ArticleJMIR human factors2022
Audio Recording Patient-Nurse Verbal Communications in Home Health Care Settings: Pilot Feasibility and Usability Study.
Article in JMIR human factors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- SpeechDETECT: an explainable automated speech processing pipeline for early detection of neurological and health changes.Health information science and systems · 2026Article
- Recruitment and Participation of Black Home Health Care Patients in Speech-Based Cognitive Research: Mixed Methods Feasibility Study.JMIR formative research · 2026Article
- Large Language Model Adaptation Strategies in Speech-Based Cognitive Screening: Systematic Evaluation.JMIR AI · 2026Article
- Developing a Multimodal Screening Algorithm for Mild Cognitive Impairment and Early Dementia in Home Health Care: Protocol for a Cross-Sectional Case-Control Study Using Speech Analysis, Large Language Models, and Electronic Health Records.JMIR research protocols · 2026Article
- Acoustic Analysis of Primary Care Patient-provider Conversations to Screen for Cognitive Impairment.medRxiv : the preprint server for health sciences · 2025Article
- Preprocessing Large-Scale Conversational Datasets: A Framework and Its Application to Behavioral Health Transcripts.JMIR formative research · 2025Article
- Tools to improve discharge equity: Protocol for the pilot TIDE trial.Contemporary clinical trials communications · 2025Article
- Beyond electronic health record data: leveraging natural language processing and machine learning to uncover cognitive insights from patient-nurse verbal communications.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Does synthetic data augmentation improve the performances of machine learning classifiers for identifying health problems in patient-nurse verbal communications in home healthcare settings?Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025Article
- Article
- CUSCO: An Unobtrusive Custom Secure Audio-Visual Recording System for Ambient Assisted Living.Sensors (Basel, Switzerland) · 2024Article
- Utilizing patient-nurse verbal communication in building risk identification models: the missing critical data stream in home healthcare.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Is the patient speaking or the nurse? Automatic speaker type identification in patient-nurse audio recordings.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- ADscreen: A speech processing-based screening system for automatic identification of patients with Alzheimer's disease and related dementia.Artificial intelligence in medicine · 2023Article
- Do nurses document all discussions of patient problems and nursing interventions in the electronic health record? A pilot study in home healthcare.JAMIA open · 2022Article
- Is Auto-generated Transcript of Patient-Nurse Communication Ready to Use for Identifying the Risk for Hospitalizations or Emergency Department Visits in Home Health Care? A Natural Language Processing Pilot Study.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPatients' 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.
objectiveThis 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.
methodsThis 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.
resultsSaramonic 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.
conclusionsThis 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.