Accuracy of Healthcare Worker Recall and Medical Record Review for Identifying Infectious Exposures to Hospitalized Patients
Bibliographic record
Abstract
OBJECTIVE: To determine the validity of using healthcare worker (HCW) recall of patient interactions and medical record review for contact tracing in a critical care setting. DESIGN: Trained observers recorded the interactions of nurses, respiratory therapists, and service assistants with study patients in a medical-surgical intensive care unit. These observers' records were used as the reference standard to test the criterion validity of using HCW recall data or medical record review data to identify exposure characteristics. We assessed the effects of previous quarantine of the HCW (because of possible exposure) and the availability of patients' medical records for use as memory aids on the accuracy of HCW recall. SETTING: A 10-bed medical-surgical intensive care unit at Mount Sinai Hospital in Toronto, Ontario. PATIENTS: Thirty-six HCWs observed caring for 16 patients, for a total of 55 healthcare worker shifts. RESULTS: Recall accuracy was better among HCWs who were provided with patient medical records as memory aids (P<.01). However, HCWs tended to overestimate exposures when they used patient medical records as memory aids. For 6 of 26 procedures or care activities, this tendency to overestimate was statistically significant (P<.05). Most HCWs with true exposures were identified by means of this technique, despite the overestimations. Documentation of the activities of the 4 service assistants could not be found in any of the patients' medical records. Similarly, the interactions between 6 (19%) of 32 other patient-HCW pairs were not recorded in patients' medical records. CONCLUSIONS: Data collected from follow-up interviews with HCWs in which they are provided with patient medical records as memory aids should be adequate for contact tracing and for determining exposure histories. Neither follow-up interviews nor medical record review alone provide sufficient data for these purposes.
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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 | 0.002 | 0.019 |
| 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".