Preservation of Person-Centered Care Through Videoconferencing for Patient Follow-up During the COVID-19 Pandemic: Case Study of a Multidisciplinary Care Team
Bibliographic record
Abstract
BACKGROUND: The Patient-Centered Team (PACT) focuses on the transitional phase between hospital and primary care for older patients in Northern Norway with complex and long-term needs. PACT emphasizes a person-centered care approach whereby the sharing of power and the patient's response to "What matters to you?" drive care decisions. However, during the COVID-19 pandemic, videoconferencing was the only option for assessing, planning, coordinating, and performing treatment and care. OBJECTIVE: The aim of this study is to report the experience of the PACT multidisciplinary health care team in shifting rapidly from face-to-face care to using videoconferencing for clinical and collaborative services during the initial phase of the COVID-19 pandemic. This study explores how PACT managed to maintain person-centered care under these conditions. METHODS: This case study takes a qualitative approach based on four semistructured focus group interviews carried out in May and June 2020 with 19 PACT members and leaders. RESULTS: The case study illustrates that videoconferencing is a good solution for many persons with complex and long-term needs and generates new opportunities for interaction between patients and health care personnel. Persons with complex and long-term needs are a heterogeneous group, and for many patients with reduced cognitive capacity or hearing and vision impairment, the use of videoconferencing was challenging and required support from relatives or health care personnel. The study shows that using videoconferencing offered an opportunity to use health care personnel more efficiently, reduce travelling time for patients, and improve the information exchange between health care levels. This suggests that the integration of videoconferencing contributed to the preservation of the person-centered focus on care during the COVID-19 pandemic. There was an overall agreement in PACT that face-to-face care needed to be at the core of the person-centered care approach; the main use of videoconferencing was to support follow-up and coordination. CONCLUSIONS: The COVID-19 pandemic and the rapid adoption of digital care have generated a unique opportunity to continue developing a health service to both preserve and improve the person-centered care approach for persons with complex and long-term needs. This creates demand for overall agreements, including guidelines and procedures for how and when to use videoconferencing to supplement face-to-face treatment and care. Implementing videoconferencing in clinical practice generates a need for systematic training and familiarization with the equipment and technology as well as for an extensive support organization. Videoconferencing can then contribute to better preparing health care services for future scenarios.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".