Exploring Contactless Vital Signs Collection in Video Telehealth Visits Among Veterans Affairs Providers and Patients: Pilot Usability Study
Bibliographic record
Abstract
BACKGROUND: To expand veterans' access to health care, the Veterans Affairs (VA) Office of Connected Care explored a novel software feature called "Vitals" on its VA Video Connect telehealth platform. Vitals uses contactless, video-based, remote photoplethysmography (rPPG) through the infrared camera on veterans' smartphones (and other devices) to automatically scan their faces to provide real-time vital statistics on screen to both the provider and patient. OBJECTIVE: This study aimed to assess VA clinical provider and veteran patient attitudes regarding the usability of Vitals. METHODS: We conducted a mixed methods evaluation of Vitals among VA providers and patients, collecting data in July and August 2023 at the VA Boston Healthcare System and VA San Diego Healthcare System. We conducted analyses in October 2023. In-person usability testing sessions consisted of a think-aloud procedure while using the software, a semistructured interview, and a 26-item web-based survey. RESULTS: Usability test sessions with 20 VA providers and 13 patients demonstrated that both groups found Vitals "useful" and "easy to use," and they rated its usability highly (86 and 82 points, respectively, on a 100-point scale). Regarding acceptability or willingness/intent to use, providers and patients generally expressed confidence and trust in Vitals readings, with high ratings of 90 and 85 points, respectively. Providers and patients rated Vitals highly for its feasibility and appropriateness for context (90 and 90 points, respectively). Finally, providers noted that Vitals' flexibility makes it appropriate and advantageous for implementation in a wide range of clinical contexts, particularly in specialty care. Providers believed that most clinical teams would readily integrate Vitals into their routine workflow because it saves time; delivers accurate, consistently collected vitals; and may reduce reporting errors. Providers and veterans suggested training and support materials that could improve Vitals adoption and implementation. CONCLUSIONS: While remote collection of vital readings has been described in the literature, this is one of the first accounts of testing a contactless vital signs measurement tool among providers and patients. If ongoing initiatives demonstrate accuracy in its readings, Vitals could enhance telemedicine by providing accurate and automatic reporting and recording of vitals; sending patients' vital readings (pending provider approval) directly to their electronic medical record; saving provider and patient time; and potentially reducing necessity of some home-based biometric devices. Understanding usability issues before US Food and Drug Administration approval of Vitals and its implementation could contribute to a seamless introduction of Vitals to VA providers and patients.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".