Risk stratification is essential to effective population health management. To know which patients require what level of care, a platform for separating patients into high-risk, low-risk, and rising-risk is necessary. Several methods for stratifying a population by risk include: Hierarchical Condition Categories (HCCs), Adjusted Clinical Groups (ACG), Elder Risk Assessment (ERA), Chronic Comorbidity Count (CCC), Minnesota Tiering, and Charlson Comorbidity Measure. At Health Catalyst, we use an analytics application called the Risk Model Analyzer to stratify patients into risk categories. This becomes a powerful tool for filtering populations to find higher-risk patients.
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Over half the Pioneer ACOs have dropped from the program in the last four years, despite achieving $304 million in savings, and fifty percent of the participating ACOs receiving shared savings reimbursements. Why the exodus? Overutilization and inconsistent performance benchmarking and attribution hindered the ability of many participants to achieve success. The overall impact of the program, however, has been a positive one for value-based care. In the next 3-5 years, providers and health systems will bear more of the financial risk of the populations they serve. The proliferation of data, and the tools to analyze and exchange it, will be critical to the long-term success of value-based care.
OSF HealthCare has committed that 75 percent of its primary care patient will be part of a value-based program by 2020. The organization’s leaders knew that success depended on how well they managed their data and decided to build a data warehouse in-house. They recognized that beneficiary claims data was essential to understanding their population better. To get that claims data, however, was no easy task. This required patient matching through master data management and getting buy-in from leaders and physicians throughout the health system. Then, they prioritize where to start efforts using the 80/20 rule and using that as a guide, loaded the claims data.
Many healthcare organizations seem to have been in perpetual pilot stage while experimenting with value-based payment models. Healthcare organizations are focusing their efforts in two primary areas: developing the skills to successfully manage at-risk contracts and, preparing for the considerable business and care delivery transformation necessary for true population health management. But what are the foundational competencies needed to take on risk? Healthcare organizations should consider the following 5 key areas: 1) at-risk contract management, 2) network management, 3) care management, 4) performance monitoring, and 5) improvement prioritization. The value of analytics in each of these competency areas is to prioritize limited resources on the highest impact area.
Healthcare organizations preparing for the value-based payment model shift have found their internal resources pushed to the limit. Often, in an attempt to address regulatory timetables, systems will use point solutions rather than move toward a long-term strategy of developing robust clinical analytics. If an organization is using their EHR for analytics, they will soon discover that these built-in analytics packages cannot help them identify opportunities for cost effectiveness and clinical best practices. Sophisticated data management and healthcare analytics solutions, however, can provide leaders with the integrated clinical, financial, and patient satisfaction data they need to transform their systems into data-driven enterprises.
The ACO concept can be generically defined as a group of health care providers, potentially including doctors, hospitals, health plans and other health care constituents, who voluntarily come together to provide coordinated high-quality care to populations of patients This article, written by two physician executives with years of accountable care experience, gives a robust overview of the ACO concept including: the history, range of payment models, the new accountability and payment structures, a comparison between traditional insurance vs ACO models, key barriers and challenges, and most importantly, the key criteria needed for ACO success.
An ACO will fail without precise patient population definitions. ACOs need to define populations for many reasons, including identifying their members and attributing those patients to the correct physician and performing population health analytics. The challenges to a good population definition are: multiple providers per member, multiple data sources, and multiple identifiers for each member. Using a clinical integration hierarchy to refine population and subpopulations will solve a lot of these issues. A data warehouse is the foundation that makes it possible.
Health systems and large physician groups will need to focus on lowering their cost structures to survive in a value-based future. To succeed, systems must understand their cost structures on a granular level. Only at this detailed view can they identify variation, find the causes for it, and fix it. An enterprise data warehouse provides the platform for aggregating data from clinical and financial system into usable analytics.
Meaningful Use and ACO reports are just two of a plethora of ever-increasing external healthcare reporting requirements. An EMR is only a partial solution due to limitations in data turnaround time, data and logic multi-purposing, and being relegated to single-vendor, homogenous environments. Learn about a solution that helps you streamline your Meaningful Use and reporting requirements and can be leveraged for clinical quality improvement, population health and predictive analytics.
The best solution for leveraging data to drive clinical and financial improvement in an ACO environment is a healthcare enterprise data warehouse (EDW) with a flexible, Late-Binding™ architecture. Why? Because a successful analytics solution for an ACO must be one that: i. 1. Gives rapid time-to-value. ii. 2. Adapts easily to the changing needs of an organization.
Accountable care is changing the way Payers and Providers look at their healthcare data. Many healthcare enterprises believed that their Electronic Health Record (EHR) would be the silver bullet to this data problem, but they are beginning to discover the limitations of the EHR for managing at the enterprise-level all of the information necessary for effective risk-sharing. Health information exchanges (HIEs) help eliminate data silos but are not designed to store or analyze the data with the level of sophistication required for supporting a risk-sharing model. The reality is, until now, providers and payers have lacked consistent incentives to share data.
How would an Accountable Care approach change how a patient is treated? It’s important to recognize that accountable care isn’t just a piece of legislation or a new organizational or payment structure. Nor is it just applying technological advances to make healthcare more efficient. It is a fundamental shift in making people accountable for how care is delivered and experienced. And it is founded in the shared responsibility we all have—patients, providers and payers—to make sure our healthcare dollars are used wisely and well. In this Insight, Luke shares an example of a healthcare episode of resource misallocation that could have been avoided by accountable care.
More and more, healthcare is molded and critically impacted by the software and information technology that surrounds and supports the industry. As a consequence, the C-level suite beyond the CIO must actively participate in the evolution of their organization’s IT strategy, particularly at the layer of technology where software directly supports workflows and business processes.There are five information systems that are indispensable to the success of an Accountable Care Organization (ACO). Those five critical information systems are 1) An Electronic Medical Record (EMR), 2) A Health Information Exchange (HIE), 3) An Activity Based Costing (ABC) system, 4) A Patient Reported Outcomes (PRO) system, and 5) An Enterprise Data Warehouse (EDW).
Many CIOs, along with their other C-suite colleagues, are anticipating a catharsis on completing massive EHR deployment projects. Before long, however, they come to the unwelcome realization that the EHR is just one component needed to provide the actionable intelligence health systems need to survive in a value-based purchasing environment.