Health Information Management for Clinical Monitoring, Research, and Quality Assurance in Living Kidney Donor Evaluation
Bibliographic record
Abstract
Background The Kidney Transplant Program at the Toronto General Hospital utilizes numerous electronic health record (EHR) platforms which house patient health information that are often not coded in a systematic manner to facilitate quality improvement and research. In recent years, the Comprehensive Living Kidney Donor (CLiKeD) database was developed for this purpose. Methods A team of multi-disciplinary professionals developed the framework and codebook for CLiKeD, which comprised data elements from the donor evaluation process, donor outcomes, costs, and quality-of-life measurements. To create CLiKeD, relational tables representing elements from performance measures and clinical tests domains, and linked to hospital-based costing data, were developed using MS Access. Training manuals for data abstraction and entry from EHR platforms were created to ensure all procedures were performed systematically. Results Currently, CLiKeD comprises over 21 data domains with 300 data elements. This includes information from all donor referrals since 1-Jan-2006 to the present. Data related to potential donors at different phases of their clinical evaluation have been systematically captured. Data linkages to matching recipient clinical information have been facilitated using appropriate data primary keys. The training manuals have led to the development of training courses and e-learning tools. A set of statistical codes developed utilizing the program Stata® were employed to perform data audits and validation checks to ensure maintenance of the quality of the databases and to produce standardized analytical datasets for ongoing and future research. Conclusion Data analysis from CLiKeD presented at programmatic retreats and scientific meetings will contribute to quality improvement and the knowledge base of living kidney donation and transplantation. Similar frameworks have a wide applicability for donor management systems in other healthcare institutions.
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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 | 0.059 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".