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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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The care management team concept has evolved over the last decade to be more patient- and data-driven. Truly modern care management teams—those that represent the future of care management—provide team-based care that is carefully planned, comprehensive, highly coordinated, data driven, evidence based, seamless, and patient centric. But what’s equally important as being patient-centric and patient-driven, is relying on a comprehensive, effective care management system—a suite of tools with features in five core competencies: Data integration. Patient stratification and intake. Care coordination. Patient engagement. Performance measurement. As the industry’s care management teams continue to evolve (e.g., using predictive analytics to proactively identify patients), their primary goal remains: achieving optimal outcomes for the patients they serve.

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If all goes according to plan, the first performance period for the new Medicare Access and Chip Reauthorization Act (MACRA) is just around the calendar corner. It’s a complicated reimbursement structure with multiple tracks that are guaranteed to reward with bonuses or inflict pain through penalties in CMS’s new zero sum game. To the physicians and practices that adopt this new program early and position themselves for the best fiscal outcomes, go the spoils. But for many smaller practices and those that consistently underperform, the outlook may be glum regardless. Here are some highlights of the new program and the financial impact it will have on clinicians and practices.

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There are clear signs the healthcare industry is in the midst of a shift to value-based reimbursement. The most noticeable signs are the recent and proposed 2015 rulings from CMS. There are four areas in value-based reimbursement that will be impacted by the end of 2015: the physician payment structure, bundled payments, Inpatient Prospective Payment Systems regulations, and commercial payers. To survive the shift to value-based reimbursement, it’s important for providers and payers to take three steps: provide access to rich data, share knowledge and learn from each other, develop strategies by doing assessments.

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Hospital readmissions rates are now at 3 percent, which means that health systems are feeling the financial burden of decreased payments from Medicare. They also need to track two more 30-day readmission rates. While there aren’t any new penalty measures planned for 2016, coronary artery bypass grafts will be added as yet another measure to track in 2017. By using three strategies to reduce readmission rates, health systems will experience better outcomes and decreased penalties. The three strategies include the following: (1) implementing a data warehouse that provides a single source of truth; (2) engaging a multidisciplinary team to lead the improvement efforts; (3) installing a sophisticated analytics platform.

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Finding the perfect data governance environment is an elusive target. It’s important to govern to the least extent necessary in order to achieve the greatest common good. With the three data governance cultures, authoritarian, tribal, and democratic, the latter is best for a balanced, productive governance strategy. The Triple Aim of data governance is: 1) ensuring data quality, 2) building data literacy, and 3) maximizing data exploitation for the organization’s benefit. The overall strategy should be guided by these three principles under the guidance of the data governance committee. Data governance committees need to be sponsored at the executive board and leadership level, with supporting roles defined for data stewards, data architects, database and systems administrators, and data analysts. Data governance committees need to avoid the most common failure modes: wandering, technical overkill, political infighting, and bureaucratic red tape. Healthcare organizations that are undergoing analytics adoption will also go through six phases of data governance including: 1) establishing the tone for becoming a data-driven organization, 2) providing access to data, 3) establishing data stewards, 4) establishing a data quality program, 5) exploiting data for the benefit of the organization, 6) the strategic acquisition of data to benefit the organization. As U.S. healthcare moves into its next stage of evolution, the organizations that will survive and thrive will be those who most effectively acquire, analyze, and utilize their data to its fullest extent. Such is the mission of data governance.

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Avoidable readmissions are a major financial major problem for the healthcare industry, especially for government payers. To tackle this problem, CMS launched the Hospital Readmissions Reduction Program (HRRP). While some hospitals may be able to absorb the financial penalties under HRRP, they still need to track increasingly complex reporting metrics. Most tracking solutions are inadequate for today’s complicated reporting needs. A healthcare enterprise data warehouse and analytics applications, however, are designed to solve the numerous reporting burdens. When used together, they also deliver a robust solution that enables hospitals to track and drive real cost and quality improvement initiatives, all without the need for users to be technical experts.

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The Medicare Access and CHIP Reauthorization Act (MACRA) overhauls the payment system for Medicare providers. It’s a complex program that requires careful study so physicians can make the best choice for how they want to report. This choice ultimately impacts reimbursement and the potential bonuses or penalties associated with each reporting option. This FAQ covers both tracks of the new rule, the Merit-based Incentive Payment System (MIPS), and the Advanced Alternative Payment Model (APM), with a background review and a comprehensive list of questions and answers. It’s a practical guide complete with next steps for strategic and tactical planning.

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A systematic approach to performance improvement initiative includes three components: analytics, content, and deployment. Taking six steps will help an organization to effectively cover all three components of success. Step 1: Integrate performance improvement into your strategic objectives. Step 2: Use analytics to unlock data and identity areas of opportunity. Step 3: Prioritize programs using a combination of analytics and a deployment system. Step 4: Define the performance improvement program’s permanent teams. Step 5: Use a content system to define program outcomes and define interventions. Step 6: Estimate the ROI.

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According to the Centers of Disease Control (CDC), an estimated 70,000 patients die each year from hospital-associated infections (HAIs): contrast the CDC statistic with the fact that only 35,000 people die each year in the U.S. from motor vehicle accidents. Learn key best practices in patient safety and quality including: patient safety as a team sport, the added challenges of healthcare being the most complex, adaptive system, and how culture, analytics, and content contribute to improve outcomes and lower costs.

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At a time when average hospital’s margins are stagnating, executives should be asking tough questions about the ROI of "indispensable" technologies. Will new technologies prove their worth or drive them further into the red? How do you measure and track ROI? We need to educate clinicians on financial metrics and finance people need to learn more about the clinical processes and outcomes. One of the historical problems with calculating ROI has been the fundamental culture divide between clinicians and finance. Gone should be the days that clinicians deliver care without knowing the financial cost of that care. This slide set give practical advice on how to set goals, measure ROI and gives excel templates that are based on years of experience by the authors

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Business intelligence may hold tremendous promise but it can’t answer healthcare’s challenges unless it’s built on the solid foundation of a clinical data warehouse. Learn the definition of business intelligence, why a clinical data warehouse is needed for any healthcare BI strategy, the various options in data warehousing, which one is most effective for hospitals and the industry and why.

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The shift from fee-for-service to value-based reimbursements has good and bad consequences for healthcare. While the shift will ultimately help health systems provide higher quality lower cost care, the transition may be financially disastrous for some. In addition, the shifting revenue mix from commercial payers to Medicare and Medicaid is creating its own set of challenges. There are, however, three keys to surviving the transition: 1) Effectively manage shared savings programs to maximize reimbursement. 2) Improve operating costs. 3) Increase patient volumes. With an analytics foundation, health systems will be able to meet and survive today’s healthcare challenges.

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Healthcare data is not linear. It is a complex, diverse beast unlike the data of any other industry. There are five ways in particular that make healthcare data unique: 1. Much of the data is in multiple places. 2. The data is structured and unstructured. 3. It has inconsistent and variable definitions; evidence-based practice and new research is coming out every day. 4. The data is complex. 5. Changing regulatory requirements. The answer for this unpredictability and complexity is the agility of a late-binding Data Warehouse.

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What are the differences between a database and a data warehouse? A database is any collection of data organized for storage, accessibility, and retrieval. A data warehouse is a type of database the integrates copies of transaction data from disparate source systems and provisions them for analytical use. The important distinction is that data warehouses are designed to handle analytics required for improving quality and costs in the new healthcare environment. A transactional database, like an EHR, doesn’t lend itself to analytics.

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