Since Maine established the first All Payer Claims Database (APCD) in 2003, 10 additional states have established APCDs and 30 others are in development or show strong interest in establishing APCDs. APCDs are generally mandated by legislation, though voluntary efforts exist. They are administered through various agencies, including state health departments or other governmental agencies and private not-for-profit organizations. APCDs receive funding from various sources, including legislative appropriations and private foundations. To ensure sustainability, APCDs must also consider the sale of data access and reports as a source of revenue. With the advent of the Affordable Care Act, there has been an increased interest in APCDs as a data source to aid in health care reform. The call for greater transparency in health care pricing and quality, development of Patient-Centered Medical Homes (PCMHs) and Accountable Care Organizations (ACOs), expansion of state Medicaid programs, and establishment of health insurance and health information exchanges have increased the demand for the type of administrative claims data contained in an APCD. Data collection, management, analysis, and reporting issues are examined with examples from implementations of live APCDs. Developing data intake, processing, warehousing, and reporting standards are discussed in light of achieving the triple aim of improving the individual experience of care; improving the health of populations; and reducing the per capita costs of care. APCDs are compared and contrasted with other sources of state-level health care data, including hospital discharge databases, state departments of insurance records, and institutional and consumer surveys. The benefits and limitations of administrative claims data are reviewed. Specific issues addressed with examples include implementing transparent reporting of service prices and provider quality, maintaining master patient and provider identifiers, validating APCD data and comparison with o ther state health care data available to researchers and consumers, defining data suppression rules to ensure patient confidentiality and HIPAA-compliant data release and reporting, and serving multiple end users, including policy makers, researchers, and consumers with appropriately consumable information.

Read the paper (PDF). | Watch the recording.

With the move to value-based benefit and reimbursement models, it is essential toquantify the relative cost, quality, and outcome of a service. Accuratelymeasuring the cost and quality of doctors, practices, and health systems iscritical when you are developing a tiered network, a shared savings program, ora pay-for-performance incentive. Limitations in claims payment systems requiredeveloping methodological and statistical techniques to improve the validityand reliability of provider's scores on cost and quality of care. This talkdiscusses several key concepts in the development of a measurement systemfor provider performance, including measure selection, risk adjustment methods,and peer group benchmark development.

Read the paper (PDF). | Watch the recording.

There are times when the objective is to provide a summary table and graph for several quality improvement measures on a single page to allow leadership to monitor the performance of measures over time. The challenges were to decide which SAS^® procedures to use, how to integrate multiple SAS procedures to generate a set of plots and summary tables within one page, and how to determine whether to use box plots or series plots of means or medians. We considered the SGPLOT and SGPANEL procedures, and Graph Template Language (GTL). As a result, given the nature of the request, the decision led us to use GTL and the SGRENDER procedure in the %BXPLOT2 macro. For each measure, we used the BOXPLOTPARM statement to display a series of box plots and the BLOCKPLOT statement for a summary table. Then we used the LAYOUT OVERLAY statement to combine the box plots and summary tables on one page. The results display a summary table (BLOCKPLOT) above each box plot series for each measure on a single page. Within each box plot series, there is an overlay of a system-level benchmark value and a series line connecting the median values of each box plot. The BLOCKPLOT contains descriptive statistics per time period illustrated in the associated box plot. The discussion points focus on techniques for nesting the lattice overlay with box plots and BLOCKPLOTs in GTL and some reasons for choosing box plots versus series plots of medians or means.

SAS/QC^® provides procedures, such as PROC SHEWHART, to produce control charts with centerlines and control limits. When quality improvement initiatives create an out-of-control process of improvement, centerlines and control limits need to be recalculated. While this is not a complicated process, producing many charts with multiple centerline shifts can quickly become difficult. This paper illustrates the use of a macro to efficiently compute centerlines and control limits when one or more recalculations are needed for multiple charts.

Researchers, patients, clinicians, and other health-care industry participants are forging new models for data-sharing in hopes that the quantity, diversity, and analytic potential of health-related data for research and practice will yield new opportunities for innovation in basic and translational science. Whether we are talking about medical records (for example, EHR, lab, notes), administrative data (claims and billing), social (on-line activity), behavioral (fitness trackers, purchasing patterns), contextual (geographic, environmental), or demographic data (genomics, proteomics), it is clear that as health-care data proliferates, threats to security grow. Beginning with a review of the major health-care data breeches in our recent history, we highlight some of the lessons that can be gleaned from these incidents. In this paper, we talk about the practical implications of data sharing and how to ensure that only the right people have the right access to the right level of data. To that end, we explore not only the definitions of concepts like data privacy, but we discuss, in detail, methods that can be used to protect data--whether inside our organization or beyond its walls. In this discussion, we cover the fundamental differences between encrypted data, 'de-identified', 'anonymous', and 'coded' data, and methods to implement each. We summarize the landscape of maturity models that can be used to benchmark your organization's data privacy and protection of sensitive data.

Read the paper (PDF). | Watch the recording.

The era of big data and health care reform is an exciting and challenging time for anyone whose work involves data security, analytics, data visualization, or health services research. This presentation examines important aspects of current approaches to quality improvement in health care based on data transparency and patient choice. We look at specific initiatives related to the Affordable Care Act (for example, the qualified entity program of section 10332 that allows the Centers for Medicare and Medicaid Services (CMS) to provide Medicare claims data to organizations for multi-payer quality measurement and reporting, the open payments program, and state-level all-payer claims databases to inform improvement and public reporting) within the context of a core issue in the era of big data: security and privacy versus transparency and openness. In addition, we examine an assumption that underlies many of these initiatives: data transparency leads to improved choices by health care consumers and increased accountability of providers. For example, recent studies of one component of data transparency, price transparency, show that, although health plans generally offer consumers an easy-to-use cost calculator tool, only about 2 percent of plan members use it. Similarly, even patients with high-deductible plans (presumably those with an increased incentive to do comparative shopping) seek prices for only about 10 percent of their services. Anyone who has worked in analytics, reporting, or data visualization recognizes the importance of understanding the intended audience, and that methodological transparency is as important as the public reporting of the output of the calculation of cost or quality metrics. Although widespread use of publicly reported health care data might not be a realistic goal, data transparency does offer a number of potential benefits: data-driven policy making, informed management of cost and use of services, as well as public health benefits through, for example, the rec ognition of patterns of disease prevalence and immunization use. Looking at this from a system perspective, we can distinguish five main activities: data collection, data storage, data processing, data analysis, and data reporting. Each of these activities has important components (such as database design for data storage and de-identification and aggregation for data reporting) as well as overarching requirements such as data security and quality assurance that are applicable to all activities. A recent Health Affairs article by CMS leaders noted that the big-data revolution could not have come at a better time, but it also recognizes that challenges remain. Although CMS is the largest single payer for health care in the U.S., the challenges it faces are shared by all organizations that collect, store, analyze, or report health care data. In turn, these challenges are opportunities for database developers, systems analysts, programmers, statisticians, data analysts, and those who provide the tools for public reporting to work together to design comprehensive solutions that inform evidence-based improvement efforts.

This unique culture has access to lots of data, unstructured and structured; is innovative, experimental, groundbreaking, and doesn't follow convention; and has access to powerful new infrastructure technologies and scalable, industry-standard computing power like never seen before. The convergence of data, and innovative spirit, and the means to process it is what makes this a truly unique culture. In response to that, SAS^® proposes The New Analytics Experience. Attend this session to hear more about the New Analytics Experience and the latest Intel technologies that make it possible.

The NH Citizens Health Initiative and the University of New Hampshire Institute for Health Policy and Practice, in collaboration with Accountable Care Project (ACP) participants, have developed a set of analytic reports to provide systems undergoing transformation a capacity to compare performance on the measures of quality, utilization, and cost across systems and regions. The purpose of these reports is to provide data and analysis on which our ACP learning collaborative can share knowledge and develop action plans that can be adopted by health-care innovators in New Hampshire. This breakout session showcases the claims-based reports, powered by SAS^® Visual Analytics and driven by the New Hampshire Comprehensive Health Care Information System (CHIS), which includes commercial, Medicaid, and Medicare populations. With the power of SAS Visual Analytics, hundreds of pages of PDF files were distilled down to a manageable, dynamic, web-based portal that allows users to target information most appealing to them. This streamlined approach reduces barriers to obtaining information, offers that information in a digestible medium, and creates a better user experience. For more information about the ACP or to access the public reports, visit http://nhaccountablecare.org/.