Electronic retrieval of health information by healthcare providers to improve practice and patient care
Bibliographic record
Abstract
BACKGROUND: The movement towards evidence-based practice makes explicit the need for access to current best evidence to improve health. Advances in electronic technologies have made health information more available, but does availability affect the rate of use of evidence in practice? OBJECTIVES: To assess the effectiveness of interventions intended to provide electronic retrieval (access to information) to health information by healthcare providers to improve practice and patient care. SEARCH STRATEGY: We obtained studies from computerized searches of multiple electronic bibliographic databases, supplemented by checking reference lists, and consultation with experts. SELECTION CRITERIA: Randomized controlled trials (RCTs) including cluster randomized trials (CRCTs), controlled clinical trials (CCT), and interrupted time series analyses (ITS) of any language publication status examining interventions of effectiveness of electronic retrieval of health information by healthcare providers. DATA COLLECTION AND ANALYSIS: Duplicate relevancy screening of searches, data abstraction and risk of bias assessment was undertaken. MAIN RESULTS: We found two studies that examined this question. Neither study found any changes in professional behavior following an intervention that facilitated electronic retrieval of health information. There was some evidence of improvements in knowledge about the electronic sources of information reported in one study. Neither study assessed changes in patient outcomes or the costs of provision of the electronic resource and the implementation of the recommended evidence-based practices. AUTHORS' CONCLUSIONS: Overall there was insufficient evidence to support or refute the use of electronic retrieval of healthcare information by healthcare providers to improve practice and patient care.
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 | 0.038 | 0.156 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".