Introducing the Health Catalyst Monitor™ Patient Safety Suite: Surveillance Module

Unlike the standard post-event reporting process, the Patient Safety Monitor Suite: Surveillance Module is a trigger-based surveillance system, enabled by the unique industry-first technological capabilities of the Health Catalyst Data Operating System platform, including predictive analytic models and AI. The Health Catalyst PSO creates a secure and safe environment where clients can collect and analyze patient safety events to learn and improve, free from fear of litigation. Coupled with patient safety services, an organization’s active all-cause harm patient safety system is fully enabled to deliver measurable and meaningful improvements.

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Five Deming Principles That Help Healthcare Process Improvement

Dr. John Haughom explains 5 key Deming processes that can be applied to healthcare process improvement. These include 1) quality improvement as the science of process management, 2) if you cannot measure it, you cannot improve it, 3) managed care means managing the processes of care (not managing physicians and nurses), 4) the importance of the right data in the right format at the right time in the right hands, and 5) engaging the “smart cogs” of healthcare.

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Linking Clinical and Financial Data: The Key to Real Quality and Cost Outcomes

Since accountable care took the healthcare industry by a storm in 2010, health systems have had to move from their predictable revenue streams based on volume to a model that includes quality measures. While the switch will ultimately improve both quality and cost outcomes, health systems now need the capability of tracking and analyzing the data from both clinical and financial systems. A late-binding enterprise data warehouse provides the flexible architecture that makes it possible to liberate both kinds of data to link it together to provide a full picture of trends and opportunities.

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Database vs Data Warehouse: A Comparative Review

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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Precision Medicine: Four Trends Make It Possible

When realized, the promise of precision medicine (to specifically tailor treatment to each individual) stands to transform healthcare for the better by delivering more effective, appropriate care. To date, to achieve precision medicine, health systems have faced financial, data management, and interoperability barriers. Current trends in healthcare, however, will give researchers and clinicians the quality and breadth of health data, biological information, and technical sophistication to overcome the challenges to achieving precision medicine. Four notable trends in healthcare will bolster to growth of precision medicine in the coming years: Decision support methods harness the power of the human genome. Healthcare leverages big data analytics and machine learning. Reimbursement methods incentivize health systems to keep patients well. Emerging tools enable more…

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Transforming Healthcare Analytics: Five Critical Steps

By committing to transforming healthcare analytics, organizations can eventually save hundreds of millions of dollars (depending on their size) and achieve comprehensive outcomes improvement. The transformation helps organizations achieve the analytics efficiency needed to navigate the complex healthcare landscape of technology, regulatory, and financial challenges and the challenges of value-based care. To achieve analytics transformation and ROI within a short timeframe, organizations can follow five phases to become data driven: Establish a data-driven culture. Acquire and access data. Establish data stewardship. Establish data quality. Spread data use.

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The Future of Healthcare AI: An Honest, Straightforward Q&A

Health Catalyst President of Technology, Dale Sanders, gives straightforward answers to tough questions about the future of AI in healthcare. He starts by debunking a common belief: We are awash in valuable data in healthcare as a consequence of EHR adoption. The truth involves a need for deeper data about a patient.

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How Healthcare Text Analytics and Machine Learning Work Together to Improve Patient Outcomes

Healthcare organizations that leverage both text analytics and machine learning are better positioned to improve patient outcomes. Used in tandem, text analytics and machine learning can significantly improve the accuracy of risk scores, used widely in healthcare to help clinicians identify patients at high risk for certain conditions and, therefore, intervene. Health systems can run machine learning models with input from text analytics to provide tailored risk predictions on both unstructured and structured data. The result? More accurate risk scores and the ability to identify every patient’s level of risk in time to inform decisions about their care.

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Healthcare Dashboards vs. Scorecards: Use Both to Improve Outcomes

Healthcare IT leaders tend to debate over which tool is best for measuring and sustaining outcomes improvement goals: healthcare dashboards or scorecards. But using both tools is the most effective approach. “Scoreboards” take advantage of the high-level, strategic capacity of scorecards and the real-time, operational functionality of dashboards. But using both effectively requires a thorough understanding of the who, what, when, and how of each tool. Who: Scorecards are for leaders; dashboards are for the frontline. What: Scorecards are strategic; dashboards are operational. When: Scorecards are daily, weekly, or monthly reports; dashboards are real-time or near real-time. How: Scorecards enforce accountability and provide actionable data; dashboards provide drill-down capability and inform root cause. Despite the different but equally important aspects…

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Value-Based Purchasing: Four Need-to-Know Domains for 2018

Health systems that meet the 2018 Hospital Value-Based Purchasing Program measures stand to benefit from CMS’s $1.9 billion incentive pool. Under the 2018 regulations, CMS continues to emphasize quality. To reduce the risk of penalty and vie for bonuses, it’s increasingly critical that organizations leverage data to build skills and processes that meet more demanding reimbursement measures. To thrive under value-based payment, healthcare systems must understand CMS’s four quality domains, and their associated measures, for 2018: Clinical Care Patient- and Caregiver-Centered Experience of Care/Care Coordination Efficiency and Cost Reduction Safety

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A Guide to Applying Quality Improvement to Healthcare: Five Principles

Healthcare is an art and a science. What many in the industry don’t understand is that systems and processes can coexist with personalized care. Quality improvement methods can be as effective in healthcare as they have been in other industries (e.g., agriculture, manufacturing, etc.). Quality improvement in healthcare is not just achievable, it’s an absolute necessity given the amount of wasteful spending in the U.S. on healthcare. Organizations can reduce this wasteful spending while improving their processes by applying these five guiding principles: Facilitate adoption through hands-on improvement projects. Define quality and get agreement. Measure for improvement, not accountability. Use a quality improvement framework and PDSA cycles. Learn from variation in data. By using these principles and starting small, organizations…

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Quality Data Is Essential for Doctors Concerned with Patient Engagement

It might be a bit of a leap to associate quality data with improving the patient experience. But the pathway is apparent when you consider that physicians need data to track patient diagnoses, treatments, progress, and outcomes. The data must be high quality (easily accessible, standardized, comprehensive) so it simplifies, rather than complicates, the physician’s job. This becomes even more important in the pursuit of population health, as care teams need to easily identify at-risk patients in need of preventive or follow-up care. Patients engaged in their own care via portals and personal peripherals contribute to the volume and quality of data and feel empowered in the process. This physician and patient engagement leads to improved care and outcomes, and,…

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Why Health Systems Must Use Data Science to Improve Outcomes

In today’s improvement-driven healthcare environment, organizations must ensure that improvement measures help them reach desired outcomes and focus on the opportunities with optimal ROI. With data science-based analysis, health systems leverage machine learning to determine if improvement measures align with specific outcomes and avoid the risk and cost of carrying out interventions that are unlikely to support their goals. There are four essential reasons that insights from data science help health systems implement and sustain improvement: Measures aligned with desired outcomes drive improvement. Improvement teams focus on processes they can impact. Outcome-specific interventions might impact other outcomes. Identifies opportunities with optimal ROI.

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Prioritizing Healthcare Projects to Optimize ROI

Healthcare organizations have long relied on traditional benchmarking to compare their performance to others and determine where they can do better; however, to identify the highest ROI improvement opportunities and understand how to take action, organizations need more comprehensive data. Next-generation opportunity analysis tools, such as Health Catalyst® Touchstone™, use machine learning to identify projects with the greatest need for improvement and the greatest potential ROI. Because Touchstone determines prioritization with data from across the continuum of care, users can drive improvement decisions with information appropriate to their patient population and the domains they’re addressing.

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Care Management Analytics: Six Ways Data Drives Program Success

To succeed in improving outcomes and lowering costs, care management leaders must begin by selecting the patients most likely to benefit from their programs. To identify the right high-risk and rising-risk patients, care managers need data from across the continuum of care and tools to help them access that knowledge when they need it. Analytics-driven technology helps care managers identify patients for their programs and manage their care to improve outcomes and lower costs in six key ways: Identifies rising-risk patients. Uses a specific social determinant assessment to capture factors beyond claims data. Integrates EMR data to achieve quality measures. Identifies patients for palliative or hospice care. Identifies patients with chronic conditions. Increases patient engagement.

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Four Population Health Management Strategies that Help Organizations Improve Outcomes

Population health management (PHM) strategies help organizations achieve sustainable outcomes improvement by guiding transformation across the continuum of care, versus focusing improvement resources on limited populations and acute care. Because population health comprises the complete picture of individual and population health (health behaviors, clinical care social and economic factors, and the physical environment), health systems can use PHM strategies to ensure that improvement initiatives comprehensively impact healthcare delivery. Organizations can leverage four PHM strategies to achieve sustainable improvement: Data transformation Analytic transformation Payment transformation Care transformation

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The Real Opportunity of Precision Medicine and How to Not Miss Out

Precision medicine, defined as a new model of patient-powered research that will give clinicians the ability to select the best treatment for an individual patient, holds the key that will allow health IT to merge advances in genomics research with new methods for managing and analyzing large data sets. This will accelerate research and biomedical discoveries. However, clinical improvements are often designed to reduce variation. So, how do systems balance tailoring medicine to each patient with standardizing care? The answer is precise registries. For example, using registries that can account for the most accurate, specific patients and disease, clinicians can use gene variant knowledge bases to provide personalized care.

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Critical Healthcare M&A Strategies: A Data-driven Approach

Historically technology and talent were primary assets used to weigh the value of M&A activity, but data is an equal pillar. Buyers (the acquiring organizations) face enormous responsibility and risk with M&A transactions. C-suite leaders have a lot to consider—enterprise-wide technology, finances, operations, facilities, talent, processes, workflows, etc.—during the due diligence process. But attention is often heavily weighted toward time-honored balance sheet and facility assets rather than next-generation assets with the long-term strategic value in the M&A process: data. The model for conducting due diligence around data involves four disciplines: Establish the strategic objectives of the M&A with the leadership team. Prioritize data along with the standardization of solutions and the design of a new IT organization (i.e., a co-equal…

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Measuring the Value of Care Management: Five Tools to Show Impact

To earn legitimacy and resources within a healthcare organization, care management programs need objective, data-driven ways to demonstrate their success. The value of care management isn’t always obvious; while these programs may, in fact, be responsible for improvements in critical metrics, such as reducing readmissions, C-suite leaders need visibility into care management’s impact and processes to understand precisely how they’re improving care and lowering costs at their organizations. Five analytics-driven technologies give healthcare leaders a comprehensive understanding of care management performance: The Patient Stratification Application The Patient Intake Tool The Care Coordination Application The Care Companion Application The Care Team Insights Tool

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Advanced Analytics Holds the Key to Achieve the Triple Aim and Survive Value-based Purchasing

Every hospital and health system has to juggle significant IT needs with a limited budget. In the middle of these demands and possibilities, hospital executives have to prioritize and decide which technology solutions are the most critical to the health of their organization. I call these most critical IT solutions “survival software.” Advanced clinical analytics solutions are the survival software of the near future, as they really hold the key to achieving the triple aim and survive value-based purchasing.

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Healthcare Data Warehouse Models Explained

Want to know the best healthcare data warehouse for your organization? You’ll need to start first by modeling the data, because the data model used to build your healthcare enterprise data warehouse (EDW) will have a significant effect on both the time-to-value and the adaptability of your system going forward. Each of the models I describe below bind data at different times in the design process, some earlier, some later. As you’ll see, we believe that binding data later is better. The three approaches are 1) the enterprise data model, 2) the independent data model, and 3) the Health Catalyst Late-Binding™ approach.

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The Top 8 Skills Every Healthcare Process Improvement Leader Must Have

Healthcare process improvement leaders not only have to be a jack-of-all-trades, but they need to be a master, as well. This is one of the most important leadership roles in the healthcare system with responsibilities that can ultimately end up saving lives, improving the patient experience, improving caregiver job satisfaction, and reducing costs. Although there are many others, these eight skills are the most critical for the efficient, and ultimately, successful process improvement leader: Communication Trust Building Coaching Understanding Process Management Understanding Care Management Personnel Constructive Accountability and Constructive Conflict Resiliency and Persistency Seeing the Big Picture Along with the right training, education, and sponsorship, it’s easy to see why this role blends many elements of art and science.

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The Dangers of Commoditized Machine Learning in Healthcare: 5 Key Differentiators that Lead to Success

Many vendors deliver machine learning models with different applications in healthcare. But they don’t all deliver accurate models that are easy to implement, targeted to a specific use case, connected to actionable interventions, and surrounded by a machine learning community and support team with extensive, exclusive healthcare experience. These machine learning qualities are possible only through a machine learning model delivered by a vendor with a unique set of capabilities. There are five differentiators behind effective machine learning models and vendors: Vendor’s expertise and exclusive focus on healthcare. Machine learning model’s access to extensive data sources. Machine learning model’s ease of implementation. Machine learning model’s interpretability and buy-in. Machine learning model’s conformance with privacy standards. These five factors separate the…

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The Key to Healthcare Mergers and Acquisitions Success: Don’t Rip and Replace Your IT

Healthcare mergers and acquisitions can involve a lot of EMRs and other IT systems. Sometimes leaders feel like they have to rip and replace these systems to fully integrate organizations. However, this is not the answer, according to Dale Sanders. This report, based upon his July 2017 webinar, outlines the importance of a data-first strategy and introduces the Health Catalyst® Data Operating System (DOS™) platform. DOS can play a critical role in facilitating IT strategy for the growing healthcare M&A landscape.

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