Characterization of Safety Events Involving Technology in Primary and Community Care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Background The adoption of technology in health care settings is often touted as an opportunity to improve patient safety. While some adverse events can be reduced by health information technologies, technology has also been implicated in or attributed to safety events. To date, most studies on this topic have focused on acute care settings. Objectives To describe voluntarily reported safety events that involved health information technology in community and primary care settings in a large Canadian health care organization. Methods Two years of safety events involving health information technology (2016–2018) were extracted from an online voluntary safety event reporting system. Events from primary and community care settings were categorized according to clinical setting, type of event, and level of harm. The Sittig and Singh sociotechnical system model was then used to identify the most prominent sociotechnical dimensions of each event. Results Of 104 reported events, most (n = 85, 82%) indicated the event resulted in no harm. Public health had the highest number of reports (n = 45, 43%), whereas home health had the fewest (n = 7, 7%). Of the 182 sociotechnical concepts identified, many events (n = 61, 59%) mapped to more than one dimension. Personnel (n = 48, 46%), Workflow and Communication (n = 37, 36%), and Content (n = 30, 29%) were the most common. Personnel and Content together was the most common combination of dimensions. Conclusion Most reported events featured both technical and social dimensions, suggesting that the nature of these events is multifaceted. Leveraging existing safety event reporting systems to screen for safety events involving health information technology, and applying a sociotechnical analytic framework can aid health organizations in identifying, responding to, and learning from reported events.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it