Empowering Patients: Making Health Information and Systems Safer for Patients and the Public
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
OBJECTIVES: The objectives of this paper are to explore issues and perspectives from four regions of the world where health information systems are contributing to patient empowerment and influencing patient safety. METHODS: Members of the IMIA Working Group for Health Information Systems Safety came together to explore global issues at the intersection of health information systems safety, patient empowerment and patient safety. The group carried out a review and synthesis of the empirical and grey literature in four different regions/countries of the world that have differing health information system safety priorities. RESULTS: Regions/countries from differing parts of the world are developing: (1) high quality, safe information for individuals to use in their health related decision making, (2) patient portals and testing them for their safety, (3) methods for identifying unsafe health information system features and functions, and (4) ways of engaging citizens in identifying unsafe features and functions of health information systems. CONCLUSIONS: Internationally, there has been a rise in the number of health information systems and technologies that are being developed to support patient care. The amount of health information available on the World Wide Web (WWW), and the use of mobile phone software to support consumer health behaviours and self-management of chronic illnesses has also grown. The use of some of these health information systems and technologies has helped citizens to improve their health status (e.g. patient portals, mobile phones). However, the safety of these systems and technologies has come into question. As a result, there is a need to refine these systems and ensure their safety when they are used by patients and their families.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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