Evaluation of a Digital Self-management Platform for Patients With Chronic Illness in Primary Care: Qualitative Study of Stakeholders’ Perspectives
Bibliographic record
Abstract
BACKGROUND: Population aging and multimorbidity has led to increasing chronic care needs associated with new challenges in managing growing costs, rising health care professional workloads, and the adoption of rigorous guidelines. These issues could all benefit from greater digitalization and a more patient-centered approach to chronic care, a situation brought to the fore by the COVID-19 pandemic. Little is known about real-life use in primary care. OBJECTIVE: This study aimed to explore the views, thoughts, usability, and experiences concerning a recently introduced digital self-care platform for chronic conditions in 3 Dutch primary care practices. METHODS: We conducted an explorative study combining questionnaires and interviews among patients and general practitioners from 3 general practices that used the digital platform. Questionnaires were sent to patients in each practice to seek the views and experiences of both patient nonusers (n=20) and patient users (n=58) of the platform, together with standardized questionnaires about illness perception and quality of life. In addition, patients (n=15) and general practitioners (n=4) who used the platform took part in semistructured interviews. We transcribed interviews verbatim and performed qualitative content analysis using a deductive approach. The results of the questionnaires were analyzed with descriptive analysis. RESULTS: Among patients who had not actively used the platform but had received an explanation, only 35% (7/20) would recommend its use due to concerns over communication and handling. However, this percentage increased to 76.3% (45/59) among the people who actively used the platform. Interviews with patients and general practitioners who used the platform uncovered several key benefits, including reduced time requirements, reduced workload, improved care quality, and improved accessibility due to the greater patient-centeredness and use of different communication tools. In addition, the self-management tool led to greater patient autonomy and empowerment. Although users considered the platform feasible, usable, and easy to use, some technical issues remained and some patients expressed concerns about the reduction in human contact and feedback. CONCLUSIONS: The overall experience and usability of the platform was good. Support for the online self-management platform for chronic care increased when patients actively used the tool and could experience or identify important advantages. However, patients still noted several areas for improvement that need to be tackled in future iterations. To ensure benefit in the wider population, we must also evaluate this platform in cohorts with lower digital and health literacy.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".