The Association of Where Patients with Prostate Cancer Live and Receive Care on Racial Treatment Inequities.

Black individuals are less likely to be treated for prostate cancer even though they are more than twice as likely to die compared to White individuals. The complex causes of these inequities are influenced by social and structural factors, including racism, which contribute to the differential delivery of care. This study investigates how factors related to the location of where individuals live and receive care affect treatment inequities for prostate cancer between Black and White individuals.

Emergency department visit frequency and health care costs following implementation of an integrated practice unit for frequent utilizers.

Objectives: The integrated practice unit (IPU) aims to improve care for patients with complex medical and social needs through care coordination, medication reconciliation, and connection to community resources. This study examined the effects of IPU enrollment on emergency department (ED) utilization and health care costs among frequent ED utilizers with complex needs.

Methods: We extracted electronic health records (EHR) data from patients in a large health care system who had at least four distinct ED visits within any 6-month period between March 1, 2018, and May 30, 2021.

Isolating the Drivers of Racial Inequities in Prostate Cancer Treatment.

Background: Black individuals in the United States are less likely than White individuals to receive curative therapies despite a 2-fold higher risk of prostate cancer death. While research has described treatment inequities, few studies have investigated underlying causes.

Methods: We analyzed a cohort of 40,137 Medicare beneficiaries (66 and older) linked to the Surveillance Epidemiology and End Results (SEER) cancer registry who had clinically significant, non-metastatic (cT1-4N0M0, grade group 2-5) prostate cancer (diagnosed 2010-2015).

Impact of the Affordable Care Act on participation in the Supplemental Nutrition Assistance Program among low-income older Medicare beneficiaries.

Background: The Affordable Care Act (ACA) provisions, especially Medicaid expansion, are believed to have "spillover effects," such as boosting participation in the Supplemental Nutrition Assistance Program (SNAP) among eligible individuals in the United States (US). However, little empirical evidence exists about the impact of the ACA, with its focus on the dual eligible population, on SNAP participation. The current study investigates whether the ACA, under an explicit policy aim of enhancing the interface between Medicare and Medicaid, has improved participation in the SNAP among low-income older Medicare beneficiaries.

An evaluation of copy and paste events in electronic notes of patients with hospital acquired conditions.

Background: The increased use of the copy and paste function (CPF) in Electronic Health Records (EHRs) has raised concerns about possible clinician miscommunication and adverse patient outcomes.

Objective: This study investigated the prevalence and extent of CPF in the EHRs of patients diagnosed with Hospital-acquired Conditions (HACs). We also examined the association between the use of CPF and patient characteristics.

Hospital value-based payment programs and disparity in the United States: A review of current evidence and future perspectives.

Beginning in the early 2010s, an array of Value-Based Purchasing (VBP) programs has been developed in the United States (U.S.) to contain costs and improve health care quality.

Inclusion of nonrandomized studies of interventions in systematic reviews of interventions: updated guidance from the Agency for Health Care Research and Quality Effective Health Care program.

Objectives: We developed guidance to inform decisions regarding the inclusion of nonrandomized studies of interventions (NRSIs) in systematic reviews (SRs) of the effects of interventions.

Study Design And Setting: The guidance workgroup comprised SR experts and used an informal consensus generation method.

Results: Instead of recommending NRSI inclusion only if randomized controlled trials (RCTs) are insufficient to address the SR key question, different topics may require different decisions regarding NRSI inclusion.

A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 1-Data From Wearable Devices.

Objectives: With the emerging use of machine learning (ML) techniques, there has been particular interest in using wearable data for health economics and outcomes research (HEOR). We aimed to understand the emerging patterns of how ML has been applied to wearable data in HEOR.

Methods: We identified studies published in PubMed between January 2016 and March 2021.

A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 2-Data From Nonwearables.

Objectives: Despite the increasing interest in applying machine learning (ML) methods in health economics and outcomes research (HEOR), stakeholders face uncertainties in when and how ML can be used. We reviewed the recent applications of ML in HEOR.

Methods: We searched PubMed for studies published between January 2020 and March 2021 and randomly chose 20% of the identified studies for the sake of manageability.

SARS-CoV-2 infection and COVID-19 severity in individuals with prior seasonal coronavirus infection.

We show that individuals with documented history of seasonal coronavirus have a similar SARS-CoV-2 infection rate and COVID-19 severity as those with no prior history of seasonal coronavirus. Our findings suggest prior infection with seasonal coronavirus does not provide immunity to subsequent infection with SARS-CoV-2.

Piloting a model-to-data approach to enable predictive analytics in health care through patient mortality prediction.

Objective: The development of predictive models for clinical application requires the availability of electronic health record (EHR) data, which is complicated by patient privacy concerns. We showcase the "Model to Data" (MTD) approach as a new mechanism to make private clinical data available for the development of predictive models. Under this framework, we eliminate researchers' direct interaction with patient data by delivering containerized models to the EHR data.

A predictive tool for identification of SARS-CoV-2 PCR-negative emergency department patients using routine test results.

Background: Testing for COVID-19 remains limited in the United States and across the world. Poor allocation of limited testing resources leads to misutilization of health system resources, which complementary rapid testing tools could ameliorate.

Objective: To predict SARS-CoV-2 PCR positivity based on complete blood count components and patient sex.

Evaluation of the secondary use of electronic health records to detect seasonal, holiday-related, and rare events related to traumatic injury and poisoning.

Background: The increasing adoption of electronic health record (EHR) systems enables automated, large scale, and meaningful analysis of regional population health. We explored how EHR systems could inform surveillance of trauma-related emergency department visits arising from seasonal, holiday-related, and rare environmental events.

Methods: We analyzed temporal variation in diagnosis codes over 24 years of trauma visit data at the three hospitals in the University of Washington Medicine system in Seattle, Washington, USA.

Electronic medical records and medical procedure choice: Evidence from cesarean sections.

This paper examines how hospital adoption of electronic medical records (EMRs) impacts medical procedure choice in the context of cesarean section deliveries. It provides a unique contribution by tying the literature on EMR diffusion to the literature on the utilization of expensive medical technology and provider practice style. Exploiting within-hospital variation in three types of EMR adoption, we find that computerized physician order entry, an advanced EMR system that typically incorporates decision support, reduces C-section rates for low-risk mothers by 2.