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As part of ongoing implementation of electronic patient-reported outcome tools at the Dana-Farber Cancer Institute, here we describe the development of the electronic New Patient Intake Questionnaire.

[This corrects the article DOI: 10.1093/jamiaopen/ooac012.].

To identify concerns, barriers and facilitators impacting the use of patient portals by older patients as well as desired features in future updates.

Apply and modify the American Essential Clinical Dataset (ECD) approach to optimize the data elements of an electronic nursing admission assessment form in a metropolitan Australian local health district.

Predicting daily trends in the Coronavirus Disease 2019 (COVID-19) case number is important to support individual decisions in taking preventative measures. This study aims to use COVID-19 case number history, demographic characteristics, and social distancing policies both independently/interdependently to predict the daily trend in the rise or fall of county-level cases.

We study the association between payment parity policies and telehealth utilization at community health centers (CHCs) before, during, and after the onset of the pandemic.

Electronic medical records are increasingly used to store patient information in hospitals and other clinical settings. There has been a corresponding proliferation of clinical natural language processing (cNLP) systems aimed at using text data in these records to improve clinical decision-making, in comparison to manual clinician search and clinical judgment alone. However, these systems have delivered marginal practical utility and are rarely deployed into healthcare settings, leading to proposals for [...]

Electronic health record-based clinical decision support (CDS) has the potential to improve health outcomes. This systematic review investigates the design, effectiveness, and economic outcomes of CDS targeting several common chronic diseases.

The predictive modeling literature for biomedical applications is dominated by biostatistical methods for survival analysis, and more recently some out of the box machine learning approaches. In this article, we show a presentation of a machine learning method appropriate for time-to-event modeling in the area of prostate cancer long-term disease progression. Using XGBoost adapted to long-term disease progression, we developed a predictive model for 118 788 patients with localized prostate cancer [...]