The Perceived Ease of Use and Usefulness of Loop: Evaluation and Content Analysis of a Web-Based Clinical Collaboration System
Bibliographic record
Abstract
BACKGROUND: Patients with complex health care needs require the expertise of many health care providers. Communication, collaboration, and patient-centered care positively impact care quality and patient outcomes. Few technologies exist that facilitate collaboration between providers across settings of care and also engage the patient. We developed a Web-based clinical collaboration system, Loop, to address this gap. The likelihood of a technological system's uptake is associated with its perceived ease of use and perceived usefulness. We engaged stakeholders in the conceptualization and development of Loop in an effort to maximize its intuitiveness and utility. OBJECTIVE: This study aimed to report end users' perceptions about the ease of use and usefulness of Loop captured during usability tests of Loop. METHODS: Participants represented three user types (patients, caregivers, and health care providers) recruited from three populations (adults with cancer, adolescents and young adults with cancer, and children with medical complexity). We conducted usability testing over three iterative cycles of testing and development in both laboratory-based and off-site environments. We performed a content analysis of usability testing transcripts to summarize and describe participant perceptions about the ease of use and usefulness of Loop. RESULTS: Participants enjoyed testing Loop and were able to use the core functions-composing, posting, and reading messages-with little difficulty. They had difficulty interpreting certain visual cues and design elements or the purpose of some features. This difficulty negatively impacted perceived ease of use but was primarily limited to auxiliary features. Participants predicted that Loop could improve the efficiency and effectiveness of communication between care team members; however, this perceived usefulness could be compromised by disruptions to personal workflow such as additional time or task requirements. CONCLUSIONS: Loop was perceived to have value as a collaboration system; however, usability testing findings indicate that some design and functional elements need to be addressed to improve ease of use. Additionally, participant concerns highlight the need to consider how a system can be implemented so as to minimize impact on workflow and optimize usefulness.
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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 | 0.018 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".