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Participation in healthcare research shapes health policy and practice; however, low trust is a barrier to participation. We evaluated whether returning health information (information transparency) and disclosing intent of data use (intent transparency) impacts trust in research.

Artificial intelligence/machine learning models are being rapidly developed and used in clinical practice. However, many models are deployed without a clear understanding of clinical or operational impact and frequently lack monitoring plans that can detect potential safety signals. There is a lack of consensus in establishing governance to deploy, pilot, and monitor algorithms within operational healthcare delivery workflows. Here, we describe a governance framework that combines current regulatory best practices [...]

Hospitals have multiple methods available to engage in health information exchange (HIE); however, it is not well understood whether these methods are complements or substitutes. We sought to characterize patterns of adoption of HIE methods and examine the association between these methods and increased availability and use of patient information.

This case study assesses the uptake, user characteristics, and outcomes of automated self-scheduling in a community-based physician group affiliated with an academic health system. We analyzed 1 995 909 appointments booked between January 1, 2019, and June 30, 2021 at more than 30 practice sites. Over the study period, uptake of self-scheduling increased from 4% to 15% of kept appointments. Younger, commercially insured patients were more likely to be users. Missed appointments [...]

The Rapid Acceleration of Diagnostics-Underserved Populations (RADx-UP) program is a consortium of community-engaged research projects with the goal of increasing access to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) tests in underserved populations. To accelerate clinical research, common data elements (CDEs) were selected and refined to standardize data collection and enhance cross-consortium analysis.

Methods to correct class imbalance (imbalance between the frequency of outcome events and nonevents) are receiving increasing interest for developing prediction models. We examined the effect of imbalance correction on the performance of logistic regression models.

Electronic consultation (eConsult) content reflects important information about referring clinician needs across an organization, but is challenging to extract. The objective of this work was to develop machine learning models for classifying eConsult questions for question type and question content. Another objective of this work was to investigate the ability to solve this task with constrained expert time resources.

Patients in the intensive care unit (ICU) are often in critical condition and have a high mortality rate. Accurately predicting the survival probability of ICU patients is beneficial to timely care and prioritizing medical resources to improve the overall patient population survival. Models developed by deep learning (DL) algorithms show good performance on many models. However, few DL algorithms have been validated in the dimension of survival time or compared [...]

Cardiac surgery patients are at high risk for readmissions after hospital discharge- few of these readmissions are preventable by mitigating barriers underlying discharge care transitions. An in-depth evaluation of the nuances underpinning the discharge process and the use of tools to support the process, along with insights on patient and clinician experiences, can inform the design of evidence-based strategies to reduce preventable readmissions.