Improving Patient Access to Hospital Pharmacists Using eConsults: Retrospective Descriptive Study
Bibliographic record
Abstract
BACKGROUND: eConsults are increasingly used worldwide to reduce specialist referrals and increase access to medical care. An additional benefit of using an eConsult tool is a reduction of health care costs while improving the quality of health care and patient participation. Currently, shared decision making is increasingly implemented and preferred by patients. eConsults are also a promising tool to improve access to the hospital pharmacist. Patients often have questions about their medication. When medication is started during a hospital admission or outpatient visit, community pharmacists are not always sufficiently informed to answer patient questions. Direct contact with hospital pharmacists may be more appropriate and efficient. This contact is facilitated through the eConsult feature in the hospital's patient portal. OBJECTIVE: This study aims to evaluate the prevalence and contents of the eConsults sent by patients to hospital pharmacists. METHODS: A first retrospective descriptive study was conducted at the Leiden University Medical Center in the Netherlands. Patients who sent at least one eConsult to a hospital pharmacist between March 2017 and December 2021 were included. Patient characteristics and the number of medications taken were extracted from electronic health records. The content of eConsults was analyzed and grouped into different subjects. Time of sending of the eConsults was analyzed. A comparison was made between the number of eConsults sent to the hospital pharmacy and the number sent to the medical center. Finally, the appropriateness for evaluation by the hospital pharmacist was assessed in all eConsults. RESULTS: During the study period, 983 eConsults (from 808 patients) were sent to the hospital pharmacist. The average patient age was 56 (SD 15.9) years, and 51.4% (415/808) were male; 47.8% (386/808) of the patients used 0 to 4 medications, 33.0% (267/808) used 5 to 9 medications, and 19.2% (155/808) used ≥10 medications. Of the eConsults, 10.9% (107/983) were excluded due to not being medication-related or not intended for the hospital pharmacist. Patients being treated in 31 medical specialties sent eConsults to the hospital pharmacist. The most common medical specialty was cardiology with 22.5% (197/876) of the eConsults. Most eConsults were sent during office hours (614/876, 70.2%). eConsult subjects were medication verification (372/876, 42.5%), logistics (243/876, 27.7%), therapeutic effect and adverse events (100/876, 11.4%), use of medication (87/876, 9.9%), and other subjects (74/876, 8.4%). CONCLUSIONS: Introducing eConsults allows patients to ask medication-related questions directly to hospital pharmacists. Our study shows that patients send medication reconciliation-related eConsults most often. Use of the eConsult tool leads to fast, direct, and documented communication between patient and hospital pharmacist. This can reduce medication-related errors, improve patient empowerment, and increase access to the hospital pharmacist.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".