How the Integration of Telehealth and Coordinated Care Approaches Impact Health Care Service Organization Structure and Ethos: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Coordinated care and telehealth services have the potential to deliver quality care to chronically ill patients. They can both reduce the economic burden of chronic care and maximize the delivery of clinical services. Such services require new behaviors, routines, and ways of working to improve health outcomes, administrative efficiency, cost-effectiveness, and user (patient and health professional) experience. OBJECTIVE: The aim of this study was to assess how health care organization setup influences the perceptions and experience of service managers and frontline staff during the development and deployment of integrated care with and without telehealth. METHODS: As part of a multinational project exploring the use of coordinated care and telehealth, questionnaires were sent to service managers and frontline practitioners. These questionnaires gathered quantitative and qualitative data related to organizational issues in the implementation of coordinated care and telehealth. Three analytical stages were followed: (1) preliminary analysis for a direct comparison of the responses of service managers and frontline staff to a range of organizational issues, (2) secondary analysis to establish statistically significant relationships between baseline and follow-up questionnaires, and (3) thematic analysis of free-text responses of service managers and frontline staff. RESULTS: Both frontline practitioners and managers highlighted that training, tailored to the needs of different professional groups and staff grades, was a crucial element in the successful implementation of new services. Frontline staff were markedly less positive than managers in their views regarding the responsiveness of their organization and the pace of change. CONCLUSIONS: The data provide evidence that the setup of health care services is positively associated with outcomes in several areas, particularly tailored staff training, rewards for good service, staff satisfaction, and patient involvement.
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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 | 0.056 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".