Frontiers in Digital Health

Wearable devices can collect changes in human behaviors related to mental health including depression and anxiety. Here, we examined whether and how digital metrics from a consumer-grade wearable smart ring (Oura Ring) differed by severity of depression and anxiety symptoms using data from a large-scale population-based sample of young adults (n=1,290, age range: 33-35). Participants wore the ring for two weeks, assessing sleep architecture, nocturnal heart rate (HR), heart rate variability (HRV...

ImportanceClinician adoption and adaptation of new tools evolve over time. Prior studies of ambient Artificial intelligence (AI) scribes have primarily relied on single time-point measurements (e.g., pre-post), potentially obfuscating their true impact on outcomes. ObjectiveTo investigate longitudinal effects of an AI scribe tool on patient encounter-level outcomes. DesignCase series across 48 weeks (24 pre, 24 post) per clinician. SettingPrimary care clinical encounters occurring between 01/...

Community health workers (CHWs) in low-resource settings deliver variable-quality care. This study used OpenAIs o3 and Googles Gemini Flash 2.5 to evaluate whether large language models (LLMs) listening to CHW-patient interactions could generate accurate referral decisions. Across 150 participating Rwandan CHWs, 429 encounters were recorded (in Kinyarwanda) and then processed by LLMs. CHWs demonstrated high referral accuracy (97.9% [95% CI: 96.1%-98.9%]), and OpenAIs o3 performed similarly to C...

BackgroundArtificial intelligence is increasingly embedded in healthcare delivery. Its legitimacy depends on institutional governance, not technical performance alone. Prior research has centered on clinicians and patients. Less attention has been given to cybersecurity professionals who sustain the digital infrastructures that support health AI. This study examines how cybersecurity professionals conceptualize AI as clinical infrastructure and how these interpretations shape understandings of t...

Structured AbstractO_ST_ABSObjectiveC_ST_ABSAmbient artificial intelligence (AI) tools are increasingly adopted in clinical practices. This study investigated whether and how clinicians edit AI-generated drafts and the linguistic differences between AI drafts and clinician-finalized notes. Materials and MethodsThis retrospective study analyzed real-world data from ambulatory clinics at a large academic health system spanning two vendor deployments. We quantified clinicians editing behavior usin...

BackgroundAssistants incorporating large language models are increasingly applied in the context of health care, where they represent a promising means of expanding access to care. However, there is growing recognition of the risks that these chatbots may fail to respond appropriately to individuals in crisis, and may adversely affect mental health in some circumstances. MethodsWe developed and implemented an automated system for assessing voice or text AI assistant response to users across a r...

BackgroundEHR documentation and chart review contribute to clinician workload and burnout. To alleviate pre-charting burden, Epic has released a new generative AI chart summarizer tool, which has become widely adopted; however, its impact has not been examined in randomized trials. ObjectiveTo evaluate whether access to an Epic generative AI chart summarization tool reduces cognitive task load among ambulatory providers compared with usual care. MethodsTwo-arm, parallel-group randomized contro...

Ambient intelligence-based systems are increasingly used for clinical documentation. To quantify linguistic differences associated with ambient documentation, we conducted a matched pre-post analysis of 6,026 outpatient clinical notes from Mass General Brigham following implementation of two ambient AI documentation systems (Nuance Dragon Ambient eXperience [DAX] and Abridge). Within-clinician comparisons focused on the History of Present Illness (HPI) and Assessment and Plan (A&P) sections and ...

BackgroundGenerative artificial intelligence (GenAI) in healthcare may reduce administrative burden and enhance quality of care. Large language models (LLMs) can generate draft responses to patient messages using electronic health record (EHR) data. This could mitigate increased workload related to high message volumes. While effectiveness and feasibility of these GenAI tools have been studied in the United States, evidence from non-English contexts is scarce, particularly regarding user experie...

Structured AbstractO_ST_ABSObjectiveC_ST_ABSThe use of ambient AI documentation tools is rapidly growing in US hospitals and clinics. Such tools generate the first draft of clinical notes from scribed patient-provider conversations, which clinicians can then review and edit before signing into electronic health records (EHR). Understanding how and why clinicians make modifications to AI-generated drafts is critical to improving AI design and clinical efficiency, yet it has been under-studied. Th...

ImportanceArtificial Intelligence (AI) voice applications have the potential to address the unmet treatment needs among patients with depression and anxiety, but their therapeutic utility is largely unknown. ObjectiveTo investigate the efficacy and mechanisms of an AI-driven voice-based coach, Lumen, delivering problem-solving treatment (PST) for patients with untreated, moderate depression and/or anxiety. DesignPhase 2, 3-arm randomized clinical trial. SettingA public university and affiliat...

Eye tracking is recognized as a gold standard for measuring visual attention and cognitive engagement. In this study, it offers a useful lens for understanding how primary care providers balance patient communication with navigation of electronic health records (EHRs). We used wearable eye tracking to collect visual information processing behavior and conducted a retrospective think-aloud protocol to examine how primary care clinicians processed suiciderelated information (CAT-MH(R)) embedded in...

ObjectiveAmbient artificial intelligence (AI) documentation is increasingly used to draft clinical notes from patient-provider conversations, but how clinicians revise and finalize these drafts is not well understood. This qualitative content analysis study characterizes real-world edits to AI-generated drafts and identifies opportunities for improvement of AI design and the implementation process. Materials and MethodsEight coders analyzed clinical documentation generated by ambient AI from 20...

ObjectiveEffective management of Major Depressive Disorder (MDD) is limited by reliance on episodic, subjective clinical scales. Passive digital phenotyping offers a potential solution for continuous, objective monitoring. We aimed to assess the concurrent validity of a novel digital biomarker--the Facial Affect Dynamics-derived Depression Severity (FADS) score--against the Patient Health Questionnaire-9 (PHQ-9). MethodsWe conducted an interim analysis of the EMC2FR study (NCT06860165), a prosp...

ImportanceHigh-quality discharge summaries are essential for safe care transitions but contribute substantially to clinician documentation burden and burnout. While retrospective studies suggest large language models (LLMs) can generate clinical summaries of comparable quality to physicians, prospective data on their safety, utility, and impact on clinician well-being in real-world environments are lacking. ObjectiveTo evaluate the safety, utilization, and impact on clinician burden of MedAgent...

BackgroundTyping in the electronic health record (EHR) takes up healthcare providers time and cognitive space and constitutes a substantial administrative burden contributing to high burnout rates in healthcare. Ambient digital scribes may improve this problem. ObjectiveTo investigate the effect of the use of Autoscriber, an ambient digital scribe, on healthcare providers administrative workload and the quality of medical notes in the EHR. MethodsA study period of 26 weeks was randomized into ...

This study evaluated Long Short-Term Memory (LSTM) and Transformer artificial intelligence (AI) models for recognizing Activities of Daily Living (ADLs) using data collected from a low-cost, non-invasive ambient in-home sensor system. Motion, temperature, luminance, and door-contact sensors were deployed in a two-participant home for 22 days, with ground truth established through volunteer logs and expert validation. Missing data were handled using Akima and linear interpolation. Models were tra...

BackgroundHealthcare utilization forecasting systems are often derived from static, annualized market share assumptions that fail to represent real-world treatment dynamics. Such approaches systematically misestimate future utilization by ignoring longitudinal treatment sequencing, discontinuation with surveillance, recurrence-driven re-entry, and provider adoption dynamics. ObjectiveThis study proposes a reusable, governance-driven health informatics forecasting framework designed to generate ...

BackgroundLarge language models (LLMs) are increasingly piloted as chat interfaces for chart review and clinical decision support. Although leading models achieve and even exceed physician-level accuracy on exam-style benchmarks such as MedQA, recent perturbation studies show large drops in accuracy after small changes to prompts, distractor content, or answer format. Prior work has not systematically examined how these vulnerabilities unintentionally manifest in clinically realistic settings, i...

ObjectiveTo develop and content-validate a brief, expert-informed Survey for Human-AI Performance Evaluation (SHAPE-AI) for near-real-time assessment of how clinical AI affects human performance. BackgroundAI-enabled clinical decision support can improve outcomes only when aligned with clinician workflows, and cognitive demands. Existing evaluations measure technical performance and adoption, providing limited assessment of how AI shapes human performance. There is a lack of concise, operationa...