Association Between State Medicaid Policies and Accrual of Black or Hispanic Patients to Cancer Clinical Trials.

Purpose: It is unknown whether Medicaid expansion under the Affordable Care Act (ACA) or state-level policies mandating Medicaid coverage of the routine costs of clinical trial participation have ameliorated longstanding racial and ethnic disparities in cancer clinical trial enrollment.

Methods: We conducted a retrospective, cross-sectional difference-in-differences analysis examining the effect of Medicaid expansion on rates of enrollment for Black or Hispanic nonelderly adults in nonobservational, US cancer clinical trials using data from Medidata's Rave platform for 2012-2019. We examined heterogeneity in this effect on the basis of whether states had pre-existing mandates requiring Medicaid coverage of the routine costs of clinical trial participation.

A randomized study on the usefulness of an electronic outpatient hypoglycemia risk calculator for clinicians of patients with diabetes in a safety-net institution.

Hypoglycemia (HG) occurs in up to 60% of patients with diabetes mellitus (DM) each year. We assessed a HG alert tool in an electronic health record system, and determined its effect on clinical practice and outcomes. The tool applied a statistical model, yielding patient-specific information about HG risk.

Factors associated with clinical inertia in type 2 diabetes mellitus patients treated with metformin monotherapy.

To assess demographic and clinical characteristics associated with clinical inertia in a real-world cohort of type 2 diabetes mellitus patients not at hemoglobin A1c goal (<7%) on metformin monotherapy. Adult (≥18 years) type 2 diabetes mellitus patients who received care at Massachusetts General Hospital/Brigham and Women's Hospital and received a new metformin prescription between 1992 and 2010 were included in the analysis. Clinical inertia was defined as two consecutive hemoglobin A1c measures ≥7% ≥3 months apart while remaining on metformin monotherapy (i.

Predictive modeling of hypoglycemia for clinical decision support in evaluating outpatients with diabetes mellitus.

Hypoglycemia occurs in 20-60% of patients with diabetes mellitus. Identifying at-risk patients can facilitate interventions to lower risk. We sought to develop a hypoglycemia prediction model.

Phenome-wide association studies across large population cohorts support drug target validation.

Phenome-wide association studies (PheWAS) have been proposed as a possible aid in drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we select 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease indications. We interrogate these SNPs by PheWAS in four large cohorts with extensive health information (23andMe, UK Biobank, FINRISK, CHOP) for association with 1683 binary endpoints in up to 697,815 individuals and conduct meta-analyses for 145 mapped disease endpoints.

Development of an Algorithm to Identify Patients with Physician-Documented Insomnia.

We developed an insomnia classification algorithm by interrogating an electronic medical records (EMR) database of 314,292 patients. The patients received care at Massachusetts General Hospital (MGH), Brigham and Women's Hospital (BWH), or both, between 1992 and 2010. Our algorithm combined structured variables (such as International Classification of Diseases 9th Revision [ICD-9] codes, prescriptions, laboratory observations) and unstructured variables (such as text mentions of sleep and psychiatric disorders in clinical narrative notes).

Predictive Modeling of Physician-Patient Dynamics That Influence Sleep Medication Prescriptions and Clinical Decision-Making.

Insomnia remains under-diagnosed and poorly treated despite its high economic and social costs. Though previous work has examined how patient characteristics affect sleep medication prescriptions, the role of physician characteristics that influence this clinical decision remains unclear. We sought to understand patient and physician factors that influence sleep medication prescribing patterns by analyzing Electronic Medical Records (EMRs) including the narrative clinical notes as well as codified data.

Characterizing Sleep Issues Using Twitter.

Background: Sleep issues such as insomnia affect over 50 million Americans and can lead to serious health problems, including depression and obesity, and can increase risk of injury. Social media platforms such as Twitter offer exciting potential for their use in studying and identifying both diseases and social phenomenon.

Objective: Our aim was to determine whether social media can be used as a method to conduct research focusing on sleep issues.