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 Health Information Technology is increasingly being used to help providers connect patients with community resources to meet health-related social needs (e.g., food, housing, transportation). Research is needed to design efficient, simple, and engaging interfaces during a sensitive process that involves multiple stakeholders. Research is also needed to understand the roles, expectations, barriers, and facilitators these different stakeholders (i.e., patients, providers, and community-based organizations [CBOs]) face during this process.

 Duplicate patient records can increase costs and medical errors. We assessed the association between demographic factors, comorbidities, health care usage, and duplicate electronic health records.

We proposed adopting billing models for secure messaging (SM) telehealth services that move beyond time-based metrics, focusing on the complexity and clinical expertise involved in patient care.

 This research study aims to advance the staging of Parkinson's disease (PD) by incorporating machine learning to assess and include a broader multifunctional spectrum of neurocognitive symptoms in the staging schemes beyond motor-centric assessments. Specifically, we provide a novel framework to modernize and personalize PD staging more objectively by proposing a hybrid feature scoring approach.

To examine changes in technology-related errors (TREs), their manifestations and underlying mechanisms at 3 time points after the implementation of computerized provider order entry (CPOE) in an electronic health record; and evaluate the clinical decision support (CDS) available to mitigate the TREs at 5-years post-CPOE.

The proliferation of electronic health record (EHR) alerts has led to widespread alert fatigue and clinician burnout, undermining the effectiveness of clinical decision support and compromising patient safety.

The COVID pandemic accelerated the adoption of telehealth. Long-term implications for continued telehealth use on a large scale in primary care are still emerging, and the key to this understanding is how telehealth adoption impacts the delivery of health care.

We aim to use large language models (LLMs) to detect mentions of nuanced psychotherapeutic outcomes and impacts than previously considered in transcripts of interviews with adolescent depression. Our clinical authors previously created a novel coding framework containing fine-grained therapy outcomes beyond the binary classification (eg, depression vs control) based on qualitative analysis embedded within a clinical study of depression. Moreover, we seek to demonstrate that embeddings from LLMs are informative [...]

Hypertension (HTN) remains a significant public health concern and the primary modifiable risk factor for cardiovascular disease, which is the leading cause of death in the United States. We applied our validated HTN computable phenotypes within the All of Us Research Program to uncover prevalence and characteristics of HTN and apparent treatment-resistant hypertension (aTRH) in United States.

Research participants value learning how their data contributions are advancing health research (ie, data stories). The All of Us Research Program gathered insights from program staff to learn what research topics they think are of interest to participants, what support staff need to communicate data stories, and how staff use data story dissemination tools.