Audio Recording Patient-Nurse Verbal Communications in Home Health Care Settings: Pilot Feasibility and Usability Study
Bibliographic record
Abstract
BACKGROUND: Patients' spontaneous speech can act as a biomarker for identifying pathological entities, such as mental illness. Despite this potential, audio recording patients' spontaneous speech is not part of clinical workflows, and health care organizations often do not have dedicated policies regarding the audio recording of clinical encounters. No previous studies have investigated the best practical approach for integrating audio recording of patient-clinician encounters into clinical workflows, particularly in the home health care (HHC) setting. OBJECTIVE: This study aimed to evaluate the functionality and usability of several audio-recording devices for the audio recording of patient-nurse verbal communications in the HHC settings and elicit HHC stakeholder (patients and nurses) perspectives about the facilitators of and barriers to integrating audio recordings into clinical workflows. METHODS: This study was conducted at a large urban HHC agency located in New York, United States. We evaluated the usability and functionality of 7 audio-recording devices in a laboratory (controlled) setting. A total of 3 devices-Saramonic Blink500, Sony ICD-TX6, and Black Vox 365-were further evaluated in a clinical setting (patients' homes) by HHC nurses who completed the System Usability Scale questionnaire and participated in a short, structured interview to elicit feedback about each device. We also evaluated the accuracy of the automatic transcription of audio-recorded encounters for the 3 devices using the Amazon Web Service Transcribe. Word error rate was used to measure the accuracy of automated speech transcription. To understand the facilitators of and barriers to integrating audio recording of encounters into clinical workflows, we conducted semistructured interviews with 3 HHC nurses and 10 HHC patients. Thematic analysis was used to analyze the transcribed interviews. RESULTS: Saramonic Blink500 received the best overall evaluation score. The System Usability Scale score and word error rate for Saramonic Blink500 were 65% and 26%, respectively, and nurses found it easier to approach patients using this device than with the other 2 devices. Overall, patients found the process of audio recording to be satisfactory and convenient, with minimal impact on their communication with nurses. Although, in general, nurses also found the process easy to learn and satisfactory, they suggested that the audio recording of HHC encounters can affect their communication patterns. In addition, nurses were not aware of the potential to use audio-recorded encounters to improve health care services. Nurses also indicated that they would need to involve their managers to determine how audio recordings could be integrated into their clinical workflows and for any ongoing use of audio recordings during patient care management. CONCLUSIONS: This study established the feasibility of audio recording HHC patient-nurse encounters. Training HHC nurses about the importance of the audio-recording process and the support of clinical managers are essential factors for successful implementation.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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, 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".