Data: Quality, Management, Governance

Insights

Keith Roberts

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:

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.
Data democratization. Leveraging a central source of truth, dashboards allow leaders at every level to access the most updated, accurate data.
Digestible data. Analysts can configure dashboards to highlight important figures and trends, so high-level leaders can understand complex data without diving into spreadsheets.

Cessily Johnson
Michael Buck

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:

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

Sam McCutcheon

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:

Population Identification and Stratification
Measurement
Data
Information
Knowledge
Insight
Wisdom
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.

Brian Eliason, MIS
Kevin Sigafoes

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:

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

Dan Soule

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.

David Grauer, MBA, MHSA

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:

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

Anna Kleckner

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:

Infrastructure
Access
Support
Privacy and Security

Stephen Hess

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:

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

Daniel Orenstein, JD

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:

Preventing Harm.
Promoting the Privacy of EHI.
Promoting the Security of EHI.
Recovering Costs Reasonably Incurred.
Responding to Request that are Infeasible.
Licensing of Interoperability Elements on Reasonable and Non-discriminatory Terms.
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.

Health Catalyst Editors

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.

Health Catalyst Editors

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:

Elevate a vision and agenda that align with organizational priorities.
Establish an organizational structure to fulfill the data governance mandate.
Execute with prioritized data governance projects, people and resource assignment, and disciplined focus on the work.
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.

Jane Felmlee

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.

Sarah Provan, BS
Caitlin Kelly

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.

Daniel Orenstein, JD

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.

Luke Skelley
Matt Denison
Rob McCrory

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.

Tim Zoph
Dale Sanders

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.

Health Catalyst

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.

Mike Dow

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:

Focus on relevant content.
Externally validate the model.
Commit to providing vital documentation.
Prioritize long-term planning.
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.

Bryan Hinton

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:

Raw data zone.
Refined data zone.
Trusted data zone.
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.

Imran Qureshi

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:

Early-Binding Data Warehouse
Late-Binding Data Warehouse
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.

Jared Crapo

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.

Daniel Orenstein, JD

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.

Eric Just

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:

Optimize text search (display, medical terminologies, and context).
Enhance context and extract values with an NLP pipeline.
Always validate the algorithm.
Focus on interoperability and integration using a Late-Binding approach.

This broad approach with position health systems for clinical and financial success.

Tom Burton

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:

Sub-optimization, over-emphasizing a single part at the expense of the whole.
Extreme over-emphasis, also called gaming the system.
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.

John Wadsworth

When Healthcare Data Analysts Fulfill the Data Detective Role

There’s a new way to think about healthcare data analysts. Give them the responsibilities of a data detective. If ever there were a Sherlock Holmes of healthcare analytics, it’s the analyst who thinks like a detective. Part scientist, part bloodhound, part magician, the healthcare data detective thrives on discovery, extracting pearls of insight where others have previously returned emptyhanded. This valuable role comprises critical thinkers, story engineers, and sleuths who look at healthcare data in a different way. Three attributes define the data detective:

They are inquisitive and relentless with their questions.
They let the data inform.
They drive to the heart of what matters.

Innovative analytics leaders understand the importance of supporting the data analyst through the data detective career track, and the need to start developing this role right away in the pursuit of outcomes improvement in all healthcare domains.