Defining evaluation indicators of health information systemsand a Model Presentation
Bibliographic record
Abstract
Introduction: Health information systems have designed in order to manage health information fluency for improving healthcare quality. It is necessary to conduct continues evaluations to do epidemiologic researches and manage health information systems, enhance quality and decrease costs. Unfortunately there isn’t a framework for that foeus on the measurement methods and indicators in our country.Objective of this research is defining steps, study design methods, data sources and indicators of health information systems evaluation. Methods: This research was cross sectional-description and conducted in 2004. At first studies books in library, and searched on internet to find related information. After that then we categorized developed indicators by Canada and England according their importance. Thereafter we sent a 20 keys questions questionnaire to review for 30 peer reviewers. Although the Questionnaires gived to 35 persons, but it full by 28. These persons were teaching staff in universities and specialists in health information systems. Selected indicators as the most important indictors were which over75% sight selected them high and very high degree. After gathering questionnaire, results analyze by SPSS. Results: There are six steps to evaluate health information system. They include Agree why an evaluation is needed,? Agree when to evaluate? Agree what to evaluate, Agree how to evaluate? analyze and report,?Assess recommendations and decide on actions. there were 13 study designs for health information systems evaluation. finaly indicators provided in three contexts. accountability, performance enhancement and knowledge development. Conclusion: It is necessary to consider human aspects and knowledge development more over economics and financial aspects.conducted evaluation of health information system is based on the accountability that conducted with randomized Controlled trial and qualitative the best evaluation is conducted when use some evaluation methods with considering these indicators.
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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 | 0.034 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".