EHR READINESS AND CLINICAL INFORMATION MANAGEMENT: STAKEHOLDER CONSULTATION AND ANALYSIS
Bibliographic record
Abstract
Electronic Health Record Systems (EHRs) are an important tool for today’s physicians. EHRs (commonly called EMRs in Canada) are used to store, retrieve and leverage patient information to achieve better clinical outcomes for patients. EHRs can also contribute to public policy by helping policy makers track population health data. There are barriers as well as drivers to successful implementation of EHRs. Also, with the introduction of EHRs and their accumulation of patient data physicians face challenges for better extraction and use of data as well as overall management of information within the clinic. This thesis performs a literature review and presents evidence on the barriers and drivers that exist in the area of EHR (Electronic Health Records) implementation in the US. The thesis also includes a survey that tracks responses of primary care physicians in the US. The responses were analyzed to determine key factors impacting EHR implementation and information management. The key factors included workflow, optimization of information technology (IT) resources that include software, hardware assets and trained personnel, and plan for extraction of data. Our research found, among other things, the need to raise awareness among physicians about optimizing clinical workflow, management of information in the EHRs, the need for additional training on the EHRs and, in case of non-urban physicians, the need for improved levels of IT and Internet expertise in the clinic.
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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 | 0.055 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".