Search for: All records

The COVID-19 pandemic has boosted digital health utilization, raising concerns about increased physicians’ after-hours clinical work (work-outside-work”). The surge in patients’ digital messages and additional time spent on work-outside-work by telemedicine providers underscores the need to evaluate the connection between digital health utilization and physicians’ after-hours commitments. We examined the impact on physicians’ workload from two types of digital demands - patients’ messages requesting medical advice (PMARs) sent to physicians’ inbox (inbasket), and telemedicine. Our study included 1716 ambulatory-care physicians in New York City regularly practicing between November 2022 and March 2023. Regression analyses assessed primary and interaction effects of (PMARs) and telemedicine on work-outside-work. The study revealed a significant effect ofPMARs on physicians’ work-outside-work and that this relationship is moderated by physicians’ specialties. Non-primary care physicians or specialists experienced a more pronounced effect than their primary care peers. Analysis of their telemedicine load revealed that primary care physicians received fewerPMARs and spent less time in work-outside-work with more telemedicine. Specialists faced increasedPMARs and did more work-outside-work as telemedicine visits increased which could be due to the difference in patient panels. ReducingPMARvolumes and efficient inbasket management strategies needed to reduce physicians’ work-outside-work. Policymakers need to be cognizant of potential disruptions in physicians carefully balanced workload caused by the digital health services.

 

Open data programs have become increasingly established at national and local levels of government. While the degree of success these programs have had in achieving their objectives remains open to question, one factor that has been identified as important to any success is the role of open data intermediaries, individuals and organizations that help others to make use of open data. In this paper we investigate how people become engaged with open data, what their motivations are, and the barriers and facilitators program participants perceive with regard to using open data effectively. We interview participants from a variety of backgrounds with differing levels of experience and engagement with open data. Participants include students learning how to train others in open data techniques and tools; people who attend open data events and use open data for commercial or social benefit; and representatives from local government, municipal agencies and a civic tech non-profit. We identify pathways to successfully developing and nurturing a community of open data intermediaries, and make five recommendations for organizations planning and managing open data programs.

 

Abstract Increasingly, laws are being proposed and passed by governments around the world to regulate artificial intelligence (AI) systems implemented into the public and private sectors. Many of these regulations address the transparency of AI systems, and related citizen-aware issues like allowing individuals to have the right to an explanation about how an AI system makes a decision that impacts them. Yet, almost all AI governance documents to date have a significant drawback: they have focused on what to do (or what not to do) with respect to making AI systems transparent, but have left the brunt of the work to technologists to figure out how to build transparent systems. We fill this gap by proposing a stakeholder-first approach that assists technologists in designing transparent, regulatory-compliant systems. We also describe a real-world case study that illustrates how this approach can be used in practice. 

Background Chatbots are being piloted to draft responses to patient questions, but patients’ ability to distinguish between provider and chatbot responses and patients’ trust in chatbots’ functions are not well established. Objective This study aimed to assess the feasibility of using ChatGPT (Chat Generative Pre-trained Transformer) or a similar artificial intelligence–based chatbot for patient-provider communication. Methods A survey study was conducted in January 2023. Ten representative, nonadministrative patient-provider interactions were extracted from the electronic health record. Patients’ questions were entered into ChatGPT with a request for the chatbot to respond using approximately the same word count as the human provider’s response. In the survey, each patient question was followed by a provider- or ChatGPT-generated response. Participants were informed that 5 responses were provider generated and 5 were chatbot generated. Participants were asked—and incentivized financially—to correctly identify the response source. Participants were also asked about their trust in chatbots’ functions in patient-provider communication, using a Likert scale from 1-5. Results A US-representative sample of 430 study participants aged 18 and older were recruited on Prolific, a crowdsourcing platform for academic studies. In all, 426 participants filled out the full survey. After removing participants who spent less than 3 minutes on the survey, 392 respondents remained. Overall, 53.3% (209/392) of respondents analyzed were women, and the average age was 47.1 (range 18-91) years. The correct classification of responses ranged between 49% (192/392) to 85.7% (336/392) for different questions. On average, chatbot responses were identified correctly in 65.5% (1284/1960) of the cases, and human provider responses were identified correctly in 65.1% (1276/1960) of the cases. On average, responses toward patients’ trust in chatbots’ functions were weakly positive (mean Likert score 3.4 out of 5), with lower trust as the health-related complexity of the task in the questions increased. Conclusions ChatGPT responses to patient questions were weakly distinguishable from provider responses. Laypeople appear to trust the use of chatbots to answer lower-risk health questions. It is important to continue studying patient-chatbot interaction as chatbots move from administrative to more clinical roles in health care. 

This paper investigates the tools and practices used by Orientation and Mobility (O&M) specialists in instructing people who are blind or have low vision in concepts, skills, and techniques for safe and independent travel. Based on interviews with experienced instructors who practice in different O&M settings we find that a shortage of qualified specialists and restrictions on in-person activities during COVID-19 has accelerated interest in remote instruction and assessment, while widespread adoption of smartphones with accessibility support has driven interest in assistive apps. This presents both opportunities and challenges for a practice that is traditionally conducted in-person and assessed through qualitative observations. In response we identify multiple opportunities for HCI research in service of O&M, including: supporting a 'physician's assistant' model of remote O&M instruction and assessment, matching O&M instructors' clients with guide dogs, highlighting clients' progress towards O&M goals, and collaboratively planning routes and monitoring clients' independent travel progress. 

Background Remote patient monitoring (RPM) technologies can support patients living with chronic conditions through self-monitoring of physiological measures and enhance clinicians’ diagnostic and treatment decisions. However, to date, large-scale pragmatic RPM implementation within health systems has been limited, and understanding of the impacts of RPM technologies on clinical workflows and care experience is lacking. Objective In this study, we evaluate the early implementation of operational RPM initiatives for chronic disease management within the ambulatory network of an academic medical center in New York City, focusing on the experiences of “early adopter” clinicians and patients. Methods Using a multimethod qualitative approach, we conducted (1) interviews with 13 clinicians across 9 specialties considered as early adopters and supporters of RPM and (2) speculative design sessions exploring the future of RPM in clinical care with 21 patients and patient representatives, to better understand experiences, preferences, and expectations of pragmatic RPM use for health care delivery. Results We identified themes relevant to RPM implementation within the following areas: (1) data collection and practices, including impacts of taking real-world measures and issues of data sharing, security, and privacy; (2) proactive and preventive care, including proactive and preventive monitoring, and proactive interventions and support; and (3) health disparities and equity, including tailored and flexible care and implicit bias. We also identified evidence for mitigation and support to address challenges in each of these areas. Conclusions This study highlights the unique contexts, perceptions, and challenges regarding the deployment of RPM in clinical practice, including its potential implications for clinical workflows and work experiences. Based on these findings, we offer implementation and design recommendations for health systems interested in deploying RPM-enabled health care. 

This article discusses novel research methods used to examine how Augmented Reality (AR) can be utilized to present “omic” (i.e., genomes, microbiomes, pathogens, allergens) information to non-expert users. While existing research shows the potential of AR as a tool for personal health, methodological challenges pose a barrier to the ways in which AR research can be conducted. There is a growing need for new evaluation methods for AR systems, especially as remote testing becomes increasingly popular. In this article, we present two AR studies adapted for remote research environments in the context of personal health. The first study ( n = 355) is a non-moderated remote study conducted using an AR web application to explore the effect of layering abstracted pathogens and mitigative behaviors on a user, on perceived risk perceptions, negative affect, and behavioral intentions. This study introduces methods that address participant precursor requirements, diversity of platforms for delivering the AR intervention, unsupervised setups, and verification of participation as instructed. The second study ( n = 9) presents the design and moderated remote evaluation of a technology probe, a prototype of a novel AR tool that overlays simulated timely and actionable environmental omic data in participants' living environment, which helps users to contextualize and make sense of the data. Overall, the two studies contribute to the understanding of investigating AR as a tool for health behavior and interventions for remote, at-home, empirical studies.