Towards a framework for teaching about information technology risk in health care: Simulating threats to health data and patient safety
Bibliographic record
Abstract
In this paper the author describes work towards developing an integrative framework for educating health information technology professionals about technology risk. The framework considers multiple sources of risk to health data quality and integrity that can result from the use of health information technology (HIT) and can be used to teach health professional students about these risks when using health technologies. This framework encompasses issues and problems that may arise from varied sources, including intentional alterations (e.g. resulting from hacking and security breaches) as well as unintentional breaches and corruption of data (e.g. resulting from technical problems, or from technology-induced errors). The framework that is described has several levels: the level of human factors and usability of HIT, the level of monitoring of security and accuracy, the HIT architectural level, the level of operational and physical checks, the level of healthcare quality assurance policies and the data risk management strategies level. Approaches to monitoring and simulation of risk are also discussed, including a discussion of an innovative approach to monitoring potential quality issues. This is followed by a discussion of the application (using computer simulations) to educate both students and health information technology professionals about the impact and spread of technology-induced and related types of data errors involving HIT.
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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".