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Researchers are increasingly collecting large amounts of deidentified data about individuals to address important health-related challenges and answer fundamental questions. Current US federal regulations permit researchers to use already collected and stored deidentified health-related data from a variety of sources without seeking consent from patients. The objective of this study was to investigate public views on the policies and processes institutions have in place for accessing, using, and sharing of [...]

Clinical workflows require the ability to synthesize and act on existing and emerging patient information. While offering multiple benefits, in many circumstances electronic health records (EHRs) do not adequately support these needs.

COVID-19 mortality forecasting models provide critical information about the trajectory of the pandemic, which is used by policymakers and public health officials to guide decision-making. However, thousands of published COVID-19 mortality forecasts now exist, many with their own unique methods, assumptions, format, and visualization. As a result, it is difficult to compare models and understand under which circumstances a model performs best. Here, we describe the construction and usability of [...]

To estimate the risk of hospital admission and mortality from COVID-19 to patients and measure the association of race and area-level social vulnerability with those outcomes.

Patient-generated health data (PGHD) have not achieved widespread clinical adoption. However, the COVID-induced shift to telemedicine may have created opportunities for PGHD as surrogates for vital signs collected in person. We assessed whether this shift was associated with greater ambulatory care PGHD use. We conducted an interrupted time series analysis of physician enrollment in, and patient-initiated vital sign transmission of non-COVID-associated PGHD through, a national PGHD platform (Validic). Ten health [...]

Substantial strategies to reduce clinical documentation were implemented by health care systems throughout the coronavirus disease-2019 (COVID-19) pandemic at national and local levels. This natural experiment provides an opportunity to study the impact of documentation reduction strategies on documentation burden among clinicians and other health professionals in the United States.

Given widespread excitement around predictive analytics and the proliferation of machine learning algorithms that predict outcomes, a key next step is understanding how this information is-or should be-communicated with patients.

Hypertension is a modifiable risk factor for numerous comorbidities and treating hypertension can greatly improve health outcomes. We sought to increase the efficiency of a virtual hypertension management program through workflow automation processes.

The objective of this study was to determine the feasibility of a computerized clinical decision support (cCDS) tool to facilitate referral to adult healthcare services for children with special healthcare needs. A transition-specific cCDS was implemented as part of standard care in a general pediatrics clinic at a tertiary care academic medical center. The cCDS alerts providers to patients 17-26 years old with 1 or more of 15 diagnoses that may [...]