Apps for Health-Related Education in Pharmacy Practice: Needs Assessment Survey Among Patients Within a Large Metropolitan Area
Bibliographic record
Abstract
BACKGROUND: Patient education resources are crucial to the effectiveness of prescribed pharmacotherapy. However, user interest and patient preference for these materials is lacking. Regardless of the field, nearly every article on designing mHealth apps references the lack of end-user involvement as a key flaw to sustainable design. The traditional paper-based methods of patient education are difficult to tailor to a patient's specific needs and learning styles, but a customizable app might be beneficial. OBJECTIVE: Regarding a mobile app for patient education, the objectives of the study were to (1) quantify patient interest, (2) determine desirable features, and (3) determine if a relationship exists between patient variables and interest in an iPad app for patient education. METHODS: A paper-based questionnaire was developed and administered to consenting patients receiving care within three sites: two suburban outpatient sites where ambulatory care services are provided and one urban hospital site where ambulatory care transition services are provided. RESULTS: A total of 121 surveys were completed. Most respondents were female (64/120, 53.3%), between 50 and 70 years of age, white/Caucasian (94/120, 78.3%), owned at least one technology device, and knew what an iPad was. Diabetes was the most common disease state (43/120, 35.8%), followed by heart failure (27/120, 22.5%), history of venous thromboembolism (VTE) (21/120, 17.5%), and asthma/chronic obstructive pulmonary disease (17/120, 14.2%). Overall interest in a mobile health app for patient education was 63.7% (72/113). Interest increased to 68.4% (78/114) if a health care provider recommended it. Respondents with one chronic health condition were more likely to be interested in an app compared to those with two or more. Respondents with a history of VTE were mostly likely to be interested in using an app on their own accord, while respondents with diabetes were mostly likely to be interested if their health care provider recommended it. CONCLUSIONS: This preliminary needs assessment identified that patients are interested in using mHealth apps for health-related education in pharmacy practice, particularly if their health care provider recommends it.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".