Using family physician Electronic Medical Record data to measure the pathways of cancer care
Bibliographic record
Abstract
IntroductionGaps in care have been identified along the disease pathway for specific cancers. However, no real-world data exists to identify wait times along these cancer pathways. Secondary use of family physician (FP) electronic medical record data (EMR) can augment existing health administrative data in measuring steps in the care pathways. Objectives and ApproachWe used FP EMR data to identify care pathways for lung cancer and breast cancer patients from the description of symptoms, to the initiation of investigations, referrals to specialty care and the receipt of specific treatments (surgery, chemotherapy, radiation treatment). Data from the Electronic Medical Record Administrative data Linked Database (EMRALD) held at the Institute for Clinical Evaluative Sciences (ICES) was used to identify a cohort of lung cancer and breast cancer patients. Data abstractors examined the FP EMR notes to identify pre-diagnostic symptoms, pre-diagnostic radiological test, biopsy results, oncology and surgical specialist referrals and post-diagnostic surgical and oncological consultations. ResultsTo date, abstractors have reviewed the FP EMR notes for 300 lung cancer patient and 1200 breast cancer patients. Abstractors identified an index date where there was documentation of the first abnormal test result and/or a FP progress note documenting a “suspicious” or “concerning” sign or symptom. For both lung cancer and breast cancer patients, a pre-diagnostic index date was identified in 88.5% of FP EMR notes. For lung cancer patients 66.7% based were based on abnormal chest x-rays and for breast cancer patients 81.1% were based on abnormal mammograms. Pre-diagnostic symptoms were identified in 62.1% of FP EMR notes and 81.6% had post-diagnostic consultation notes. Wait times from the index date to seeing an oncological specialist were less than four weeks for all patients. Conclusion/ImplicationsWe are able to use information from FP EMRs linked to health administrative data to identify pre-diagnostic care received by patients prior to their cancer diagnosis. This information can be used to identify care gaps and measure wait times in receiving cancer care from a patient’s perspective.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".