Maintaining the continuity of HIV-care records for patients transferring care between centers: challenges, workloads, needs and risks
Bibliographic record
Abstract
With improved life expectancy, the medical records of HIV-infected patients are likely to be transferred repeatedly between HIV caregivers. The challenges, and risk for introducing medical error from incomplete record transfers are poorly understood. We measured number of requests for record transfer, the workload incurred, and explore, using genotypic antiretroviral resistance testing results (GART), the potential risk of incomplete records. Using retrospective database and chart review, we examined all patients followed at the Southern Alberta Clinic between 1 January 2004 and 1 January 2015, and determined how many patients transferred care into and out our program, the associated requests and the workload for record transfer. Using a complete record of all GART tests, the potential importance of absent historic records in current treatment decisions was analyzed. The annual churn rate was 22 ± 3.4%. We received requests for only 70% of patient records who had left our care. Median time for receipt of incoming medical records was 28 days; average clerical time for processing data was 2 hours/record. Of all GART results, 25% exhibited resistance. Of 111 patients with potentially misleading GART results (i.e., documented historical resistance not visible on more recent GART), 34 (30.6%) had moved in from elsewhere. Rigorous maintenance of the continuity of the HIV record is not universally practiced. Resources, costs and logistic challenges as well as a lack of appreciation of risks clearly shown by GART testing, may be relevant barriers. Addressing such issues is pressing as aging and transfers of care are increasingly common.
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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 | 0.028 | 0.151 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".