Characteristics of consistently high primary health care users in the DELPHI database: Retrospective study of electronic medical records
Bibliographic record
Abstract
OBJECTIVE: To identify consistently high users of primary health care and describe their use of services, characteristics, and comorbidities. DESIGN: Retrospective analysis of de-identified patient data from 23 physicians contributing to the DELPHI (Deliver Primary Healthcare Information) database of electronic medical records between October 1, 2005, and June 30, 2010. SETTING: Ten primary care practice sites in southwestern Ontario. PARTICIPANTS: A total of 1971 patients whose data were coded with the International Classification of Primary Care. MAIN OUTCOME MEASURES: Patient characteristics analyzed included sex, age, chronic conditions diagnosed by the end of the first year, multimorbidity (defined as 3 or more total chronic conditions), urban or rural postal code, and median family income quintile. Consistency of high primary health care use was measured using the total number of primary care visits in each of the 4 years that were studied (July 1, 2006, to June 30, 2010), creating 3 outcome groups: never high users, sometimes high users (above the 90th percentile in 1 to 2 years), and consistent high users (above the 90th percentile in 3 to 4 years). Bivariate analyses and multinomial logistic regression were used to test for effects of patient characteristics on consistency of high use. RESULTS: Older patients were significantly more likely to become sometimes or consistent high users (P < .05). Multimorbidity at baseline increased the odds of being a sometimes high user by 2.3 times (P < .001) and a consistent high user by 4.1 times (P < .001). Patients in rural locations were 1.8 times more likely to become consistent high users (P = .010). In the multinomial regression, sex and income were not associated with odds of high use. Significantly higher prevalences of chronic respiratory, musculoskeletal, and psychological conditions were seen in the consistent high users (P < .05). CONCLUSION: Older patients with multimorbidity and those in rural locations are at a significantly higher risk of becoming consistent high users of primary health care. Several years of electronic medical record data were essential to conducting this research on the characteristics associated with becoming consistent high users of primary health 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".