Integrated Digital Patient Education at the Bedside for Patients with Chronic Conditions: Observational Study
Bibliographic record
Abstract
BACKGROUND: Patient education delivered by a health care provider increases patients' understanding and adherence to medical instructions, which helps to improve patient health. Multiple challenges exist to delivering effective patient education to patients with multiple chronic conditions, including giving the necessary time, range, and types of learning materials, and assessing the level of understanding. To help overcome these challenges, it is important to study new electronic means to assist in patient education, such as the use of mobile devices, interactive media, 3-dimensional images, and multimedia educational content at the bedside. OBJECTIVE: The goal of this study was to address the need for blended learning strategies combining technical and workflow integration of digital patient education systems for patients with chronic conditions within and across the regular process of care. Studies are needed to evaluate the utility and benefits of these technologies for providers and patients alike. METHODS: A mixed-methods approach was employed including survey administration to 178 patients after they received digital patient education in person with a health care provider, and qualitative interviews with 16 nurse educators who used the mobile digital health education technology to deliver instruction to patients. Patient survey data were analyzed using chi-square statistical tests. Qualitative interviews were analyzed for user acceptance and perceived value themes. RESULTS: Patients who were counseled using a blended digital health education approach reported improved understanding of educational content (P=.034) and chronic health conditions (P<.001), were more motivated to care for themselves at home (P<.001), were more likely to say that they felt capable of making health care decisions with their doctors (P<.001) and on their own (P=.001), and were more likely to report their intention to follow their doctor's instructions (P<.001) than were patients whose education was not computer-based. Nurse educators felt that the digital education system and content enhanced their education efforts and could be easily integrated into the outpatient clinical workflow. CONCLUSIONS: Patient education for individuals with chronic conditions may be more effective than traditional formats when provided in blended digital formats supervised by a health care provider.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".