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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2016

Open Access 01-12-2015 | Research article

Critical factors influencing physicians’ intention to use computerized clinical practice guidelines: an integrative model of activity theory and the technology acceptance model

Authors: Ju-Ling Hsiao, Rai-Fu Chen

Published in: BMC Medical Informatics and Decision Making | Issue 1/2016

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Abstract

Background

With the widespread use of information communication technologies, computerized clinical practice guidelines are developed and considered as effective decision supporting tools in assisting the processes of clinical activities. However, the development of computerized clinical practice guidelines in Taiwan is still at the early stage and acceptance level among major users (physicians) of computerized clinical practice guidelines is not satisfactory. This study aims to investigate critical factors influencing physicians’ intention to computerized clinical practice guideline use through an integrative model of activity theory and the technology acceptance model.

Methods

The survey methodology was employed to collect data from physicians of the investigated hospitals that have implemented computerized clinical practice guidelines. A total of 505 questionnaires were sent out, with 238 completed copies returned, indicating a valid response rate of 47.1 %. The collected data was then analyzed by structural equation modeling technique.

Results

The results showed that attitudes toward using computerized clinical practice guidelines (γ = 0.451, p < 0.001), organizational support (γ = 0.285, p < 0.001), perceived usefulness of computerized clinical practice guidelines (γ = 0.219, p < 0.05), and social influence (γ = 0.213, p < 0.05) were critical factors influencing physicians’ intention to use computerized clinical practice guidelines, and these factors can explain 68.6 % of the variance in intention to use computerized clinical practice guidelines.

Conclusions

This study confirmed that some subject (human) factors, environment (organization) factors, tool (technology) factors mentioned in the activity theory should be carefully considered when introducing computerized clinical practice guidelines. Managers should pay much attention on those identified factors and provide adequate resources and incentives to help the promotion and use of computerized clinical practice guidelines. Through the appropriate use of computerized clinical practice guidelines, the clinical benefits, particularly in improving quality of care and facilitating the clinical processes, will be realized.
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Literature
1.
Field MJ, Lohr KN. Guidelines for clinical practice: from development to use. Washington, D.C: National Academy Press; 1992.
2.
3.
de Jong JD, Groenewegen PP, Spreeuwenberg P, Schellevis F, Westert GP. Do guidelines create uniformity in medical practice? Soc Sci Med. 2010;70(2):209–16.PubMedCrossRef
4.
Grando A, Peleg M, Glasspool D. A goal-oriented framework for specifying clinical guidelines and handling medical errors. J Biomed Inform. 2010;43(2):287–99.PubMedCrossRef
5.
Chen CF. Glocalization: the outlook for Taiwan evidence based health care. J Nurs. 2014;61(6):12–6.
6.
Chen C, Chen K, Hsu CY, Chiu WT, Li YC. A guideline-based decision support for pharmacological treatment can improve the quality of hyperlipidemia management. Comput Methods Programs Biomed. 2010;97(3):280–5.PubMedCrossRef
7.
Jian WS, Hsu CY, Hao TH, Wen HC, Hsu MH, Lee YL, et al. Building a portable data and information interoperability infrastructure-framework for a standard Taiwan Electronic Medical Record Template. Comput Methods Programs Biomed. 2007;88(2):102–11.PubMedCrossRef
8.
Wu CT, Chung KP, Miao NF, Wang CH, Hsu YH, Chang KJ, et al. Exploring factors of physicians’ willingness on implementation of clinical practice guidelines-focus on characters of clinical practice guidelines. J Healthc Manag. 2005;6(2):153–72.
9.
Giguere A, Legare F, Grimshaw J, Turcotte S, Fiander M, Grudniewicz A, et al. Printed educational materials: effects on professional practice and health care outcomes. Cochrane Database Syst Rev. 2012;10, CD004398.PubMed
10.
11.
Handler J, Lackland DT. Translation of hypertension treatment guidelines into practice: a review of implementation. J Am Soc Hypertens. 2011;5(4):197–207.PubMedCrossRef
12.
Larisch A, Reuss A, Oertel W, Eggert K. Does the clinical practice guideline on Parkinson’s disease change health outcomes? A cluster randomized controlled trial. J Neurol. 2011;258(5):826–34.PubMedCrossRef
13.
Mitchell C, Dwyer R, Hagan T, Mathers N. Impact of the QOF and the NICE guideline in the diagnosis and management of depression: a qualitative study. Brit J Gen Pract. 2011;61(586):e279–89.CrossRef
14.
Mourad SM, Hermens RPMG, Liefers J, Akkermans RP, Zielhuis GA, Adang E, et al. A multi-faceted strategy to improve the use of national fertility guidelines; a cluster-randomized controlled trial. Hum Reprod. 2011;26(4):817–26.PubMedCrossRef
15.
Schnoor M, Meyer T, Suttorp N, Raspe H, Welte T, Schafer T, et al. Development and evaluation of an implementation strategy for the German guideline on community-acquired pneumonia. Qual Saf Health Care. 2010;19(6):498–502.PubMed
16.
17.
Lyng KM. From clinical practice guidelines, to clinical guidance in practice -impacts for computerization. Int J Med Inform. 2013;82(12):358–63.CrossRef
18.
Grimshaw J, Eccles M, Tetroe J. Implementing clinical guidelines: current evidence and future implications. J Contin Educ Health Prof. 2004;24 Suppl 1:S31–7.PubMedCrossRef
19.
Johnson CM, Turley JP. The significance of cognitive modeling in building healthcare interfaces. Int J Med Inform. 2006;75(2):163–72.PubMedCrossRef
20.
Bouaud J, Seroussi B, Falcoff H, Venot A. Design factors for success or failure of guideline-based decision support systems: an hypothesis involving case complexity. AMIA Annu Symp Proc. 2006;71–5.
21.
Cabana MD, Rand CS, Powe NR, Wu AW, Wilson MH, Abboud PA, et al. Why don’t physicians follow clinical practice guidelines? a framework for improvement. JAMA. 1999;282(15):1458–65.PubMedCrossRef
22.
Larson E. Status of practice guidelines in the United States: CDC guidelines as an example. Prev Med. 2003;36(5):519–24.PubMedCrossRef
23.
Shahar Y, Young O, Shalom E, Galperin M, Mayaffit A, Moskovitch R, et al. A framework for a distributed, hybrid, multiple-ontology clinical-guideline library, and automated guideline-support tools. J Biomed Inform. 2004;37(5):325–44.PubMedCrossRef
24.
Goud R, Hasman A, Strijbis AM, Peek N. A parallel guideline development and formalization strategy to improve the quality of clinical practice guidelines. Int J Med Inform. 2009;78(8):513–20.PubMedCrossRef
25.
Tu SW, Campbell JR, Glasgow J, Nyman MA, McClure R, McClay J, et al. The SAGE guideline model: achievements and overview. J Am Med Inform Assoc. 2007;14(5):589–98.PubMedPubMedCentralCrossRef
26.
Boxwala AA, Peleg M, Tu S, Ogunyemi O, Zeng QT, Wang D, et al. GLIF3: a representation format for sharable computer-interpretable clinical practice guidelines. J Biomed Inform. 2004;37(3):147–61.PubMedCrossRef
27.
Wang D, Peleg M, Tu SW, Boxwala AA, Ogunyemi O, Zeng Q, et al. Design and implementation of the GLIF3 guideline execution engine. J Biomed Inform. 2004;37(5):305–18.PubMedCrossRef
28.
Abidi S. A conceptual framework for ontology based automating and merging of clinical pathways of comorbidities. In: Knowledge management for health care procedures. Berlin: Springer; 2009. p. 55–66.CrossRef
29.
Abidi S, Cox J, Shepherd M, Abidi SR. Using OWL ontologies for clinical guidelines based comorbid decision support, 45th Hawaii international conference on system sciences. 2012. p. 3030–8.
30.
Butler T, Feller J, Pope A, Emerson B, Murphy C. Designing a core IT artefact for knowledge management systems using participatory action research in a government and a non-government organization. J Strategic Inf Syst. 2008;17(4):249–67.CrossRef
31.
Colombet I, Aguirre-Junco AR, Zunino S, Jaulent MC, Leneveut L, Chatellier G. Electronic implementation of guidelines in the EsPeR system: a knowledge specification method. Int J Med Inform. 2005;74(7–8):597–604.PubMedCrossRef
32.
Dufour JC, Fieschi D, Fieschi M. Coupling computer-interpretable guidelines with a drug-database through a web-based system - the PRESGUID project. BMC Med Inform Decis Mak. 2004;4:2.PubMedPubMedCentralCrossRef
33.
Fraccaro P, Casteleiro MA, Ainsworth J, Buchan I. Adoption of clinical decision support in multimorbidity: A systematic review. JMIR Med Inform. 2015;3(1):1–18.CrossRef
34.
Jafarpour B, Abidi S. Merging disease-specific clinical guidelines to handle comorbidities in a clinical decision support setting. Artif Intell Med. 2013;7885:28–32.CrossRef
35.
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:f657.PubMedCrossRef
36.
Verhoeven F, Steehouder MF, Hendrix RMG, Van Gemert-Pijnen JEWC. From expert-driven to user-oriented communication of infection control guidelines. Int J Hum-Comput St. 2010;68(6):328–43.CrossRef
37.
Isern D, Moreno A. Computer-based execution of clinical guidelines: a review. Int J Med Inform. 2008;77(12):787–808.PubMedCrossRef
38.
Lyng KM, Pedersen BS. Participatory design for computerization of clinical practice guidelines. J Biomed Inform. 2011;44(5):909–18.PubMedCrossRef
39.
Wilk S, Michalowski W, Michalowski M, Farion K, Hing MM, Mohapatra S. Mitigation of adverse interactions in pairs of clinical practice guidelines using constraint logic programming. J Biomed Inform. 2013;46(2):341–53.PubMedCrossRef
40.
Francke AL, Smit MC, de Veer AJE, Mistiaen P. Factors influencing the implementation of clinical guidelines for health care professionals: a systematic meta-review. BMC Med Inform Decis Mak. 2008;8:38.PubMedPubMedCentralCrossRef
41.
Kortteisto T, Kaila M, Komulainen J, Mäntyranta T, Rissanen P. Healthcare professionals’ intentions to use clinical guidelines: A survey using the theory of planned behaviour. Implement Sci. 2010;5:51.PubMedPubMedCentralCrossRef
42.
Flottorp S, Oxman A, Krause J, Musila N, Wensing M, Godycki-Cwirko M, et al. A checklist for identifying determinants of practice: A systematic review and synthesis of frameworks and taxonomies of factors that prevent or enable improvements in healthcare professional practice. Implement Sci. 2013;8:35.PubMedPubMedCentralCrossRef
43.
Vygotsky LS. Psychology of Art. Cambridge: MIT Press; 1971.
44.
Kuutti K. Activity Theory as a potential framework for human-computer interaction research. In: Nardi BA, editor. Context and consciousness: activity theory and human computer Interaction. Cambridge: MIT Press; 1995. p. 17–44.
45.
Leontiev AN. Problems of the development of the mind. Moscow: Progress; 1981 (Russian edition published 1959).
46.
Nardi BA. Studying context a comparison of activity theory situated action models and distributed cognition. In: Nardi BA, editor. Context and consciousness: activity theory and human-computer interaction. Cambridge: MIT Press; 1995. p. 35–52.
47.
Tuikka T. Remote concept design from an activity theory perspective, In proceedings of the 2002 ACM conference on computer supported cooperative work. 2002. p. 186–95.
48.
Liaw SS, Huang HM, Chen GD. An activity-theoretical approach to investigate learners’ factors toward e-learning systems. Comput Hum Behav 2007. 2007;23(4):1906–20.CrossRef
49.
Lim CP, Hang D. An activity theory approach to research of ICT integration in Singapore schools. Comput Educ. 2003;41(1):49–63.CrossRef
50.
Beaudry A, Carillo KD. The customer-centered B2C literature through the lens of activity theory: a review and research agenda. Commun Assoc Inf Syst. 2006;17:428–503.
51.
Lin F, Chaboyer W, Wallis M, Miller A. Factors contributing to the process of intensive care patient discharge: an ethnographic study informed by activity theory. Int J Nnurs Stud 2013. 2013;50(8):1054–66.
52.
Boisen E, Bygholm A, Cavan D, Hejlesen OK. Copability, coping, and learning as focal concepts in the evaluation of computerised diabetes disease management. Int J Med Inform. 2003;70(2):353–63.PubMedCrossRef
53.
Sun J, Koong KS, Poole MS. Critical success factors for context-aware mobile communication systems. Int J Mobile Communication. 2009;7(3):290–307.CrossRef
54.
Yusof MM, Kuljis J, Papazafeiropoulou A, Stergioulas LS. An evaluation framework for health information systems: human, organization and technology-fit factors (HOT-fit). Int J Med Inform. 2008;77(6):386–98.PubMedCrossRef
55.
Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. Mis Quart. 1989;13(3):319–40.CrossRef
56.
Wu IL, Li JY, Fu CY. The adoption of mobile healthcare by hospital’s professionals: an integrative perspective, Decis. Support Syst. 2011;51:587–96.CrossRef
57.
Yarbrough AK, Smith TB. Technology acceptance among physicians: a new take on TAM. Med Care Res Rev. 2007;64(6):650–72.PubMedCrossRef
58.
Hsaio JL, Wu WC, Chen RF. Factors of accepting pain management decision support systems by nurse anesthetists. BMC Med Inform Decis Mak. 2013;13:16.CrossRef
59.
Chang IC, Hwang HG, Hung WF, Li YC. Physicians’ acceptance of pharmacokinetics-based clinical decision support systems. Expert Syst Appl. 2007;33(2):296–303.CrossRef
60.
Esmaeilzadeh P, Sambasivan M, Kumar N, Nezakati H. (2015). 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(8):548–60.PubMedCrossRef
61.
Venkatesh V, Sykes T, Zhang X. Just what the doctor ordered: a revised UTAUT for EMR system adoption and use by doctors, 44th Hawaii international conference on system sciences (HICSS). 2014. p. 1–10.
62.
Venkatesh V, Morris MG, Davis G, Davis F. User acceptance of information technology: toward a unified view. Mis Quart. 2003;27(3):425–78.
63.
Rogers EM. Diffusion of innovation. 5th ed. New York: Free Press; 2003.
64.
Wakefield DS, Halbesleben JRB, Ward MM, Qiu Q, Brokel J, Crandall D. Development of a measure of clinical information systems expectations and experiences. Med Care. 2007;45(9):884–90.PubMedCrossRef
65.
Chen RF, Hsaio JL. An investigation on physicians’ acceptance of hospital information systems: a case study. Int J Med Inform. 2012;81:810–20.PubMedCrossRef
66.
Teng JTC, Grover V, Guttler W. Information technology innovations: general diffusion patterns and its relationships to innovation characteristics. IEEE Trans Eng Manage. 2002;49(1):13–27.CrossRef
67.
Aldosari B. User acceptance of a picture archiving and communication system (PACS) in a Saudi Arabian hospital radiology department. BMC Med Inform Decis. 2012;12:44.CrossRef
68.
Craig VS, France B, Christie LC. Factors influencing the adoption of web based shopping: the impact of trust. ACM SIGMIS Database. 2004;35(2):32–49.CrossRef
69.
Tung FC, Chang SC, Chou CM. An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry. Int J Med Inform. 2008;77(5):324–35.PubMedCrossRef
70.
Vogelzang M, Zijlstra F, Nijsten MW. Design and implementation of GRIP: a computerized glucose control system at a surgical intensive care unit. BMC Med Inform Decis Mak. 2005;5:38.PubMedPubMedCentralCrossRef
71.
Fishbein M, Ajzen I. Belief attitude, intention, and behavior: an introduction to theory and research. Reading: Addison Wesley; 1975.
72.
Alexander LW, Randolph WA. The fit between technology and structure as a predictor of performance in nursing subunits. Acad Manage J. 1985;28(4):844–59.PubMedCrossRef
73.
Blili S, Raymond L, Rivard S. Impact of task uncertainty, end-user involvement, and competence on the success of end-user computing. Inform Manage. 1998;33(3):137–53.CrossRef
74.
Saillour-Glenisson F, Michel P. Individual and collective facilitators of and barriers to the use of clinical practice guidelines by physicians: a literature review. Rev Epidemiol Sante Publique. 2003;51(1 Pt 1):65–80.PubMed
75.
Venkatesh V, Morris MG. Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. Mis Quart. 2000;24(1):115–39.CrossRef
76.
Simpson SH, Marrie TJ, Majumdar SR. Do guidelines guide pneumonia practice? a systematic review of interventions and barriers to best practice in the management of community-acquired pneumonia. Respir Care Clin N Am. 2005;11(1):1–13.PubMedCrossRef
77.
Hilton A, Skrutkowski M. Translating instruments into other languages: development and testing processes. Cancer Nurs. 2002;25(1):1–7.PubMedCrossRef
78.
Petrick JF. Development of a multi-dimensional scale for measuring the perceived value of a service. J Leis Res. 2002;34(2):119–34.
79.
Davis DA, Taylor-Vaisey A. Translating guidelines into practice: a systematic review of theoretic concepts, practical experience and research evidence in the adoption of clinical practice guidelines. CMAJ. 1997;157(4):408–16.PubMedPubMedCentral
80.
Warner BW, Kulick RM, Stoop MM, Mehta S, Stephan M, Kotagal UR. An evidence-based clinical pathway for acute appendicitis decreases hospital duration and cost. J Pediatr Surg. 1998;33(9):1371–5.PubMedCrossRef
81.
Wolff M, Bower DJ, Marbella AM, Casanova JE. US family physicians’ experiences with practice guidelines. Fam Med. 1998;30(2):117–21.PubMed
82.
Flores G, Lee M, Bauchner H, Kastner B. Pediatricians attitudes, beliefs, and practices regarding clinical practice guidelines: a national survey. Pediatrics. 2000;105:496–501.PubMedCrossRef
83.
Davis FD. User acceptance of information technology: system characteristics, user perception and behavioral impacts. Int J Man Mach Stud. 1993;38:319–40.CrossRef
84.
Goodhue DL, Thompson RL. Task-technology fit and individual performance. Mis Quart. 1995;19(2):213–36.CrossRef
85.
Bhattacherjee A, Hikmet N. Physicians’ resistance toward healthcare information technology: a theoretical model and empirical test. Eur J Inform Syst. 2007;16(6):725–37.CrossRef
86.
Hair JF, Anderson RE, Tatham RL, Black WC. Multivariate data analysis. 6th ed. New Jersey: Prentice Hall; 1998.
87.
Chen DR. Psychosocial work environment and self-rated health: a comparative study of DC and ERI models in a sample of medical specialists. Taiwan J Public Health. 2006;25:93–106.
88.
Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res. 1981;18(1):39–50.CrossRef
89.
Stoddard JJ, Grossmen JM, Rudell LS. Physicians more likely to face guality incentives than incentives that may restrain care. Issue Brief Cent Stud Health Syst Change. 2002;48:1–4.PubMed
90.
Chau PYK, Hu PJ. Examining a model of information technology acceptance by individual professionals: an exploratory study. J Manage Inform Syst. 2002;18(4):191–229.
91.
Raymond L, Bergeron F. Personal DSS success in small enterprises. Inform Manage. 1992;22(5):301–8.CrossRef
Metadata
Title
Critical factors influencing physicians’ intention to use computerized clinical practice guidelines: an integrative model of activity theory and the technology acceptance model
Authors
Ju-Ling Hsiao
Rai-Fu Chen
Publication date
01-12-2015
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2016
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-016-0241-3

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