Predictive Models for Medical Costs in Private Healthcare PDF Download
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Author: L. R. Lopes
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: L. R. Lopes
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Ian Duncan
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Predictive models of healthcare costs have become mainstream in much healthcare actuarial work. The Affordable Care Act requires the use of predictive modeling-based risk-adjuster models to transfer revenue between different health exchange participants. While the predictive accuracy of these models has been investigated in a number of studies, the accuracy and use of models for applications other than risk adjustment has not been the subject of much investigation. We investigate predictive modeling of future healthcare costs using a number of different statistical techniques. Our analysis was performed based on a dataset of 30,000 insureds containing claims information from two contiguous years. The dataset contains over a hundred covariates for each insured, including detailed breakdown of past costs and causes encoded via coexisting condition (CC) flags. We discuss statistical models for the relationship between next-year costs and medical and cost information to predict the mean and quantiles of future cost, ranking risks and identifying most predictive covariates. A comparison of multiple models is presented, including (in addition to the traditional linear regression model underlying risk adjusters) Lasso GLM, multivariate adaptive regression splines, random forests, decision trees, and boosted trees. A detailed performance analysis shows that the traditional regression approach does not perform well and that more accurate models are possible.
Author: World Health Organization
Publisher: World Health Organization
ISBN: 924006401X
Category : Health & Fitness
Languages : en
Pages : 40
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Author: Adam Bohr
Publisher: Academic Press
ISBN: 0128184396
Category : Computers
Languages : en
Pages : 385
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Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
Author: Institute of Medicine
Publisher: National Academies Press
ISBN: 0309083435
Category : Medical
Languages : en
Pages : 213
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Book Description
Many Americans believe that people who lack health insurance somehow get the care they really need. Care Without Coverage examines the real consequences for adults who lack health insurance. The study presents findings in the areas of prevention and screening, cancer, chronic illness, hospital-based care, and general health status. The committee looked at the consequences of being uninsured for people suffering from cancer, diabetes, HIV infection and AIDS, heart and kidney disease, mental illness, traumatic injuries, and heart attacks. It focused on the roughly 30 million-one in seven-working-age Americans without health insurance. This group does not include the population over 65 that is covered by Medicare or the nearly 10 million children who are uninsured in this country. The main findings of the report are that working-age Americans without health insurance are more likely to receive too little medical care and receive it too late; be sicker and die sooner; and receive poorer care when they are in the hospital, even for acute situations like a motor vehicle crash.
Author: Kevin Li
Publisher:
ISBN:
Category :
Languages : en
Pages : 65
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It is widely known that the minority of patients make up the majority of healthcare costs. Research being done aims at identifying these patients through predictive modeling. In the hopes that providing targeted resources to these patients can prevent inurnment of the high-cost. Lowing the bottom line to the hospital and helping the patient. Yet what degree of utility do these models provide? Most models are applied in a less than realistic setting or fail to state which predicted patients can even be impacted. In this study, I went through patient's clinical notes to better understand how practical such predictive models are. First, I sought after literature to better understand what variables most predictive models use as a base. I compare these to what was available in the patient's profile. Then revise what necessary for me to predict high cost given the patient's clinical notes. With access to UWMC/Harborview and NW Hospital databases, I went through clinical notes to evaluate each patient's possible predictability. These determinations were later verified by a physician for accuracy. This was further reflected on Northwest(NW) Hospital data, which is a relatively smaller hospital with a focus on inpatient/outpatient patients. Each patient was categorized on the nature of their high expenditure. This work's importance is in how to consider predictive models moving forward. Assuming modeling will always have the solution to predict high-cost patients is misguided. Instead, understanding the underlying dynamic of the patient's cause is a better target. The conclusions made in this study can help better guide models to be more cognizant in how they approach predicting high-cost patients.
Author: Ana Aizcorbe
Publisher: University of Chicago Press
ISBN: 022653099X
Category : Business & Economics
Languages : en
Pages : 535
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Book Description
Health care costs represent a nearly 18% of U.S. gross domestic product and 20% of government spending. While there is detailed information on where these health care dollars are spent, there is much less evidence on how this spending affects health. The research in Measuring and Modeling Health Care Costs seeks to connect our knowledge of expenditures with what we are able to measure of results, probing questions of methodology, changes in the pharmaceutical industry, and the shifting landscape of physician practice. The research in this volume investigates, for example, obesity’s effect on health care spending, the effect of generic pharmaceutical releases on the market, and the disparity between disease-based and population-based spending measures. This vast and varied volume applies a range of economic tools to the analysis of health care and health outcomes. Practical and descriptive, this new volume in the Studies in Income and Wealth series is full of insights relevant to health policy students and specialists alike.
Author: Peter Muennig
Publisher: John Wiley & Sons
ISBN: 1119011280
Category : Medical
Languages : en
Pages : 561
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Book Description
The field's bestselling reference, updated with the latest tools, data, techniques, and the latest recommendations from the Second Panel on Cost-Effectiveness in Health and Medicine Cost-Effectiveness Analysis in Health is a practical introduction to the tools, methods, and procedures used worldwide to perform cost-effective research. Covering every aspect of a complete cost-effectiveness analysis, this book shows you how to find which data you need, where to find it, how to analyze it, and how to prepare a high-quality report for publication. Designed for the classroom or the individual learner, the material is presented in simple and accessible language for those who lack a biostatistics or epidemiology background, and each chapter includes real-world examples and "tips and tricks" that highlight key information. Exercises throughout allow you to test your understanding with practical application, and the companion website features downloadable data sets for students, as well as lecture slides and a test bank for instructors. This new third edition contains new discussion on meta-analysis and advanced modeling techniques, a long worked example using visual modeling software TreeAge Pro, and updated recommendations from the U.S. Public Health Service's Panel on Cost-Effectiveness in Health and Medicine. This is the second printing of the 3rd Edition, which has been corrected and revised for 2018 to reflect the latest standards and methods. Cost-effectiveness analysis is used to evaluate medical interventions worldwide, in both developed and developing countries. This book provides process-specific instruction in a concise, structured format to give you a robust working knowledge of common methods and techniques. Develop a thoroughly fleshed-out research project Work accurately with costs, probabilities, and models Calculate life expectancy and quality-adjusted life years Prepare your study and your data for publication Comprehensive analysis skills are essential for students seeking careers in public health, medicine, biomedical research, health economics, health policy, and more. Cost-Effectiveness Analysis in Health walks you through the process from a real-world perspective to help you build a skillset that's immediately applicable in the field.
Author: Amy Finkelstein
Publisher: Columbia University Press
ISBN: 0231538685
Category : Medical
Languages : en
Pages : 161
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Book Description
Addressing the challenge of covering heath care expenses—while minimizing economic risks. Moral hazard—the tendency to change behavior when the cost of that behavior will be borne by others—is a particularly tricky question when considering health care. Kenneth J. Arrow’s seminal 1963 paper on this topic (included in this volume) was one of the first to explore the implication of moral hazard for health care, and Amy Finkelstein—recognized as one of the world’s foremost experts on the topic—here examines this issue in the context of contemporary American health care policy. Drawing on research from both the original RAND Health Insurance Experiment and her own research, including a 2008 Health Insurance Experiment in Oregon, Finkelstein presents compelling evidence that health insurance does indeed affect medical spending and encourages policy solutions that acknowledge and account for this. The volume also features commentaries and insights from other renowned economists, including an introduction by Joseph P. Newhouse that provides context for the discussion, a commentary from Jonathan Gruber that considers provider-side moral hazard, and reflections from Joseph E. Stiglitz and Kenneth J. Arrow. “Reads like a fireside chat among a group of distinguished, articulate health economists.” —Choice
Author: Ian G. Duncan
Publisher: ACTEX Publications
ISBN: 1566987695
Category : Business & Economics
Languages : en
Pages : 350
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Book Description
This text is listed on the Course of Reading for SOA Fellowship study in the Group & Health specialty track. Healthcare Risk Adjustment and Predictive Modeling provides a comprehensive guide to healthcare actuaries and other professionals interested in healthcare data analytics, risk adjustment and predictive modeling. The book first introduces the topic with discussions of health risk, available data, clinical identification algorithms for diagnostic grouping and the use of grouper models. The second part of the book presents the concept of data mining and some of the common approaches used by modelers. The third and final section covers a number of predictive modeling and risk adjustment case-studies, with examples from Medicaid, Medicare, disability, depression diagnosis and provider reimbursement, as well as the use of predictive modeling and risk adjustment outside the U.S. For readers who wish to experiment with their own models, the book also provides access to a test dataset.
