Remote patient monitoring (RPM) is the use of digital technologies to improve patient care at a distance. However, current RPM solutions are often biased toward tech-savvy patients. To foster health equity, researchers have studied how to address the socio-economic and cognitive needs of diverse patient groups, but their emotional needs have remained largely neglected. We perform the first qualitative study to explore the emotional needs of diverse patients around RPM. Specifically, we conduct a thematic analysis of 18 interviews and 4 focus groups at a large US healthcare organization. We identify emotional needs that lead to four emotional tensions within and across stakeholder groups when applying an equity focus to the design and implementation of RPM technologies. The four emotional tensions are making diverse patients feel: (i) heard vs. exploited; (ii) seen vs. deprioritized for efficiency; (iii) empowered vs. anxious; and (iv) cared for vs. detached from care. To manage these emotional tensions across stakeholders, we develop design recommendations informed by a paradox mindset (i.e., "both-and" rather than "and-or" strategies).

OBJECTIVES: To evaluate demographic biases in diagnostic accuracy and health advice between generative artificial intelligence (AI) (ChatGPT GPT-4) and traditional symptom checkers like WebMD. MATERIALS AND METHODS: Combination symptom and demographic vignettes were developed for 27 most common symptom complaints. Standardized prompts, written from a patient perspective, with varying demographic permutations of age, sex, and race/ethnicity were entered into ChatGPT (GPT-4) between July and August 2023. In total, 3 runs of 540 ChatGPT prompts were compared to the corresponding WebMD Symptom Checker output using a mixed-methods approach. In addition to diagnostic correctness, the associated text generated by ChatGPT was analyzed for readability (using Flesch-Kincaid Grade Level) and qualitative aspects like disclaimers and demographic tailoring. RESULTS: ChatGPT matched WebMD in 91% of diagnoses, with a 24% top diagnosis match rate. Diagnostic accuracy was not significantly different across demographic groups, including age, race/ethnicity, and sex. ChatGPT's urgent care recommendations and demographic tailoring were presented significantly more to 75-year-olds versus 25-year-olds (P < .01) but were not statistically different among race/ethnicity and sex groups. The GPT text was suitable for college students, with no significant demographic variability. DISCUSSION: The use of non-health-tailored generative AI, like ChatGPT, for simple symptom-checking functions provides comparable diagnostic accuracy to commercially available symptom checkers and does not demonstrate significant demographic bias in this setting. The text accompanying differential diagnoses, however, suggests demographic tailoring that could potentially introduce bias. CONCLUSION: These results highlight the need for continued rigorous evaluation of AI-driven medical platforms, focusing on demographic biases to ensure equitable care.

Population health management (PHM) is an important approach to promote wellness and deliver health care to targeted individuals who meet criteria for preventive measures or treatment. A critical component for any PHM program is a data analytics platform that can target those eligible individuals. OBJECTIVE: The aim of this study was to design and implement a scalable standards-based clinical decision support (CDS) approach to identify patient cohorts for PHM and maximize opportunities for multi-site dissemination. MATERIALS AND METHODS: An architecture was established to support bidirectional data exchanges between heterogeneous electronic health record (EHR) data sources, PHM systems, and CDS components. HL7 Fast Healthcare Interoperability Resources and CDS Hooks were used to facilitate interoperability and dissemination. The approach was validated by deploying the platform at multiple sites to identify patients who meet the criteria for genetic evaluation of familial cancer. RESULTS: The Genetic Cancer Risk Detector (GARDE) platform was created and is comprised of four components: (1) an open-source CDS Hooks server for computing patient eligibility for PHM cohorts, (2) an open-source Population Coordinator that processes GARDE requests and communicates results to a PHM system, (3) an EHR Patient Data Repository, and (4) EHR PHM Tools to manage patients and perform outreach functions. Site-specific deployments were performed on onsite virtual machines and cloud-based Amazon Web Services. DISCUSSION: GARDE's component architecture establishes generalizable standards-based methods for computing PHM cohorts. Replicating deployments using one of the established deployment methods requires minimal local customization. Most of the deployment effort was related to obtaining site-specific information technology governance approvals.

OBJECTIVE: Through the coronavirus disease 2019 (COVID-19) pandemic, telemedicine became a necessary entry point into the process of diagnosis, triage, and treatment. Racial and ethnic disparities in healthcare have been well documented in COVID-19 with respect to risk of infection and in-hospital outcomes once admitted, and here we assess disparities in those who access healthcare via telemedicine for COVID-19. MATERIALS AND METHODS: Electronic health record data of patients at New York University Langone Health between March 19th and April 30, 2020 were used to conduct descriptive and multilevel regression analyses with respect to visit type (telemedicine or in-person), suspected COVID diagnosis, and COVID test results. RESULTS: Controlling for individual and community-level attributes, Black patients had 0.6 times the adjusted odds (95% CI: 0.58-0.63) of accessing care through telemedicine compared to white patients, though they are increasingly accessing telemedicine for urgent care, driven by a younger and female population. COVID diagnoses were significantly more likely for Black versus white telemedicine patients. DISCUSSION: There are disparities for Black patients accessing telemedicine, however increased uptake by young, female Black patients. Mean income and decreased mean household size of a zip code were also significantly related to telemedicine use. CONCLUSION: Telemedicine access disparities reflect those in in-person healthcare access. Roots of disparate use are complex and reflect individual, community, and structural factors, including their intersection-many of which are due to systemic racism. Evidence regarding disparities that manifest through telemedicine can be used to inform tool design and systemic efforts to promote digital health equity.

Digital Diabetes Prevention Programs (dDPP) are novel mHealth applications that leverage digital features such as tracking and messaging to support behavior change for diabetes prevention. Despite their clinical effectiveness, long-term engagement to these programs remains a challenge, creating barriers to adherence and meaningful health outcomes. We partnered with a dDPP vendor to develop a personalized automatic message system (PAMS) to promote user engagement to the dDPP platform by sending messages on behalf of their primary care provider. PAMS innovates by integrating into clinical workflows. User-centered design (UCD) methodologies in the form of iterative cycles of focus groups, user interviews, design workshops, and other core UCD activities were utilized to defined PAMS requirements. PAMS uses computational tools to deliver theory-based, automated, tailored messages, and content to support patient use of dDPP. In this article, we discuss the design and development of our system, including key requirements and features, the technical architecture and build, and preliminary user testing.

This study provides data on the feasibility and impact of video-enabled telemedicine use among patients and providers and its impact on urgent and nonurgent healthcare delivery from one large health system (NYU Langone Health) at the epicenter of the coronavirus disease 2019 (COVID-19) outbreak in the United States. Between March 2nd and April 14th 2020, telemedicine visits increased from 102.4 daily to 801.6 daily. (683% increase) in urgent care after the system-wide expansion of virtual urgent care staff in response to COVID-19. Of all virtual visits post expansion, 56.2% and 17.6% urgent and nonurgent visits, respectively, were COVID-19-related. Telemedicine usage was highest by patients 20 to 44 years of age, particularly for urgent care. The COVID-19 pandemic has driven rapid expansion of telemedicine use for urgent care and nonurgent care visits beyond baseline periods. This reflects an important change in telemedicine that other institutions facing the COVID-19 pandemic should anticipate.

Applications in a range of domains, including route planning and well-being, offer advice based on the social information available in prior users' aggregated activity. When designing these applications, is it better to offer: a) advice that if strictly adhered to is more likely to result in an individual successfully achieving their goal, even if fewer users will choose to adopt it? or b) advice that is likely to be adopted by a larger number of users, but which is sub-optimal with regard to any particular individual achieving their goal? We identify this dilemma, characterized as Goal-Directed vs. Adoption-Directed advice, and investigate the design questions it raises through an online experiment undertaken in four advice domains (financial investment, making healthier lifestyle choices, route planning, training for a 5k run), with three user types, and across two levels of uncertainty. We report findings that suggest a preference for advice favoring individual goal attainment over higher user adoption rates, albeit with significant variation across advice domains; and discuss their design implications.