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High-risk pregnancy (HRP) conditions such as gestational diabetes mellitus (GDM), hypertension (HTN), and peripartum depression (PPD) affect maternal and neonatal health. Patient engagement is critical for effective HRP management (HRPM). While digital technologies and analytics hold promise, emerging research indicates limited and suboptimal support offered by the highly prevalent pregnancy digital solutions within the commercial marketplace. In this article, we describe our efforts to develop a portfolio of digital products [...]

In retrospective secondary data analysis studies, researchers often seek waiver of consent from institutional Review Boards (IRB) and minimize risk by utilizing complex software. Yet, little is known about the perspectives of IRB experts on these approaches. To facilitate effective communication about risk mitigation strategies using software, we conducted two studies with IRB experts to co-create appropriate language when describing a software to IRBs.

The Observational Health Data Sciences and Informatics (OHDSI) community has emerged as a leader in observational research on real-world clinical data for promoting evidence for healthcare and decision-making. The community has seen rapid growth in publications, citations, and the number of authors. Components of its successful uptake have been attributed to an open science and collaborative culture for research and development. Investigating the adoption of OHDSI as a field of [...]

To use natural language processing (NLP) of clinical notes to augment existing structured electronic health record (EHR) data for classification of a patient's menopausal status.

In the United States, end-stage kidney disease (ESKD) is responsible for high mortality and significant healthcare costs, with the number of cases sharply increasing in the past 2 decades. In this study, we aimed to reduce these impacts by developing an ESKD model for predicting its occurrence in a 2-year period.

During the COVID-19 pandemic, federal and state health policies allowed temporary flexibilities for Medicare and Medicaid beneficiaries, leading to a sharp increase in telemedicine use. However, many of the flexibilities that enabled innovation and growth in telemedicine continue temporarily since the federal emergency declaration ended in May 2023, and the United States has not made permanent decisions about telemedicine policy. Analysts have raised concerns about increased spending, program integrity, safety [...]

Despite the importance of using information for ovarian cancer (OvCa) disease management and decision-making, some women with OvCa do not actively seek out information. The purpose of this study is to investigate factors that influence information seeking behaviors and information avoidance behaviors and information resources among women with OvCa and their caregivers.

Partially observed confounder data pose a major challenge in statistical analyses aimed to inform causal inference using electronic health records (EHRs). While analytic approaches such as imputation are available, assumptions on underlying missingness patterns and mechanisms must be verified. We aimed to develop a toolkit to streamline missing data diagnostics to guide choice of analytic approaches based on meeting necessary assumptions.

To design a novel artificial intelligence-based software platform that allows users to analyze text data by identifying various coherent topics and parts of the data related to a specific research theme-of-interest (TOI).

Existing research on social determinants of health (SDoH) predominantly focuses on physician notes and structured data within electronic medical records. This study posits that social work notes are an untapped, potentially rich source for SDoH information. We hypothesize that clinical notes recorded by social workers, whose role is to ameliorate social and economic factors, might provide a complementary information source of data on SDoH compared to physician notes, which primarily [...]