Multidisciplinary stakeholder engagement in a type 2 diabetes comparative effectiveness study utilizing real-world data.

Introduction: Patient and stakeholder involvement enhances the conduct and applicability of comparative effectiveness research (CER). However, examples of engagement practices for CER leveraging real-world data (i.e.

Outcomes and Attributes Patients Value When Choosing Glucose-Lowering Medications: A Mixed-Methods Study.

This mixed-methods study sought to identify pharmacotherapy preferences among 40 noninsulin-treated adults with type 2 diabetes receiving care at two U.S. health care systems.

Derivation of an Annualized Claims-Based Major Adverse Cardiovascular Event Estimator in Type 2 Diabetes.

Background: Major adverse cardiovascular events (MACE) are a leading cause of morbidity and mortality among adults with type 2 diabetes. Currently, available MACE prediction models have important limitations, including reliance on data that may not be routinely available, narrow focus on primary prevention, limited patient populations, and longtime horizons for risk prediction.

Objectives: The purpose of this study was to derive and internally validate a claims-based prediction model for 1-year risk of MACE in type 2 diabetes.

Effectiveness of glucose-lowering medications on cardiovascular outcomes in patients with type 2 diabetes at moderate cardiovascular risk.

Cardiovascular disease (CVD) is the leading cause of death among people with type 2 diabetes, most of whom are at moderate CVD risk, yet there is limited evidence on the preferred choice of glucose-lowering medication for CVD risk reduction in this population. Here, we report the results of a retrospective cohort study where data for US adults with type 2 diabetes and moderate risk for CVD are used to compare the risks of experiencing a major adverse cardiovascular event with initiation of glucagon-like peptide-1 receptor agonists (GLP-1RA; = 44,188), sodium-glucose cotransporter 2 inhibitors (SGLT2i; = 47,094), dipeptidyl peptidase-4 inhibitors (DPP4i; = 84,315) and sulfonylureas ( = 210,679). Compared to DPP4i, GLP-1RA (hazard ratio (HR) 0.

Comparative Effectiveness of Glucagon-Like Peptide-1 Receptor Agonists, Sodium-Glucose Cotransporter 2 Inhibitors, Dipeptidyl Peptidase-4 Inhibitors, and Sulfonylureas for Sight-Threatening Diabetic Retinopathy.

Objective: To investigate whether the choice of glucose-lowering agent for type 2 diabetes (T2D) impacts a patient's risk of developing sight-threatening diabetic retinopathy complications.

Design: Retrospective observational database study emulating an idealized target trial.

Subjects: Adult (≥21 years) enrollees in United States commercial, Medicare Advantage, and Medicare fee-for-service plans from January 1, 2014, to December 31, 2021, with T2D and moderate cardiovascular disease (CVD) risk who had no baseline history of advanced diabetic retinal complications, initiating treatment with glucagon-like peptide-1 receptor agonists (GLP-1 RA), sodium-glucose cotransporter 2 inhibitors (SGLT2i), dipeptidyl peptidase-4 inhibitors (DPP-4i), and sulfonylureas.

Implementation of Artificial Intelligence-Based Clinical Decision Support to Reduce Hospital Readmissions at a Regional Hospital.

Background: Hospital readmissions are a key quality metric, which has been tied to reimbursement. One strategy to reduce readmissions is to direct resources to patients at the highest risk of readmission. This strategy necessitates a robust predictive model coupled with effective, patient-centered interventions.

What's in a name? A comparison of attitudes towards artificial intelligence (AI) versus augmented human intelligence (AHI).

Background: "Artificial intelligence" (AI) is often referred to as "augmented human intelligence" (AHI). The latter term implies that computers support-rather than replace-human decision-making. It is unclear whether the terminology used affects attitudes and perceptions in practice.

A lesson in implementation: A pre-post study of providers' experience with artificial intelligence-based clinical decision support.

Background: To explore attitudes about artificial intelligence (AI) among staff who utilized AI-based clinical decision support (CDS).

Methods: A survey was designed to assess staff attitudes about AI-based CDS tools. The survey was anonymously and voluntarily completed by clinical staff in three primary care outpatient clinics before and after implementation of an AI-based CDS system aimed to improve glycemic control in patients with diabetes as part of a quality improvement project.