Factors that Influence the Recognition, Reporting, and Resolution of Incidents Related to Medical Devices and an Investigation of the Continuous Quality Improvement Data Automatically Reported by Wireless Smart Infusion Pumps
Bibliographic record
Abstract
Medical devices are used to diagnose, treat, or prevent a disease or abnormal physical condition without any chemical action in the body. They can also result in unintended incidents and other errors. This thesis was divided into three chapters: i) a systematic review on the recognition, reporting and resolution of incidents related to medical devices and other health technologies; ii) telephone interviews with physicians and registered nurses (RNs) to solicit information on the resolution, reporting and resolution of medical device-related incidents based on their professional experience; and iii) a case study to review the continuous quality improvement (CQI) data retrieved from the wireless smart infusion pump system at The Ottawa Hospital (TOH) and to propose a CQI data analysis process. The systematic review included 30 studies on factors that influence the recognition, reporting and resolution of incidents in hospitals and interventions to improve patient safety. Central themes that emerged for incident reporting were personal attitudes, awareness and perception of incident reporting systems, organizational culture, and feedback to healthcare professionals. In our telephone interviews, physicians and RNs attributed incident recognition to devices not operating based on the manufacturer’s instructions, and to the hospital staff’s knowledge of and professional experience with the use of the medical device, and clinical manifestations of patients. Suggestions to improve medical device safety surveillance centered on education and training to ensure that the staff is able to use the medical device properly and know what would be considered an error, and how to report these errors. The results of the systematic review and interviews helped to inform the design of a medical device surveillance framework in a hospital setting. Our case study assessed the Dose Error Reduction Software compliance and frequency of soft and hard limit alerts with wireless smart infusion pump systems over a one year period. A CQI data analysis process to monitor the performance of wireless smart infusion pumps is proposed. The findings of this doctoral thesis can contribute to the development of a medical device surveillance system that would help to improve health care delivery and patient safety in a health care institution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.062 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".