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Data: Quality, Management, Governance

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The Surprising Benefits of Bad Healthcare Data

Bad healthcare data is inevitable. Whether it happens as a result of human input error or an incorrect rule, bad healthcare data will happen. And rather than ignoring it, hiding it, or scrubbing it, health systems need to take a more transparent approach. Bad healthcare data, when approached correctly, has four surprising benefits:

  1. Provides valuable feedback to application users/data consumers.
  2. Inspires an improvement culture.
  3. Creates a Snowball Effect of Success.
  4. Improves Data Accuracy.
It’s not easy to make the shift from fearing bad data to embracing it, but there are several steps systems can take to start creating a data transparency culture:
  1. Empower: encourage data consumers to provide feedback.
  2. Share: Provide a mechanism for sharing feedback.
  3. Act: dedicate time and resources to respond and act.
Health systems prepared and willing to fix bad data will ultimately improve data quality.

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Why the Data Steward’s Role Is Critical to Sustained Outcomes Improvement in Healthcare

The data steward is critical to sustained outcomes improvement, yet they tend to be underappreciated members of the healthcare analytics family. Combining the invaluable technical expertise of a data analyst with the vital clinical knowledge of an experienced caregiver, the data steward’s skills and proficiency at both positions brings value beyond measure to any outcomes improvement project. Unfortunately, all too often, their role is non-existent even though potential candidates for the job are located in multiple data sources throughout the organization. Among other responsibilities, the data steward:

  1. Reinforces the global data governance principles.
  2. Helps develop and refine details of local data governance practices.
  3. Is the eyes and ears of the organization with respect to data governance and the governance committee.
  4. Provides direction to peers regarding appropriate data definitions, usage, and access.
  5. Anticipates local consequences of global changes
For innovative health system leaders who have specifically recognized this emerging role, the ROI of data stewards who help achieve improved outcomes is very worthwhile.

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Demystifying Healthcare Data Governance: An Executive Report

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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Data: Quality, Management, Governance - Additional Content

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Exceptions to Information Blocking Defined in Proposed Rule: Here’s What You Need to Know

Information blocking practices inhibit care coordination, interoperability, and healthcare’s forward progress.  The ONC’s proposed rule ushers in the next phase of the Cures Act by defining information blocking practices and allowed exceptions. To make the final rule as strong as possible, exceptions should be narrowly defined. In proposed form these include the following:

  1. Preventing Harm.
  2. Promoting the Privacy of EHI.
  3. Promoting the Security of EHI.
  4. Recovering Costs Reasonably Incurred.
  5. Responding to Request that are Infeasible.
  6. Licensing of Interoperability Elements on Reasonable and Non-discriminatory Terms.
  7. Maintaining and Improving Health IT Performance.
This article covers each of these exceptions and discusses what to watch for in the final version of the rule.

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Emergency Department Quality Improvement: Transforming the Delivery of Care

Overcrowding in the emergency department has been associated with increased inpatient mortality, increased length of stay, and increased costs for admitted patients. ED wait times and patients who leave without seeing a qualified medical provider are indicators of overcrowding. A data-driven system approach is needed to address these problems and redesign the delivery of emergency care. This article explores common problems in emergency care and insights into embarking on a successful quality improvement journey to transform care delivery in the ED, including an exploration of the following topics:

  • A four-step approach to redesigning the delivery of emergency care.
  • Understanding ED performance.
  • Revising High-Impact Workflows.
  • Revising Staffing Patterns.
  • Setting Leadership Expectations.
  • Improving the Patient Experience.

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Four Critical Phases for Effective Healthcare Data Governance

Based on a 2018 Healthcare Analytics Summit presentation, this report details the four phases necessary for successful healthcare data governance:

  1. Elevate a vision and agenda that align with organizational priorities.
  2. Establish an organizational structure to fulfill the data governance mandate.
  3. Execute with prioritized data governance projects, people and resource assignment, and disciplined focus on the work.
  4. Extend data governance investments and efforts through established practices.
Each step must follow the core principles of stakeholder engagement, shared understanding, alignment, and focus. Effective healthcare data governance is not a one-time event and requires ongoing and iterative efforts.

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Clinical Data Management: 3 Improvement Strategies

Most health systems suffer from data clutter and efficiency problems. As a result, analysts spend most of their time searching for data, not performing high value work.  There are three steps that can help you address your data management issues: 1) find all your dispersed analysts in the organization, 2) assess your analytics risks and challenges, 3) champion the creation of an EDW as the foundation for clinical data management.

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The Number One Secret of Highly Effective Healthcare Data Analysts

Data-driven quality improvement is propelling healthcare transformation. The ability to strategically leverage healthcare data is essential, making highly effective data analysts more valuable than ever. So, what attributes differentiate a good data analyst from a great analyst? Stephen Covey’s well-known book “The 7 Habits of Highly Effective People,” has long had far-reaching impacts in the business world. These same principles are relevant today and applicable in the world of healthcare analytics. Learn how Covey’s second habit, “Begin With the End in Mind,” drives great healthcare data analysts.

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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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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:

  1. Data availability.
  2. Data access.
  3. Data aggregation.
  4. Data analysis.
  5. Data adoption.
  6. Data application.

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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 effort for data, tools, and talent).
  • Identify the near-term data strategic priorities, stakeholders, and tools.
  • Assess the talent and consider creating an analytics center of excellence (ACOE) to harness organizational capabilities.

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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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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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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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What Is a Healthcare Data Lake and Why Do You Need One? Imagine a Supermarket

Using a supermarket analogy, this article helps healthcare leaders understand what data lakes are (open reservoirs for vast amounts of data), why they’re essential (they reduce the time and resources required to map data), and how they integrate with three common analytic architectures:

  1. Early-Binding Data Warehouse
  2. Late-Binding Data Warehouse
  3. Map-Reduce Hadoop System
Data lakes are useful parts of all three platforms, but deciding which platform to integrate a data lake with depends heavily on a health system’s resources and infrastructure. Once understood and appropriately integrated with the optimal analytics platform, data lakes save health systems time, money, and resources by adding structure to data only as use cases arise.

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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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Interoperability in Healthcare Delivers Critical Health Information at the Point of Care

Interoperability in healthcare, despite frequent objections by EHR vendors and health systems (e.g. “EHR integration is too difficult to manage”), is integral to delivering high quality patient care. Interoperability means different things to different health system stakeholders, from leaders seeing it as a purchase they must defend, to clinicians relying on it to get the information they need, when they need it. But it boils down to delivering the highest-quality, most effective, and most efficient care to patients—a goal that’s easier to define than achieve. One of interoperability’s most important use cases, EHR integration, is challenged by EHR vendors and health systems worried about integration challenges, from HIT vendors wanting to integrate too many tools, to EHR access fears. Fortunately, objections are dissipating with the introduction of national interoperability policy and better cooperation among industry participants. Amidst these distractions, health systems need to regain focus on interoperability’s top goal: improving patient care by making the best information available at the point of care.

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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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Data for Improving Healthcare vs. Data for Exasperating Healthcare Workers

For better or worse, hospitals are obligated to collect and report data for regulatory purposes. Or they feel compelled to meet some reputational metric. The problem is, an inordinate amount of time can be spent on what is considered data for accountability or punishment, when the real focus should be on data for learning and improvement. When time, effort, and resources are dedicated to the latter, it leads to real outcomes improvement. Deming has three views of focusing on a process and this article applies them to healthcare:

  1. Sub-optimization, over-emphasizing a single part at the expense of the whole.
  2. Extreme over-emphasis, also called gaming the system.
  3. The right amount of focus, the only path to improvement.
With data for learning as the primary goal, improving clinical, operational, and financial processes becomes an internal strategy that lifts the entire healthcare system.

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