Evaluative Frameworks and Models for Health Information Systems (HIS) and Health Information Technologies (HIT)
Bibliographic record
Abstract
Evaluation criteria for health information systems (HIS) and health information technologies (HIT) is broad, diverse and lacks a gold standard approach that could be leveraged, to evaluate clinical systems at various stages of their system development life cycle (SDLC). Without generalizable tools such as frameworks or models, comparative analysis across HIS and HIT is not possible. This paper presents the findings from a scoping review, utilizing the Arksey and O'Malley methodology [1]. The objective of this review is two-fold: 1) to classify models and frameworks published between the years 2010-2020 according to their level of evaluative focus (e.g. micro, meso, macro, multi), 2) to identify the countries where these models and frameworks have been employed for the purpose of evaluation, using the International Medical Informatics Association (IMIA) Represented Regions [3]. The results demonstrated the heterogeneity of evaluation models and frameworks currently used in health informatics and reflected the necessity for more adaptive approaches to HIS and HIT evaluation.
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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 | 0.063 | 0.066 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| 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".