Health Advocate, Inc. is a US national health advocacy, patient advocacy and assistance company. The privately held company was founded in 2001 by former Aetna executives and is headquartered in Plymouth Meeting, Pennsylvania, currently run by Teleperformance. The company employs registered nurses, medical directors and benefits specialists who address a range of health care and health insurance issues. Personal Health Advocates can help members locate providers, address errors on medical bills, answer questions about coverage denials and assist with insurance appeals.The company's products include brands called Wellness Advocate, Benefits Gateway+Health Information Dashboards, EAP and Worklife, Pricing Decision Support, Personalized Health Communications, Chronic Care Management, and HR. The company offers a direct-to-consumer advocacy service, called Health Proponent to individuals who are not part of groups..
Long COVID is a novel condition primarily studied in outpatient settings, failing to capture the full spectrum of affected patients, particularly those from disadvantaged populations. Demographic differences in the medical encounter setting of initial long COVID diagnosis suggest disparities in symptom severity, care access, and utilization. To identify demographic factors associated with the encounter setting at initial long COVID diagnosis. Retrospective study utilizing data from the electronic medical record within the largest Midwestern non-profit healthcare system. In total, 8008 patients aged 18+ with initial long COVID diagnosis (ICD-10 U09.9) between March 1, 2020, and October 31, 2023. Demographic factors (age, sex, race/ethnicity, insurance, median household income) and encounter setting at first long COVID diagnosis were extracted. We used multinomial logistic regression to estimate adjusted odds of encounter setting at first diagnosis by demographic subgroup. Patients were most frequently diagnosed with long COVID in an outpatient non-diagnostic encounter (92
More real-world evidence is needed to understand how telemedicine in primary care affects downstream healthcare use. To assess the impact of primary care visit modes on prescription orders and follow-up healthcare use. Retrospective cohort study of adult primary care visits in a large Midwestern healthcare system from January to December 2022. Visits were categorized as in-person, video, or audio-only. Inverse probability of treatment weights (IPTW) was used to balance baseline patient demographic and visit characteristics. In total, 993,029 patients with 2,195,735 primary care visits. Prescription orders and healthcare use within 30 days (follow-up primary care visit, emergency department (ED) visit, and hospital visit). Generalized linear mixed models with IPTW were used to assess associations between visit mode and outcomes. Telemedicine accounted for 3.5
Objectives: Artificial intelligence (AI) offers health systems opportunities to enhance care delivery, improve efficiency, and expand patient access. However, rapid innovation introduces new risks requiring careful oversight. This study examines how diverse stakeholders shaped the design and early evaluation of the Framework for the Appropriate Implementation and Review of AI (FAIR-AI), a system-wide AI governance framework implemented within a large, multi-state health system. Methods: We conducted two rounds of semi-structured interviews-before FAIR-AI development and shortly after FAIR-AI was approved-with executive leaders (N = 5), risk/compliance/legal leaders (N = 11), and data developers (N = 8) to identify initial design needs and evaluate the approved framework. Pre-development interviews also included patients (N = 5) and clinicians (N = 5) to capture AI end-user expectations. Data were analyzed using thematic analysis and inductive and deductive coding methodologies. Results: Pre-development interviews highlighted three central priorities: balancing risk tolerance with potential benefits, ensuring direct human oversight, and streamlining review for low-risk solutions. Patients and clinicians emphasized the need for clinician control over care decisions, with AI serving as supplemental support. Post-approval interviews identified seven elements critical to success: (1) transparent and consistent reviews; (2) timely evaluations; (3) ongoing solution monitoring; (4) iterative framework refinement; (5) alignment with institutional priorities and regulatory standards; (6) multi-modal teammate education; and (7) diverse patient dissemination efforts. Conclusions: Our findings highlight the importance of AI governance frameworks integrating both pre-deployment risk assessment and post-implementation solution monitoring, while remaining adaptable through feedback loops and in response to changing regulatory and technological contexts. This stakeholder-informed approach provides practical guidance for responsible AI at enterprise scale.
The US Centers for Disease Control and Prevention (CDC) has listed vaccination as one the top 10 public health achievements,1 and vaccines have led to a tremendous reduction in deaths due to vaccine-preventable disease in the United States alone.2 There are over 22 million persons employed in healthcare in the United States, making healthcare personnel (HCP) an important population to target for vaccination efforts. Promoting vaccination for HCP as defined by the CDC is likely to become even more essential given the rising incidence in the United States of vaccine-preventable diseases such as measles and pertussis.3.
