Defining, Measuring, and Analyzing
Quality of Care: Statistical and Computational Challenges
Organizer: Sally C. Morton
(Sally_Morton@rand.org)
RAND

Description:
Quality of health care is generally thought to encompass two dimensions: the technical aspect of care, and the art of care. Poor quality can mean too much, too little, or the wrong care. How do we clearly define quality of care, measure it, assess whether it's changing over time, or analyze quality in other ways? This session will address these questions and others, with special focus on the statistical and computational challenges that arise.

After an introduction by a clinical expert, the first talk will focus on questions raised about the quality of care being delivered given the rapid expansion of managed care. In response to these concerns, quality management efforts have relied heavily on the measurement of performance. Associated with these measurement and improvement activities are various statistical and computing considerations. The first talk will provide an overview of a number of these considerations including: measure definitions, data availability and quality, risk adjustment, analytical and interventional use of results, predictive modeling, and thoughts about the future.

The second talk will consider the need to summarize information as multiple measures of health care quality proliferate, e.g., HEDIS, CAHPS and others. The speaker will discuss various ways of building aggregate scales and discuss some of the technical issues that must be overcome to produce reliable aggregate information. These issues include appropriate standard error calculations, developing weights for measures, and the communication of statistical uncertainty to the lay audience.

The final talk will describe the monitoring by the AIDS Institute of the New York State Department of Health of the quality of care delivered by hospitals, community health centers and drug treatment centers to individuals infected with HIV. A medical peer review organization visits these facilities each year and applies a number of protocols reflecting the standard of care to random samples of medical records. Bayesian techniques are used to model aspects of the quality of care. In order to make inferences from the models, simulation methods are applied and trellis graphics are used to display overall and among facility trends.

Format:
The session will consist of an introduction to quality of care by a clinical expert, followed by three methodological talks, and an open discussion period. A related Invited Poster Session is being organized, it will focus more widely on statistical methods used in health services research.

Participants:
Bruce Agins (presentation, The HIV Performance Measurement Perspective from NYC, Framing the Clinical Context)
Medical Director, AIDS Institute, New York State Department of Health.

Randall K. Spoeri (presentation, Measuring and Improving Quality in Managed Care: Some Statistical and Computing Issues)
The first talk will be given by Randall K. Spoeri, Vice President, Medical and Quality Informatics, HIP Health Plans, New York, New York. Dr. Randall K. Spoeri is Vice President of Medical and Quality Informatics at HIP Health Plans, a company with over one million enrollees in metropolitan New York City and Florida. He is responsible for all the data and analytical activities of a staff of over 40, located in New York City, New Jersey and Florida. He has a variety of experience in the health care industry, over the past 10 years, holding positions at Humana Inc., the U.S. Health Care Financing Administration (HCFA), the National Committee for Quality Assurance (NCQA), and most recently at NYLCare Health Plans, Inc.

John Adams (presentation, Building Aggregate Health Care Quality Scales)
The second talk will be given by John Adams, RAND, Santa Monica, California. Dr. John Adams is a Statistician in the RAND Health Program and is the head statistician for RAND's Survey Research Group. His research interests include computer-aided experimental design and model-based enhancement of sampling efficiency. Dr. Adams has participated in a wide range of research including developing rankings for hospital programs based on health outcomes, designing a national quality monitoring system for Medicare beneficiaries, and analyzing global climate change and strategies for its abatement. He recently taught a seminar on quality of care methods ("The Quality Toolbox" with co-instructor Dr. Beth McGlynn) at the 1999 Association for Health Services Research (AHSR) meetings.

Karl Heiner (presentation, Simulation in Models of Health Care Quality)
The third talk will be given by Karl Heiner, SUNY at New Paltz. Dr. Karl Heiner is a professor of Statistics in the Department of Business Administration at the State University of New York at New Paltz. He is a statistical consultant to the New York State AIDS Institute where he works on Bayesian approaches to the monitoring of the quality of health care. His research also includes statistical auditing. Dr. Heiner's co-author is David Laws, University of Sheffield. Dr. David Laws is a lecturer in the Department of Probability and Statistics at the University of Sheffield, England. His research is primarily into Bayesian modeling and methodology for financial and medical applications.

Marc N. Elliott, RAND; Richard Swartz, Rice University; John Adams, RAND; and Ron D. Hays, UCLA (poster, Casemix Adjustment of the National CAHPSO Benchmarking Data 1.0)
The poster will describe several casemix adjustment models. The authors will compare these models in terms of the nature and magnitude of the resulting adjustments using a national benchmarking database of the Consumer Assessment of Health Plans Survey (CAHPS) project's 1.0 survey of consumer satisfaction with health care plans.

I. Elaine Allen and Ingram Olkin (poster, Imputing Treatment Differences in Meta-Analyses with Missing Data)
I. Elaine Allen is from Babson College in Wellesley, Massachusetts and Ingram Olkin is from Stanford University, Stanford, California. The poster will discuss how to handle missing data in the meta-analysis of clinical data. Several imputation techniques will be described with examples from published meta-analyses. Methods include Bayesian hierarchical modeling to estimate random effects; multilevel mixed models to estimate treatment differences; and a proposed method to test the difference between active treatments when only placebo controlled studies exist.