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

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.

Like its predecessors, the fourth edition of Risk Adjustment for Measuring Healthcare Outcomes presents the fundamental principles and concepts of risk adjustment for comparing outcomes of care and explains why risk adjustment is a critical tool for measuring quality and setting reimbursement rates. This book is a comprehensive guide to the issues raised by risk adjustment, including the pros and cons of different data sources, the validity and reliability of risk adjustment methods, the effects of various statistical modeling approaches, and concerns relating to special populations. The fourth edition features: A new chapter on the role of risk adjustment in managing healthcare organizations New information on risk factors, including genetics and social and environmental determinants of health Perspectives on electronic health records and new health information technologies Explanations of new statistical methods for comparing provider outcomes and their implications for risk adjustment

An Essential text on transforming raw data into concrete health care improvements Now in its second edition, The Health Care Data Guide: Learning from Data for Improvement delivers a practical blueprint for using available data to improve healthcare outcomes. In the book, a team of distinguished authors explores how health care practitioners, researchers, and other professionals can confidently plan and implement health care enhancements and changes, all while ensuring those changes actually constitute an improvement. This book is the perfect companion resource to The Improvement Guide: A Practical Approach to Enhancing Organizational Peformance, Second Edition, and offers fulsome discussions of how to use data to test, adapt, implement, and scale positive organizational change. The Health Care Data Guide: Learning from Data for Improvement, Second Edition provides: Easy to use strategies for learning more readily from existing health care data Clear guidance on the most useful graph for different types of data used in health care A step-by-step method for making use of highly aggregated data for improvement Examples of using patient-level data in care Multiple methods for making use of patient and other feedback data A vastly better way to view data for executive leadership Solutions for working with rare events data, seasonality and other pesky issues Use of improvement methods with epidemic data Improvement case studies using data for learning A must read resource for those committed to improving health care including allied health professionals in all aspects of health care, physicians, managers, health care leaders, and researchers.

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.

The U.S. healthcare system is in "complete chaos-disarray." Medical costs have increased significantly over the past 6 years with 70% increase for deductibles and 24% or more for health insurance premiums. All the while, workers earnings have either not increased or if they did, the pay raises were for less than the increase in the cost of medical care. The situation is unsustainable and the public wants the system fixed. This book offers ways of fixing the problems in healthcare. HEALTHCARE's OUT SICK - PREDICTING A CURE - Solutions that WORK !!!! first defines the "healthcare in crisis" problem. Through real patient experiences, the book describes the difficulties of getting through the maze of complexity among the plethora of "silo providers" which make up the industry. The heart of the book provides readers with a comprehensive solution that can work, a disruption that is necessary to provide Americans the medical care they need without the US public and healthcare providers and payors going into bankruptcy, insolvency or closure. This book delves into digitized medicine, payor and provider reimbursement models, and value-based healthcare delivery. It also includes a philosophy or mode of thinking and operation for the solutions that are needed for diagnosis-effective, cost-effective, and time-efficient healthcare delivery, of which digitized medicine, value-based care, and payor reimbursement modes are just some of the factors. The authors propose that the real solution involves having the patient at the center of the issues and changing from an archaic gold standard way of thinking to a "Predictive Analytic thinking" where one gets at the real truth by doing "real science" that in the end becomes effective not only for the population but for the individual person. This all leads to real person-centered and person-directed medicine and healthcare delivery.