Investigating Clinicians’ Intentions and Influencing Factors for Using an Intelligence-Enabled Diagnostic Clinical Decision Support System in Health Care Systems: Cross-Sectional Survey
BackgroundAn intelligence-enabled clinical decision support system (CDSS) is a computerized system that integrates medical knowledge, patient data, and clinical guidelines to assist health care providers make clinical decisions. Research studies have shown that CDSS utilizati...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2025-04-01
|
| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2025/1/e62732 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850264638261821440 |
|---|---|
| author | Rui Zheng Xiao Jiang Li Shen Tianrui He Mengting Ji Xingyi Li Guangjun Yu |
| author_facet | Rui Zheng Xiao Jiang Li Shen Tianrui He Mengting Ji Xingyi Li Guangjun Yu |
| author_sort | Rui Zheng |
| collection | DOAJ |
| description |
BackgroundAn intelligence-enabled clinical decision support system (CDSS) is a computerized system that integrates medical knowledge, patient data, and clinical guidelines to assist health care providers make clinical decisions. Research studies have shown that CDSS utilization rates have not met expectations. Clinicians’ intentions and their attitudes determine the use and promotion of CDSS in clinical practice.
ObjectiveThe aim of this study was to enhance the successful utilization of CDSS by analyzing the pivotal factors that influence clinicians’ intentions to adopt it and by putting forward targeted management recommendations.
MethodsThis study proposed a research model grounded in the task-technology fit model and the technology acceptance model, which was then tested through a cross-sectional survey. The measurement instrument comprised demographic characteristics, multi-item scales, and an open-ended query regarding areas where clinicians perceived the system required improvement. We leveraged structural equation modeling to assess the direct and indirect effects of “task-technology fit” and “perceived ease of use” on clinicians’ intentions to use the CDSS when mediated by “performance expectation” and “perceived risk.” We collated and analyzed the responses to the open-ended question.
ResultsWe collected a total of 247 questionnaires. The model explained 65.8% of the variance in use intention. Performance expectations (β=0.228; P<.001) and perceived risk (β=–0.579; P<.001) were both significant predictors of use intention. Task-technology fit (β=–0.281; P<.001) and perceived ease of use (β=–0.377; P<.001) negatively affected perceived risk. Perceived risk (β=–0.308; P<.001) negatively affected performance expectations. Task-technology fit positively affected perceived ease of use (β=0.692; P<.001) and performance expectations (β=0.508; P<.001). Task characteristics (β=0.168; P<.001) and technology characteristics (β=0.749; P<.001) positively affected task-technology fit. Contrary to expectations, perceived ease of use (β=0.108; P=.07) did not have a significant impact on use intention. From the open-ended question, 3 main themes emerged regarding clinicians’ perceived deficiencies in CDSS: system security risks, personalized interaction, seamless integration.
ConclusionsPerceived risk and performance expectations were direct determinants of clinicians’ adoption of CDSS, significantly influenced by task-technology fit and perceived ease of use. In the future, increasing transparency within CDSS and fostering trust between clinicians and technology should be prioritized. Furthermore, focusing on personalized interactions and ensuring seamless integration into clinical workflows are crucial steps moving forward. |
| format | Article |
| id | doaj-art-d3729fb6a49a4e718d1ca0c21acd9f4b |
| institution | OA Journals |
| issn | 1438-8871 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj-art-d3729fb6a49a4e718d1ca0c21acd9f4b2025-08-20T01:54:40ZengJMIR PublicationsJournal of Medical Internet Research1438-88712025-04-0127e6273210.2196/62732Investigating Clinicians’ Intentions and Influencing Factors for Using an Intelligence-Enabled Diagnostic Clinical Decision Support System in Health Care Systems: Cross-Sectional SurveyRui Zhenghttps://orcid.org/0009-0003-2463-7858Xiao Jianghttps://orcid.org/0009-0009-8791-7834Li Shenhttps://orcid.org/0000-0003-4838-0770Tianrui Hehttps://orcid.org/0000-0003-2754-2902Mengting Jihttps://orcid.org/0000-0003-0670-3797Xingyi Lihttps://orcid.org/0000-0001-8148-9415Guangjun Yuhttps://orcid.org/0000-0002-3159-4652 BackgroundAn intelligence-enabled clinical decision support system (CDSS) is a computerized system that integrates medical knowledge, patient data, and clinical guidelines to assist health care providers make clinical decisions. Research studies have shown that CDSS utilization rates have not met expectations. Clinicians’ intentions and their attitudes determine the use and promotion of CDSS in clinical practice. ObjectiveThe aim of this study was to enhance the successful utilization of CDSS by analyzing the pivotal factors that influence clinicians’ intentions to adopt it and by putting forward targeted management recommendations. MethodsThis study proposed a research model grounded in the task-technology fit model and the technology acceptance model, which was then tested through a cross-sectional survey. The measurement instrument comprised demographic characteristics, multi-item scales, and an open-ended query regarding areas where clinicians perceived the system required improvement. We leveraged structural equation modeling to assess the direct and indirect effects of “task-technology fit” and “perceived ease of use” on clinicians’ intentions to use the CDSS when mediated by “performance expectation” and “perceived risk.” We collated and analyzed the responses to the open-ended question. ResultsWe collected a total of 247 questionnaires. The model explained 65.8% of the variance in use intention. Performance expectations (β=0.228; P<.001) and perceived risk (β=–0.579; P<.001) were both significant predictors of use intention. Task-technology fit (β=–0.281; P<.001) and perceived ease of use (β=–0.377; P<.001) negatively affected perceived risk. Perceived risk (β=–0.308; P<.001) negatively affected performance expectations. Task-technology fit positively affected perceived ease of use (β=0.692; P<.001) and performance expectations (β=0.508; P<.001). Task characteristics (β=0.168; P<.001) and technology characteristics (β=0.749; P<.001) positively affected task-technology fit. Contrary to expectations, perceived ease of use (β=0.108; P=.07) did not have a significant impact on use intention. From the open-ended question, 3 main themes emerged regarding clinicians’ perceived deficiencies in CDSS: system security risks, personalized interaction, seamless integration. ConclusionsPerceived risk and performance expectations were direct determinants of clinicians’ adoption of CDSS, significantly influenced by task-technology fit and perceived ease of use. In the future, increasing transparency within CDSS and fostering trust between clinicians and technology should be prioritized. Furthermore, focusing on personalized interactions and ensuring seamless integration into clinical workflows are crucial steps moving forward.https://www.jmir.org/2025/1/e62732 |
| spellingShingle | Rui Zheng Xiao Jiang Li Shen Tianrui He Mengting Ji Xingyi Li Guangjun Yu Investigating Clinicians’ Intentions and Influencing Factors for Using an Intelligence-Enabled Diagnostic Clinical Decision Support System in Health Care Systems: Cross-Sectional Survey Journal of Medical Internet Research |
| title | Investigating Clinicians’ Intentions and Influencing Factors for Using an Intelligence-Enabled Diagnostic Clinical Decision Support System in Health Care Systems: Cross-Sectional Survey |
| title_full | Investigating Clinicians’ Intentions and Influencing Factors for Using an Intelligence-Enabled Diagnostic Clinical Decision Support System in Health Care Systems: Cross-Sectional Survey |
| title_fullStr | Investigating Clinicians’ Intentions and Influencing Factors for Using an Intelligence-Enabled Diagnostic Clinical Decision Support System in Health Care Systems: Cross-Sectional Survey |
| title_full_unstemmed | Investigating Clinicians’ Intentions and Influencing Factors for Using an Intelligence-Enabled Diagnostic Clinical Decision Support System in Health Care Systems: Cross-Sectional Survey |
| title_short | Investigating Clinicians’ Intentions and Influencing Factors for Using an Intelligence-Enabled Diagnostic Clinical Decision Support System in Health Care Systems: Cross-Sectional Survey |
| title_sort | investigating clinicians intentions and influencing factors for using an intelligence enabled diagnostic clinical decision support system in health care systems cross sectional survey |
| url | https://www.jmir.org/2025/1/e62732 |
| work_keys_str_mv | AT ruizheng investigatingcliniciansintentionsandinfluencingfactorsforusinganintelligenceenableddiagnosticclinicaldecisionsupportsysteminhealthcaresystemscrosssectionalsurvey AT xiaojiang investigatingcliniciansintentionsandinfluencingfactorsforusinganintelligenceenableddiagnosticclinicaldecisionsupportsysteminhealthcaresystemscrosssectionalsurvey AT lishen investigatingcliniciansintentionsandinfluencingfactorsforusinganintelligenceenableddiagnosticclinicaldecisionsupportsysteminhealthcaresystemscrosssectionalsurvey AT tianruihe investigatingcliniciansintentionsandinfluencingfactorsforusinganintelligenceenableddiagnosticclinicaldecisionsupportsysteminhealthcaresystemscrosssectionalsurvey AT mengtingji investigatingcliniciansintentionsandinfluencingfactorsforusinganintelligenceenableddiagnosticclinicaldecisionsupportsysteminhealthcaresystemscrosssectionalsurvey AT xingyili investigatingcliniciansintentionsandinfluencingfactorsforusinganintelligenceenableddiagnosticclinicaldecisionsupportsysteminhealthcaresystemscrosssectionalsurvey AT guangjunyu investigatingcliniciansintentionsandinfluencingfactorsforusinganintelligenceenableddiagnosticclinicaldecisionsupportsysteminhealthcaresystemscrosssectionalsurvey |