Healthcare Risk Adjustment And Predictive Modeling
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Healthcare Risk Adjustment and Predictive Modeling by Ian G. Duncan Pdf
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.
Predictive Modeling by Healthcare Intelligence Network,Patricia Donovan Pdf
As technology makes possible the rapid access of patient data, past patterns of behavior, health claims history and pharmaceutical information could hold the key to improving managed care and reining in healthcare costs. In this special report, "Predictive Modeling: Improving Margins by Identifying and Targeting High-Risk Populations," a panel of experts detail ways health plans use predictive modeling to identify plan members who may need proactive care management. By identifying this at-risk population, health plans can accurately gauge future patient expenses based on prior treatments. Using a combination of technology and web-based tools, health plans can use predictive modeling to project future member and group healthcare costs and price more appropriately for risk. You''ll hear from Howard Brill, Manager of Medical Informatics at Monroe Plan for Medical Care Inc.; Danielle Butin, Manager, Health Promotion and Wellness, Oxford Health Plans; Michael Cousins, Ph.D, Director of Informatics, Health Management Corporation; James M. Dolstad, ASA, MAAA, Vice President of Actuarial Services, SHPS Inc.; Dr. Stanley Hochberg, Medical Director, Provider Service Network; Marilyn Schlein Kramer, CEO and President, DxCG Inc.; and Jerry Osband, MD, Chief Medical Officer, SHPS Inc., on theories, application and results of predictive modeling programs. This report is based on the June 16, 2004 audio conference "Predictive Modeling: Strategies, Trends & Forecasts" and the November 30, 2004 audio conference "Improving the Quality of Data Collection for Effective Predictive Modeling" during which Brill, Butin, Cousins, Dolstad, Hochberg, Kramer and Osband described the types of predictive models, the impact of predictive modeling programs and how predictive modeling results can be improved. You''ll get details on: -Trends in predictive modeling; -Evidence-based medicine and predictive modeling; -Diseases best suited to predictive modeling; -The role of health risk assessments in predictive modeling; -Validating the integrity of the data;and -The bottom line impact of predictive modeling programs. Table of Contents Improving the Quality of Data Collection for Effective Predictive Modeling -Risk Groupers -Statistical Models -Artificial Intelligence -The Potential of Neural Networks -Features of Neural Networks -The Impact of Modeling Tools on the Healthcare Industry -One Predictive Model Doesnt Necessarily Fit AllStrategies, Trends and Forecasts -Incremental Cost of Chronic Disease -Models Address Top 10 Healthcare Issues -Identifying Potentially Expensive Patients -Adding DCGs Improves Margins -Medicare Drives Healthcare TrendsThe Impact of Evidence-based Medicine on Predictive Modeling -Pros and Cons of Health Risk Assessments -Diseases Best Suited to Predictive Modeling -HRAs Match High-Risk Patients to Interventions -Variables for Diabetes in Predictive Models -The Struggle to Manage Re-Admissions -Transitional Coaches Conduct Patient Assertiveness Training -Using Predictive Modeling to Identify High-Risk Members -Telephonic Training Reaches Out to Homebound COPD Patients -Pain Management Program Nets $142 PMPMPredictive Modelings Impact and EBMs Role -Ensuring Data Integrity -Elements of Data Mining -Net Savings Forecast -Identifying a Members Willingness to Change -Where Predictive Modeling Has an Impact -Formulating an Intervention Strategy -Enhanced Engagement ProcessPredictive Modeling in an Integrated Delivery System -Predictive Modelings Effect on PMPM -Current Concerns Predictive Modeling and Medicaid Care Management-Pareto 80/20 Rule: Monroe Asthma Patients, 2002 -The Value of Prediction -Components of a Coherent Care Management Process -Targeted Interventions Change Predicted Outcomes -Challenges of Risk Adjustment Based on Predictive ModelingQ&A: Ask the Experts -Predictive Modeling in the Self-Insured Market -Maximizing Enrollment in Opt-In Plans -Software Recommendations -The High-Risk Patient and Bedside Tools -Developing Predictors for Intervenable Cases -The Value of Telephonic vs. Online Communications -Specifying Physician Incentives -Adjusting Forecasts for Exaggeration or Overestimation -Updating Predictive Models for New Treatments, Drugs -Drawbacks to Predictive Modeling -Boosting ROI
Author : Lisa I. Iezzoni Publisher : Asociation of University Programs in Health Administration/Health Administration Press Page : 0 pages File Size : 52,7 Mb Release : 2013 Category : Decision making ISBN : 1567934374
Managing and Evaluating Healthcare Intervention Programs by Ian Duncan, FSA, FIA, FCIA, MAAA Pdf
Since its publication in 2008, Managing and Evaluating Healthcare Intervention Programs has become the premier textbook for actuaries and other healthcare professionals interested in the financial performance of healthcare interventions. The second edition updates the prior text with discussion of new programs and outcomes such as ACOs, Bundled Payments and Medication Management, together with new chapters that include Opportunity Analysis, Clinical Foundations, Measurement of Clinical Quality, and use of Propensity Matching.
Institute of Medicine,Board on Health Sciences Policy,Committee on Disability in America
Author : Institute of Medicine,Board on Health Sciences Policy,Committee on Disability in America Publisher : National Academies Press Page : 619 pages File Size : 53,9 Mb Release : 2007-10-24 Category : Medical ISBN : 9780309104722
The Future of Disability in America by Institute of Medicine,Board on Health Sciences Policy,Committee on Disability in America Pdf
The future of disability in America will depend on how well the U.S. prepares for and manages the demographic, fiscal, and technological developments that will unfold during the next two to three decades. Building upon two prior studies from the Institute of Medicine (the 1991 Institute of Medicine's report Disability in America and the 1997 report Enabling America), The Future of Disability in America examines both progress and concerns about continuing barriers that limit the independence, productivity, and participation in community life of people with disabilities. This book offers a comprehensive look at a wide range of issues, including the prevalence of disability across the lifespan; disability trends the role of assistive technology; barriers posed by health care and other facilities with inaccessible buildings, equipment, and information formats; the needs of young people moving from pediatric to adult health care and of adults experiencing premature aging and secondary health problems; selected issues in health care financing (e.g., risk adjusting payments to health plans, coverage of assistive technology); and the organizing and financing of disability-related research. The Future of Disability in America is an assessment of both principles and scientific evidence for disability policies and services. This book's recommendations propose steps to eliminate barriers and strengthen the evidence base for future public and private actions to reduce the impact of disability on individuals, families, and society.
Artificial Intelligence in Healthcare by Adam Bohr,Kaveh Memarzadeh Pdf
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
Text Mining Techniques for Healthcare Provider Quality Determination: Methods for Rank Comparisons by Cerrito, Patricia Pdf
The quest for quality in healthcare has led to attempts to develop models to determine which providers have the highest quality in healthcare, with the best outcomes for patients. Text Mining Techniques for Healthcare Provider Quality Determination: Methods for Rank Comparisons discusses the general practice of defining a patient severity index in order to make risk adjustments to compare patient outcomes across multiple providers with the intent of ranking the providers in terms of quality. This innovative reference source, valuable to medical practitioners, researchers, and academicians, brings together research from across the globe focusing on how severity indices are generally defined when determining the best outcome for patient
Richard D. Riley,Danielle van der Windt,Peter Croft,Karel G. M. Moons
Author : Richard D. Riley,Danielle van der Windt,Peter Croft,Karel G. M. Moons Publisher : Oxford University Press Page : 384 pages File Size : 48,6 Mb Release : 2019-01-17 Category : Medical ISBN : 9780192516657
Prognosis Research in Healthcare by Richard D. Riley,Danielle van der Windt,Peter Croft,Karel G. M. Moons Pdf
"What is going to happen to me?" Most patients ask this question during a clinical encounter with a health professional. As well as learning what problem they have (diagnosis) and what needs to be done about it (treatment), patients want to know about their future health and wellbeing (prognosis). Prognosis research can provide answers to this question and satisfy the need for individuals to understand the possible outcomes of their condition, with and without treatment. Central to modern medical practise, the topic of prognosis is the basis of decision making in healthcare and policy development. It translates basic and clinical science into practical care for patients and populations. Prognosis Research in Healthcare: Concepts, Methods and Impact provides a comprehensive overview of the field of prognosis and prognosis research and gives a global perspective on how prognosis research and prognostic information can improve the outcomes of healthcare. It details how to design, carry out, analyse and report prognosis studies, and how prognostic information can be the basis for tailored, personalised healthcare. In particular, the book discusses how information about the characteristics of people, their health, and environment can be used to predict an individual's future health. Prognosis Research in Healthcare: Concepts, Methods and Impact, addresses all types of prognosis research and provides a practical step-by-step guide to undertaking and interpreting prognosis research studies, ideal for medical students, health researchers, healthcare professionals and methodologists, as well as for guideline and policy makers in healthcare wishing to learn more about the field of prognosis.
Leo Anthony Celi,Maimuna S. Majumder,Patricia Ordóñez,Juan Sebastian Osorio,Kenneth E. Paik,Melek Somai
Author : Leo Anthony Celi,Maimuna S. Majumder,Patricia Ordóñez,Juan Sebastian Osorio,Kenneth E. Paik,Melek Somai Publisher : Springer Nature Page : 471 pages File Size : 47,6 Mb Release : 2020-07-31 Category : Medical ISBN : 9783030479947
Leveraging Data Science for Global Health by Leo Anthony Celi,Maimuna S. Majumder,Patricia Ordóñez,Juan Sebastian Osorio,Kenneth E. Paik,Melek Somai Pdf
This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.
Author : Max Kuhn,Kjell Johnson Publisher : Springer Science & Business Media Page : 600 pages File Size : 46,9 Mb Release : 2013-05-17 Category : Medical ISBN : 9781461468493
Applied Predictive Modeling by Max Kuhn,Kjell Johnson Pdf
Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.
Institute of Medicine,Board on Health Care Services,Committee on the Consequences of Uninsurance
Author : Institute of Medicine,Board on Health Care Services,Committee on the Consequences of Uninsurance Publisher : National Academies Press Page : 213 pages File Size : 42,5 Mb Release : 2002-06-20 Category : Medical ISBN : 9780309083430
Care Without Coverage by Institute of Medicine,Board on Health Care Services,Committee on the Consequences of Uninsurance Pdf
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.
Institute of Medicine,Committee on Employment-Based Health Benefits
Author : Institute of Medicine,Committee on Employment-Based Health Benefits Publisher : National Academies Press Page : 381 pages File Size : 55,7 Mb Release : 1993-02-01 Category : Medical ISBN : 9780309048279
Employment and Health Benefits by Institute of Medicine,Committee on Employment-Based Health Benefits Pdf
The United States is unique among economically advanced nations in its reliance on employers to provide health benefits voluntarily for workers and their families. Although it is well known that this system fails to reach millions of these individuals as well as others who have no connection to the work place, the system has other weaknesses. It also has many advantages. Because most proposals for health care reform assume some continued role for employers, this book makes an important contribution by describing the strength and limitations of the current system of employment-based health benefits. It provides the data and analysis needed to understand the historical, social, and economic dynamics that have shaped present-day arrangements and outlines what might be done to overcome some of the access, value, and equity problems associated with current employer, insurer, and government policies and practices. Health insurance terminology is often perplexing, and this volume defines essential concepts clearly and carefully. Using an array of primary sources, it provides a store of information on who is covered for what services at what costs, on how programs vary by employer size and industry, and on what governments doâ€"and do not doâ€"to oversee employment-based health programs. A case study adapted from real organizations' experiences illustrates some of the practical challenges in designing, managing, and revising benefit programs. The sometimes unintended and unwanted consequences of employer practices for workers and health care providers are explored. Understanding the concepts of risk, biased risk selection, and risk segmentation is fundamental to sound health care reform. This volume thoroughly examines these key concepts and how they complicate efforts to achieve efficiency and equity in health coverage and health care. With health care reform at the forefront of public attention, this volume will be important to policymakers and regulators, employee benefit managers and other executives, trade associations, and decisionmakers in the health insurance industry, as well as analysts, researchers, and students of health policy.
Data Mining and Predictive Analytics by Daniel T. Larose Pdf
Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.
