Current use of electronic medical records in primary care of chronic disease
Bibliographic record
Abstract
Purpose This paper aims to explore the perceptions of facilitators and barriers to their using electronic medical records (EMRs) for these functions and contributes baseline data about the use of EMRs for chronic disease management. The sub‐study reported here is a baseline process evaluation of EMRs and their current use, preliminary to a larger, pragmatic, randomized controlled trial. Its purpose is to understand how EMRs are currently being used by primary care physicians to facilitate chronic disease prevention and screening in their practices. Design/methodology/approach This is a qualitative case study where the lead physician at each of eight primary care clinics (four in Alberta, four in Ontario) participated in semi‐structured interviews. Data were analyzed using thematic content analysis. Findings Although EMRs are being used in a limited fashion for chronic disease prevention and screening, clinicians identified few current benefits. Participants noted some instances in which paper charts were preferred and that the lack of human and financial resources is inhibiting the use of chronic disease applications already incorporated in EMRs. Research limitations/implications To understand fully how EMRs can best be used in the logistical management of chronic disease prevention and screening requires research efforts towards improvement of the data structures they contain. Practical implications Data extraction needs to be easier so that screening of patients, at risk or living with chronic disease, can be facilitated. Social implications Evaluation of the benefits, for the content of care and care relationships, conferred by this new method of communicating, needs to be complemented by a parallel exploration of the risks. Originality/value The paper illustrates that with the tremendous investments in EMRs it is important to learn how changes in their design could facilitate improvements in patient care in this important area.
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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 | 0.054 | 0.222 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".