Implementation of a novel linkage of primary care electronic medical record data with hospital data in South Eastern Ontario
Bibliographic record
Abstract
<h3>Context:</h3> Currently, primary care data, community data, and hospital data are not linked in Ontario, resulting in a disconnect in continuity of care. Combining these datasets in a consolidated data repository could result in an improved understanding of the care journey, support the healthcare needs of Ontario Health Team priority populations, promote continuity of care improvements across sectors, and decrease burden on emergency departments (EDs) and primary care providers. <h3>Objectives:</h3> To link primary care electronic medical record (EMR) data with community and hospital data and to test the utility of the merged dataset through a targeted quality improvement (QI) intervention among high-risk patients with chronic obstructive pulmonary disease (COPD). <h3>Datasets:</h3> Primary care EMR data from the Eastern Ontario Network and acute care, post-acute, and community mental health and addictions data from the Shared Health Integrated Information Portal. <h3>Population and Intervention:</h3> Patients attending an academic family health team who were at risk of COPD-related ED visits were identified and targeted for a QI intervention in which patients saw a COPD Specialist for pulmonary function testing, action plan development, medication review, and education. <h3>Results:</h3> Robust legal, privacy, and technical processes were developed and applied to securely link and merge datasets. Privacy risks were mitigated through a privacy impact assessment and execution of data use agreements between stakeholders. 1072 patients with COPD were identified within the merged dataset, 50% of whom visited the ED within two years. Risk factors (i.e., comorbid disease, smoking status) were determined to predict those at highest risk for future ED visits. Following patient review by clinician, 77 patients were deemed eligible. A total of 25 patients (32%) were booked for the intervention highlighting a simple pathway for patient care improvements in line with best practice guidelines. <h3>Conclusions:</h3> Despite privacy, legal, and technical considerations when combining datasets from different sources, we were able to successfully and safely bridge the gap between primary care EMR and hospital data. We demonstrated the capacity to implement data-drive QI approaches to support patient care across health care sectors using the novel merged datasets. Overall, this project highlights a robust linkage process which can be scaled and spread across primary care clinics and health conditions.
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 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 | 0.005 | 0.000 |
| 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 | 0.001 | 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".