Bibliographic record
Abstract
There is a growing interest in using public data for open government policy involving health informatics and healthcare systems. This paper investigated the characteristics of publically available data sets in health informatics that were derived from electronic health records (EHRs), healthcare systems, and a variety of open-government libraries, data marts, or data catalogues.Data used in this study consisted of public data sets that did not require any registration to access online. In total, nine web-based platforms on the Internet were used that included: British Columbia (BC) Data Catalogue, Canadian Institute for Health Information (CIHI), Harvard Dataverse, MIMIC-eICU, FigShare, GitHub, Google Dataset, UCI Machine Learning Repository, and Zenodo. Our initial search across these platforms found over 10,000 public use files that had data sets related to health informatics.We found 558 data sets that matched search criterion that ranged from years 2013-2022. The data source types were mostly found using the health informatics search filters followed by the combination of health informatics and healthcare systems, but fewer data sets were found when using EHR as the criterion. Almost 85% of the total data sets were from 2020-2022. The range of data sizes were 11KB to 7.8MB. The eICU (hosted by MIT’s MIMIC data mart) platform had the largest data set followed by Zenodo, and GitHub. Additionally, any bioinformatics in the 558 data sets were excluded and further classification on the content and usability, and dashboard visualization towards experiential learning resulted in 117 data sets.Of these 117 data sets, we further tested their usability to graph and create a dashboard within 2-5 minutes of loading the data to Tableau© that then used a Data Usability Scale (DUS) scoring developed from the industry standard of System Usability Scale (SUS). Data were deemed usable and useful for >60% average DUS scoring. Finally, 25 sets of data could be used effectively in classroom exercises dealing with electronic records and decision support for health care. Best data for dashboard usability were from MIMIC-eICU, and other websites like Zenodo produced low to high usability. The data sets with low to poor usability were from FigShare, Dataverse, CIHI, and BC Data Catalogue, respectively.Overall, 25 data sets with high usability of data related health informatics and healthcare systems showed 60-85% usability. Moreover, all nine platforms showed ease-of-use search patterns to establish the criteria in a short amount of time. However, more investigation is needed to compare data-to-dashboard visualization for single to multiple files for experiential learning in health informatics.
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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 | 0.032 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.052 | 0.055 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".