Exploring Clinicians’ and Patients’ Acceptance and Utilization of a Digital Solution to Support Individualized Care in Diabetes Specialist Outpatient Care (DigiDiaS): Qualitative Study
Bibliographic record
Abstract
Background: With the increasing prevalence of type 1 diabetes alongside limited health care resources, the need for more sustainable health care services is apparent. Central to ensuring the standard of diabetes care while simultaneously optimizing resource utilization is improved patient-clinician communication and the provision of individualized care. Digital outpatient solutions incorporating patient-reported outcome measures (PROMs) have been introduced in diabetes outpatient care over recent years; however, features and delivery methods differ, and existing studies on their use and perceived clinical value are limited. Furthermore, clinicians' acceptance has been suggested as a key factor in the sustainability of digital solutions. Thus, to support the implementation of digital outpatient solutions perceived as valuable by clinicians and patients, we need more knowledge about how they are accepted and utilized in clinical practice. Objective: This study investigates how clinicians and patients with type 1 diabetes accept and utilize a digital outpatient solution to support individualized care in the context of full-scale implementation at a diabetes specialist outpatient clinic. Furthermore, we aim to explore the synchronous interaction between patients and clinicians when they are allowed to prepare through the filling and reviewing of asynchronous PROMs before consultations. Methods: This qualitative study uses interpretive description as a methodological approach. The digital outpatient solution features various components, including PROM questionnaires, asynchronous chat, remote consultations, e-learning, and information distribution. Data were collected through semistructured interviews with 10 clinicians and 20 patients with type 1 diabetes and observations of consultations. The data from the patient and clinician interviews (267 A4 pages) were analyzed separately before being jointly analyzed in the context of the findings from the observations (40 A4 pages). Results: Our analysis resulted in the following three main themes that describe the interplay between clinicians' and patients' acceptance, utilization, and perceived clinical value of a digital outpatient solution: (1) clinicians' acceptance of the digital outpatient solution influences patients' acceptance, (2) variations in the use of different features influence the extent of individualized care, and (3) clinicians' and patients' utilization influences perceived care efficiency and quality. Those who demonstrated higher acceptance and more extensive utilization reported that the solution was more valuable in enhancing individualized care efficiency and quality. Conclusions: This study highlights the interplay between clinicians' and patients' acceptance, utilization, and perceived clinical value of a digital outpatient solution in diabetes specialist outpatient care. Our findings suggest that when clinicians and patients understand why and how digital solutions are used, such solutions can enhance care efficiency and quality, contributing to sustainable health care. Future research should aim to gain an in-depth understanding of clinicians' and patients' acceptance, as well as the effectiveness of change management strategies when implementing digital outpatient solutions in diabetes specialist outpatient care.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".