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We developed and evaluated a privacy-preserving One-shot Distributed Algorithm to fit a multicenter Cox proportional hazards model (ODAC) without sharing patient-level information across sites.

The COVID-19 pandemic has led to the rapid expansion of telehealth services as healthcare organizations aim to mitigate community transmission while providing safe patient care. As technology adoption rapidly increases, operational telehealth teams must maintain awareness of critical information, such as patient volumes and wait times, patient and provider experience, and telehealth platform performance. Using a model of situation awareness as a conceptual foundation and a user-centered design approach we [...]

Large observational data networks that leverage routine clinical practice data in electronic health records (EHRs) are critical resources for research on coronavirus disease 2019 (COVID-19). Data normalization is a key challenge for the secondary use of EHRs for COVID-19 research across institutions. In this study, we addressed the challenge of automating the normalization of COVID-19 diagnostic tests, which are critical data elements, but for which controlled terminology terms were published [...]

The purpose of this systematic review is to summarize information on the use of teledentistry in the diagnosis of oral lesions.

Machine learning (ML) diagnostic tools have significant potential to improve health care. However, methodological pitfalls may affect diagnostic test accuracy studies used to appraise such tools. We aimed to evaluate the prevalence and reporting of design characteristics within the literature. Further, we sought to empirically assess whether design features may be associated with different estimates of diagnostic accuracy.

The study sought to create an integrated vocabulary system that addresses the lack of standardized health terminology in gender and sexual orientation.

Responding to the COVID-19 pandemic requires accurate forecasting of health system capacity requirements using readily available inputs. We examined whether testing and hospitalization data could help quantify the anticipated burden on the health system given shelter-in-place (SIP) order.

Prediction of disease phenotypes and their outcomes is a difficult task. In practice, patients routinely seek second opinions from multiple clinical experts for complex disease diagnosis. Our objective is to mimic such a practice of seeking second opinions by training 2 agents with different focuses: the primary agent studies the most recent visit of the patient to learn the current health status, and then the second-opinion agent considers the entire [...]

The screening of healthcare workers for COVID-19 (coronavirus disease 2019) symptoms and exposures prior to every clinical shift is important for preventing nosocomial spread of infection but creates a major logistical challenge. To make the screening process simple and efficient, University of California, San Francisco Health designed and implemented a digital chatbot-based workflow. Within 1 week of forming a team, we conducted a product development sprint and deployed the digital [...]

In December 2019, coronavirus disease 2019 (COVID-19) occurred in Wuhan, China. Online fever clinics were developed by hospitals, largely relieving the hospital's burden. Online fever clinics could help people stay out of crowded hospitals and prevent the risk of cross infections. The objective of our study was to describe the patient characteristics of an online fever clinic and explore the most important concerns and question of online patients.