Interrater reliability and ease of use of the High-Alert Medication Stratification Tool‒Revised Prospective (HAMST-R PRO): A prospective, multisite study
Bibliographic record
Abstract
OBJECTIVE: The High-Alert Medication Stratification Tool‒Revised (HAMST-R) was originally designed to standardize the identification of high-alert medications (HAMs) according to safety risk. The primary objective of this multisite study was to assess interrater reliability of the HAMST-R PRO, a version of the tool designed to prospectively evaluate safety risk of medications during evaluation for formulary addition. METHODS: HAMST-R was designed as an objective tool to evaluate HAMs at a single site during the HAMST-R phase I study. Phase II of the study demonstrated the validity of the tool in a multisite, national study. In this third study, 11 medication safety experts from 8 health systems across the United States and 1 in Canada facilitated evaluation of medications prospectively with the HAMST-R PRO during the formulary review process for 27 medications. At each site, at least 5 individuals were asked to review each medication. Interrater reliability was evaluated using Kendall's coefficient of concordance. Ease of use was determined by participant interviews. RESULTS: Overall interrater reliability for HAMST-R PRO was found to be 0.76 (P < 0.001) across all sites, indicating substantial agreement between users. Interrater reliability among individual sites ranged from 0.52 to 0.82 (P < 0.05 for all sites). CONCLUSION: Interrater reliability of HAMST-R PRO is substantial, indicating consistency and agreement among pharmacists utilizing this tool to evaluate safety risk of medications before their addition to a health-system formulary. This information can be used to identify potential interventions for each step of the medication-use process that institutions may implement to decrease a medication's potential safety risk.
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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 | 0.001 |
| 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".