Impact of COVID-19 on Primary Care Practice Sites and Their Vulnerable Patients.

Purpose: While COVID-19 has upended lives, it has also catalyzed innovation with potential to advance health delivery. Yet, we know little about how the delivery system, and primary care in particular, has responded and how this has impacted vulnerable patients. We aimed to understand the impact of COVID-19 on primary care practice sites and their vulnerable patients and to identify explanations for variation. Approach: We developed and administered a survey to practice managers and physician leaders from 173 primary care practice sites, October-November 2020. We report and graphically depict results from univariate analysis and examine potential explanations for variation in practices' process innovations in response to COVID-19 by assessing bivariate relationships between seven dependent variables and four independent variables. Findings: Among 96 (55.5%) respondents, primary care practice sites on average took more safety (8.5 of 12) than financial (2.5 of 17) precautions in response to COVID-19. Practice sites varied in their efforts to protect patients with vulnerabilities, providing care initially postponed, and experience with virtual visits. Financial risk, practice size, practitioner age, and emergency preparedness explained variation in primary care practices' process innovations. Many practice sites plan to sustain virtual visits, dependent mostly on patient and provider preference and continued reimbursement. Value: While findings indicate rapid and substantial innovation, conditions must enable primary care practice sites to build on and sustain innovations, to support care for vulnerable populations, including those with multiple chronic conditions and socio-economic barriers to health, and to prepare primary care for future emergencies.

Ethical issues in using ambient intelligence in health-care settings.

Ambient intelligence is increasingly finding applications in health-care settings, such as helping to ensure clinician and patient safety by monitoring staff compliance with clinical best practices or relieving staff of burdensome documentation tasks. Ambient intelligence involves using contactless sensors and contact-based wearable devices embedded in health-care settings to collect data (eg, imaging data of physical spaces, audio data, or body temperature), coupled with machine learning algorithms to efficiently and effectively interpret these data. Despite the promise of ambient intelligence to improve quality of care, the continuous collection of large amounts of sensor data in health-care settings presents ethical challenges, particularly in terms of privacy, data management, bias and fairness, and informed consent. Navigating these ethical issues is crucial not only for the success of individual uses, but for acceptance of the field as a whole.