Impact of digital health on the patient-provider relationship in respiratory secondary care settings: a mixed methods systematic review from IHI DRAGON/CONNECT CRC
Bibliographic record
Abstract
Patients and providers have expressed concerns about how digital care affects their relationship. We systematically reviewed the evidence on how digital health affects the patient-provider relationship. Methods: We searched 7 databases (date-Nov 2023) using terms for respiratory, digital health and patient-provider relationship (in secondary care). 38 volunteers from ELF, DRAGON and CONNECT undertook duplicate screening/data extraction. Analysis was thematic. Results: Of 116 included studies, 32 explicitly explored the patient-provider relationship. Existing frameworks for patient-provider relationships (Ridd) proved only partially applicable. Trust was foundational and depended on providers’ beliefs about the technology. It was obstructed if clinicians expressed fear and scepticism. Care was demonstrated through monitoring which created an emotional presence, making patients feel taken care of. Connection between the patient/provider was enhanced if communication was seamless, but burdensome technology could create a disconnect due to disengagement. Shared decision-making enhanced by self-monitoring devices changed the dynamics and created a more equal patient-doctor relationship, through improved self-efficacy, and empowerment. Conclusion: Digital care is widely used post-COVID [as shown in DRAGON IHI], however, the impact on the patient/provider relationship is still under-researched. Seamless connection, and strategies to increase trust (both in the technology and in clinician support) are important considerations for strengthening the relationship. These findings will inform the work of CONNECT CRC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".