Factors influencing Chinese doctors to use medical large language models
Objective The integration of medical large language models (MLLMs) into healthcare has garnered global interest, however, the determinants of their adoption by medical professionals remain underexplored. This study aims to elucidate the factors influencing doctors’ intention to utilize MLLMs, encomp...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2024-11-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076241297237 |
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| _version_ | 1850062440292679680 |
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| author | Shujuan Qu Lin Liu Min Zhou Chuting Zhou Kathryn S. Campy |
| author_facet | Shujuan Qu Lin Liu Min Zhou Chuting Zhou Kathryn S. Campy |
| author_sort | Shujuan Qu |
| collection | DOAJ |
| description | Objective The integration of medical large language models (MLLMs) into healthcare has garnered global interest, however, the determinants of their adoption by medical professionals remain underexplored. This study aims to elucidate the factors influencing doctors’ intention to utilize MLLMs, encompassing both psychological determinants and demographic attributes. Methods An extended theoretical model was developed using constructs derived from the Technology Acceptance Model (TAM) and five constructs. A hybrid online and offline survey was conducted from March to December 2023, including 955 Chinese medical practitioners. Structural equation modeling was utilized to test the research hypotheses. Results The measurement model exhibited satisfactory reliability and validity, with fit indices meeting scholarly standards. Perceived ease of use emerged as a significant predictor of both perceived usefulness and satisfaction. Content quality was identified as a substantial influence on perceived satisfaction but did not significantly predict perceived usefulness. Technical support and social influence were found to significantly affect perceived usefulness without directly impacting satisfaction. Perceived usefulness positively influenced both satisfaction and usage behavior, while perceived risk had a negative effect. A significant relationship between perceived satisfaction and usage behavior was established, with gender, age, education, and professional title moderating this relationship. Conclusions The study provides empirical evidence for understanding the adoption of MLLMs by Chinese doctors, offering management implications for future technical research, development, and implementation in the medical field. |
| format | Article |
| id | doaj-art-4cd744a643af4a48860cc4cfb61a2435 |
| institution | DOAJ |
| issn | 2055-2076 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj-art-4cd744a643af4a48860cc4cfb61a24352025-08-20T02:49:55ZengSAGE PublishingDigital Health2055-20762024-11-011010.1177/20552076241297237Factors influencing Chinese doctors to use medical large language modelsShujuan Qu0Lin Liu1Min Zhou2Chuting Zhou3Kathryn S. Campy4 Department of Pediatrics, The Third Xiangya Hospital, , Changsha, Hunan, China Department of Pediatrics, The Third Xiangya Hospital, , Changsha, Hunan, China Weldon School of Biomedical Engineering, , West Lafayette, USA Changsha Social Laboratory of Artificial Intelligence, Changsha, Hunan, China Center for Public Health Initiatives, , Philadelphia, USAObjective The integration of medical large language models (MLLMs) into healthcare has garnered global interest, however, the determinants of their adoption by medical professionals remain underexplored. This study aims to elucidate the factors influencing doctors’ intention to utilize MLLMs, encompassing both psychological determinants and demographic attributes. Methods An extended theoretical model was developed using constructs derived from the Technology Acceptance Model (TAM) and five constructs. A hybrid online and offline survey was conducted from March to December 2023, including 955 Chinese medical practitioners. Structural equation modeling was utilized to test the research hypotheses. Results The measurement model exhibited satisfactory reliability and validity, with fit indices meeting scholarly standards. Perceived ease of use emerged as a significant predictor of both perceived usefulness and satisfaction. Content quality was identified as a substantial influence on perceived satisfaction but did not significantly predict perceived usefulness. Technical support and social influence were found to significantly affect perceived usefulness without directly impacting satisfaction. Perceived usefulness positively influenced both satisfaction and usage behavior, while perceived risk had a negative effect. A significant relationship between perceived satisfaction and usage behavior was established, with gender, age, education, and professional title moderating this relationship. Conclusions The study provides empirical evidence for understanding the adoption of MLLMs by Chinese doctors, offering management implications for future technical research, development, and implementation in the medical field.https://doi.org/10.1177/20552076241297237 |
| spellingShingle | Shujuan Qu Lin Liu Min Zhou Chuting Zhou Kathryn S. Campy Factors influencing Chinese doctors to use medical large language models Digital Health |
| title | Factors influencing Chinese doctors to use medical large language models |
| title_full | Factors influencing Chinese doctors to use medical large language models |
| title_fullStr | Factors influencing Chinese doctors to use medical large language models |
| title_full_unstemmed | Factors influencing Chinese doctors to use medical large language models |
| title_short | Factors influencing Chinese doctors to use medical large language models |
| title_sort | factors influencing chinese doctors to use medical large language models |
| url | https://doi.org/10.1177/20552076241297237 |
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