Inter-professional delirium education and care: a qualitative feasibility study of implementing a delirium Smartphone application
Bibliographic record
Abstract
BACKGROUND: Delirium is a common medical condition with a high prevalence in hospital settings. Effective delirium management requires a multi-component intervention, including the use of Interprofessional teams and evidence-based interventions at the point of care. One vehicle for increasing access of delirium practice tools at the point of care is E-health. There has been a paucity of studies describing the implementation of delirium related clinical application. The purpose of this current study is to acquire users' perceptions of the utility, feasibility and effectiveness of a smartphone application for delirium care in a general surgery unit. In addition, the authors aimed to elucidate the potential challenges with implementing this application. METHODS: This quantitative study was conducted between January 2015 and June 2015 at the University Health Network, Toronto General Hospital site. Participants met inclusion criteria if they were clinical staff on the General Surgery Unit at the Toronto General Hospital site and had experience caring for patients with delirium. At the conclusion of the 4 weeks after the implementation of the intervention, participants were invited by email to participate in a focus group to discuss their perspectives related to using the delirium application RESULTS: Our findings identified several themes related to the implementation and use of this smartphone application in an acute care clinical setting. These themes will provide clinicians preparing to use a smartphone application to support delirium care with an implementation framework. CONCLUSIONS: This study is one of the first to demonstrate the potential utility of a smartphone application for delirium inter-professional education. While this technology does appeal to healthcare professionals, it is important to note potential implementation challenges. Our findings provide insights into these potential barriers and can be used to assist healthcare professionals considering the development and use of an inter-professional clinical care application in their setting.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".