An analysis of database management systems in mitigating patient misidentification through unique patient identifiers
Bibliographic record
Abstract
Patient misidentification is a serious issue within the US healthcare system that needs to be addressed. A solution that has been recommended to address this issue is a unique patient identifier (UPI), which is a unique ID for a patient that links all health records. This thesis assesses 3 UPI models from a database management perspective, with the goal of recommending the most viable solution for future analysis and research. Model 1, which utilizes a national identifier, and Model 2, which links a distinct UPI to a national identifier, face major challenges in security and maintenance, respectively. Model 3, which requires creating an independent UPI solely for healthcare, emerges as the most viable solution, with enhanced security and HIPAA compliance. Although the utilization of a UPI theoretically seems like a promising solution, further retrospective studies are needed to analyze the impact of a UPI once successfully implemented. Future research and work are needed to assess the models from additional perspectives, such as cost-effectiveness, inoperability with existing healthcare IT infrastructure, and HIPAA compliance. Ultimately, the crisis of patient identification will require a diverse set of solutions, but prioritizing a promising one, such as a UPI, may assist the US healthcare system sooner by reducing the prevalence of misidentification.--Author's abstract
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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 | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".