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Data: Quality, Management, Governance

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Three Must-Haves for a Successful Healthcare Data Strategy

Healthcare is confronting rising costs, aging and growing populations, an increasing focus on population health, alternative payment models, and other challenges as the industry shifts from volume to value. These obstacles drive a growing need for more digitization, accompanied by a data-centric improvement strategy. To establish and maintain data as a primary strategy that guides clinical, financial, and operational transformation, organizations must have three systems in place:

  1. Best practices to identify target behaviors and practices.
  2. Analytics to accelerate improvement and identify gaps between best practices and analytic results.
  3. Adoption processes to outline the path to transformation.

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The Biggest Barriers to Healthcare Interoperability

Improving healthcare interoperability is a top priority for health systems today. Fundamental problems around improving interoperability include standardization of terminology and normalization of data to those standards. And, the volume of data healthcare IT systems produce exacerbates these problems. While interoperability regulations focus on trying to make it easy to find and exchange patient data across multiple organizations and HIEs, the legislation’s lack of fine print and aggressive implementation timelines nearly ensures the proliferation of existing interoperability problems. This article discusses the biggest barriers to interoperability, possible solutions to interoperability problems, and why it matters.

Three Must-Haves for a Successful Healthcare Data Strategy

Healthcare is confronting rising costs, aging and growing populations, an increasing focus on population health, alternative payment models, and other challenges as the industry shifts from volume to value. These obstacles drive a growing need for more digitization, accompanied by a data-centric improvement strategy. To establish and maintain data as a primary strategy that guides clinical, financial, and operational transformation, organizations must have three systems in place:

  1. Best practices to identify target behaviors and practices.
  2. Analytics to accelerate improvement and identify gaps between best practices and analytic results.
  3. Adoption processes to outline the path to transformation.

Healthcare Data Literacy: A Must-Have for Becoming a Data-Driven Organization

The journey for healthcare organizations to become data driven is complex but absolutely critical for success in today’s increasingly digitized environment. Data literacy is an essential capability because it empowers team members at every level of the organization—from individual learners to executives—to aggregate, analyze, and utilize data to drive decision making. To optimize data usage and reach high levels of data literacy, health systems can create a data literacy program based on four foundational elements:

  1. Infrastructure
  2. Access
  3. Support
  4. Privacy and Security

Five Practical Steps Towards Healthcare Data Governance

Health systems increasingly recognize data as one of their top strategic assets, but how many organization have the processes and frameworks in place to protect their data? Without effective data governance, organizations risk losing trust in their data and its value in process and outcomes improvement; a 2018 survey indicated less than half of healthcare CIOs have strong trust in their data. By following five steps towards data governance, health systems can effectively steward data and grow and maintain trust in it as a critical asset:

  1. Identify the organizational priorities.
  2. Identify the data governance priorities.
  3. Identify and recruit the early adopters.
  4. Identify the scope of the opportunity appropriately.
  5. Enable early adopters to become enterprise data governance leaders and mentors.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.