts B, Vandvik PO, et al. The GUIDES checklist: development of a tool to improve the successful use of guideline-based computerised clinical decision support. Implement Sci. 2018;13:86.
Article Google Scholar
Van de Velde S, Heselmans A, Delvaux N, Brandt L, Marco-Ruiz L, Spitaels D, et al. A systematic review of trials evaluating success factors of interventions with computerised clinical decision support. Implement Sci. 2018;13:114.
Article Google Scholar
Bright TJ, Wong A, Dhurjati R, Bristow E, Bastian L, Coeytaux RR, et al. Effect of clinical decision-support systems. Ann Intern Med. 2012;157:29.
Article Google Scholar
Shekelle PG, Morton SC, Keeler EB. Costs and benefits of health information technology. Evid Rep Technol Assess (Full Rep). 2006;132:1–71.
Google Scholar
Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: toward a unified view. MIS Q. 2003;27:425.
Article Google Scholar
Chang I-C, Hsu H-M. Predicting medical staff intention to use an online reporting system with modified unified theory of acceptance and use of technology. Telemed e-Health. 2012;18:67–73.
Article Google Scholar
Brighton B, Bhandari M, Tornetta P, Felson DT. Hierarchy of evidence: from case reports to randomized controlled trials. Clin Orthop Relat Res. 2003;413:19–24.
Article Google Scholar
Bailey KD. Typologies and taxonomies: an introduction to classification techniques. London: Sage Publications; 1994.
Book Google Scholar
Oh J-C, Yoon S-J. Predicting the use of online information services based on a modified UTAUT model. Behav Inf Technol. 2014;33:716–29.
Article Google Scholar
Cimino JJ. Improving the electronic health record—Are clinicians getting what they wished for? JAMA. 2013;309:991.
CAS Article Google Scholar
Deci EL, Ryan RM. The ‘what’ and ‘why’ of goal pursuits: human needs and the self-determination of behavior. Psychol Inq. 2000;11:227–68.
Article Google Scholar
Bandura A. Human agency in social cognitive theory. Am Psychol. 1989;44:1175–84.
CAS Article Google Scholar
Csikszentmihalyi M. Beyond boredom and anxiety. Jossey-Bass; 2000.
Emaeilzadeh P, Sambasivan M, Nezakati H. The limitations of using the existing TAM in adoption of clinical decision support system in hospitals. Int J Res Bus Soc Sci. 2014;3:56–68.
Google Scholar
Walter Z, Lopez MS. Physician acceptance of information technologies: role of perceived threat to professional autonomy. Decis Support Syst. 2008;46:206–15.
Article Google Scholar
Esmaeilzadeh P. Interaction with clinical decision support systems: the challenge of having a steak with no knife. In: eHealth and remote monitoring. InTech; 2012.
Esmaeilzadeh P, Sambasivan M, Kumar N, Nezakati H. Adoption of clinical decision support systems in a developing country: antecedents and outcomes of physician’s threat to perceived professional autonomy. Int J Med Inform. 2015;84:548–60.
Article Google Scholar
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 Decis Mak. 2012;12:142.
Article Google Scholar
Ajzen I. From intentions to actions: a theory of planned behavior. In: Action control. Springer, Berlin; 1985. p. 11–39.
Hill RJ, Fishbein M, Ajzen I. Belief, attitude, intention, and behavior: an introduction to theory and research. Contemp Sociol. 1977;6:244.
Article Google Scholar
Fillmore CL, Rommel CA, Welch BM, Zhang M, Kawamoto K, Lake S, et al. The perils of meta-regression to identify clinical decision support system success factors. J Biomed Inform. 2016;56:65–8.
Article Google Scholar
Lobach D, Sanders GD, Bright TJ, Wong A, Dhurjati R, Bristow E, et al. Enabling health care decisionmaking through clinical decision support and knowledge management. Evid Rep Technol Assess (Full Rep). 2012;1–784.
Garg AX, Adhikari NKJ, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005;293:1223–38.
CAS Article Google Scholar
Chang I-C, Hwang H-G, Hung W-F, Li Y-C. Physicians’ acceptance of pharmacokinetics-based clinical decision support systems. Expert Syst Appl. 2007;33:296–303.
Article Google Scholar
Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4:50.
Article Google Scholar
Kitson A, Harvey G, McCormack B. Enabling the implementation of evidence based practice: a conceptual framework. Qual Saf Heal Care. 1998;7:149–58.
CAS Article Google Scholar
Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas. 1960;20:37–46.
Article Google Scholar
McHugh ML. Interrater reliability: the kappa statistic. Biochem Medica. 2012;22:276–82.
Article Google Scholar
Kawamoto K, Lobach DF. Clinical decision support provided within physician order entry systems: a systematic review of features effective for changing clinician behavior. In: AMIA Annual Symposium proceedings AMIA Symposium. 2003;361–5.
Miller K, Capan M, Weldon D, Noaiseh Y, Kowalski R, Kraft R, et al. The design of decisions: matching clinical decision support recommendations to Nielsen’s design heuristics. Int J Med Inform. 2018;117:19–25.
Article Google Scholar
Meeker D, Linder JA, Fox CR, Friedberg MW, Persell SD, Goldstein NJ, et al. Effect of behavioral interventions on inappropriate antibiotic prescribing among primary care practices a randomized clinical trial. JAMA. 2016;315:562.
CAS Article Google Scholar
Duke JD, Li X, Dexter P. Adherence to drug-drug interaction alerts in high-risk patients: a trial of context-enhanced alerting. J Am Med Informatics Assoc. 2013;20:494–8.
Article Google Scholar
Scheepers-Hoeks AMJ, Grouls RJ, Neef C, Ackerman EW, Korsten EH. Physicians’ responses to clinical decision support on an intensive care unit-comparison of four different alerting methods. Artif Intell Med. 2013;59:33–8.
Article Google Scholar
Alaiad A, Zhou L. Patients’ behavioral intention toward using healthcare robots. Proc Ninet Am Conf Inf Syst. 2013;15–17:1–11.
Google Scholar
Hoque R, Sorwar G. Understanding factors influencing the adoption of mHealth by the elderly: an extension of the UTAUT model. Int J Med Inform. 2017;2017(101):75–84.
Article Google Scholar
Maillet É, Mathieu L, Sicotte C. Modeling factors explaining the acceptance, actual use and satisfaction of nurses using an Electronic Patient Record in acute care settings: an extension of the UTAUT. Int J Med Inform. 2015;84:36–47.
Article Google Scholar
Carayon P, Schoofs Hundt A, Karsh B-T, Gurses AP, Alvarado CJ, Smith M, et al. Work system design for patient safety: the SEIPS model. Qual Heal Care. 2006;15(suppl 1):i50–8.
Article Google Scholar
Armstrong K. Methods in comparative effectiveness research. J Clin Oncol. 2012;30:4208–14.