Data on Patient Record Trajectory for Linkage (DataPRinT Linkage).
Bibliographic record
Abstract
The linkage of Electronic Medical Records, Administrative and other data sources is highly valuable for research and health system monitoring. Once linked, combined resources can be analyzed to provide the answers to a variety of health questions that otherwise could not be answered. However, legislative and administrative barriers, including lengthy processes for data sharing agreements, may preclude timely linkage which is a key requirement during pandemics. ObjectiveTo develop a method using a patient’s health trajectory to probabilistically link primary care Electronic Medical Record (EMR) data with administrative and other data, without the need to transfer large datasets or identifiable information. To determine the legislative feasibility, accuracy and validity of this linkage process. Study DesignIdentify data strings that do not directly identify patients and could be used as unique linkage variables. The data strings, which we are calling dataprints, are sufficiently similar over time in different databases. One example in Ontario, Canada, is the pattern of submitted health claims. For every patient seen by a family physician, there exists a unique pattern of dates/billing codes/diagnoses over time. These unique patterns are reasonably similar in EMR and administrative datasets. We will apply an algorithm which turns the string in the selected dataprints to an irreversibly hashed code for each person. The hashed code and no additional information will be provided by both data controllers to a trusted-third party who will determine which records match and send a mapping table to both. This enables analyses to be run in parallel, without divulging any direct person identifiers. DatasetIndividuals contained in the University of Toronto Practice Based Research Network (UTOPIAN). Outcome MeasuresLinkage quality will be assessed by the number of true matches and represented by sensitivity, specificity and positive and negative predictive values. ResultsThe method will be evaluated against a validated, deterministically linked reference standard at North York General Hospital using de-identified EMR and hospital data. Results will inform processes to enable analyses across datasets while adhering to privacy legislation.
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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 | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.017 | 0.007 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".