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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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The Powerhouse Data Analytics and Visualization Tool That Excels

There are many advanced tools that come to mind when considering healthcare data analytics and visualization. Microsoft Excel may not necessarily make the list, but it has distinct advantages, the least of which are that it’s already installed on your system and that you already know how to use it. Healthcare finance folks already know the capabilities of Excel when it comes to quantitative analysis. Excel also deserves a place on the podium when it comes to pulling data from the warehouse and from various source marts. Excel pivot tables are extraordinary for providing ad hoc analysis. And when preceded by dimensional modeling—with the help of Health Catalyst’s data architects—Excel can easily transform large datasets. This article summarizes all of the surprising features that Excel brings to the data analytics and visualization table.

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Healthcare Analytics Platform: DOS Delivers the 7 Essential Components

The Data Operating System (DOS™) is a vast data and analytics ecosystem whose laser focus is to rapidly and efficiently improve outcomes across every healthcare domain. DOS is a cornerstone in the foundation for building the future of healthcare analytics. This white paper from Imran Qureshi details the seven capabilities of DOS that combine to unlock data for healthcare improvement:

  1. Acquire
  2. Organize
  3. Standardize
  4. Analyze
  5. Deliver
  6. Orchestrate
  7. Extend
These seven components will reveal how DOS is a data-first system that can extract value from healthcare data and allow leadership and analytics teams to fully develop the insights necessary for health system transformation.

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Survey Shows the Role of Technology in the Progress of Patient Safety

A lack of effective technology is impeding the progress of patient safety, according to a 2018 survey of healthcare professionals. Even though most healthcare organizations claim safety as a priority, serious challenges remain to making a significant impact on patient safety outcomes. Survey respondents said ineffective information technology and the related lack of real-time warnings for possible harm events were the top barriers to improving patient safety. They cited a number of key obstacles:

  1. Lack of resources.
  2. Organization structure.
  3. Lack of reimbursement for safety measures.
  4. Changes in patient population.
This survey of more than 400 healthcare professionals tackles a big question many hospital leaders are asking: Why aren’t we seeing improvements in patient safety despite our efforts?

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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:

  1. Identifies rising-risk patients.
  2. Uses a specific social determinant assessment to capture factors beyond claims data.
  3. Integrates EMR data to achieve quality measures.
  4. Identifies patients for palliative or hospice care.
  5. Identifies patients with chronic conditions.
  6. Increases patient engagement.

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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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Introducing Touchstone: The Next-Generation Healthcare Benchmarking and Opportunity Prioritization Tool

To do healthcare benchmarking effectively and efficiently, healthcare organizations need to know where they’re underperforming, where they’re performing well, and how to focus and prioritize their improvement efforts. They also need a new approach to benchmarking that isn’t limited to the inpatient setting. The Health Catalyst® Touchstone™ product is the next-generation healthcare benchmarking and prioritization tool that delivers what antiquated benchmarking technologies cannot:

  • Risk-adjusted benchmarking across the full continuum of care.
  • Artificial intelligence-powered recommendations.
  • Ranked lists of improvement opportunities.
  • Detailed analytics and an intuitive user interface that enable the easy exploration of factors driving performance issues.
  • Democratized benchmarking that’s available to as many people as the organization wants.
Touchstone was designed with many users and use cases in mind, from population health analysts looking to improve ACO performance to C-suite leaders who need a data-driven approach to prioritizing improvement opportunities.

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The Impact of FDA Digital Health Guidance on CDS, Medical Software, and Machine Learning

The FDA recently released guidance documents on the use of clinical decision support (CDS) and medical software that may be of concern to forward-thinking healthcare innovators who rely on these technologies to deliver exceptional care and improve outcomes. What will be the impact of this guidance on machine learning and predictive analytics efforts? How will the guidance affect timelines, costs, and effectiveness of ongoing machine learning implementation? As healthcare delivery increasingly relies on digital innovation and support, more questions emerge about the governance of the accompanying tools and technology. This article provides a summary of the FDA guidance on CDS, how CDS is defined, whether or not CDS is exempt from regulation, and how the FDA intends to enforce compliance. It also summarizes the FDA guidance on medical software, what software is exempt from regulation, and helps to answer some of the questions surrounding the digital health space.

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Five Lessons for Building Adaptive Healthcare Data Models that Support Innovation

Healthcare data models are the backbone of innovation in healthcare, without which many new technologies may never come to fruition, so it’s important to build models that focus on relevant content and specific use cases. Health Catalyst has been continuously refining its approach to building concise yet adaptive healthcare data models for years. Because of our experience, we’ve learned five key lessons when it comes to building healthcare data models:

  1. Focus on relevant content.
  2. Externally validate the model.
  3. Commit to providing vital documentation.
  4. Prioritize long-term planning.
  5. Automate data profiling.
These lessons are essential to apply when building adaptive healthcare data models (and their corresponding methodologies, tools, and best practices) given the prominent role they play in fueling the technologies designed to solve healthcare’s toughest problems.

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Healthcare Analytics Platform: DOS Delivers the 7 Essential Components

The Data Operating System (DOS™) is a vast data and analytics ecosystem whose laser focus is to rapidly and efficiently improve outcomes across every healthcare domain. DOS is a cornerstone in the foundation for building the future of healthcare analytics. This white paper from Imran Qureshi details the seven capabilities of DOS that combine to unlock data for healthcare improvement:

  1. Acquire
  2. Organize
  3. Standardize
  4. Analyze
  5. Deliver
  6. Orchestrate
  7. Extend
These seven components will reveal how DOS is a data-first system that can extract value from healthcare data and allow leadership and analytics teams to fully develop the insights necessary for health system transformation.

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The Four Essential Zones of a Healthcare Data Lake

The role of a data lake in healthcare analytics is essential in that it creates broad data access and usability across the enterprise. It has symbiotic relationships with an enterprise data warehouse and a data operating system. To avoid turning the data lake into a black lagoon, it should feature four specific zones that optimize the analytics experience for multiple user groups:

  1. Raw data zone.
  2. Refined data zone.
  3. Trusted data zone.
  4. Exploration zone.
Each zone is defined by the level of trust in the resident data, the data structure and future purpose, and the user type. Understanding and creating zones in a data lake behooves leadership and management responsible for maximizing the return on this considerable investment of human, technical, and financial resources.

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Seven Ways DOS™ Simplifies the Complexities of Healthcare IT

Health Catalyst Data Operating System (DOS) is a revolutionary architecture that addresses the digital and data problems confronting healthcare now and in the future. It is an analytics galaxy that encompasses data platforms, machine learning, analytics applications, and the fabric to stitch all these components together. DOS addresses these seven critical areas of healthcare IT:

  1. Healthcare data management and acquisition
  2. Integrating data in mergers and acquisitions
  3. Enabling a personal health record
  4. Scaling existing, homegrown data warehouses
  5. Ingesting the human health data ecosystem
  6. Providers becoming payers
  7. Extending the life and current value of EHR investments
This white paper illustrates these healthcare system needs detail and explains the attributes of DOS. Read how DOS is the right technology for tackling healthcare’s big issues, including big data, physician burnout, rising healthcare expenses, and the productivity backfire created by other healthcare technologies.

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The Top Three Healthcare Financial Trends in 2017: Payment Transitions, Disruption, and New Skills

Influential healthcare financial trends in 2017 emerged in three areas:

  1. Transitions in payment.
  2. Disruption from familiar players and newcomers.
  3. Emerging data skillsets.
Uncertainty has been a common theme for 2017. Organizations continue waiting for clarity on the future of the Affordable Care Act (ACA), while working to implement value-based care. Changes from established healthcare organizations as well as the arrival of prominent newcomers (e.g., Amazon) add to the unsettled outlook, as do emerging data skillsets. Amid the uncertainty, however, healthcare is clearly continuing on the path to patient-centered care. Organizations best positioned for 2018 will understand their performance in 2017’s top three healthcare financial trends as they evaluate their preparedness for the coming year.

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A Behind-the-Scenes Look at Healthcare IT Analyst Rankings and Reports: What You Should Know

Healthcare leaders often turn to healthcare IT analyst rankings and reports for information that drives vendor-related decision making. Knowing the key differences between several notable healthcare and cross-industry IT analysts—what methodologies they employ to gather data, their missions and goals (ranking vs. consulting), and how much of their own opinions they interject (unbiased vs. opinionated)—will help healthcare leaders be more educated consumers of the reports and rankings that saturate healthcare. This article provides a high-level overview of the key differences between several healthcare IT analysts:

  • KLAS Research (ranking focus)
  • Black Book Rankings (ranking focus)
  • Chilmark Research (ranking and consulting focus)
  • Advisory Board (consulting focus)
It also looks at the most notable cross-industry IT analysts that apply a healthcare-specific lens to their findings:
  • Gartner
  • International Data Corporation
  • Frost & Sullivan
Healthcare leaders with the ability to interpret these rankings and reports to extract the information they need, will make them more effective decision makers.

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How to Use Text Analytics in Healthcare to Improve Outcomes—Why You Need More than NLP

Given the fact that up to 80 percent of clinical data is stored in unstructured text, healthcare organizations need to harness the power of text analytics. But, surprisingly, less than five percent of health systems use it due to resource limitations and the complexity of text analytics. But given the industry’s necessity to use text analytics to create precise patient registries, enhance their understanding of high-risk patient populations, and improve outcomes, this executive report explains why systems must start using it—and explains how to get started. Health systems can start using text analytics to improve outcomes by focusing on four key components:

  1. Optimize text search (display, medical terminologies, and context).
  2. Enhance context and extract values with an NLP pipeline.
  3. Always validate the algorithm.
  4. Focus on interoperability and integration using a Late-Binding approach.
This broad approach with position health systems for clinical and financial success.

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Healthcare Information Systems: A Look at the Past, Present, and Future

Healthcare information systems are integral to hospital operations and clinical care for patients. In the 1960s healthcare was driven by Medicare and Medicaid and HIT developed shared hospital accounting systems. In the 1970s communication between departments and individual transactional systems became important. DRGs drove healthcare in the 1980s and HIT needed to find ways to pull both clinical and financial data in order for reimbursements. The 1990s saw competition and consolidation drive technology to create IDN-like integration. In the 2000s outcomes-based reimbursement became the drive behind developing real-time clinical decision support. For the future, ACOs and value-based purchasing means that CIOs will need to implement data warehouses and analytics application to provide the insights to drive performance improvement necessary for hospital survival.  

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The Powerhouse Data Analytics and Visualization Tool That Excels

There are many advanced tools that come to mind when considering healthcare data analytics and visualization. Microsoft Excel may not necessarily make the list, but it has distinct advantages, the least of which are that it’s already installed on your system and that you already know how to use it. Healthcare finance folks already know the capabilities of Excel when it comes to quantitative analysis. Excel also deserves a place on the podium when it comes to pulling data from the warehouse and from various source marts. Excel pivot tables are extraordinary for providing ad hoc analysis. And when preceded by dimensional modeling—with the help of Health Catalyst’s data architects—Excel can easily transform large datasets. This article summarizes all of the surprising features that Excel brings to the data analytics and visualization table.

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No More Excuses: We Need Disruptive Innovation in Healthcare Now

U.S. healthcare is one of the most technologically advanced industries in the world, yet it has such a difficult time transforming some of its most mundane problems (cost, quality, and service). With these problems, we are not so different from many other industries, so we should be able to learn from the individuals and industries that have succeeded in finding answers. At the same time, we need to recognize that healthcare is incredibly complex, so we need to search within for barriers that prevent disruption and innovation. The future of healthcare lies in technology, but more importantly, in our ability to pave the way for its implementation starting right now.

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Factoids Reveal Healthcare Trends in Analytics and Technology

We hand-picked the most interesting, useful, credible factoids from 2015 (including the plethora of facts that came out of the 2nd Healthcare Analytics Summit) to create an easy-to-share presentation. The 32 factoids included in this presentation revealed several interesting healthcare trends:

  • Trend #1: Healthcare analytics continue to improve outcomes and save money. For example, OSF’s predictive readmission model reduced its all-cause readmission rate to less than 10%.
  • Trend #2: New technologies are improving patient engagement. For example, 73 percent of health executives surveyed see positive ROI from personalization technologies, and 76 percent of doctors say patient use of wearable health devices improves engagement.
  • Trend #3: Patients and providers agree on data is useful but have security and interoperability concerns. For example, 83 percent of patients don’t trust EHR safety and security, and 83 percent of physicians are frustrated by EHR interoperability.
Although a majority of healthcare leaders understand the importance of using analytics to improve outcomes and reduce costs, only 15% of hospitals use predictive analytics. We hope to see analytics use increase in 2016, and we’re excited to see how technology will continue to engage patients and lead to better health outcomes.

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