BACKGROUND: Mild cognitive impairment and early-stage dementia significantly impact healthcare utilization and costs, yet more than half of affected patients remain underdiagnosed. This study leverages audio-recorded patient-nurse verbal communication in home healthcare settings to develop an artificial intelligence-based screening tool for early detection of cognitive decline. OBJECTIVE: To develop a speech processing algorithm using routine patient-nurse verbal communication and evaluate its performance when combined with electronic health record (EHR) data in detecting early signs of cognitive decline. METHOD: We analyzed 125 audio-recorded patient-nurse verbal communication for 47 patients from a major home healthcare agency in New York City. Out of 47 patients, 19 experienced symptoms associated with the onset of cognitive decline. A natural language processing algorithm was developed to extract domain-specific linguistic and interaction features from these recordings. The algorithm's performance was compared against EHR-based screening methods. Both standalone and combined data approaches were assessed using F1-score and area under the curve (AUC) metrics. RESULTS: The initial model using only patient-nurse verbal communication achieved an F1-score of 85 and an AUC of 86.47. The model based on EHR data achieved an F1-score of 75.56 and an AUC of 79. Combining patient-nurse verbal communication with EHR data yielded the highest performance, with an F1-score of 88.89 and an AUC of 90.23. Key linguistic indicators of cognitive decline included reduced linguistic diversity, grammatical challenges, repetition, and altered speech patterns. Incorporating audio data significantly enhanced the risk prediction models for hospitalization and emergency department visits. DISCUSSION: Routine verbal communication between patients and nurses contains critical linguistic and interactional indicators for identifying cognitive impairment. Integrating audio-recorded patient-nurse communication with EHR data provides a more comprehensive and accurate method for early detection of cognitive decline, potentially improving patient outcomes through timely interventions. This combined approach could revolutionize cognitive impairment screening in home healthcare settings.

BACKGROUND: In the United States, over 12 000 home healthcare agencies annually serve 6+ million patients, mostly aged 65+ years with chronic conditions. One in three of these patients end up visiting emergency department (ED) or being hospitalized. Existing risk identification models based on electronic health record (EHR) data have suboptimal performance in detecting these high-risk patients. OBJECTIVES: To measure the added value of integrating audio-recorded home healthcare patient-nurse verbal communication into a risk identification model built on home healthcare EHR data and clinical notes. METHODS: This pilot study was conducted at one of the largest not-for-profit home healthcare agencies in the United States. We audio-recorded 126 patient-nurse encounters for 47 patients, out of which 8 patients experienced ED visits and hospitalization. The risk model was developed and tested iteratively using: (1) structured data from the Outcome and Assessment Information Set, (2) clinical notes, and (3) verbal communication features. We used various natural language processing methods to model the communication between patients and nurses. RESULTS: Using a Support Vector Machine classifier, trained on the most informative features from OASIS, clinical notes, and verbal communication, we achieved an AUC-ROC = 99.68 and an F1-score = 94.12. By integrating verbal communication into the risk models, the F-1 score improved by 26%. The analysis revealed patients at high risk tended to interact more with risk-associated cues, exhibit more "sadness" and "anxiety," and have extended periods of silence during conversation. CONCLUSION: This innovative study underscores the immense value of incorporating patient-nurse verbal communication in enhancing risk prediction models for hospitalizations and ED visits, suggesting the need for an evolved clinical workflow that integrates routine patient-nurse verbal communication recording into the medical record.

OBJECTIVES: Patient-clinician communication provides valuable explicit and implicit information that may indicate adverse medical conditions and outcomes. However, practical and analytical approaches for audio-recording and analyzing this data stream remain underexplored. This study aimed to 1) analyze patients' and nurses' speech in audio-recorded verbal communication, and 2) develop machine learning (ML) classifiers to effectively differentiate between patient and nurse language. MATERIALS AND METHODS: Pilot studies were conducted at VNS Health, the largest not-for-profit home healthcare agency in the United States, to optimize audio-recording patient-nurse interactions. We recorded and transcribed 46 interactions, resulting in 3494 "utterances" that were annotated to identify the speaker. We employed natural language processing techniques to generate linguistic features and built various ML classifiers to distinguish between patient and nurse language at both individual and encounter levels. RESULTS: A support vector machine classifier trained on selected linguistic features from term frequency-inverse document frequency, Linguistic Inquiry and Word Count, Word2Vec, and Medical Concepts in the Unified Medical Language System achieved the highest performance with an AUC-ROC = 99.01 ± 1.97 and an F1-score = 96.82 ± 4.1. The analysis revealed patients' tendency to use informal language and keywords related to "religion," "home," and "money," while nurses utilized more complex sentences focusing on health-related matters and medical issues and were more likely to ask questions. CONCLUSION: The methods and analytical approach we developed to differentiate patient and nurse language is an important precursor for downstream tasks that aim to analyze patient speech to identify patients at risk of disease and negative health outcomes.