Impact of Digital Health on Patient-Provider Relationships in Respiratory Secondary Care Based on Qualitative and Quantitative Evidence: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Digital health technology adoption has accelerated in respiratory care, particularly since the COVID-19 pandemic, supporting various applications from self-management to telerehabilitation. While these technologies have transformed health care delivery, their impact on the patient-provider relationship in specialist respiratory care remains poorly understood. OBJECTIVE: This study aims to systematically review the literature on the impact of digital health technology on the patient-provider relationship in respiratory secondary care settings and to understand the factors that enhance or diminish this relationship. METHODS: In December 2023, we conducted a systematic review following Cochrane methodology, searching MEDLINE, Embase, CINAHL, Cochrane databases, and PsycINFO. We included qualitative, quantitative, and mixed methods studies examining digital health interventions in respiratory secondary care. Trained volunteers from the European Respiratory Society CONNECT Clinical Research Collaboration performed screening and data extraction. We conducted a qualitative meta-synthesis of findings, followed by an abductive quantitative data analysis. A total of 3 stakeholder workshops were held to interpret findings collaboratively with patients and health care professionals. RESULTS: From 15,779 papers screened, 97 met the inclusion criteria (55 qualitative/mixed-methods studies, 42 quantitative studies). Studies covered various respiratory conditions, including COPD (32%), asthma (26%), and COVID-19 (13%). Four main themes emerged: trust (foundational to the relationship), adoption factors (including clinical context and implementation drivers), confidence in technology (based on functionality and the evidence base), and connection (encompassing communication and a caring presence). Digital health technology can either enhance or diminish trust between patients and clinicians, with patients' perceptions of the motivations behind its implementation being crucial. While technology facilitated access and communication, remote consultations risked depersonalisation, particularly when not balanced with in-person interactions. Self-monitoring and access to information empowered patients and promoted more equitable patient-provider relationships. CONCLUSIONS: Digital health technology can either strengthen or weaken patient-provider relationships in respiratory care, with effects impacted by adoption factors, confidence in technology, connection, and patient empowerment. Maintaining trust in the era of digital care requires transparent implementation of motivations, consideration of individual circumstances, and reliable technology that supports rather than replaces the therapeutic relationship. TRIAL REGISTRATION: PROSPERO CRD42024493664; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024493664.
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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 | 0.054 | 0.179 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".