Department of Emergency Medicine (UCI)

The objective of this study was to investigate the state of the interoperable mobile health application (app) ecosystem at the start of the 21st Century Cures Act to understand the opportunities currently available to patients for accessing and using their computable clinical data. Thus, we sought to identify third-party apps in the Apple App and Google Play Stores that seem to be capable of automatically downloading clinical data via a FHIR-based application programming interface through a targeted review of health apps. We found that few of the apps in this review have this capability (1.4% of 1,272 iOS apps and 0.6% of 1,449 Android apps). Ultimately, our results suggest that this is a nascent marketspace. If barriers to app development are not identified and addressed, and efforts are not made to educate patients and improve discoverability of apps, it could mean that patients will not benefit from these interoperability measures.

Objective: In order to manage COVID-19 patient population and bed capacity issues, remote patient monitoring (RPM) is a strategy used to transition patients from inpatients to home. We describe our RPM implementation process for post-acute care COVID-19 pneumonia patients. We also evaluate the impact of RPM on patient outcomes, including hospital length of stay (LOS), post-discharge Emergency Department (ED) visits, and hospital readmission. Materials and Methods: We utilized a cloud-based RPM platform (Vivify Health) and a nurse-monitoring service (Global Medical Response) to enroll COVID-19 patients who required oxygen supplementation after hospital discharge. We evaluated patient participation, biometric alerts, and provider communication. We also assessed the program's impact by comparing RPM patient outcomes with a retrospective cohort of Control patients who similarly required oxygen supplementation after discharge but were not referred to the RPM program. Statistical analyses were performed to evaluate the 2 groups' demographic characteristics, hospital LOS, and readmission rates. Results: The RPM program enrolled 75 patients with respondents of a post-participation survey reporting high satisfaction with the program. Compared to the Control group (n = 150), which had similar demographics and baseline characteristics, the RPM group was associated with shorter hospital LOS (median 4.8 vs 6.1 days; P=.03) without adversely impacting return to the ED or readmission. Conclusion: We implemented a RPM program for post-acute discharged COVID-19 patients requiring oxygen supplementation. Our RPM program resulted in a shorter hospital LOS without adversely impacting quality outcomes for readmission rates and improved healthcare utilization by reducing the average LOS.

OBJECTIVE: Facial masks are an essential personal protective measure to fight the COVID-19 (coronavirus disease) pandemic. However, the mask adoption rate in the United States is still less than optimal. This study aims to understand the beliefs held by individuals who oppose the use of facial masks, and the evidence that they use to support these beliefs, to inform the development of targeted public health communication strategies. MATERIALS AND METHODS: We analyzed a total of 771 268 U.S.-based tweets between January to October 2020. We developed machine learning classifiers to identify and categorize relevant tweets, followed by a qualitative content analysis of a subset of the tweets to understand the rationale of those opposed mask wearing. RESULTS: We identified 267 152 tweets that contained personal opinions about wearing facial masks to prevent the spread of COVID-19. While the majority of the tweets supported mask wearing, the proportion of anti-mask tweets stayed constant at about a 10% level throughout the study period. Common reasons for opposition included physical discomfort and negative effects, lack of effectiveness, and being unnecessary or inappropriate for certain people or under certain circumstances. The opposing tweets were significantly less likely to cite external sources of information such as public health agencies' websites to support the arguments. CONCLUSIONS: Combining machine learning and qualitative content analysis is an effective strategy for identifying public attitudes toward mask wearing and the reasons for opposition. The results may inform better communication strategies to improve the public perception of wearing masks and, in particular, to specifically address common anti-mask beliefs.