Physician Satisfaction With Clinical Decision Support Systems: Impact of Technology Identity and Computer Self-Efficacy
Bibliographic record
Abstract
Clinical decision support systems (CDSS) use data analytics to provide critical information to aid physicians’ decision-making. With timely access to data considered crucial in effective healthcare delivery, CDSS are vital for improving healthcare outcomes. Nevertheless, physician satisfaction is key to their adoption. Previous articles on the adoption of CDSS by physicians have focused on their system-related structures, however there is a lack of research on system identity. This article aims to fill this important gap by integrating the theories of task-technology fit (TTF) and information technology (IT) identity into a research model that explains physicians’ satisfaction with CDSS. To validate the model, data were collected from 349 Chinese physicians who use CDSS in clinical practice via an online questionnaire. The article's findings demonstrate that TTF positively influences CDSS identity. CDSS identity not only impacts physician satisfaction directly but also serves as a mediator in the influence of TTF on their satisfaction. Moreover, computer self-efficacy (CSE) was found to act as a negative moderator between TTF and CDSS identity, indicating that higher levels of CSE weaken the impact of TTF on CDSS identity. This article extends current understanding on physician satisfaction with CDSS by integrating IT identity with TTF in healthcare IT research. In doing so, this research integration contributes to the more effective application of CDSS in healthcare settings.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".