Stakeholders’ perceptions of a nurse-led telehealth case management intervention in primary care for patients with complex care needs: a qualitative descriptive study
Bibliographic record
Abstract
OBJECTIVE: With the onset of the COVID-19 pandemic, telehealth case management (TCM) was introduced in primary care for patients requiring care by distance. While not all healthcare needs can be addressed via telehealth, the use of information and communication technology to support healthcare delivery has the potential to contribute to the management of patients with chronic conditions and associated complex care needs. However, few qualitative studies have documented stakeholders' perceptions of TCM. This study aimed to describe patients', primary care providers' and clinic managers' perceptions of the use of a nurse-led TCM intervention for primary care patients with complex care needs. DESIGN: Qualitative descriptive study. SETTING: Three primary care clinics in three Canadian provinces. PARTICIPANTS: Patients with complex care needs (n=30), primary care providers (n=11) and clinic managers (n=2) participated in qualitative individual interviews and focus groups. INTERVENTION: TCM intervention was delivered by nurse case managers over a 6-month period. RESULTS: Participants' perceptions of the TCM intervention were summarised in three themes: (1) improved patient access, comfort and sense of reassurance; (2) trusting relationships and skilled nurse case managers; (3) activities more suitable for TCM. TCM was a generally accepted mode of primary care delivery, had many benefits for patients and providers and worked well for most activities that do not require physical assessment or treatment. Participants found TCM to be useful and a viable alternative to in-person care. CONCLUSIONS: TCM improves access to care and is successful when a relationship of trust between the nurse case manager and patient can develop over time. Healthcare policymakers and primary care providers should consider the benefits of TCM and promote this mode of delivery as a complement to in-person care for patients with complex care needs.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".