The value of connected health information: perceptions of electronic health record users in Canada
Bibliographic record
Abstract
BACKGROUND: As health care becomes more complex, it becomes more important for clinicians and patients to share information. Electronic health information exchange can help address this need. To this end, all provinces and territories (PTs) in Canada have created interoperable electronic health records (iEHRs). These secure systems offer authorized users an integrated view of a person's healthcare history across the continuum of care. They include information such as lab results, medications, diagnostic images, clinical reports and immunization profiles. This study explores user experiences and perceived outcomes of iEHR use. METHODS: Surveys conducted between 2006 and 2014 asked iEHR users in six Canadian PTs about system, information and service quality; iEHR use and user satisfaction; and net quality and productivity benefits. The surveys had a core set of questions that used Likert-type scales. Results were synthesized across surveys for each evaluative dimension. Consensus among researchers and subject matter experts on whether to classify the outcomes as positive, mixed/neutral, or negative was established using a modified Delphi technique. RESULTS: A total of 2316 iEHR users responded to the six surveys. Information quality was the most studied area. Results varied across PTs, but positive outcomes were more common than mixed/neutral or negative outcomes by a 19:1:1 ratio across this dimension. The next most frequently studied aspects were user satisfaction, the impact of iEHR use on quality of care, and the impact on productivity. In all three areas, there were more positive than mixed/neutral or /negative results (ratios of 13:1:1, 14:3:1, and 15:2:1respectively). CONCLUSIONS: Overall, users of iEHRs that provide secure access to patient information collated from across the health system tend to report positive outcomes, including quality of care and productivity. This study is an important first step in understanding user perspectives on iEHRs and health information exchange more broadly.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".