Patients Assess an eConsult Model's Acceptability at 5 US Academic Medical Centers.

PURPOSE: Electronic consultation (eConsult), involving asynchronous primary care clinician-to-specialist consultation, is being adopted at a growing number of health systems. Most evaluations of eConsult programs have assessed clinical and financial impacts and clinician acceptability. Less attention has been focused on patients' opinions. We set out to understand patient perspectives and preferences for hypothetical eConsult use at 5 US academic medical centers in the process of adopting an eConsult model. METHODS: We invited adult primary care patients to participate in focus groups. Participants were introduced to the eConsult model, considered its potential benefits and drawbacks, judged the acceptability of a hypothetical copay, and expressed their preferences for future involvement in eConsult decision making and communication. Thematic analysis was used for data interpretation. RESULTS: One focus group was conducted at each of the 5 sites with a total of 52 participants. Focus groups responded positively to the idea of eConsult, with quicker access to specialty care and convenience identified as key benefits. Approval was particularly high among those with a trusted primary care clinician. Preference for involvement in eConsult decision making and communication varied and enthusiasm about eConsult waned when a hypothetical copay was introduced. Concerns included potential misuse of eConsult and exclusion of the patient's illness narrative in the eConsult exchange. CONCLUSIONS: Primary care patients expressed strong support for eConsult, particularly when used by a trusted primary care clinician, in addition to voicing several concerns. Patient involvement in eConsult outreach and education efforts could help to enhance the model's effectiveness and acceptability.

A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine.

Since 2019, the COVID-19 pandemic has had an extremely high impact on all facets of the society and will potentially have an everlasting impact for years to come. In response to this, over the past years, there have been a significant number of research efforts on exploring approaches to combat COVID-19. In this paper, we present a survey of the current research efforts on using mobile Internet of Thing (IoT) devices, Artificial Intelligence (AI), and telemedicine for COVID-19 detection and prediction. We first present the background and then present current research in this field. Specifically, we present the research on COVID-19 monitoring and detection, contact tracing, machine learning based approaches, telemedicine, and security. We finally discuss the challenges and the future work that lay ahead in this field before concluding this paper.