Corpus ID: 5615780

A Disease Management Program Utilizing "Life Coaches" for Children with Asthma

  title={A Disease Management Program Utilizing "Life Coaches" for Children with Asthma},
  author={Randy C. Axelrod and Kathie S. Zimbro and Rhonda Chetney and Janis Sabol and Valerie J. Ainsworth},
An estimated 17 million Americans suffer from asthma, a costly disease accounting for 1.8 million emergency department visits and 10 million physician office visits annually [1]. Asthma is the most common chronic childhood disease, affecting more than 1 child in 20 and accounting for an annual loss of approximately 10 million school days per year [1]. Asthma-related hospitalization rates increased significantly during the past 2 decades even as overall hospitalization rates declined… Expand
The impact of a large-scale population-based asthma management program on pediatric asthma patients and their caregivers.
These findings demonstrate that a large-scale population-based intervention program can produce measurable clinical and economic benefits, thereby lessening the burden of asthma on the family unit. Expand
Direct and Indirect Costs of Asthma in School-age Children
The economic impact of asthma on school-age children, families, and society is immense, and more public health efforts to better control asthma in children are needed. Expand
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A machine learning classification model to predict the hospital encounters for asthma in the following year in asthmatic patients is developed and could be integrated into a decision support tool to guide asthma care management allocation. Expand
Developing a Predictive Model for Asthma-Related Hospital Encounters in Patients With Asthma in a Large, Integrated Health Care System: Secondary Analysis
A machine learning model is constructed on Intermountain Healthcare data to predict asthma-related hospital encounters in patients with asthma and exhibited acceptable generalizability to KPSC and resulted in a model that is more accurate than those formerly built by others. Expand
Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis
Background Asthma and chronic obstructive pulmonary disease (COPD) impose a heavy burden on health care. Approximately one-fourth of patients with asthma and patients with COPD are prone toExpand
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A new machine learning model is built to forecast future asthma hospital encounters of patients with asthma at Intermountain Healthcare, a nonacademic health care system, and shows excellent generalizability to the University of Washington Medicine, leading to a model with an AUC that is higher than all of the AUCs previously reported in the literature. Expand
Using Computational Approaches to Improve Risk-Stratified Patient Management: Rationale and Methods
Methods developed in this study will help transform risk-stratified patient management for better clinical outcomes, higher patient satisfaction and quality of life, reduced health care use, and lower costs. Expand
A Roadmap for Optimizing Asthma Care Management via Computational Approaches
To maximize benefit, patients anticipated to have the highest costs or worst prognosis should be enrolled, and multiple machine learning techniques to address them are outlined, providing a roadmap for future research. Expand
Coaching for behaviour change in chronic disease: A review of the literature and the implications for coaching as a self-management intervention
It was apparent that education-based interventions have a significant role in self-management, but that these were not sufficient by themselves and the role of behaviour change-focused coaching was also shown to be an important factor. Expand
Generalizability of an Automatic Explanation Method for Machine Learning Prediction Results on Asthma-Related Hospital Visits in Patients With Asthma: Quantitative Analysis
For forecasting asthma-related hospital visits, the automatic explanation method worked well for explaining the forecasting results of the authors' Intermountain Healthcare model, but its generalizability to other health care systems remains unknown. Expand


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