Evaluation of Secure Mobile and Clinical Communication Solution (SMaCCS) across acute and community practice settings
Bibliographic record
Abstract
Aims Clinicians struggle to provide information to each other that supports safe patient transitions, especially across acute and community care jurisdictions. They need flexible communication tools to improve care coordination. Island Health introduced a Secure Mobile and Clinical Communication Solution (SMaCCS) to address these challenges in 2018. In this study we evaluated the SMaCCS system to understand the (1) volume and flow of healthcare communication, (2) degree of adoption and accessibility of the system and (3) user experience. Methods This was a prospective, cross-sectional, observational study. Island Health Information Management/Information Technology (IMIT) selected Vocera Collaboration Suite as the secure messaging platform. We invited healthcare providers in various roles in the hospital and community to use SMaCCS for their daily communications and system and survey data were collected between February and August 2018. System data and survey data were used to determine outcomes. Results A Sankey diagram represents the volume and flow of communication. A total of 2542 messages were sent and 79% of conversations included more than a single message. Eighty-one per cent of participants agreed that using a secure communication tool made them feel more comfortable sharing patient information. Most users (65%) perceived that the application was a useful method for transmitting simple information. Conclusion However, our study showed that different occupational roles require different frequencies and volumes of communication and there are numerous barriers to adoption that must be addressed before secure messaging can be an effective, ubiquitous method of clinical communication.
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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 | 0.030 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".