Toward Data-Informed Care in Long-Term Care: Qualitative Analysis
Bibliographic record
Abstract
Background: In long-term care (LTC) for older adults, data on client, employee, and organization levels are collected in various ways, covering quality of care, life, and work. There is, however, a lack of understanding of how to introduce data-informed care in LTC and thus create value from data. Objective: This study aims to investigate the experiences and perceptions of various stakeholders in LTC regarding data and data-informed care. Methods: A qualitative study using the World Café cocreation technique was conducted with a diverse group of LTC stakeholders. Four questions were addressed: (1) What thoughts do you have when you hear the term "data" in relation to LTC? (2) What purposes do data have (in the future) in LTC? (3) What knowledge and skills are needed to enable data-informed care? (4) How can data contribute to and improve multidisciplinary learning? Stakeholders' notes and the plenary summary were analyzed using conventional content analysis. Results: Stakeholders included nurses, members of client councils, data specialists, researchers, and managers (N=20; mean age 50, SD 13 years). Five themes were identified: (1) despite uncertainty, the benefits of using data outweigh the associated risks; (2) the lack of accessibility and uniformity hinders integrating data-informed care; (3) human resources and finance departments pioneer data usage; however, potential lies in clinical decision-making; (4) data-informed care demands individual, collective, and organizational prerequisites; and (5) multidisciplinary collaboration enriches collective knowledge regarding data. Conclusions: Introducing data-informed care requires enhancing data literacy of health care professionals, establishing clear communication about the role of data within the organization, and introducing new job positions, such as data scientists. Data-informed care was considered a multidisciplinary approach in which data have a supportive role to enhance collective understanding and are considered crucial for improving quality of care. .
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".