Best Practices


Sean Whitaker

Healthcare Data Management: Three Principles of Using Data to Its Full Potential

Author Douglas Laney is now tackling the topic of Infonomics: the practice of information economics. In his 2017 book, Infonomics: How to Monetize, Manage, and Measure Information as an asset for competitive advantage, Laney provides detailed rationale as well as a thoughtful framework for treating information as a modern-day organization’s most valuable asset.
This article walks through how healthcare organizations can leverage data to its full potential using this framework and the three principles of infonomics:

Measure – How much data does the organization have? What is it worth?
Manage – What data does the organization have? Where is it stored?
Monetize – How does the organization use data?

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Dan LeSueur

How to Run Your Healthcare Analytics Operation Like a Business

A robust data analytics operation is necessary for healthcare systems’ survival. Just like any business, the analytics enterprise needs to be well managed using the principles of successful business operations.
This article walks through how to run an analytics operation like a business using the following five-question framework:

Who does the analytics team serve and what are those customers trying to do?
What services does the analytics team provide to help customers accomplish their goals?
How does the analytics team know they’re doing a great job and how do they communicate that effectively to the leadership team?
What is the most efficient way to provide analytics services?
What is the most effective way to organize?

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Health Catalyst Editors

Why Clinical Quality Should Drive Healthcare Business Strategy

Healthcare today is in the midst of a massive transformation. The opportunities for improvement are great if healthcare systems can do the following:

Reduce clinical variation.
Reduce rates of inappropriate care and care-associated patient injury and death.
Follow accepted best care practices.
Eliminate waste.

This article covers the different types of waste in healthcare systems, ways to reduce them, financial alignment around waste reduction opportunities, and the importance of reducing clinical variation. The core driver of healthcare systems must be improving clinical quality. Almost always, with proper clinical management, better care is cheaper care through waste management.

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Elaine St. James, BSN, RN, CPHQ
Josh Ferguson APRN, ACNP, ANP-BC
Nancy Casazza, BSN, MMI, RN

How to Achieve Your Clinical Data Analytics Goals

Healthcare organizations know that they need to an effective clinical data analytics strategy to improve and survive in today’s challenging environment. In order to make these necessary improvements, healthcare leaders need to establish clear goals for their clinical data analytics initiatives.
Achieving these goals requires clinical teams to clearly identify problems and plan for how to achieve them. This article walks improvement teams through sometimes confusing process of identifying problems, setting clear, achievable goals, and common pitfalls along the way. Topics covered include:

Six categories of clinical data.
Three types of goals: outcome, process, and balance.
How to write an outcome goal.
Internal vs. External Benchmarks.
Mitigation strategies.
Getting clinical buy-in.

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Heather Schoonover

Six Steps to Managing an Infection Control Breach

Despite widespread efforts to improve patient safety, infection control breaches still happen at an alarming rate. In order to improve patient safety and prevent infections, healthcare organizations need to have infection control procedures in place and regularly assess protocols and adherence to these policies. In the case of an infection control breach, organizations need to be prepared to act quickly and follow a six-step evaluation procedure outlined by the CDC:

Identify the infection control breach.
Gather additional data.
Notify and involve key stakeholders.
Perform a qualitative assessment.
Make decisions about patient notification and testing.
Handle communications and logistical issues.

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Taylor Larsen
Michael Barton
Jennifer Van Pelt

Three Ways Evidence-Based Medicine Improves Machine Learning

As health systems continue to adopt machine learning to impact significant outcomes (e.g., reducing readmissions, preventing hospital-acquired infections, and reducing length of stay), they must also leverage evidence-based medicine. Evidence adds critical insight to machine learning models, ensuring that models incorporate all necessary variables in their risk prediction, and builds credibility among clinicians.
Evidence-based medicine brings three essential elements to healthcare machine learning:

Boosts machine learning model credibility.
Engages data experts around healthcare projects.
Saves time and money and increases ROI.

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Heather Schoonover

Four Effective Opioid Interventions for Healthcare Leaders

The crisis of opioid abuse in the U.S. is well known. What may not be so well known are the ways for clinicians and healthcare systems to minimize misuse of these addictive drugs. This article describes the risks for patients when they are prescribed opioids and the need for opioid intervention. It offers four approaches that healthcare systems can take to tackle the crisis while still relieving pain and suffering for the patients they serve:

Use data and analytics to inform strategies that reduce opioid availability
Adopt prescription drug monitoring programs to prevent misuse
Adopt evidence-based guidelines
Consider promising state strategies for dealing with prescription opioid overdose

Opioid misuse is a public health epidemic, but treatments are available and it’s time for those involved in the delivery of healthcare to change practices.

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John Wadsworth

The Healthcare Analytics Ecosystem: A Must-Have in Today’s Transformation

Healthcare organizations seeking to achieve the Quadruple Aim (enhancing patient experience, improving population health, reducing costs, and reducing clinician and staff burnout), will reach their goals by building a rich analytics ecosystem. This environment promotes synergy between technology and highly skilled analysts and relies on full interoperability, allowing people to derive the right knowledge to transform healthcare.
Five important parts make up the healthcare analytics ecosystem:

Must-have tools.
People and their skills.
Reactive, descriptive, and prescriptive analytics.
Matching technical skills to analytics work streams.

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Kathleen Merkley, DNP, APRN, FNP

Sepsis Treatment: Target Five Key Areas to Improve Sepsis Outcomes

More people in the U.S. die from sepsis than from prostate cancer, breast cancer, and AIDS…combined. Although health systems continue working to improve outcomes for septic patients, there is tremendous room for improvement.
Preparing health systems to most effectively tackle sepsis starts with an awareness of consensus definitions of sepsis and continues with following evidence-based recommendations from credible organizations, such as the Surviving Sepsis Campaign and the Sepsis Alliance.
Distilling ever-evolving recommendations and best practices for sepsis is time intensive. This article facilitates healthcare’s distillation effort by highlighting the five key areas health systems can target to improve sepsis outcomes (based on evidence-based guidelines and Health Catalyst’s first-hand experience with healthcare partners):

Early ED recognition
Three-hour sepsis bundle compliance
Six-hour sepsis bundle compliance
In-house recognition of sepsis
Sepsis readmissions: prioritize risk stratification

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Jared Crapo

Three Essential Systems for Effective Population Health Management

An effective population health management program must include three systems: Healthcare Analytics, Best Practice, and Adoption. Organizations with only one or two of these systems often display symptoms of weak and ineffective capability for population health management.  But when you have a analytics foundation based upon a data warehouse, combined with evidence-based practices contained in a best practice system, and the ability to deploy and implement systematic changes to healthcare processes, health systems are truly prepared to manage population of patients.

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Josh Ferguson APRN, ACNP, ANP-BC

Reducing Unwanted Variation in Healthcare Clears the Way for Outcomes Improvement

According to statistician W. Edwards Deming, “Uncontrolled variation is the enemy of quality.” The statement is particularly true of outcomes improvement in healthcare, where variation threatens quality across processes and outcomes. To improve outcomes, health systems must recognize where and how inconsistency impacts their outcomes and reduce unwanted variation.
There are three key steps to reducing unwanted variation:

Remove obstacles to success on a communitywide level.
Maintain open lines of communication and share lessons learned.
Decrease the magnitude of variation.

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Leslie Falk

Improving Patient Safety and Quality through Culture, Clinical Analytics, Evidence-Based Practices, and Adoption

According to the Centers of Disease Control (CDC), an estimated 70,000 patients die each year from hospital-associated infections (HAIs): contrast the CDC statistic with the fact that only 35,000 people die each year in the U.S. from motor vehicle accidents.  Learn key best practices in patient safety and quality including:  patient safety as a team sport, the added challenges of healthcare being the most complex, adaptive system, and how culture, analytics, and content contribute to improve outcomes and lower costs.

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Holly Rimmasch

How to Develop a Robust Clinical Content System

On average it takes seventeen years for medical research findings to make their way into standard clinical practice. Healthcare organizations are being challenged to find much faster ways to implement evidence-based best practices into everyday care delivery. Successful implementation of a robust content system is critical for hospitals to drive clinical integration more quickly and efficiently across the organization to achieve improved, sustainable outcomes. There are 5 major steps including:. 1. Performing key process analysis; 2. Understanding the population; 3. Understanding best practices and current state; 4. Identifying key metrics; and 5. Defining specific goals

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Ed Corbett, MD

The Clinical Integration Hierarchy: A Primer on the Backbone of Data-driven Quality and Cost Improvement

Healthcare delivery is typically siloed into departments and care settings. But accountable care and value-based payment models require organizations to coordinate care across the continuum. To accomplish this, the Clinical Integration Hierarchy groups healthcare into work process that reflect how care is actually delivered. At the most granular level are care processes such as AMI and Cardiac Rehab (some of which are further divided into sub-care processes such as when AMI is divided into PCI and CABG). Next, care process families form the link between care processes through common pathologic conditions. Finally, the care process families comprise clinical programs such as Cardiovascular and Behavioral Health. The Clinical Integration Hierarchy forms the foundation for systematically tackling quality and cost improvement.

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Eric Just

The Three Systems Critical to Healthcare Analytics Success

Health systems looking to improve the lives of patients through analytics often face problems that prevent them from making the improvements they desire. But by using the three systems: Analytics, Best Practice, and Adoption, organizations can be successful. The analytics system ensures that data is aggregated, easy-to-access, and distributed efficiently by implementing a data warehouse. The best practice system provides the framework for best practices and baselines, which provide context and actionable insight to metrics provided by the analytics system. Finally, the adoption system consists of a permanent, multidisciplinary team to enact those actionable insights from the best practice system. All three systems together form the base for organizations to make the journey to data-driven improvement successful.

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John Haughom, MD

Knowledge Management in Healthcare: It’s More Important Than You Realize

Healthcare is becoming an increasingly complex environment that requires organizations to have access to knowledge from both inside and outside its walls. An organized and effective knowledge management strategy can help organizations achieve operational excellence, foster innovation, and provide the best possible care for its patients. Three reasons for its important are: It facilitate decision-making capabilities. It build learning organizations by making learning routing (and this fosters innovation). And it stimulates cultural change. Using IT systems to enable knowledge management is possible today thanks to the widespread adoption of EHRs and advanced analytical tools.

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Tom Burton

The Best Approach to Healthcare Analytics

Healthcare has remained entrenched in its cottage industry-style of operation, even within huge medical centers and significant medical innovation. The result, as documented by Dr. John Wennberg’s Dartmouth Atlas of Health Care project , is unwarranted variation in the practice of medicine and in the use of medical resources including underuse of effective care, misuse of care, and overuse of care provided to specific patient populations. The root of the problem, Wennberg concludes, is that there is no healthcare “system.” At Health Catalyst, we agree. Healthcare needs to be systematized and standardized in three key areas:1) healthcare analytics or measurement, 2) adoption or how teams and work are organized, and 3) best practice or how evidence/knowledge is gathered, evaluated, and disseminated for adoption.

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