Developing an artificial intelligence-based clinical decision-support system for chest tube management: user evaluations & patient perspectives of the Chest Tube Learning Synthesis and Evaluation Assistant (CheLSEA) system
Bibliographic record
Abstract
Background: Chest tube management aims to balance the risks of early chest tube removal (such as postoperative complications and reinsertion) and detriments of excessive and prolonged drainage (e.g., infection, pain, and increased length of stay). The Chest tube Learning Synthesis and Evaluation Assistant (CheLSEA) is an artificial intelligence-based clinical decision support system, designed to combine, interpret, and learn from postoperative patient monitoring data to provide safe and effective recommendations for healthcare providers managing chest tube care. CheLSEA user-interface is an interactive dashboard developed to access recommendations produced by the system. The purpose of this study was to gain an understanding of healthcare professionals’ perceptions, and patient’s views towards an artificial intelligence-based clinical decision support system for chest tube care. An evaluation to assess the usability of the user-interface was also conducted. Methods: This mixed-methods study was conducted in three phases: (I) a survey of healthcare professionals’ perceptions towards artificial intelligence-based clinical decision support system, (II) usability testing sessions with potential users of the system’s user-interface, using a think-aloud approach followed by interviews with closed and open-ended questions organized in a structured worksheet, and (III) semi-structured interviews with patients to ascertain their views toward the use of artificial intelligence-based clinical decision support system in their chest tube care. Results: Survey results showed an overall positive outlook on the usefulness of CheLSEA in chest tube management and its potential to improve patient care. Healthcare professionals helped identify any challenging elements of CheLSEA’s interface and provided suggestions during usability testing. Interface evaluation interviews generated major themes including visibility, understandability, usability, navigation, workflow, and usefulness. Patient interviews highlighted themes such as optimistic attitudes, implementation considerations, transparent communication with healthcare team, overall trust in the surgeon, and desirable features of artificial intelligence clinical decision support systems (AI-CDSS). Conclusions: For CheLSEA to be functional in a clinical setting, the system must have a user-friendly interface that can be integrated with users’ workflow, meet clinical needs, and undergo continual usability reviews.
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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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".