Adoption of Automated Clinical Decision Support System: A Recent Literature Review and a Case Study
Bibliographic record
Abstract
Automated clinical decision support systems (CDSS) are knowledge-based systems that provide patient-specific information and data to clinicians at the proper time for enhancing the clinical workflow of hospital organizations. Nowadays, it is adopted by most of the health care professionals for clinical decision-making that helps to reduce the adverse clinical care events occurring during the treatment. In this article, we present a recent literature review on the adoption of computer-based CDSSs in the area of health care based on qualitative and quantitative techniques, published between 2007 and 2022. For this purpose, we searched Google Scholar and identified different adoption factors by using textual analysis from the included publications. We then ranked the different factors based on the total number of occurrences and represented them as a conceptual framework. A total of 14 different adoption factors were found from 13 studies, among them the usefulness of the system is the most prominent factor that influences the adoption of CDSS to a great extent. This literature review and the framework could be helpful to researchers and healthcare professionals working in the field of technology adoption, providing an overall idea of factors and techniques in this field of research. We have also mentioned the limitations and future research gaps of different studies, which will help the researchers to take an initiation towards these types of research. We also conducted a case study on adoption of fully automatic digital blood pressure monitor and identified that “usefulness” and “ease of use” could influence the adoption of fully automatic digital blood pressure monitor system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".