H-DRIVE: A Big Health Data Analytics Platform for Evidence-Informed Decision Making
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Healthcare operations generates large volumes of data. Big data analytics methods are needed to derive actionable and decision-quality 'intelligence' from 'big' healthcare data in order to improve patient care. Given the technical challenges to big health data analytics, in this paper we present a specialized health analytics platform -- H-DRIVE (Health Data Reconciliation Inferencing and Visualization Environment). H-DRIVE is an integrated, end-to-end health data analytics service-oriented workbench designed to empower data analysts and researchers to design analytical experiments and then perform complex analytics on their health data. We present the high-level functional and technical architecture of H-DRIVE. As a case study, we demonstrate the application of H-DRIVE in the context of optimizing the operations of a provincial pathology lab, where we analyze province-wide lab orders to prepare scorecards outlining physician lab testing performance and offer an operational dashboard to provide an overview of lab utilization.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it