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Equitable distribution of vaccines is necessary to ensure those at highest risk of illness are protected from COVID-19 (coronavirus disease 2019). Unfortunately, there is significant evidence that vaccines have not been reaching the most vulnerable. At our large hospital system, we created interactive online tools to measure and visualize equitability of vaccine administrations and to help stakeholders identify populations at highest risk within state-designated eligible vaccine groups. Using race, ethnicity [...]

The study sought to evaluate the expected clinical utility of automatable prediction models for increasing goals-of-care discussions (GOCDs) among hospitalized patients at the end of life (EOL).

The COVID-19 (coronavirus disease 2019) pandemic has expanded telehealth utilization in unprecedented ways and has important implications for measuring geographic access to healthcare services. Established measures of geographic access to care have focused on the spatial impedance of patients in seeking health care that pertains to specific transportation modes and do not account for the underlying broadband network that supports telemedicine and e-health. To be able to measure the impact [...]

To develop prediction models for intensive care unit (ICU) vs non-ICU level-of-care need within 24 hours of inpatient admission for emergency department (ED) patients using electronic health record data.

Evidence is scarce regarding the safety of long-term drug use, especially for drugs treating chronic diseases. To bridge this knowledge gap, this research investigated the differences in drug exposure between clinical trials and clinical practice.

To assess fairness and bias of a previously validated machine learning opioid misuse classifier.

This study: 1) characterized the app market by EHR app gallery and type of app; 2) tracked changes in the EHR app galleries from the end of 2019 through 2020; and 3) examined how apps connect to EHR data systems, and if the apps support the HL7 FHIR standard.

To determine user and electronic health records (EHR) integration requirements for a scalable remote symptom monitoring intervention for asthma patients and their providers.

In intensive care units (ICUs), a patient's brain function status can shift from a state of acute brain dysfunction (ABD) to one that is ABD-free and vice versa, which is challenging to forecast and, in turn, hampers the allocation of hospital resources. We aim to develop a machine learning model to predict next-day brain function status changes.

The study sought to assess the feasibility of replacing the International Classification of Diseases-Tenth Revision-Clinical Modification (ICD-10-CM) with the International Classification of Diseases-11th Revision (ICD-11) for morbidity coding based on content analysis.