Breaking the 80:20 rule in health research using large administrative data sets
Bibliographic record
Abstract
Objective: To explore the application of online analytic processing (OLAP) to improve the efficiency of analytics using large administrative health data sets. Methods: 18 years of administrative health data (1994/95 to 2012/13) were obtained from the Alberta Ministry of Health in Canada. The data sets included hospitalization, ambulatory care and practitioner claims data. Reference files were obtained that provided information including patient demographics, resident postal code, facility, and provider details. Population counts and projections for each year, sex, age were included for rate calculations. These sources were used to develop a data cube using OLAP tools. Results: Time required for analyses was reduced to 5% of that required when comparing run-time for simple queries that did not require linkage of data sets. The data cube negated the need for many intermediary steps for data extraction and analyses for research activities. Conventional methods required over 250 GB of server space for multiple analytic subsets, compared to only 10.3 GB for the data cube. Conclusions: Cross-training in information technology and health analytics is recommended to provide capacity to better leverage OLAP tools which are available with many common applications.
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.
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 | 0.288 | 0.485 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.019 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".