Four Essential Ways Control Charts Guide Healthcare Improvement

Control charts are a critical asset to any health system seeking effective, sustainable improvement. With a simple three-line format, control charts show process change over time, including the average of the data, upper control limit, and lower control limit. This insight helps improvement teams monitor projects, understand opportunities and the impact of initiatives, and sustain improved processes. Also known as Shewhart charts or statistical process control charts, control charts drive effective improvement by addressing three fundamental questions: What is the goal of the improvement project? How will the organization know that a change is an improvement? What change can the organization make that will result in improvement?

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Employer Health Plans: Keys to Lowering Cost, Boosting Benefits

Employers that offer robust employee health plans at affordable costs are more likely to attract and retain a great workforce. Healthcare, however, is often a top expense for organizations, making balancing attractive benefits with attractive costs a complex undertaking. Employers need a deep understanding of employee populations and opportunities to manage health plan costs without sacrificing quality. An analytics-driven approach to employee population health management gives employers insight into two key steps to lower healthcare costs and enhance benefits: Manage easily fixed cost issues. Use healthcare cost savings to fund expanded benefits.

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Machine Learning in Healthcare: What C-Suite Executives Must Know to Use it Effectively in Their Organizations

Machine learning (ML) is gaining in popularity throughout healthcare. ML’s far-reaching benefits, from automating routine clinical tasks to providing visibility into which appointments are likely to no-show, make it a must-have in an industry that’s hyper focused on improving patient and operational outcomes. This executive report—co-written by Microsoft Worldwide Health and Health Catalyst—is a basic guide to training machine learning algorithms and applying machine learning models to clinical and operational use case. This report shares practical, proven techniques healthcare organizations can use to improve their performance on a range of issues.

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Identifying Vulnerable Patients and Why They Matter

The vulnerable individuals in a health system’s patient population are at risk of becoming some of the organization’s most complex and costly members. Because vulnerability can be determined by long-term health status and social determinants of health (versus acute episodes), managing risk for these patients relies on a whole-person approach to care. Fee-for-service reimbursement hasn’t incentivized this comprehensive approach to care, but, under value-based payment models, health systems are increasingly rewarded for care that keeps patients well. The first challenge in addressing the needs of vulnerable patients is identifying those patients. Analytics-driven technologies can help health systems understand who is vulnerable in their populations and take actions to control risk for these patients.

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EHR Integration: Achieving this Digital Health Imperative

As the digital trajectory of healthcare rises, health systems have an array of new resources available to make more effective and timely care decisions. However, to use these data analytics, machine learning, predictive analytics, and wellness applications to gain real-time, data-driven insight at the point of care, health systems must fully integrate the tools with their EHRs. Integration brings technical and administrative challenges, requiring organizations to coordinate around standards, administrative processes, regulatory principles, and functional integration, as well as develop compelling integration use cases that drive demand. When realized, full EHR integration will allow clinicians to leverage data from across the continuum of care (from health plan to patient-generated data) to improve patient diagnosis and treatment.

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How UPMC and Health Catalyst Improve Outcomes Using Innovation in Activity-based Costing

UPMC and Health Catalyst created a great business partnership focused on sharing risks and rewards to innovate how activity-based costing (ABC) is done in healthcare. The partners relied on complementary intellectual property, complementary talent, and complementary risks and rewards to drive benefits that extend beyond either organization’s borders. Health Catalyst licensed UPMC’s activity-based costing software, which served as the foundation for the Health Catalyst CORUS suite. Together, the partners will continue to work for innovations in ABC to drive outcomes improvements in healthcare.

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Five Reasons Why Health Catalyst Acquired Medicity and What It Means for Interoperability, as Explained by Dale Sanders, President of Technology

Why did Health Catalyst acquire Medicity? Dale Sanders, President of Technology, shares five reasons and what it means for interoperability: Medicity has several petabytes of valuable data content. Medicity’s data governance expertise. Medicity’s 7 x 24 real-time cloud operations expertise. Medicity’s expertise in real-time EHR integration. Medicity’s presence and expertise in the loosely affiliated, community ambulatory care management space.

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Data Warehousing in Healthcare: A Guide to Success

Looking for a way to share his extensive experience with data warehousing in healthcare, in 2002 Dale Sanders wrote what many consider to be the “EDW Bible.” It’s a document with guidance that, if followed, will drive value and utilization from a data warehouse. We’ve made that report available now.

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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. Additionally, once listed, the Health Catalyst PSO will create 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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Six Challenges to Becoming a Data-Driven Payer Organization

As healthcare transitions from fee-for-service to value-based payment, payer organizations are increasingly looking to population health management strategies to help them lower costs. To manage individuals within their populations, payers must become data driven and establish the technical infrastructure to support expanding access to and reliance on data from across the continuum of care. To fully leverage the breadth and depth of data that an effective health management strategy requires, payers must address six key challenges of becoming data driven: Data availability. Data access. Data aggregation. Data analysis. Data adoption. Data application.

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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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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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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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