The perceived ease of use and perceived usefulness of a web-based interprofessional communication and collaboration platform in the hospital setting: lessons for future system designers. (Preprint)
Bibliographic record
Abstract
BACKGROUND Hospitalized patients with complex care needs require an interprofessional team of health professionals working together to support their care in hospital and during discharge planning. However, interprofessional communication and collaboration in the inpatient setting is often fragmented and inefficient, leading to poor patient outcomes and provider frustration. Health information technology can potentially help improve team communication and collaboration, but to date, evidence of their effectiveness is lacking. There are also concerns that current implementations further fragment and increase clinician burden without proven benefits. OBJECTIVE To generate transferrable lessons for future designers of health information technology tools to facilitate team communication and collaboration. METHODS A secondary analysis of the qualitative component of a mixed methods evaluation was carried out. The electronic communication and collaboration platform was implemented on two general internal medicine wards in a large community teaching hospital in Mississauga, Ontario, Canada. Fifteen inpatient clinicians on those wards, including nurses, physicians, and allied healthcare providers, were recruited to participate in semi-structured interviews about their experience with a co-designed electronic communication and collaboration tool. Data was analyzed using the Technology Acceptance Model (TAM), and themes relating to the constructs of Perceived Ease Of Use (PEOU) and Perceived Usefulness (PU) were identified. RESULTS : A secondary analysis guided by the TAM highlighted the following. Intuitive design removed training as a barrier for use, but lack of training may hinder participants’ PEOU if features designed for efficiency are not discovered by users. Organized information was found to be useful for creating a comprehensive clinical picture of each patient and facilitating improved handovers. However, information needs to be both comprehensive and succinct at the same time, or else information overload may negatively impact PEOU. The mixed paper and electronic practice environment also negatively impacted PEOU due to unavoidable double documentation and need for printing. Participants perceived the tool to be useful as it improved efficiency in information retrieval and documentation, improved the handovers process, afforded another mode of communication when face-to-face communication was impractical, and improved share awareness. The PU of this tool is dependent on its utilization, and is optimized when all team members use it. CONCLUSIONS Electronic tools can support communication and collaboration for interprofessional teams caring for complex patients. There are transferable lessons learned that can improve PU and PEOU of future systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".