Designing a Health Information Management model for elderly care centers in Iran
Bibliographic record
Abstract
Introduction: Nursing care facilities are among a variety of health care services. Nursing care facilities refers to a broad spectrum of health, social, supportive, medical and rehabilitation cares .People that lives in these facilities can choose their services .Then, nursing care facilities need some professional organizing and standards about health information management. Methods: This is a comparison-qualitative study .The data was collected from professional texts, articles, internet, and elderly care centers. In this research health information management in elderly care centers in America, Canada, Japan and Iran were compared. Considering similarity and differences characteristics of health information management, a model were suggested. Using Delphi methods, the recommended model was put into practice in two phases. The data collection tool were questionnaires. Findings were analyzed, and a final model for Iran was presented. Results: Findings divide in to five section: 1. documentation elements (social data, clinical data), 2. documentation standard (general standards, authentication, permanency, manner of documentation, guidelines for handing correction , errors , omission) 3. storage and retrieval standards (policy and procedure of storage and retrieval, filing method , maintenance of record, time, manner of filing, filing equipment), 4. coding system(policy and procedure of coding , coding books) 5. health information management standards (request for medical record, confidentiality, release of information, maintenance standard, destruction, staff training). Conclusion: In conclusion, the final model of health information management tend to American and Canada model of health information management. It has the lowest similarity to Japanning model. It is suggested that guidelines about documentation elements, documentation standard, filing and retrieval, coding and health information management standards were published and updated annually.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".