Healthcare Risk Adjustment And Predictive Modeling

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.

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

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 (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

"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.

This book shows healthcare professionals how to turn data points into meaningful knowledge upon which they can take effective action. Actionable intelligence can take many forms, from informing health policymakers on effective strategies for the population to providing direct and predictive insights on patients to healthcare providers so they can achieve positive outcomes. It can assist those performing clinical research where relevant statistical methods are applied to both identify the efficacy of treatments and improve clinical trial design. It also benefits healthcare data standards groups through which pertinent data governance policies are implemented to ensure quality data are obtained, measured, and evaluated for the benefit of all involved. Although the obvious constant thread among all of these important healthcare use cases of actionable intelligence is the data at hand, such data in and of itself merely represents one element of the full structure of healthcare data analytics. This book examines the structure for turning data into actionable knowledge and discusses: The importance of establishing research questions Data collection policies and data governance Principle-centered data analytics to transform data into information Understanding the "why" of classified causes and effects Narratives and visualizations to inform all interested parties Actionable Intelligence in Healthcare is an important examination of how proper healthcare-related questions should be formulated, how relevant data must be transformed to associated information, and how the processing of information relates to knowledge. It indicates to clinicians and researchers why this relative knowledge is meaningful and how best to apply such newfound understanding for the betterment of all.

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.

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