Development of Predictive Models to Inform a Novel Risk Categorization Framework for Antibiotic Resistance in Escherichia coli-Caused Uncomplicated Urinary Tract Infection.

Background: In clinical practice, challenges in identifying patients with uncomplicated urinary tract infections (uUTIs) at risk of antibiotic nonsusceptibility may lead to inappropriate prescribing and contribute to antibiotic resistance. We developed predictive models to quantify risk of nonsusceptibility to 4 commonly prescribed antibiotic classes for uUTI, identify predictors of nonsusceptibility to each class, and construct a corresponding risk categorization framework for nonsusceptibility.

Methods: Eligible females aged ≥12 years with Escherichia coli-caused uUTI were identified from Optum's de-identified Electronic Health Record dataset (1 October 2015-29 February 2020).

Combining the Power of Artificial Intelligence with the Richness of Healthcare Claims Data: Opportunities and Challenges.

Combinations of healthcare claims data with additional datasets provide large and rich sources of information. The dimensionality and complexity of these combined datasets can be challenging to handle with standard statistical analyses. However, recent developments in artificial intelligence (AI) have led to algorithms and systems that are able to learn and extract complex patterns from such data.

The impact of psoriasis on health care costs and patient work loss.

Background: There are few comprehensive estimates of the cost of psoriasis in the United States.

Objective: We sought to quantify the incremental direct medical and indirect work loss costs associated with psoriasis.

Methods: A de-identified claims database from 31 self-insured employers during the period 1998 to 2005 was used.

The direct and indirect cost burden of atopic dermatitis: an employer-payer perspective.

The goal of this study was to quantify the incremental direct medical and indirect work-loss costs associated with patients diagnosed with atopic dermatitis (AD). A de-identified administrative claims database was used comprising 5.1 million covered beneficiaries from 31 Fortune 500 self-insured employers between 1998 and 2005.

Does use of a PACS increase the number of images per study? A case study in ultrasound.

The purpose of this study was to determine if the use of a picture archiving and communications system (PACS) in ultrasonography increased the number of images acquired per examination. The hypothesis that such an increase does occur was based on anecdotal information; this study sought to test the hypothesis. A random sample of all ultrasound examination types was drawn from the period 1998 through 1999.