Barriers to engagement of patients in medication reconciliation.
Bibliographic record
Abstract
362 Background: Medication reconciliation (MedRec) is a process where a detailed medication list is compiled and reviewed with an appropriate health care professional for completeness and drug-drug interactions, then communicated across all of the patient’s healthcare teams. While effective MedRec can minimize the risk of adverse events, uptake in outpatient oncology can be poor. Engagement of patients and caregivers in collection of medication lists through patient portals has been suggested as a potential change idea to improve uptake. To understand barriers and facilitators of patient engagement in the MedRec process through an electronic patient portal we undertook semi structured interviews in a comprehensive cancer center in Ontario, Canada. Methods: Patients in outpatient oncology clinics and chemo-daycare waiting areas were approached to participate in interviews; as we wished to understand barriers both users and non-users of the medication list feature of the patient portal were eligible to participate. Participant recruitment continued until saturation of data was reached. Interviews were carried out virtually following a semi-structured guide between 08/2022 and 12/2022. Interviews were recorded and transcribed verbatim; thematic coding was completed in NVIVO by two members of the study team. Results: A total of 10 patients participated in interviews; participants ranged in age between 30 to 89 years, and all completed post-secondary education. Patients’ emphasized issues with provider/care team communication expressing it was unclear why they were asked to enter medications and did not know if anyone was reviewing the medications entered. Additionally, patients had a fear of inputting erroneous medications resulting in their overall avoidance to use the portal. Portal features such as the use of generic vs trade name medications created lack of clarity for patients when entering medications into the system. Presently, the language is limited to English which may reduce the number of users. Accessibility issues such as font size and overall layout, especially on mobile devices were noteworthy during interviews. Patients recommended the addition of the following features: refill information including requests, medication reminder features via text/email and linkable medication information. Conclusions: Our findings indicate that although patients are using the portal to upload their medication lists, there are technical and systemic improvements that can be made to increase patient usage. In addition to updates to the patient portal to create added value to patients, and improve comprehension and accessibility, patient education is needed to raise awareness of the importance of MedRec and their role in the process and improve buy in.
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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 | 0.015 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".