Machine learning to identify chronic cough from administrative claims data.

Accurate identification of patient populations is an essential component of clinical research, especially for medical conditions such as chronic cough that are inconsistently defined and diagnosed. We aimed to develop and compare machine learning models to identify chronic cough from medical and pharmacy claims data. In this retrospective observational study, we compared 3 machine learning algorithms based on XG Boost, logistic regression, and neural network approaches using a large claims and electronic health record database.

A machine learning-based algorithm to identify U-500R insulin candidates among adults with type 2 diabetes mellitus in US retrospective databases.

Objective: To develop a machine learning-based predictive algorithm to identify patients with type 2 diabetes mellitus (T2DM) who are candidates for initiation of U-500R insulin (U-500R).

Methods: A retrospective cohort of patients with T2DM was used from a large US administrative claims and electronic health records (EHR) database affiliated with Optum. Predictor variables derived from the data were used to identify appropriate supervised machine learning models including least absolute shrinkage and selection operator (LASSO) and extreme gradient boosted (XGBoost) methods.

Predicting Response to Bolus Insulin Therapy in Patients With Type 2 Diabetes.

Background: The aim of this study was to develop a predictive model to classify people with type 2 diabetes (T2D) into expected levels of success upon bolus insulin initiation.

Methods: Machine learning methods were applied to a large nationally representative insurance claims database from the United States (dNHI database; data from 2007 to 2017). We trained boosted decision tree ensembles (XGBoost) to assign people into Class 0 (never meeting HbA1c goal), Class 1 (meeting but not maintaining HbA1c goal), or Class 2 (meeting and maintaining HbA1c goal) based on the demographic and clinical data available prior to initiating bolus insulin.

Hearing and Health Outcomes: Recognizing and Addressing Hearing Loss in Hospitalized Older Adults.

As age increases, the prevalence of hearing loss significantly increases, reaching up to 89% of those 80 years and older. Hearing loss in older patients is often unrecognized and its consequences are often underappreciated. Hearing loss can interfere with the ability to exchange important health information and to participate in health care decision-making.

Sustainability and scalability of the hospital elder life program at a community hospital.

The Hospital Elder Life Program (HELP), an effective intervention to prevent delirium in older hospitalized adults, has been successfully replicated in a community teaching hospital as a quality improvement project. This article reports on successfully sustaining the program over 7 years and expanding its scale from one to six inpatient units at the same hospital. The program currently serves more than 7,000 older patients annually and is accepted as the standard of care throughout the hospital.

Designs that make a difference: the Cardiac Universal Bed model.

Information contained in this article includes some of the findings from a joint research project conducted by Corazon Consulting and Ohio State University Medical Center on national trends in Cardiac Universal Bed (CUB) utilization. This article outlines current findings and "best practice" standards related to the benefits of developing care delivery models to differentiate an organization with a competitive advantage in the highly dynamic marketplace of cardiovascular care. (OSUMC, a Corazon client, is incorporating the CUB into their Ross Heart Hospital slated to open this spring.