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Wearable fitness devices are increasingly being used by the general population, with many new applications being proposed for healthy adults as well as for adults with chronic diseases. Fewer, if any, studies of these devices have been conducted in healthy adolescents and teenagers, especially over a long period of time. The goal of this work was to document the successes and challenges involved in 5 years of a wearable fitness [...]

[This corrects the article DOI: 10.1093/jamiaopen/ooab008.].

The objectives of this pilot study were (1) to assess the feasibility of a larger evaluation of Smart About Meds (SAM), a patient-centered medication management mobile application, and (2) to evaluate SAM's potential to improve outcomes of interest, including adherence to medication changes made at hospital discharge and the occurrence of adverse events.

The effects of shared clinical notes on patients, care partners, and clinicians ("open notes") were first studied as a demonstration project in 2010. Since then, multiple studies have shown clinicians agree shared progress notes are beneficial to patients, and patients and care partners report benefits from reading notes. To determine if implementing open notes at a hematology/oncology practice changed providers' documentation style, we assessed the length and readability of clinicians' [...]

The COVID-19 pandemic and subsequent expansion of telehealth may be exacerbating inequities in ambulatory care access due to institutional and structural barriers. We conduct a repeat cross-sectional analysis of ambulatory patients to evaluate for demographic disparities in the utilization of telehealth modalities.

The widespread deployment of electronic health records (EHRs) has introduced new sources of error and inefficiencies to the process of ordering medications in the hospital setting. Existing work identifies orders that require pharmacy intervention by comparing them to a patient's medical records. In this work, we develop a machine learning model for identifying medication orders requiring intervention using only provider behavior and other contextual features that may reflect these new [...]

Patient-generated health data (PGHD) are important for tracking and monitoring out of clinic health events and supporting shared clinical decisions. Unstructured text as PGHD (eg, medical diary notes and transcriptions) may encapsulate rich information through narratives which can be critical to better understand a patient's condition. We propose a natural language processing (NLP) supported data synthesis pipeline for unstructured PGHD, focusing on children with special healthcare needs (CSHCN), and demonstrate [...]

Clinico-genomic data (CGD) acquired through routine clinical practice has the potential to improve our understanding of clinical oncology. However, these data often reside in heterogeneous and semistructured data, resulting in prolonged time-to-analyses.

We help identify subpopulations underrepresented in randomized clinical trials (RCTs) cohorts with respect to national, community-based or health system target populations by formulating population representativeness of RCTs as a machine learning (ML) fairness problem, deriving new representation metrics, and deploying them in easy-to-understand interactive visualization tools.

Ambulatory providers were administered an EHR skills self-assessment survey to assess their confidence in learning about and using the electronic health record (EHR). Seventy-one providers participated. Only 35% of respondents felt that they had strong EHR skills, 92% felt confident that they could learn new skills, and 90% felt they could improve with practice. Forty-five percent of faculty physicians felt confident that they could use the EHR in a time-efficient [...]