Improving Diabetes Management With a Patient Portal: A Qualitative Study of Diabetes Self-Management Portal
Bibliographic record
Abstract
BACKGROUND: Effective management and care of diabetes is crucial to reducing associated risks such as heart disease and kidney failure. With increasing access and use of the Internet, online chronic disease management is being explored as a means of providing patients with support and the necessary tools to monitor and manage their disease. OBJECTIVE: The objective of our study was to evaluate the experience of patients and providers using an online diabetes management portal for patients. METHODS: Participants were recruited from a large sample population of 887 for a follow-up questionnaire to be completed after 6 months of using the patient portal. Participants were presented with the option to participate in an additional interview and, if the participant agreed, a time and date was scheduled for the interview. A 5-item, open-ended questionnaire was used to capture providers' opinions of the patient portal. Providers included general practitioners (GPs), nurses, nurse practitioners (NPs), dieticians, diabetes educators (DECs), and other clinical staff. RESULTS: A total of 854 patients were consented for the questionnaire. Seventeen (8 male, 9 female) patients agreed to participate in a telephone interview. Sixty-four health care providers completed the five open-ended questions; however, an average of 48.2 responses were recorded per question. Four major themes were identified and will be discussed in this paper. These themes have been classified as: facilitators of disease management, barriers to portal use, patient-provider communication and relationship, and recommendations for portal improvements. CONCLUSIONS: This qualitative study shows that online chronic disease management portals increase patient access to information and engagement in their health care, but improvements in the portal itself may improve usability and reduce attrition. Furthermore, this study identifies a grey area that exists in the roles that GPs and AHPs should play in the facilitation of online disease management.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".