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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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Data Visualization Dashboards: Three Ways to Maximize Data

With an unpredictable future due to COVID-19, health systems must leverage data to drive decision making at every organizational level. Data visualization dashboards allow health systems to optimize their data and create a data-driven culture by displaying large, real-time data sets in an easy-to-understand dashboard. Health systems that rely on dashboard reporting maximize their data in three important ways:

  1. Time to value. Decision makers do not have time to wait for manually-created reports; dashboards quickly convey information so leaders can make swift decisions.
  2. Data democratization. Leveraging a central source of truth, dashboards allow leaders at every level to access the most updated, accurate data.
  3. Digestible data. Analysts can configure dashboards to highlight important figures and trends, so high-level leaders can understand complex data without diving into spreadsheets.

Self-Service Data Tools Unlock Healthcare’s Most Valuable Asset

Data is increasingly critical to the delivery of healthcare. However, due to its complexity and scope, frontline clinicians and other end users can’t always access the data they need when they need it. In addition, expectations for data at the point of care unduly burden data analysts, keeping them from advancing more sophisticated organizational analytics goals. In response to data productivity and efficiency challenges, self-service data solutions models only the high-value data, versus all available data, giving analysts and nontechnical users immediate and direct access to the data. These reusable models address three key challenges healthcare analytics programs face:

  1. Cost—avoid additional expense and labor of producing single-use models.
  2. Efficiency—save times associated with routinely producing new models.
  3. Maintenance—allow updates across the organization’s models, versus separate updates.

Achieve Data-Informed Healthcare in Eight Steps

Becoming a data-informed healthcare system starts with raw data and ends with meaningful change, driven by raw data. Health systems can follow an eight-step analytics ascension model to transform data into intelligence:

  1. Population Identification and Stratification
  2. Measurement
  3. Data
  4. Information
  5. Knowledge
  6. Insight
  7. Wisdom
  8. Action
Following the analytics ascension model allows improvement teams to avoid feeling overwhelmed, focus on each step, and see how each step fits into the overall objective, allowing health systems to maximize data.

Interoperability in Healthcare: Making the Most of FHIR

With the CMS and ONC March 2020 endorsement of HL7 FHIR R4, FHIR is positioned to grow from a niche application programming interface (API) standard to a common API framework. With broader adoption, FHIR promises to support expanding healthcare interoperability and prepare the industry for complex use cases by addressing significant challenges:

  1. Engaging consumers.
  2. Sharing data with modern standards.
  3. Building a solid foundation for healthcare interoperability.

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.