Factors Influencing Health Workers’ Acceptance of Guideline-Based Clinical Decision Support Systems for Preventive Services in Thailand: Questionnaire-Based Study
Bibliographic record
Abstract
Background: A guideline-based clinical decision support system (CDSS) is a knowledge-based system designed to collect crucial data from electronic medical records to generate decision-making based on system data requirements and inputs from standard guidelines. Despite the potential to enhance health care delivery, the adoption rate of CDSSs in clinical practice remains suboptimal. Objective: This study aimed to evaluate the determinants influencing the intention to use a new CDSS in preventive care within clinical practice. Methods: A single-center, questionnaire-based, cross-sectional study was conducted among physicians and medical students responsible for providing comprehensive preventive services at the Continuity of Care Clinic, Siriraj Hospital, Thailand. Results: In total, 89 participants were enrolled. Relationships between factors impacting the adoption of CDSSs were analyzed using correlation and regression analysis. We found that physicians' intentions to adopt the CDSS for preventive care were high, with 79% (70/89) of participants expressing their intention to use the system. According to the study's conceptual framework, modified from the original unified theory of acceptance and use of technology model, physicians' positive attitudes toward CDSS use in preventive services and a high level of effort expectancy emerged as crucial factors influencing the intention to use the new CDSS. The odds ratios for these factors were 5.44 (95% CI 1.62-18.34, P=.006) and 7.60 (95% CI 1.55-31.37, P=.01), respectively. Similar results were observed for medical students and for physicians who had graduated. The most prevalent barriers to CDSS implementation were related to physicians' attitudes, followed by issues such as the accuracy and burden of data input, time constraints for clinicians, and the risk of workflow disruption. Conclusions: There was a high intention to adopt the CDSS in preventive care. Positive physician attitudes toward CDSS use in preventive services and effort expectancy were found to be critical factors influencing the intention to use the new CDSS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".