eHealth Technologies, Multimorbidity, and the Office Visit: Qualitative Interview Study on the Perspectives of Physicians and Nurses
Bibliographic record
Abstract
BACKGROUND: eHealth is a broad term referring to the application of information and communication technologies in the health sector, ranging from health records to telemedicine and multiple forms of health education and digital tools. By providing increased and anytime access to information, opportunities to exchange experiences with others, and self-management support, eHealth has been heralded as transformational. It has created a group of informed, engaged, and empowered patients as partners, equipped to take part in shared decision making and effectively self-manage chronic illness. Less attention has been given to health care professionals' (HCPs) experiences of the role of eHealth in patient encounters. OBJECTIVE: The objective of this study was to examine HCPs' perspectives on how eHealth affects their relationships with patients living with multiple chronic conditions, as well as its ethical and practical ramifications. METHODS: We interviewed HCPs about their experiences with eHealth and its impact on the office visit. Eligible participants needed to report a caseload of ≥25% of patients with multimorbidity to address issues of managing complex chronic conditions and coordination of care. We used a semistructured discussion guide for in-depth interviews, and follow-up interviews served to clarify and expand upon initial discussions. Constant comparisons and a narrative approach guided the analyses, and a relational ethics conceptual lens was applied to the data to identify emergent themes. RESULTS: A total of 12 physicians and nurses (6 male, 6 female; median years of practice=13) participated. eHealth tools most frequently described were Web-based educational resources for patients and Web-based resources for HCPs such as curated scientific summaries on diagnostic criteria, clinical therapies, and dosage calculators. Analysis centered on a grand theme of the two-way conversation between HCPs and patients, which addresses a general recentering of the ethical relationship between HCPs and patients around engagement. Subthemes explain the evolution of the two-way conversation, and having, using, and supporting the two-way conversation with patients, primarily as this relates to achieving adherence and health outcomes. CONCLUSIONS: Emerging ethical concerns were related to the ambiguity of the ideal of empowered patients and the ways in which health professionals described enacting those ideals in practice, showing how the cultural shift toward truly mutually respectful and collaborative practice is in transition. HCPs aim to act in the best interests of their patients; the challenge is to benefit from emergent technologies that may enhance patient-HCP interactions and effective care, while abiding by regulations, dealing with the strictures of the technology itself, and managing changing demands on their time.
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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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".