An RCT Simulation Study on Performance and Accuracy of Inexact Matching Algorithms for Patient Identity in Ambulatory Care Settings
Bibliographic record
Abstract
Patient misidentification has been identified as a primary cause of electronic medical record (EMR) related patient harm. One potential function at which such errors can occur is the user interface, during patient look-up. In this study we apply a within participant randomized control trial (RCT) simulation to assess the impact of a variety of inexact matching techniques on the rate of patient misidentification. We compare a string edit distance algorithm (Jaro-Winkler) and a phonetic algorithm (Metaphone) against a commonly used leading sequence match algorithm baseline. The algorithms were evaluated based on response distribution to the stimulus and on processing time. Our participant group of 24 consisted primarily of students. Our results indicate that the baseline algorithm performs significantly better than either intervention algorithms on the basis of processing time, but on the basis of correctness it performs more poorly than the interventions when the stimulus is in the searchable set. The primary outcome of our study is the recommendation to only employ the interventions in clinical settings where patients' who's names are sought are present rather than novel (e.g., Community clinics and inpatient settings). Where patients are typically novel (e.g., Walk in clinics and emergency rooms), we recommend the use of the baseline algorithm instead.
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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 | 0.052 | 0.245 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".