RemoteHealthConnect: Innovating patient monitoring with wearable technology and custom visualization
Bibliographic record
Abstract
Objective This paper introduces RemoteHealthConnect, a novel web-based healthcare system designed to enable healthcare professionals to monitor patients remotely with enhanced efficacy. Central to our system is its integration with the Vitaliti™ wearable, equipped with biosensors for real-time vital signs monitoring. RemoteHealthConnect distinguishes itself by offering advanced, custom visualizations for interactive engagement with medical data, facilitating rapid clinical decision-making through intuitive access to vital signs and trends. The primary research question we sought to answer was: ‘Which design of vital sign visualizations is most effective in improving intuitive and rapid understanding for healthcare practitioners?’ Methods An iterative agile/SCRUM methodology was employed in the design and development of RemoteHealthConnect. We describe the architectural design of our web-based application, data visualization techniques, and user interface design. A user interface/user experience (UI/UX) study was conducted to assess the efficacy of our system. Results The usability study revealed the system's capacity to translate complex bedside data into accessible, real-world visualizations, promoting efficient pattern recognition and anomaly detection. This is crucial for enhancing clinician performance, regardless of the patient's location. The paper further details a usability study involving healthcare practitioners to ascertain RemoteHealthConnect's efficacy. The System Usability Scale (SUS) assessment yielded a score of 71.5, indicating high usability. This score is significant, positioning our system above the average usability threshold for healthcare technologies, and suggesting it as a valuable tool for remote patient monitoring. Conclusion Our web-based healthcare system and findings from the usability study contribute to the domains of Mobile Health (mHealth) and e-Health by advancing remote monitoring capabilities and offering a promising avenue for healthcare IT to improve patient care and clinician workflow.
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
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".