The American journal of managed care
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Observational Study
Impact of food affordability on diabetes-related preventable hospitalization.
This study aims to estimate the burden of food affordability on diabetes-related preventable hospitalizations among Medicaid enrollees in the United States. ⋯ This study provides real-world evidence about the impact of SDOH on diabetes-related preventable hospitalizations. Federal and state policies that can help improve accessibility of healthy foods are needed to ameliorate the burden of diabetes on society.
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The Advance Premium Tax Credit (APTC) is designed to remedy lack of health insurance due to cost; however, approximately 30 million Americans remain without health insurance and millions of households leave billions in tax credits unclaimed each year. A prerequisite of APTC is to file one's taxes; however, few studies have examined tax filing and APTC jointly. This study examined the relationship between tax filing and applying for APTC, as well as perceived barriers to and sociodemographic characteristics associated with applying for the APTC. ⋯ Barriers to applying for the APTC were unrelated to tax filing and were specific to a lack of knowledge about the APTC and eligibility. These results indicate the need to build knowledge and awareness of the APTC and eligibility and to target groups least likely to apply. Implications and future directions are discussed.
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Drug-drug interactions (DDIs) are among the most common causes of adverse drug reactions and are further complicated by genetic variants of drug-metabolizing enzymes. The aim of this study is to quantify and describe potential DDIs, drug-gene interactions (DGIs), and drug-drug-gene interactions (DDGIs) in a community-based population. ⋯ The probability of drug interaction risk increased when phenotypes associated with genetic polymorphisms were attributed to the population. These data suggest that pharmacogenomic assessment may be useful in predicting drug interactions and severity when evaluating patient medication profiles.
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To determine whether a risk prediction model using artificial intelligence (AI) to combine multiple data sources, including claims data, demographics, social determinants of health (SDOH) data, and admission, discharge, and transfer (ADT) alerts, more accurately identifies high-cost members than traditional models. ⋯ Identification of high-cost members can be improved by using AI to combine traditional sources of data (eg, claims and demographic information) with nontraditional sources (eg, SDOH, admission alerts).
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As we reset post pandemic, providers and payers are in an excellent position to prioritize a reallocation of health care expenditures driven primarily by individual and population health gains.