The EMITT Study: Development and Evaluation of a Medication Information Transfer Tool
Bibliographic record
Abstract
BACKGROUND: Continuity of care is required as patients move from the care of one pharmacist to another. The appropriate transfer of medication information between pharmacists as well as to patients at these times is essential in order to prevent drug-related problems (DRPs). OBJECTIVE: To develop a tool to transfer medication information between various pharmacists caring for the same patients. Secondary objectives were to evaluate the tool based on utility in practice and satisfaction of pharmacists. METHODS: The project consisted of a needs assessment involving in-depth interviews with patients and pharmacists and a literature review. These data were used to develop an optimal tool for medication information transfer between pharmacists in different practice settings. The tool was evaluated in a feasibility pilot for potential utility and pharmacist satisfaction. RESULTS: The tool created called EMITT (electronic medication information transfer tool) facilitates the communication of information to outpatient pharmacists including a letter and an up-to-date list of the patient's drugs. A total of 187 medication issues were communicated within 40 transferred letters, 61 of which required active follow-up, which potentially prevented 348 DRPs if the receiver of the information acted on the information that was provided. The 3 most common issues that required follow-up were restarting a held medication (n = 13), adjustment of doses based on laboratory results (n = 11), and starting a new indicated medication in the future (n = 7). CONCLUSIONS: A tool can be created to help address the gap in communication between pharmacists when patients move between interfaces of care by evaluating the needs of healthcare professionals involved in the information transfer process. It is envisioned that the elements of our tool can be easily adapted to other institutions to improve medication information transfer.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".