Empowering Patients: Making Health Information and Systems Safer for Patients and the Public
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.
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 | 0.029 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".