Health Care Professionals’ Perspectives on Using eHealth Tools in Advanced Home Care: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: The rising demand for advanced home care services, driven by an aging population and the preference for aging in place, presents both challenges and opportunities. While advanced home care can improve cost-effectiveness and patient outcomes, gaps remain in understanding how eHealth technologies can optimize these services. eHealth tools have the potential to offer personalized, coordinated care that increases patient engagement. However, research exploring health care professionals' (HCPs) perspectives on the use of eHealth tools in advanced home care and their impact on the HCP-patient relationship is limited. OBJECTIVE: This study aims to explore HCPs' perspectives on using eHealth tools in advanced home care and these tools' impact on HCP-patient relationships. METHODS: In total, 20 HCPs from 9 clinics specializing in advanced home care were interviewed using semistructured interviews. The discussions focused on their experiences with 2 eHealth tools: a mobile documentation tool and a mobile preconsultation form. The data were analyzed using content analysis to identify recurring themes. RESULTS: The data analysis identified one main theme: optimizing health care with eHealth; that is, enhancing care delivery and overcoming challenges for future health care. Two subthemes emerged: (1) enhancing care delivery, collaboration, and overcoming adoption barriers and (2) streamlining implementation and advancing eHealth tools for future health care delivery. Five categories were also identified: (1) positive experiences and benefits, (2) interactions between HCPs and patients, (3) challenges and difficulties with eHealth tools, (4) integration into the daily workflow, and (5) future directions. Most HCPs expressed positive experiences with the mobile documentation tool, highlighting improved efficiency, documentation quality, and patient safety. While all found the mobile preconsultation form beneficial, patient-related factors limited its utility. Regarding HCP-patient relationships, interactions with patients remained unchanged with the implementation of both tools. HCPs successfully maintained their interpersonal skills and patient-centered approach while integrating eHealth tools into their practice. The tools allowed more focused, in-depth discussions, enhancing patient engagement without affecting relationships. Difficulties with the tools originated from tool-related issues, organizational challenges, or patient-related complexities, occasionally affecting the time available for direct patient interaction. CONCLUSIONS: The study underscores the importance of eHealth tools in enhancing advanced home care while maintaining the HCP-patient relationship. While eHealth tools modify care delivery techniques, they do not impact the core dynamics of the relationships between HCPs and patients. While most of the HCPs in the study had a positive attitude toward using the eHealth tools, understanding the challenges they encounter is crucial for improving user acceptance and success in implementation. Future development should focus on features that not only improve efficiency but also actively enhance HCP-patient relationships, such as facilitating more meaningful interactions and supporting personalized care in the advanced home care setting.
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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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".