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Implementation Science
Published in: Implementation Science 1/2023

Open Access 01-12-2023 | Systematic review

Identifying barriers and facilitators to successful implementation of computerized clinical decision support systems in hospitals: a NASSS framework-informed scoping review

Authors: Bridget Abell, Sundresan Naicker, David Rodwell, Thomasina Donovan, Amina Tariq, Melissa Baysari, Robin Blythe, Rex Parsons, Steven M. McPhail

Published in: Implementation Science | Issue 1/2023

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Abstract

Background

Successful implementation and utilization of Computerized Clinical Decision Support Systems (CDSS) in hospitals is complex and challenging. Implementation science, and in particular the Nonadoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) framework, may offer a systematic approach for identifying and addressing these challenges. This review aimed to identify, categorize, and describe barriers and facilitators to CDSS implementation in hospital settings and map them to the NASSS framework. Exploring the applicability of the NASSS framework to CDSS implementation was a secondary aim.

Methods

Electronic database searches were conducted (21 July 2020; updated 5 April 2022) in Ovid MEDLINE, Embase, Scopus, PyscInfo, and CINAHL. Original research studies reporting on measured or perceived barriers and/or facilitators to implementation and adoption of CDSS in hospital settings, or attitudes of healthcare professionals towards CDSS were included. Articles with a primary focus on CDSS development were excluded. No language or date restrictions were applied. We used qualitative content analysis to identify determinants and organize them into higher-order themes, which were then reflexively mapped to the NASSS framework.

Results

Forty-four publications were included. These comprised a range of study designs, geographic locations, participants, technology types, CDSS functions, and clinical contexts of implementation. A total of 227 individual barriers and 130 individual facilitators were identified across the included studies. The most commonly reported influences on implementation were fit of CDSS with workflows (19 studies), the usefulness of the CDSS output in practice (17 studies), CDSS technical dependencies and design (16 studies), trust of users in the CDSS input data and evidence base (15 studies), and the contextual fit of the CDSS with the user’s role or clinical setting (14 studies). Most determinants could be appropriately categorized into domains of the NASSS framework with barriers and facilitators in the “Technology,” “Organization,” and “Adopters” domains most frequently reported. No determinants were assigned to the “Embedding and Adaptation Over Time” domain.

Conclusions

This review identified the most common determinants which could be targeted for modification to either remove barriers or facilitate the adoption and use of CDSS within hospitals. Greater adoption of implementation theory should be encouraged to support CDSS implementation.
Appendix
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Literature
1.
Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med. 2020;3(1):17.PubMedCrossRefPubMedCentral
2.
Dissanayake PI, Colicchio TK, Cimino JJ. Using clinical reasoning ontologies to make smarter clinical decision support systems: a systematic review and data synthesis. J Am Med Inform Assoc. 2020;27(1):159–74.PubMedCrossRef
3.
4.
World Health Organization (WHO). WHO guideline: recommendations on digital interventions for health system strengthening. Geneva: WHO; 2019.
5.
Laka M, Milazzo A, Merlin T. Why provision of clinical decision support (CDS) is not enough? Factors influencing the CDS adoption. Eur J Public Health. 2020;30(Supplement_5):ckaa165.223.CrossRef
6.
Kouri A, Yamada J, Lam Shin Cheung J, Van de Velde S, Gupta S. Do providers use computerized clinical decision support systems? A systematic review and meta-regression of clinical decision support uptake. Implement Sci. 2022;17(1):21.PubMedCrossRefPubMedCentral
7.
Laka M, Milazzo A, Merlin T. Factors that impact the adoption of Clinical Decision Support Systems (CDSS) for antibiotic management. Int J Environ Res Public Health. 2021;18(4):1901.PubMedCrossRefPubMedCentral
8.
Petkus H, Hoogewerf J, Wyatt JC. What do senior physicians think about AI and clinical decision support systems: quantitative and qualitative analysis of data from specialty societies. Clin Med (Lond). 2020;20(3):324–8.PubMedCrossRef
9.
Stone EG. Unintended adverse consequences of a clinical decision support system: two cases. J Am Med Inform Assoc. 2018;25(5):564–7.PubMedCrossRef
10.
Cresswell KM, Lee L, Mozaffar H, Williams R, Sheikh A, Robertson A, et al. Sustained user engagement in health information technology: the long road from implementation to system optimization of computerized physician order entry and clinical decision support systems for prescribing in hospitals in England. Health Serv Res. 2017;52(5):1928–57.PubMedCrossRef
11.
12.
Liberati EG, Ruggiero F, Galuppo L, Gorli M, González-Lorenzo M, Maraldi M, et al. What hinders the uptake of computerized decision support systems in hospitals? A qualitative study and framework for implementation. Implement Sci. 2017;12(1):113.PubMedCrossRefPubMedCentral
13.
Donovan T, Abell B, Fernando M, McPhail SM, Carter HE. Implementation costs of hospital-based computerised decision support systems: a systematic review. Implement Sci. 2023;18(1):7.PubMedCrossRefPubMedCentral
14.
Iyamu I, Gómez-Ramírez O, Xu AX, Chang H-J, Watt S, Mckee G, et al. Challenges in the development of digital public health interventions and mapped solutions: findings from a scoping review. Digit Health. 2022;8:20552076221102256.PubMedPubMedCentral
15.
Bauer MS, Kirchner J. Implementation science: what is it and why should I care? Psychiatry Res. 2020;283:112376.PubMedCrossRef
16.
Peters DH, Adam T, Alonge O, Agyepong IA, Tran N. Republished research: implementation research: what it is and how to do it. Br J Sports Med. 2014;48(8):731.PubMedCrossRef
17.
Greenhalgh T, Wherton J, Papoutsi C, Lynch J, Hughes G, A’Court C, et al. Beyond adoption: a new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. J Med Internet Res. 2017;19(11):e367.PubMedCrossRefPubMedCentral
18.
Greenhalgh T, Wherton J, Papoutsi C, Lynch J, Hughes G, A’Court C, et al. Analysing the role of complexity in explaining the fortunes of technology programmes: empirical application of the NASSS framework. BMC Med. 2018;16(1):66.PubMedCrossRefPubMedCentral
19.
Abimbola S, Patel B, Peiris D, Patel A, Harris M, Usherwood T, et al. The NASSS framework for ex post theorisation of technology-supported change in healthcare: worked example of the TORPEDO programme. BMC Med. 2019;17(1):233.PubMedCrossRefPubMedCentral
20.
KadesjöBanck J, Bernhardsson S. Experiences from implementation of internet-delivered cognitive behaviour therapy for insomnia in psychiatric health care: a qualitative study applying the NASSS framework. BMC Health Serv Res. 2020;20(1):729.CrossRef
21.
Grady A, Barnes C, Wolfenden L, Lecathelinais C, Yoong SL. Barriers and enablers to adoption of digital health interventions to support the implementation of dietary guidelines in early childhood education and care: cross-sectional study. J Med Internet Res. 2020;22(11):e22036.PubMedCrossRefPubMedCentral
22.
Darley S, Coulson T, Peek N, Moschogianis S, van der Veer SN, Wong DC, et al. Understanding how the design and implementation of online consultations affect primary care quality: systematic review of evidence with recommendations for designers, providers, and researchers. J Med Internet Res. 2022;24(10):e37436.PubMedCrossRefPubMedCentral
23.
Khong PCB, Holroyd E, Wang W. A critical review of the theoretical frameworks and the conceptual factors in the adoption of clinical decision support systems. Comput Inform Nurs. 2015;33(12):555–70.PubMedCrossRef
24.
Khanijahani A, Iezadi S, Dudley S, Goettler M, Kroetsch P, Wise J. Organizational, professional, and patient characteristics associated with artificial intelligence adoption in healthcare: a systematic review. Health Policy Technol. 2022;11(1):100602.CrossRef
25.
Munn Z, Peters MDJ, Stern C, Tufanaru C, McArthur A, Aromataris E. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol. 2018;18(1):143.PubMedCrossRef
26.
Arksey H, O’Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19–32.CrossRef
27.
28.
Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467–73.PubMedCrossRef
29.
Scells H, Zuccon G. searchrefiner: a query visualisation and understanding tool for systematic reviews. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management. Torino: Association for Computing Machinery; 2018. p. 1939–42.
30.
Clark JM, Sanders S, Carter M, Honeyman D, Cleo G, Auld Y, et al. Improving the translation of search strategies using the Polyglot Search Translator: a randomized controlled trial. J Med Libr Assoc. 2020;108(2):195–207.PubMedCrossRefPubMedCentral
31.
The Endnote Team. Endnote. Endnote X20 ed. Philadelphia: Clarivate; 2013.
32.
33.
Sim I, Gorman P, Greenes RA, Haynes RB, Kaplan B, Lehmann H, et al. Clinical decision support systems for the practice of evidence-based medicine. J Am Med Inform Assoc. 2001;8(6):527–34.PubMedCrossRefPubMedCentral
34.
Pollock D, Peters MDJ, Khalil H, McInerney P, Alexander L, Tricco AC, et al. Recommendations for the extraction, analysis, and presentation of results in scoping reviews. JBI Evid Synth. 2022;21(3):520–32.CrossRef
35.
Blanco N, O’Hara LM, Robinson GL, Brown J, Heil E, Brown CH, et al. Health care worker perceptions toward computerized clinical decision support tools for Clostridium difficile infection reduction: a qualitative study at 2 hospitals. Am J Infect Control. 2018;46(10):1160–6.PubMedCrossRef
36.
Flynn D, Nesbitt DJ, Ford GA, McMeekin P, Rodgers H, Price C, et al. Development of a computerised decision aid for thrombolysis in acute stroke care. BMC Med Inform Decis Mak. 2015;15(1):1–15.CrossRef
37.
Hasnie AA, Kumbamu A, Safarova MS, Caraballo PJ, Kullo IJ. A clinical decision support tool for familial hypercholesterolemia based on physician input. Mayo Clin Proc Innov Qual Outcomes. 2018;2(2):103–12.PubMedCrossRefPubMedCentral
38.
Melnick ER, Holland WC, Ahmed OM, Ma AK, Michael SS, Goldberg HS, et al. An integrated web application for decision support and automation of EHR workflow: a case study of current challenges to standards-based messaging and scalability from the EMBED trial. JAMIA Open. 2019;2(4):434–9.PubMedCrossRefPubMedCentral
39.
Mugabe KV. Barriers and facilitators to the adoption of artificial intelligence in radiation oncology: a New Zealand study. Tech Innov Patient Support Radiat Oncol. 2021;18:16–21.CrossRef
40.
Ploegmakers KJ, Medlock S, Linn AJ, Lin Y, Seppälä LJ, Petrovic M, et al. Barriers and facilitators in using a Clinical Decision Support System for fall risk management for older people: a European survey. Eur Geriatr Med. 2022;13(2):395–405.PubMedCrossRef
41.
Westafer LM, Kunz A, Bugajska P, Hughes A, Mazor KM, Schoenfeld EM, et al. Provider perspectives on the use of evidence-based risk stratification tools in the evaluation of pulmonary embolism: a qualitative study. Acad Emerg Med. 2020;27(6):447–56.PubMedCrossRefPubMedCentral
42.
Yadav K, Chamberlain JM, Lewis VR, Abts N, Chawla S, Hernandez A, et al. Designing real-time decision support for trauma resuscitations. Acad Emerg Med. 2015;22(9):1076–84.PubMedCrossRefPubMedCentral
43.
Ballard DW, Vemula R, Chettipally UK, Kene MV, Mark DG, Elms AK, et al. Optimizing clinical decision support in the electronic health record: clinical characteristics associated with the use of a decision tool for disposition of ED patients with pulmonary embolism. Appl Clin Inform. 2016;7(3):883–98.PubMedCrossRefPubMedCentral
44.
Bersani K, Fuller TE, Garabedian P, Espares J, Mlaver E, Businger A, et al. Use, perceived usability, and barriers to implementation of a patient safety dashboard integrated within a vendor EHR. Appl Clin Inform. 2020;11(1):034–45.CrossRef
45.
Bowen SP, Johnson KMPA, Reed MHF, Zhang L, Curry LP. The effect of incorporating guidelines into a computerized order entry system for diagnostic imaging. J Am Coll Radiol. 2011;8(4):251–8.PubMedCrossRef
46.
Campion TR, Waitman LR, Lorenzi NM, May AK, Gadd CS. Barriers and facilitators to the use of computer-based intensive insulin therapy. Int J Med Inform (Shannon, Ireland). 2011;80(12):863–71.CrossRef
47.
Chow A, Lye DCB, Arah OA. Psychosocial determinants of physicians’ acceptance of recommendations by antibiotic computerised decision support systems: a mixed methods study. Int J Antimicrob Agents. 2014;45(3):295–304.PubMedCrossRef
48.
Chua AQ, Tang SSL, Lee LW, Yii DYC, Kong ST, Lee W, et al. Psychosocial determinants of physician acceptance toward an antimicrobial stewardship program and its computerized decision support system in an acute care tertiary hospital. J Am Coll Clin Pharm. 2018;1(1):e1–8.CrossRef
49.
Chung P, Scandlyn J, Dayan PS, Mistry RD. Working at the intersection of context, culture, and technology: provider perspectives on antimicrobial stewardship in the emergency department using electronic health record clinical decision support. Am J Infect Control. 2017;45(11):1198–202.PubMedCrossRef
50.
Collins I, Breathnach O, Felle P. Electronic clinical decision support systems attitudes and barriers to use in the oncology setting. Ir J Med Sci. 2012;181(4):521–5.PubMedCrossRef
51.
de Vries AE, van der Wal MHL, Nieuwenhuis MMW, de Jong RM, van Dijk RB, Jaarsma T, et al. Perceived barriers of heart failure nurses and cardiologists in using clinical decision support systems in the treatment of heart failure patients. BMC Med Inform Decis Mak. 2013;13(1):54.PubMedCrossRefPubMedCentral
52.
English D, Ankem K, English K. Acceptance of clinical decision support surveillance technology in the clinical pharmacy. Inform Health Soc Care. 2017;42(2):135–52.PubMedCrossRef
53.
Giuliano CA, Binienda J, Kale-Pradhan PB, Fakih MG. “I never would have caught that before”: pharmacist perceptions of using clinical decision support for antimicrobial stewardship in the United States. Qual Health Res. 2018;28(5):745–55.PubMedCrossRef
54.
Glassman PA, Simon B, Belperio P, Lanto A. Improving recognition of drug interactions: benefits and barriers to using automated drug alerts. Med Care. 2002;40(12):1161–71.PubMedCrossRef
55.
Goud R, van Engen-Verheul M, de Keizer NF, Bal R, Hasman A, Hellemans IM, et al. The effect of computerized decision support on barriers to guideline implementation: a qualitative study in outpatient cardiac rehabilitation. Int J Med Inform (Shannon, Ireland). 2010;79(6):430–7.CrossRef
56.
Grau LE, Weiss J, O’Leary TK, Camenga D, Bernstein SL. Electronic decision support for treatment of hospitalized smokers: a qualitative analysis of physicians’ knowledge, attitudes, and practices. Drug Alcohol Depend. 2019;194:296–301.PubMedCrossRef
57.
Green TA, Whitt S, Belden JL, Erdelez S, Shyu C-R. Medical calculators: prevalence, and barriers to use. Comput Methods Programs Biomed. 2019;179:105002.PubMedCrossRef
58.
Gutenstein M, Pickering JW, Than M. Development of a digital clinical pathway for emergency medicine: lessons from usability testing and implementation failure. Health Informatics J. 2019;25(4):1563–71.PubMedCrossRef
59.
Jacobs J, Weir C, Evans RS, Staes C. Assessment of readiness for clinical decision support to aid laboratory monitoring of immunosuppressive care at U.S. liver transplant centers. Appl Clin Inform. 2014;5(4):988–1004.PubMedCrossRefPubMedCentral
60.
Johnson R, Evans M, Cramer H, Bennert K, Morris R, Eldridge S, et al. Feasibility and impact of a computerised clinical decision support system on investigation and initial management of new onset chest pain: a mixed methods study. BMC Med Inform Decis Mak. 2015;15(1):71.PubMedCrossRefPubMedCentral
61.
Lai F, Macmillan J, Daudelin DH, Kent DM. The potential of training to increase acceptance and use of computerized decision support systems for medical diagnosis. Hum Factors. 2006;48(1):95–108.PubMedCrossRef
62.
Lesselroth BJ, Yang J, McConnachie J, Brenk T, Winterbottom L. Addressing the sociotechnical drivers of quality improvement: a case study of post-operative DVT prophylaxis computerised decision support. BMJ Qual Saf. 2011;20(5):381–9.PubMedCrossRefPubMedCentral
63.
Masterson Creber RM, Dayan PS, Kuppermann N, Ballard DW, Tzimenatos L, Alessandrini E, et al. Applying the RE-AIM Framework for the evaluation of a clinical decision support tool for pediatric head trauma: a mixed-methods study. Appl Clin Inform. 2018;9(3):693–703.PubMedCrossRefPubMedCentral
64.
Miller MK, Mollen C, Behr K, Dowd MD, Miller E, Satterwhite CL, et al. Development of a novel computerized clinical decision support system to improve adolescent sexual health care provision. Acad Emerg Med. 2019;26(4):420–33.PubMedCrossRef
65.
Petitgand C, Motulsky A, Denis J-L, Régis C. Investigating the barriers to physician adoption of an artificial intelligence-based decision support system in emergency care: an interpretative qualitative study. In: Digital personalized health and medicine. Amsterdam: IOS Press; 2020. p. 1001–5.
66.
Salwei ME, Carayon P, Hoonakker PL, Hundt AS, Wiegmann D, Pulia M, et al. Workflow integration analysis of a human factors-based clinical decision support in the emergency department. Appl Ergon. 2021;97:103498.PubMedCrossRefPubMedCentral
67.
Santucci W, Day RO, Baysari MT. Evaluation of hospital-wide computerised decision support in an intensive care unit: an observational study. Anaesth Intensive Care. 2016;44(4):507–12.PubMedCrossRef
68.
Sheehan B, Nigrovic LE, Dayan PS, Kuppermann N, Ballard DW, Alessandrini E, et al. Informing the design of clinical decision support services for evaluation of children with minor blunt head trauma in the emergency department: a sociotechnical analysis. J Biomed Inform. 2013;46(5):905–13.PubMedCrossRef
69.
Strohm L, Hehakaya C, Ranschaert ER, Boon WP, Moors EH. Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors. Eur Radiol. 2020;30(10):5525–32.PubMedCrossRefPubMedCentral
70.
71.
Vandenberg AE, Vaughan CP, Stevens M, Hastings SN, Powers J, Markland A, et al. Improving geriatric prescribing in the ED: a qualitative study of facilitators and barriers to clinical decision support tool use. Int J Qual Health Care. 2017;29(1):117–23.PubMed
72.
Weber S, Crago EA, Sherwood PR, Smith T. Practitioner approaches to the integration of clinical decision support system technology in critical care. J Nurs Adm. 2009;39(11):465–9.PubMedCrossRef
73.
Yılmaz AA, Ozdemir L. Development and Implementation of the clinical decision support system for patients with cancer and nurses’ experiences regarding the system. Int J Nurs Knowl. 2017;28(1):4–12.PubMedCrossRef
74.
Zaidi STR, Marriott JL. Barriers and facilitators to adoption of a web-based antibiotic decision support system. South Med Rev. 2012;5(2):42–9.PubMedPubMedCentral
75.
Zaidi STR, Thursky KA. Using formative evaluation to improve uptake of a web-based tool to support antimicrobial stewardship. J Clin Pharm Ther. 2013;38(6):490–7.PubMedCrossRef
76.
Gama F, Tyskbo D, Nygren J, Barlow J, Reed J, Svedberg P. Implementation frameworks for artificial intelligence translation into health care practice: scoping review. J Med Internet Res. 2022;24(1):e32215.PubMedCrossRefPubMedCentral
77.
Ronan CE, Crable EL, Drainoni ML, Walkey AJ. The impact of clinical decision support systems on provider behavior in the inpatient setting: a systematic review and meta-analysis. J Hosp Med. 2022;17(5):368–83.PubMedCrossRef
78.
Metadata
Title
Identifying barriers and facilitators to successful implementation of computerized clinical decision support systems in hospitals: a NASSS framework-informed scoping review
Authors
Bridget Abell
Sundresan Naicker
David Rodwell
Thomasina Donovan
Amina Tariq
Melissa Baysari
Robin Blythe
Rex Parsons
Steven M. McPhail
Publication date
01-12-2023
Publisher
BioMed Central
Published in
Implementation Science / Issue 1/2023
Electronic ISSN: 1748-5908
DOI
https://doi.org/10.1186/s13012-023-01287-y

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