Access to electronic health records by care setting and provider type: perceptions of cancer care providers in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: The use of electronic health records (EHRs) to support the organization and delivery of healthcare is evolving rapidly. However, little is known regarding potential variation in access to EHRs by provider type or care setting. This paper reports on observed variation in the perceptions of access to EHRs by a wide range of cancer care providers covering diverse cancer care settings in Ontario, Canada. METHODS: Perspectives were sought regarding EHR access and health record completeness for cancer patients as part of an internet survey of 5663 cancer care providers and administrators in Ontario. Data were analyzed using a multilevel logistic regression model. Provider type, location of work, and access to computer or internet were included as covariates in the model. RESULTS: A total of 1997 of 5663 (35%) valid responses were collected. Focusing on data from cancer care providers (N = 1247), significant variation in EHR access and health record completeness was observed between provider types, location of work, and level of computer access. Providers who worked in community hospitals were half as likely as those who worked in teaching hospitals to have access to their patients' EHRs (OR 0.45 95% CI: 0.24-0.85, p < 0.05) and were six times less likely to have access to other organizations' EHRs (OR 0.15 95% CI: 0.02-1.00, p < 0.05). Compared to surgeons, nurses (OR 3.47 95% CI: 1.80-6.68, p < 0.05), radiation therapists/physicists (OR 7.86 95% CI: 2.54-25.34, p < 0.05), and other clinicians (OR 4.92 95% CI: 2.15-11.27, p < 0.05) were more likely to report good access to their organization's EHRs. CONCLUSION: Variability in access across different provider groups, organization types, and geographic locations illustrates the fragmented nature of EHR adoption in the cancer system. Along with focusing on technological aspects of EHR adoption within organizations, it is essential that there is cross-organizational and cross-provider access to EHRs to ensure patient continuity of care, system efficiency, and high quality care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".