Barriers and Facilitators to the Use of Clinical Decision Support Systems in Primary Care: A Mixed-Methods Systematic Review
Open accessAbstract
PURPOSE: To identify and quantify the barriers and facilitators to the use of clinical decision support systems (CDSSs) by primary care professionals (PCPs). METHODS: A mixed-methods systematic review was conducted using a sequential synthesis design. PubMed/MEDLINE, PsycInfo, Embase, CINAHL, and the Cochrane library were searched in July 2021. Studies that evaluated CDSSs providing recommendations to PCPs and intended for use during a consultation were included. We excluded CDSSs used only by patients, described as concepts or prototypes, used with simulated cases, and decision supports not considered as CDSSs. A framework synthesis was performed according to the HOT-fit framework (Human, Organizational, Technology, Net Benefits), then a quantitative synthesis evaluated the impact of the HOT-fit categories on CDSS use. RESULTS: A total of 48 studies evaluating 45 CDSSs were included, and 186 main barriers or facilitators were identified. Qualitatively, barriers and facilitators were classified as human (eg, perceived usefulness), organizational (eg, disruption of usual workflow), and technological (eg, CDSS user-friendliness), with explanatory elements. The greatest barrier to using CDSSs was an increased workload. Quantitatively, the human and organizational factors had negative impacts on CDSS use, whereas the technological factor had a neutral impact and the net benefits dimension a positive impact. CONCLUSIONS: Our findings emphasize the need for CDSS developers to better address human and organizational issues, in addition to technological challenges. We inferred core CDSS features covering these 3 factors, expected to improve their usability in primary care.
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Increased workload and misalignment with clinical workflows are among the most common barriers to CDS adoption in clinical settings.
“Increased workload and misalignment with clinical workflows are among the most common barriers to CDS adoption in clinical settings.12,13”
From Electronic health record activity changes around new decision support implementation: monitoring using audit logs and topic modeling (2025) · cites this paper as [13]
The Framework Synthesis reports increased workload for 33 of 45 CDSSs, and the barriers table lists disruption of usual workflow for 25. These findings support both barriers in primary care, but the review does not establish that they are among the most common barriers across clinical settings generally. Page numbers were not available in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe main failure modes of safety-net AI-ECG deployment are excess downstream testing, incomplete follow-up, and alert fatigue.
“Its main failure modes are excess downstream testing, incomplete follow-up, and alert fatigue.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [52]
Table 3 lists alert fatigue as a barrier to clinical decision support systems in primary care. The cited review does not establish that excess downstream testing or incomplete follow-up are failure modes, nor does it identify these three as the main failure modes of safety-net AI-ECG deployment. (LLM, conf=0.84)
Experimental — accuracy not yet measuredEHR-based CDS tools can streamline workflows by automatically presenting relevant information, thus reducing the need for providers to manually search through extensive records.
“EHR-based CDS tools can streamline workflows by automatically presenting relevant information, thus reducing the need for providers to manually search through extensive records.10,13”
From Electronic health record activity changes around new decision support implementation: monitoring using audit logs and topic modeling (2025) · cites this paper as [13]
Increased workload (either perceived or experienced) associated with CDS has been identified as a major barrier to CDS adoption.
“In addition, increased workload (either perceived or experienced) associated with CDS has been identified as a major barrier to CDS adoption.12,13”
From Electronic health record activity changes around new decision support implementation: monitoring using audit logs and topic modeling (2025) · cites this paper as [13]
Existing approaches for evaluating the impact of CDS on provider EHR-use activities and clinical workflow heavily rely on manual data collected through surveys, qualitative interviews, direct observation, and audio/video recording.
“Existing approaches for evaluating the impact of CDS on provider EHR-use activities and clinical workflow heavily rely on manual data collected through surveys, qualitative interviews, direct observation, and audio/video recording.10,13,16–19”
From Electronic health record activity changes around new decision support implementation: monitoring using audit logs and topic modeling (2025) · cites this paper as [13]
The Results section says most included studies were mixed-methods or qualitative, and the workflow-barriers table discusses CDSS effects on clinical workflow. Neither passage establishes that evaluations of EHR-use activities and workflow heavily rely on manually collected data. Searches for surveys, interviews, direct observation, and audio/video recording found no passage establishing those methods or their prevalence. (LLM, conf=0.72)
Experimental — accuracy not yet measured
A safety-net model may be useful when positive AI-ECG results enrich echocardiography yield and lead to timely confirmatory evaluation.
“A safety-net model may be useful when positive AI-ECG results enrich echocardiography yield and lead to timely confirmatory evaluation.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [52]
The Quantitative Synthesis discusses clinical decision support systems used for test ordering and cardiovascular care, but does not address AI-ECG results or echocardiography yield. Searches for AI-ECG, echocardiography, safety-netting, and confirmatory follow-up found no passage supporting the proposed model. (LLM, conf=0.77)
Experimental — accuracy not yet measuredGatekeeper use carries a different clinical risk profile because false-negative or poorly calibrated low-risk outputs may delay diagnosis.
“By contrast, gatekeeper use carries a different clinical risk profile because false-negative or poorly calibrated low-risk outputs may delay diagnosis.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [52]
The Implications section discusses automation bias and the need for reliable CDSS recommendations, but does not address gatekeeper use or low-risk outputs delaying diagnosis. Searches for false negatives, low-risk calibration, and diagnostic delay found no passage supporting that specific risk in our copy. (LLM, conf=0.72)
Experimental — accuracy not yet measured
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Sorted by citations
Barriers and facilitators to utilizing digital health technologies by healthcare professionals
do Nascimento, I. J. B., Abdulazeem, H. M., Vasanthan, L., Martínez, E. Z., Zucoloto, M. L., Østengaard, L., Muscat, N. A., Zapata, T., & Novillo-Ortiz, D. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00899-4 · Cited by 759 · see citing works
Hirosawa, T., Kawamura, R., Harada, Y., Mizuta, K., Tokumasu, K., Kaji, Y., Suzuki, T., & Shimizu, T. (2023) · JMIR Medical Informatics · doi:10.2196/48808 · Cited by 130 · see citing works
“However, despite the potential of CDS systems to boost diagnostic accuracy and efficiency, they often increase clinicians’ workload [9], particularly due to the need for structured input data.”
BackgroundChatGPT and Clinical Decision Support: Scope, Application, and Limitations
Ferdush, J., Begum, M., & Hossain, S. T. (2023) · Annals of Biomedical Engineering · doi:10.1007/s10439-023-03329-4 · Cited by 74 · see citing works
Gomez-Cabello, C. A., Borna, S., Pressman, S., Haider, S. A., Haider, C. R., & Forte, A. J. (2024) · European Journal of Investigation in Health, Psychology and Education · doi:10.3390/ejihpe14030045 · Cited by 74 · see citing works
“Moreover, PCPs may face difficulties co-managing patients with specialties due to discrepancies in the recommendations given by the specialists and their CDSSs or outdated EHRs [11].”
BackgroundInfluentialClinical Decision Support Tools in the Electronic Medical Record
Alexiuk, M. R., Elgubtan, H., & Tangri, N. (2023) · Kidney International Reports · doi:10.1016/j.ekir.2023.10.019 · Cited by 83 · see citing works
“12,28 This can result in limited engagement with the CDS tools, which undermines their potential bene fi ts. 16,23,28 The lack of user acceptance, or the resistance to change also contributes to the barrier of work fl ow integration.”
BackgroundScipion, C. E. A., Manchester, M., Federman, A. D., Wang, Y., & Arias, J. J. (2025) · BMJ Open · doi:10.1136/bmjopen-2024-092624 · Cited by 78 · see citing works
Liao, X., Yao, C., Jin, F., Zhang, J., & Liu, L. (2024) · BMJ Open · doi:10.1136/bmjopen-2024-084398 · Cited by 50 · see citing works
Rodrigues, D. A., Fiol-deRoque, M. A., Mateos-Campos, R., Figueiras, A., Herdeiro, M. T., & Roque, F. (2024) · Digital Health · doi:10.1177/20552076231225133 · Cited by 59 · see citing works
“…for the implementation of eHealth or mHealth in general, 21 or the acceptance of medication-related CDSS, 28 or in a speci fi c setting 29,30 showed the following as the most reported barriers and facilitators: time consuming, 28,29 security and privacy concerns, 21,23,31 limited…”
BackgroundComparative Evaluation of Diagnostic Accuracy Between Google Bard and Physicians
Hirosawa, T., Mizuta, K., Harada, Y., & Shimizu, T. (2023) · The American Journal of Medicine · doi:10.1016/j.amjmed.2023.08.003 · Cited by 43 · see citing works
Overcoming Barriers to Discovery and Implementation of Equitable Pharmacogenomic Testing in Oncology
Shriver, S. P., Adams, D. V., McKelvey, B. A., McCune, J. S., Miles, D., Pratt, V. M., Ashcraft, K. C., McLeod, H. L., Williams, H., & Fleury, M. E. (2024) · Journal of Clinical Oncology · doi:10.1200/jco.23.01748 · Cited by 35 · see citing works
Gani, I., Litchfield, I. J., Shukla, D., Delanerolle, G., Cockburn, N., & Pathmanathan, A. (2025) · Journal of Medical Internet Research · doi:10.2196/62763 · Cited by 41 · see citing works
“The need for systemwide coordination and collaboration in introducing CRs into primary care, as described in the review’s findings [59,63,66], has also been recognized in other health care settings [104-106].”
Grechuta, K., Shokouh, P., Alhussein, A., Müller‐Wieland, D., Meyerhoff, J., Gilbert, J. D., Purushotham, S., & Rolland, C. (2024) · Interactive Journal of Medical Research · doi:10.2196/58036 · Cited by 34 · see citing works
Derksen, C., Walter, F. M., Akbar, A. B., Parmar, A. V. E., Saunders, T. S., Round, T., Rubin, G. P., & Scott, S. E. (2025) · Implementation Science · doi:10.1186/s13012-025-01445-4 · Cited by 31 · see citing works
The use of educational technology in inquiry-based elementary science education: a systematic review
Yun, M., & Crippen, K. J. (2023) · International Journal of Science Education · doi:10.1080/09500693.2023.2284689 · Cited by 21 · see citing works
“Notably, it has previously been applied in several medical review papers to gauge the quality and transparency of various types of studies, encompassing qualitative, quantitative, and mixed-methods research (Donnelly et al., 2022; Meunier et al., 2023; Zhang et al., 2023).”
Accuracy Evaluation of GPT-Assisted Differential Diagnosis in Emergency Department
Shah-Mohammadi, F., & Finkelstein, J. (2024) · Diagnostics · doi:10.3390/diagnostics14161779 · Cited by 22 · see citing works
“Despite their potential to enhance diagnostic precision and efficiency, these systems often increase the workload for clinicians [10], largely due to the need for structured input data, which can hinder widespread adoption.”
BackgroundUsing Large Language Models to Retrieve Critical Data from Clinical Processes and Business Rules
Yu, Y., Gomez-Cabello, C. A., Makarova, S., Parte, Y., Borna, S., Haider, S. A., Genovese, A., Prabha, S., & Forte, A. J. (2024) · Bioengineering · doi:10.3390/bioengineering12010017 · Cited by 18 · see citing works
“Additionally, studies have demonstrated that physicians perceived that CDSSs increase their cognitive load [7,32] and feel no need for their assistance or disagree with their recommendations [33].”
Rayan, A., Al‐Ghabeesh, S. H., Fawaz, M. A., Behar, A., & Toumi, A. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1259409 · Cited by 13 · see citing works
Berkhout, M., Smit, K., Sent, D., Kusters, R. R., Versendaal, J., & van Houwelingen, T. (2025) · Journal of Medical Internet Research · doi:10.2196/76025 · Cited by 13 · see citing works
“Prior reviews cover clinicians broadly or cover nonhospital settings, with limited qualitative evidence from hospital nurses and little integration of findings within an explicit implementation lens [8,17].”
InfluentialProvision of Digital Primary Health Care Services: Overview of Reviews
Fava, V. M. D., & Lapão, L. M. V. (2024) · Journal of Medical Internet Research · doi:10.2196/53594 · Cited by 15 · see citing works
Electronic Immunization Registry in Rwanda: Qualitative Study of Health Worker Experiences
Uwera, T., Venkateswaran, M., Bhutada, K., Papadopoulou, E., Rukundo, E., Tumusiime, D. K., & Frøen, J. F. (2024) · JMIR Human Factors · doi:10.2196/53071 · Cited by 9 · see citing works
“We performed a framework analysis and worked together to place the extracted data within the HOT-Fit framework [20,22].”
van Zyl, L. E. (2026) · AI & Society · doi:10.1007/s00146-025-02814-9 · Cited by 11 · see citing works
“Evidence from digital health and medicine shows that when practitioners frequently turn to technological decision-making, their level of peer engagement decreases and collaborative sense-making declines significantly (Meunier et al. 2023; Smith et al. 2024).”
Xu, Z., Evans, L., Song, J., Chae, S., Davoudi, A., Bowles, K. H., McDonald, M. V., & Topaz, M. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae247 · Cited by 8 · see citing works
Development and expert inspections of the user interface for a primary care decision support system
Neff, M. C., Schütze, D., Holtz, S., Köhler, S. M., Vasseur, J., Ahmadi, N., Storf, H., & Schaaf, J. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105651 · Cited by 8 · see citing works
Cuadros, P., McCord, E., McDonnell, C., Apathy, N. C., Sanner, L. M., Adams, M. C. B., Mamlin, B. W., Vest, J. R., Hurley, R. W., Harle, C. A., et al. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105649 · Cited by 7 · see citing works
Böhm-Hustede, A. K., Lubasch, J. S., Hoogestraat, A. T., Buhr, E., & Wulff, A. (2025) · Systematic Reviews · doi:10.1186/s13643-024-02745-4 · Cited by 8 · see citing works
Abuzour, A. S., Wilson, S. A., Woodall, A., Mair, F. S., Aslam, A., Clegg, A., Shantsila, E., Gabbay, M., Abaho, M., Bollegala, D., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/74304 · Cited by 7 · see citing works
“This can be seen in the literature that reflects HCP preference for knowledge-based CDSSs over non–knowledge-based CDSSs [19,20].”
InfluentialThorakkattil, S. A., Sridhar, S. B., Abdulsalim, S., Karattuthodi, M. S., Chandra, P., & Unnikrishnan, M. K. (2025) · Healthcare · doi:10.3390/healthcare13172220 · Cited by 7 · see citing works
“For instance, Meunier et al. (2023) noted that digital health interventions, including a CDSS, led to a 30% improvement in clinical guideline adherence in sub-Saharan Africa, compared to 15% in high-income countries [3].”
HELIOT: LLM-Based CDSS for adverse drug reaction management
Vito, G. D., Ferrucci, F., & Angelakis, A. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114184 · Cited by 6 · see citing works
“However, traditional CDSSs face several limitations [4, 5, 6].”
InfluentialHooker, S. A., Solberg, L. I., Miley, K. M., Borgert-Spaniol, C. M., & Rossom, R. C. (2024) · The Journal of the American Board of Family Medicine · doi:10.3122/jabfm.2023.230308r1 · Cited by 6 · see citing works
Al-Zubaidy, M., Hogg, H. D. J., Maniatopoulos, G., Talks, J. S., Teare, M. D., Keane, P. A., & Beyer, F. R. (2022) · JMIR Research Protocols · doi:10.2196/33145 · Cited by 9 · see citing works
Samal, L., Chen, S. W., Lipsitz, S. R., et al. (2025) · Applied Clinical Informatics · doi:10.1055/a-2554-3969 · Cited by 5 · see citing works
Hnatchuk, Y., Hovorushchenko, T. O., & Pavlova, O. (2023) · RADIOELECTRONIC AND COMPUTER SYSTEMS · doi:10.32620/reks.2023.1.03 · Cited by 5 · see citing works
“As a result, there is an increase in medical errors [7-9].”
BackgroundKim, M. S., & Kang, M. (2025) · Asian Nursing Research · doi:10.1016/j.anr.2025.03.008 · Cited by 4 · see citing works
Digital health interventions use cases: Classification and taxonomy development, a scoping review
Harrison, S., Islam, S. U., Waseem, H. M., et al. (2025) · Digital Health · doi:10.1177/20552076251375958 · Cited by 4 · see citing works
Pardoux, É., & Kerasidou, A. (2025) · Science and Engineering Ethics · doi:10.1007/s11948-025-00562-z · Cited by 4 · see citing works
“CDSS may not be user-friendly, they could slow down healthcare prac - titioners, disrupt clinical workflows, display useless recommendations or provoke alert-fatigue (for a review of obstacles to CDSS integration see Meunier et al., 2023 ).”
Barriers and Facilitators of Digital Transformation in Health Care: Mixed Methods Study
Veldanova, M., Glazkova, P., Krasilnikova, E., Kazanfarova, M., Bezuglova, M., Sosunova, E., & Zhuravleva, M. (2025) · Journal of Participatory Medicine · doi:10.2196/83551 · Cited by 5 · see citing works
“It is important for them that the technology makes work easier and speeds it up, rather than adding extra tasks [1,19,20].”
Giacobbe, D. R., Ahmad, R., Akıllı, F. M., Ascandari, A., Eyre, D. W., Gallardo‐Pizarro, A., García‐Vidal, C., Lopes, B. S., Lyutsova, E., Rakhimov, R., et al. (2026) · Expert Review of Anti-infective Therapy · doi:10.1080/14787210.2026.2625382 · Cited by 4 · see citing works
Šafran, V., Smrke, U., Ilijevec, B., Horvat, S., Flis, V., Plohl, N., & Mlakar, I. (2025) · Digital Health · doi:10.1177/20552076251339012 · Cited by 4 · see citing works
Evaluating the Prototype of a Clinical Decision Support System in Primary Care: Qualitative Study
Köhler, S. M., Holtz, S., Neff, M. C., Schaaf, J., Wagner, M., Müller, B. S., & Schütze, D. (2025) · JMIR Formative Research · doi:10.2196/69875 · Cited by 6 · see citing works
“A mixed-methods systematic review evaluating CDSS providing recommendations to primary care professionals by Meunier et al [7] including studies from 1998 to 2021 revealed that most systems were developed and used in the United States.”
Hamasaki, T., Briand, C., Mahroug, A., & Sauvageau, A. (2025) · Disability and Rehabilitation Assistive Technology · doi:10.1080/17483107.2025.2526175 · Cited by 8 · see citing works
Rukhlenko, O. S., Imoto, H., Tambde, A., McGillycuddy, A., Junk, P., Tuliakova, A., Kölch, W., & Kholodenko, B. Ν. (2024) · Cancers · doi:10.3390/cancers16132354 · Cited by 4 · see citing works
“This property makes cSTAR recommendations fully tractable and explainable, which is critical for many biomedical applications including the development of digital twins [71].”
BackgroundChima, S., Hunter, B., Martínez-Gutiérrez, J., Lumsden, N. G., Nelson, C., Manski-Nankervis, J.-A. E., & Emery, J. D. (2024) · Family Practice · doi:10.1093/fampra/cmae046 · Cited by 4 · see citing works
Chen, J., Cutrona, S. L., Dharod, A., Moses, A., Bridges, A., Ostasiewski, B., Foley, K. L., Houston, T. K., iDAPT (Implementation & Informatics Developing Adaptable Processes and Technologies for Cancer Control) Implementation Science Center for Cancer Control, Chen, J., et al. (2025) · JAMIA Open · doi:10.1093/jamiaopen/ooaf050 · Cited by 3 · see citing works
“12,13 Therefore, healthcare organizations must monitor and continuously refine CDS tools to ensure they achieve clinical goals without inadvertently diminishing over-all provider productivity or patient care quality.”
Mouazer, A., Dubois, S., Léguillon, R., Boudegzdame, N., Levrard, T., Bars, Y. L., Simon, C., Brigitte Séroussi, A., Julien Grosjean, A., Lelong, R., et al. (2025) · Research in Social and Administrative Pharmacy · doi:10.1016/j.sapharm.2025.06.006 · Cited by 3 · see citing works
Hwang, G. W., Hejazian, S. S., Sadr, A. V., Wagner, J. K., Hao, P., Vemuri, A. K., Kawamura, Y., Nawab, K., Hijjawi, S. B., Zand, R., et al. (2025) · Bioengineering · doi:10.3390/bioengineering12121363 · Cited by 3 · see citing works
“These barriers parallel physician frustrations with traditional CDSS and EHR technology implementations, such as excessive clicks and alert pop-ups, lack of integration with EHR, lack of technical support for malfunctions, and inability to advance screens if alerts are not addressed [50,51].”
Spoon, D., de Vroed, A., Greup, S., van Dijk, M., & Ista, E. (2026) · Journal of Clinical Nursing · doi:10.1111/jocn.70212 · Cited by 3 · see citing works
Rosati, A., Criscione, M., Lilli, L., Petrillo, M., Capobianco, G., Patarnello, S., & Fagotti, A. (2026) · International Journal of Gynecological Cancer · doi:10.1016/j.ijgc.2026.104558 · Cited by 3 · see citing works
Santema, J. J. M. M., van Wijngaarden, J. D. H., Hiddink, E., Deken, F., Kleinsmann, M. S., & van Os, H. J. A. (2026) · Journal of Medical Internet Research · doi:10.2196/83084 · Cited by 3 · see citing works
“Despite its potential, CDSS adoption remains limited due to interdependent development and implementation challenges [1,5-7], particularly in primary care [2,8,9].”
Šteinmiller, J., & Ross, P. (2024) · Digital Health · doi:10.1177/20552076241271816 · Cited by 3 · see citing works
Surendran, R., Pasupathy, V., Natarajan, C., et al. (2024) · IEEE International Conference on Engineering Technologies and Applied Sciences (ICETAS) · doi:10.1109/icetas62372.2024.11119918 · Cited by 3 · see citing works
“They include convolutional neural networks and recurrent neural networks [26-29].”
McGlynn, A., Shé, É. N., Bennett, P., Liaw, S., Jackson, T., & Harris‐Roxas, B. (2023) · Journal of Integrated Care · doi:10.1108/jica-01-2023-0006 · Cited by 4 · see citing works
Kersey, E., Li, J., Kay, J. L., Adler‐Milstein, J., Yazdany, J., & Schmajuk, G. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooae061 · Cited by 3 · see citing works
Salt, E., Khalid, M., van der Windt, D., et al. (2024) · Physiotherapy · doi:10.1016/j.physio.2024.101445 · Cited by 2 · see citing works
Clinician Needs and Requirements for a Decision Aid Navigator: Qualitative Study
Morse, B., Reale, C., Nguyen, A., Latella, E., Bauguess, H. D., Anders, S. H., Roberts, P. S., SooHoo, S. L., El-Kareh, R. E., Soares, A., et al. (2025) · JMIR Human Factors · doi:10.2196/69756 · Cited by 2 · see citing works
“Similar to other clinical decision support (CDS) tools, DAs face many implementation barriers, such as increased workload and knowledge demands [17], and organizational factors (eg, culture and priorities) [18].”
Leveraging Technology to Enhance Rural Access to Care
Gillaspie, E. A., Shipman, S. A., Timperley, J. B., et al. (2025) · World Journal of Surgery · doi:10.1002/wjs.12692 · Cited by 2 · see citing works
Amuli, K., Decabooter, K., Germanes, C., Parys, A.-S. V., Verschelde, S., Saey, E., Moulin, M., Cornu, P., Delvaux, N., den Berghe, S. V., et al. (2025) · Midwifery · doi:10.1016/j.midw.2025.104610 · Cited by 2 · see citing works
Mazurenko, O., Apathy, N. C., Sanner, L. M., McCord, E., Adams, M. C. B., Hurley, R. W., Grout, R. W., Mamlin, B. W., Erazo, S. F., Blackburn, J. L., et al. (2025) · Applied Clinical Informatics · doi:10.1055/a-2639-4974 · Cited by 2 · see citing works
Mittman, B. G., Hu, B., Schulte, R., Le, P., Pappas, M. A., Hamilton, A. C., & Rothberg, M. B. (2026) · Journal of Thrombosis and Haemostasis · doi:10.1016/j.jtha.2026.02.004 · Cited by 3 · see citing works
“Evidence-based threshold selection could maximize the efficiency of clinical decision support by emphasizing the net benefit [46].”
Leibovitch, L., Ahituv, A., Gorenshtein, A., Aran, D., Sorka, M., Miron, K., & Shelly, S. (2026) · Nature Medicine · doi:10.1038/s41591-026-04601-5 · Cited by 4 · see citing works
“…it is defined as the interval between two EHR-generated timestamps: electronic consultation order entry (start) and specialist recommendation documentation (stop) 40,41 , analyzed through three estimands: ITT, per protocol and CACE using ward allocation as an instrumental variable 27,42–46 .”
Appropriate artificial intelligence algorithms will ultimately contribute to health equity
Kalina, J. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-21598-8.00008-7 · Cited by 2 · see citing works
Secor, A., Justafort, J., Torrilus, C., Honoré, J. G., Kiche, S. W., Sandifer, T. K., Beima‐Sofie, K., Wagner, A. D., Pintye, J. C., & Puttkammer, N. H. (2023) · Health Policy and Technology · doi:10.1016/j.hlpt.2024.100880 · Cited by 3 · see citing works
Graph-Based Grounding in a Conversational Clinical Decision Support System
Barham, S., Kelbaugh, M. T., Reddy, A., Ramsden, D., Chandler, C., Tobin-Williams, A., Elhadad, B. S., Cooper, G. R., & Alexander, K. (2024) · IEEE International Conference on Assured Autonomy (ICAA) · doi:10.1109/icaa64256.2024.00014 · Cited by 2 · see citing works
“A common finding across studies is that clinicians either perceive no need for CDSS assistance, or they find its recommendations irrelevant [23], disregarding anywhere from 49% to 96% of its alerts [24].”
Umoh, I., Xia, X., Winblad, B. G., Aye, S., Aho, E., Rhodius‐Meester, H. F. M., & Jönsson, L. (2025) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000544801 · Cited by 2 · see citing works
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Marcellis, L. H. M., Rutgers, K. M., Spruijt, S., Teijink, J. A. W., van der Wees, P. J., & Hoogeboom, T. J. (2025) · Annals of Medicine · doi:10.1080/07853890.2025.2540022 · Cited by 1 · see citing works
“A positive user attitude and improvements in the clinical process are frequently cited as key facilitators for the uptake of decision aids and shared decision-making [36,48,49].”
Binuya, M. A. E., Linn, S. C., Boekhout, A. H., Schmidt, M. K., & Engelhardt, E. G. (2025) · MDM Policy & Practice · doi:10.1177/23814683251328377 · Cited by 1 · see citing works
Goldberg, D. G., Soylu, T. G., Hoffman, C. F., Kishton, R. E., & Cronholm, P. F. (2025) · Digital Health · doi:10.1177/20552076251334043 · Cited by 1 · see citing works
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Alfonso, D. I. (2025) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2025.1675804 · Cited by 1 · see citing works
Taylor, L. C., Peek, N. B., Ercole, A., Lyratzopoulos, G., & Usher-Smith, J. (2026) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2025-101896 · Cited by 1 · see citing works
Barriers to and enablers of use of infectious disease decision support systems: a scoping review
Johncox, E., Zai, B., Hobson, S. J., Parmley, E. J., Sharif, S., & Grant, L. E. (2026) · BMC Digital Health · doi:10.1186/s44247-026-00258-9 · Cited by 1 · see citing works
Böhm-Hustede, A. K., Lubasch, J. S., Hoogestraat, A. T., Buhr, E., & Wulff, A. (2026) · Systematic Reviews · doi:10.1186/s13643-026-03200-2 · Cited by 1 · see citing works
“…to hospitals [36, 41, 43, 52–54, 60] , with two reviews With regard to the medical condition or field of support considered, 53.33% ( n = 16 ) of the reviews did not concentrate on any specific medical condition but examined a broad range of studies [13, 22, 36, 37, 41–45, 51, 53, 54, 56, 58–60].”
InfluentialNilius, H., Kaufmann, J., Adler, M., Minervini, F., Greguare-Sander, A. W., Alberio, L., Gerber, B., Kröll, D., Veerakatty, S., Kashev, A., et al. (2026) · The Lancet Digital Health · doi:10.1016/j.landig.2026.101019 · Cited by 1 · see citing works
Janssen, E., Knoop, J., Farabolini, G., Baldini, N., Hoogeboom, T. J., Ceravolo, M. G., Négrini, S., Kiekens, C., PREPARE Project Group, Emin, A., et al. (2026) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocag080 · Cited by 1 · see citing works
Hill, J. C., Birkinshaw, H., Usman, M., Millward, T., Bailey, J., Wathall, S., Naylor, R., Malcolm, R. J., Pegg, M., Holmes, H., et al. (2026) · Journal of Medical Internet Research · doi:10.2196/89734 · Cited by 1 · see citing works
“While systematic reviews show CDSSs can improve diagnostic accuracy, referral quality, and guideline adherence across various clinical domains [11,12], their effectiveness in MSK care specifically remains unclear.”
Olson, A. W., Haapala, J. L., Hooker, S. A., Solberg, L. I., Borgert-Spaniol, C. M., Romagnoli, K. M., Allen, C. I., Tusing, L. D., Wright, E. A., Haller, I. V., et al. (2023) · Health Affairs Scholar · doi:10.1093/haschl/qxad051 · Cited by 1 · see citing works
“To help address these barriers, our team developed a CDS tool (hereafter referred to as OUD-CDS) with features amen - able to a streamlined care experience and high utilization by PCCs. 19 The OUD-CDS was systematically designed in close collaboration with PCCs to support clinician screening, diagnosing, treating, and…”
MethodInfluentialBeccia, C., Hunter, B., Manski-Nankervis, J.-A. E., & White, M. (2024) · JMIR Formative Research · doi:10.2196/60411 · Cited by 1 · see citing works
“However, these issues have been explored in the broader general practice context, where concerns about overreferral [25], the tool’s influence on patient perceptions, and its impact on communication have been documented [26].”
BackgroundQuality of Care and Quality of Life
MINKOFF, H. L., O'Brien, J., & Berkowitz, R. L. (2024) · Obstetrics and Gynecology · doi:10.1097/aog.0000000000005681 · Cited by 1 · see citing works
Moghaddasi, H., Rahimi, F., Seddighi, A. S., Akbarpour, L., & Roshanpoor, A. (2024) · Diagnosis · doi:10.1515/dx-2024-0072 · Cited by 1 · see citing works
Olson, A. W., Bucaloiu, A., Allen, C. I., Tusing, L. D., Henzler-Buckingham, H. A., Gregor, C. M., Freitag, L. A., Hooker, S. A., Rossom, R. C., Solberg, L. I., et al. (2025) · BMJ Open · doi:10.1136/bmjopen-2024-090462 · Cited by 1 · see citing works
Farran, D., Cheang, H. W., Onwumere, J., et al. (2025) · JMIR Cardio · doi:10.2196/66428 · Cited by 1 · see citing works
“In this review, the reported benefits of the digital tools were improving quality of care, saving time, facilitating decision-making, improving professional self-confidence, and updating knowledge [25].”
InfluentialConcept of Information Technology for Recognition of Cardiac Pacemaker Dysfunction
Hovorushchenko, T. O., ГРИЩУК, І., Rei, K., et al. (2024) · International Conference on Dependable Systems, Services and Technologies (DESSERT) · doi:10.1109/dessert65323.2024.11122180 · Cited by 1 · see citing works
“Information technologies make modern medicine more accessible, functional and of higher quality [4-6].”
A balancing act: the promise and pitfalls of clinical decision support
Minor, J., & Youens, K. E. (2025) · Baylor University Medical Center Proceedings · doi:10.1080/08998280.2025.2478758 · Cited by 1 · see citing works
Thapa, P., Shukla, P., Joshi, C., Sayood, S. J., Sinha, P. K., Timilsina, D., Dutta, M., Pai, M., Rahimi, S. A., & Gandra, S. (2025) · medRxiv · doi:10.1101/2025.09.06.25335195 · Cited by 1 · see citing works
Naye, F., Ouellet, S., Yousefi, F., Cachinho, C., Bergeron, F., Bhattacharyya, O. K., Stamenova, V., & Sasseville, M. (2026) · doi:10.2196/preprints.96882 · Cited by 1 · see citing works
ربیع, م. إ. (2023) · Al-Maǧallaẗ Al-Miṣriyyaẗ Lil Dirāsāt Al-Tiǧāriyyaẗ /Al-Maǧallaẗ Al-Miṣriyyaẗ Lil Dirāsāt Al-Tiǧāriyyaẗ · doi:10.21608/alat.2023.317829
Clinical Decision Support Systems in Adults with Chronic Respiratory Disease: A Systematic Review
O’Neill, K., Parrott, H., Neely, C., McLeese, R. H., Fleming, G. F., Scott, M. J., & Bradley, J. M. (2024) · Current Respiratory Medicine Reviews · doi:10.2174/011573398x281958240531065955
Farran, D., Cheang, H. W., Onwumere, J., et al. (2024) · doi:10.2196/preprints.66428
Entscheidungsunterstützungssysteme – der Weg zu mehr Evidenz am Patientenbett?
Kücking, F. (2025) · intensiv · doi:10.1055/a-2442-4985
Köhler, S. M., Holtz, S., Neff, M. C., Schaaf, J., Wagner, M., Müller, B. S., & Schütze, D. (2024) · doi:10.2196/preprints.69875
Siddiqi, K. A., Canidate, S. S., Liu, Y., et al. (2025) · medRxiv · doi:10.1101/2025.01.30.25321379
Mitchell, S. G., Gryczynski, J., Worley, D. C., Asche, S. E., Truitt, A. R., & Rindal, D. B. (2025) · Implementation Research and Practice · doi:10.1177/26334895251319810
“A recent mixed-methods systematic review of barriers and facilitators to CDS use by primary care providers examined fi ndings in terms of human, organizational, and technological factors (Meunier et al., 2023; Yusof et al., 2008).”
Khan, U., Amin, A. M., Khlidj, Y., Majeed, Z., Ayyad, M., Al‐Shammari, A. S., Imran, M., Ali, J., & Abuelazm, M. T. (2025) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x251323489
Hovorushchenko, T. O., Kysil, V. M., & Osyadlyi, V. (2024) · doi:10.1109/csit65290.2024.10982683
“Medical information technologies improve the quality of medical services, reduce time and costs, and optimize the work of medical professionals [5, 6].”
Tseng, O. L.-I., Brar, S., Dawes, M. G., Ranchod, H., Lacaille, D. V., Su, V., & Mitton, C. (2025) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.70143
“Despite their potential for positively impacting patient care, CDSSs encounter limitations concerning data integration and operational efficiency [59].”
Clinical Decision Support Systems (CDSS) and Symptom Checkers in Primary Healthcare
Demurtas, J., Crescitelli, M. E. D., Meer, A., Laranjo, L., Oyugi, J. O., Kirk, U. B., & Huibers, L. (2025) · TELe-Health · doi:10.1007/978-3-031-89661-3_26
Sitasuwan, T., Suriyaphol, P., Lawpoolsri, S., Soonthornworasiri, N., & Ngum, W. P. (2025) · JMIR Human Factors · doi:10.2196/57314
“The challenges encountered during CDSS implementation stem from human factors related to system use, information quality, availability of EMR implementation, system quality, and system maintenance [11,12].”
Hentzen, C., Biardeau, X., Perrouin‐Verbe, M., et al. (2025) · World Journal of Urology · doi:10.1007/s00345-025-05838-4
Milosavljević, I., Petrović, I., Ilić, A., Dabović, D., Milovančev, A., Remer, G., Vuković, V., Zdravković, D., Stefanović, M., Stojšić, S., et al. (2025) · ESC Heart Failure · doi:10.1002/ehf2.15397
Veldanova, M., Сергеевна, К. Е., Glazkova, P., Ажифендиевна, К. М., Bezouglova, M., Sosunova, E., Romanenko, S., & Zhuravleva, M. V. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5393420
- Authors
- Meunier, P.-Y., Raynaud, C., Guimaraes, E., Gueyffier, F., & Letrilliart, L.
- Affiliations
- Lyon 1 Université (FR)
- Venue
- The Annals of Family Medicine
- Publisher
- Annals of Family Medicine
- Published
- 2023-01
- Volume
- 21
- Issue
- 1
- Pages
- 57-69
- ISSN
- 1544-1709 (ISSN-L)
- 1544-1717
- PMC
- PMC9870646
- OpenAlex
- W4317773813
- Cited by
- 149
- References
- 91
- Citations by year
- 2026: 56 · 2025: 51 · 2024: 31 · 2023: 10 · 2022: 1
- FWCI
- 5.80
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- The Annals of Family Medicine (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Funders
- Hospices Civils de Lyon
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Keywords
- clinical decision support systems, primary care, barriers to adoption, HOT-Fit framework, perceived usefulness, workflow disruption, workload, ease of use, primary care providers
- MeSH terms
- Health Personnel, Humans, Primary Health Care, Technology, Decision Support Systems, Clinical
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