Analytics

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Health Catalyst Editors

Three Analytics Strategies to Drive Patient-Centered Care

The cost of uncoordinated care that fails to prioritize patient needs is estimated to be over $27.2 billion. One of the primary reasons behind these wasted healthcare dollars is a failure to effectively leverage data to understand patient needs—a must-have to deliver patient-centered, value-based care (VBC).
Three analytics strategies enable health systems to focus on patients while also meeting the financial standards for VBC delivery:

Prioritize patient outreach by risk level.
Deploy data tools to combat COVID-19.
Promote data literacy.

Detailed information from comprehensive data sets allows health systems to understand patient needs at a granular level and then use that insight to drive care decisions. More informed care ensures health systems are also meeting the core elements of VBC—managing costs, delivering quality, and ensuring an excellent patient experience.

Health Catalyst Editors

To Safely Restart Elective Procedures, Look to the Data

Many health systems have realized they lack the data and analytics infrastructure to guide a sustainable reactivation plan and recover lost revenue from months of halted procedures due to COVID-19. However, with operational, clinical, and financial data, augmented by analytics tools, leaders have the visibility into hospital and resource capacity to guide a safe, sustainable elective surgery restart plan.
The first step on the road to recovery for health systems is access to robust analytics to understand the full impact of COVID-19 on clinical, financial, and operational outcomes. Second, organizations need data-sharing tools, like data displays and dashboards, allowing leaders to make decisions based on consistent data that support the organization’s reactivation goals. Leaders can even take the data one step further with predictive models and forecast procedure count, staff, and resources.

Health Catalyst Editors

Medical Practices’ Survival Depends on Four Analytics Strategies

With limited resources compared to large healthcare organizations and fewer personnel to shoulder burdens like COVID-19, medical practices must find ways to deliver better care with less. Delivering quality care, especially in a pandemic, is challenging, but analytics insight can guide effective care delivery methods, especially for smaller practices.
Comprehensive data combined with team members who can turn numbers into real-world information are essential for medical practices to ensure a strong financial, clinical, and operational future. Independent medical practices can rely on four analytics strategies to survive the uncertain healthcare market and plan for a sustainable future:

Prioritize access to up-to-date, comprehensive data sources.
Form a multidisciplinary approach to data governance.
Translate data into analytics insight.
Invest in analytics infrastructure to support rapid response.

Health Catalyst Editors

Shifting to Virtual Care in the COVID-19 Era: Analytics for Financial Success and an Optimized Patient Experience

The COVID-19 era has seen a decline in visits to ambulatory care practices by 60 percent and an estimated financial loss for primary care of over $15 billion. Shutting down elective care is financially unsustainable for health systems and for patients, who continue to need non-pandemic-related care. While virtual medicine has emerged as a viable and mutually beneficial solution for patients and providers, the shift from in-person to virtual health is logistically and financially complicated.
Processes and workflows from in-person care don’t directly translate to the virtual setting, and a financially successful shift requires deep understanding of the factors driving patient engagement and revenue in the new normal. As such, meeting patient needs and financial goals requires robust enterprisewide analytics that drill down to the provider level.

Health Catalyst Editors

Four Strategies Drive High-Value Healthcare Analytics for COVID-19 Recovery

COVID-19 response and recovery is pushing healthcare to operate at an unprecedented level. To meet these demands and continue to improve outcomes and lower costs, healthcare analytics must perform more actionably and with broader organizational impact than ever. Health systems can follow four strategies to produce high-value analytics to withstand the pandemic and make healthcare better in the long term:

Minimize benchmarking.
Outsource regulatory reporting.
Grow risk-based stratification capabilities.
Run activity-based costing plus at-risk contracting.

Dan Lowder
Trevor Smith

Hiring Top Healthcare Analytics Talent: Five Best Practices

COVID-19 has escalated healthcare’s decision-making demands, reinforcing the industry’s need for highly skilled analytics team members. As a result, health systems face mounting pressure to hire the best-suited analytics talent in a timely manner and with minimal burden on existing team members.
Five proven inclusive strategies will help hiring managers efficiently build an analytics team that can adapt to healthcare’s shifting environment and also fit within an organization’s culture:

Open positions to remote employees and conduct interviews via video conferencing.
Insert “tollgates” into the hiring process.
Use scenario-based role play to assess many competencies concurrently.
Assess cultural fit.
Follow up with and provide feedback to all candidates.

Health Catalyst Editors

Six Ways Health Systems Use Analytics to Improve Patient Safety

With preventable patient harm associated with over 400,000 deaths in the U.S. annually, improving safety is a top priority for healthcare organizations. To reduce risks for hospitalized patients, health systems are using patient safety analytics and trigger-based surveillance tools to better understand and recognize the types of harm occurring at their facilities and intervene as early as possible.
Six examples of analytics-driven patient safety success cover improvement in the following areas:

Wrong-patient order errors.
Blood management.
Clostridioides difficile (C. diff).
Opioid dependence.
Event reporting.
Sepsis.

Health Catalyst Editors

The Healthcare Analytics Adoption Model: A Roadmap to Analytic Maturity

The focus on analytics is contributing to the “EHR problem”—doctors prioritizing the EHR over patients. The Healthcare Analytics Adoption Model (HAAM) walks healthcare organizations through nine levels that lay the framework to fully leverage analytic capabilities to improve patient outcomes:
Level 1. Enterprise Data Operating System
Level 2. Standardized Vocabulary & Patient Registries
Level 3. Automated Internal Reporting
Level 4. Automated External Reporting
Level 5. Waste and Care Variability Reduction
Level 6. Population Health Management & Suggestive Analytics
Level 7. Clinical Risk Intervention & Predictive Analytics
Level 8. Personalized Medicine & Prescriptive Analytics
Level 9. Direct-To-Patient Analytics & Artificial Intelligence
Analytics are crucial to becoming a data-driven organization, but providers and administrators can’t forget about the why behind the data—to improve outcomes. Following the HAAM enables organizations to build a sustainable, analytic platform and empower patients to become data-driven when it comes to their own care.

Eric Denna, PhD
Ryan Smith, MBA

Improving Strategic Engagement for Healthcare CIOs with Five Key Questions

A healthcare CIO’s role can demand such an intense focus on technology that IT leaders may struggle to find natural opportunities to engage with their C-suite peers in non-technical conversations. To bridge the gap, healthcare CIOs can answer five fundamental questions to better align their programs with organizational strategic goals and guide IT services to their full potential:

Whom do we serve?
What services do we provide?
How do we know we are doing a great job?
How do we provide the services?
How do we organize?

Josh Ferguson APRN, ACNP, ANP-BC

ICD-10 PCS: Harnessing the Power of Procedure Codes

The transition to ICD-10 in 2015 saw the number of available procedure codes increase from roughly 3,000 to more than 70,000. This change gives clinicians the ability to code procedures to a much higher degree of specificity and provides health systems the ability to unlock powerful clinical insights into how inpatient procedural care is delivered.
This article covers the benefits and drawback of ICD-10 PCS, as well as concrete ways health systems can use these procedure codes to provide new clinical insights. The article also walks through the anatomy of the seven-digit alphanumeric codes and provides specific clinical examples of how healthcare organizations can slice and dice this data.

Dale Sanders

Academic Medical Centers: A Triple Threat Approach to Leveraging Healthcare Analytics

Academic medical centers (AMCs) are a triple threat on the healthcare court with their combined medical center, education, and research sections. With a unique set of resources, AMCs have the ability to take a  comprehensive, holistic approach to patient care. However, one of the challenges they still face is utilizing healthcare analytics effectively within the patient care setting. With the Healthcare Analytics Adoption Model and other data expertise, AMCs can learn how to merge siloed data, while improving operations, and delivering the highest quality of care to each patient.

Matt Denison

Healthcare Analytics for Payers: How to Thrive Through Shifting Financial Risk

To stay in sync with healthcare’s transition to value-based care, payers today must develop the analytics capability to support alternative payment models and drive more value to their members. Payers can follow an analytics roadmap to develop a strategy that extends their data, analytics, and risk management expertise to meet growing demands.
The analytics roadmap helps the payer meet these common challenges of establishing a data-driven culture:

Recruiting and retaining high-quality providers in a competitive market.
Managing increasing numbers of high-risk/high-cost members with limited resources.
Efficiently reacting to federal and state legislative and payment changes.
Controlling the rising costs of healthcare services and pharmaceuticals.

Health Catalyst Editors

Four Steps to Effective Opportunity Analysis

Opportunity analysis uses data to identify potential improvement initiatives and quantifies the value of these initiatives—both in terms of patient care benefits and financial impact. This process is an effective way to find unwarranted and costly clinical variation and, in turn, develop strategies to reduce it, improving outcomes and saving costs along the way. Standardizing the opportunity analysis process makes it repeatable and prioritizes actionable opportunities.
Quarterly opportunity analysis should follow four steps:

Kicking off the analysis by getting analysts together to do preliminary analysis and brainstorm.
Engaging with clinicians to identify opportunities and, in the process, get clinician buy in.
Digging deeper into the suggested opportunities to prioritize those that offer the greatest benefits.
Presenting findings to the decision makers.

Health Catalyst Editors

The Top Five 2019 Healthcare Trends

Bobbi Brown, MBA, and Stephen Grossbart, PhD have analyzed the biggest changes in the healthcare industry and 2018 and forecasted the trends to watch for in 2019. This report, based on their January 2019, covers the biggest 2019 healthcare trends, including the following:

The business of healthcare including new market entrants, business models and shifting strategies to stay competitive.
Increased consumer demand for more transparency
Continuous quality and cost control monitoring across populations.
CMS proposals to push ACOs into two-sided risk models.
Fewer process measures but more quality outcomes scrutiny for providers.

Health Catalyst Editors

How to Build a Healthcare Analytics Team and Solve Strategic Problems

Health systems have vast amounts of data, but frequently struggle to use that data to solve strategic problems in a timely fashion. A healthcare analytics team, made up of the right people with the right tools and skillsets, can help address these challenges. This article walks through the steps organizations need to take to put an effective analytics team in place.
These include the following:

Recognizing the need for change.
Demonstrating the value of an analytics team.
Conducting a current state assessment.
Identifying solutions.
Implementing a phased approach.
Building a roadmap.
Making the pitch.
Putting the roadmap into action.

The article also includes the foundation skills to look for when putting together the team and tips on how best to organize.

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?

Leslie Falk

Increase Healthcare Analytics ROI Through the Rapid Response Analytics Solution

Health systems feel mounting pressure to demonstrate ROI from analytics investments but are faced with inefficacies and delays. Fortunately, the Rapid Response Analytics Solution delivers a 10x increase in analytics productivity and a 90 percent decrease in the time required to develop new analytic insights. The Rapid Response Analytics Solution solves these tough analytics problems through two primary elements: curated, modular data kits called DOS Marts; and Population Builder, a powerful self-service tools that lets any time of user, from physician executive to frontline nurse, explore data and quality build cohorts of patients without relying on IT staff and with no need for sophisticated and customized SQL and data science coding.

Health Catalyst Editors

How to Evaluate Emerging Healthcare Technology With Innovative Analytics

As healthcare systems are pressured to cut costs and still provide high-quality care, they will need to look across the care continuum for answers, reduce variation in care, and look to emerging technologies. This article walks through how to evaluate the safety and effectiveness and of emerging healthcare technology and prioritize high-impact improvement projects using a robust data analytics platform. Topics covered include:

The importance of identifying variation in innovation.
Ways to improve outcomes and decrease costs.
The value of an analytics platform.
The reliable information that produce sparks for innovation.
Identifying and evaluating emerging healthcare technology.
Knowing what data to use.
The difference between efficacy and effectiveness in evaluation of emerging healthcare technology.

Health Catalyst Editors

Reducing Hospital Readmissions: A Case for Integrated Analytics

Health systems continue to prioritize reducing hospital readmissions as part of their value-based payment and population health strategies. But organizations that aren’t fully integrating analytics into their readmission reduction workflows struggle to meet improvement goals. By embedding predictive models across the continuum of care, versus isolated them in episodes of care, health systems can leverage analytics for meaningful improvement.
Organizations that integrate predictive models into readmissions reduction workflows have achieved as much as a 40 percent reduction in risk-adjusted readmissions indexes. Effective analytics integration strategies use a multidisciplinary development approach to meet the needs of a patient’s entire care team and deliver common tools for all involved in the patient’s healthcare journey.

John Wadsworth

The Number One Skill for a Healthcare Data Analyst

In today’s high-pressured world of healthcare, health systems don’t need report writers. They need highly valuable healthcare data analysts. A top healthcare data analyst becomes a partner for clinical and operational improvement by using a five-step method for solving complex problems.
This article walks through this step-by-step approach and demonstrates its application using the real-world example of building a diabetes registry. In addition to this specialized approach to solving problems, the article discusses the five essential skills for data analysts needed in the diabetes registry example:

Data query
Data movement
Data modeling
Data analysis
Data visualization

John Wadsworth

6 Essential Data Analyst Skills for Your Healthcare Organization

Healthcare organizations are turning to the enterprise data warehouse (EDW) as the foundation of their analytics strategy. But simply implementing an EDW doesn’t guarantee an organization’s success. One obstacle organizations come up against is that their analytics team members don’t have the right skills to maximize the effectiveness of the EDW. The following six skills are essential for analytics team members: structured query language (SQL); the ability to perform export, transform, and load (ETL) processes; data modeling; data analysis; business intelligence (BI) reporting; and the ability to tell a story with data.

Health Catalyst Editors

The Digitization of Healthcare: Why the Right Approach Matters and Five Steps to Get There

While many industries are leveraging digital transformation to accelerate their productivity and quality, healthcare ranks among the least digitized sectors. Healthcare data is largely incomplete when it comes to fully representing a patient’s health and doesn’t adequately support diagnoses and treatment, risk prediction, and long-term health care plans. But even with the obvious urgency for increased healthcare digitization, the industry must raise this trajectory with sensitivity to the impacts on clinicians and patients. The right digital strategy will not only aim for more comprehensive information on patient health, but also leverage data to empower and engage the people involved.
Health systems can follow five guidelines to digitize in a sustainable, impactful way:

Achieve and maintain clinician and patient engagement.
Adopt a modern commercial digital platform.
Digitize the assets (the patients) and the processes.
Understand the importance of data to drive AI insights.
Prioritize data volume.

Ryan Smith, MBA

The Missing Ingredient in Healthcare Analytics: The Executive Sponsor

Despite the complexity of healthcare analytics, one key strategy for effective, sustainable analytics stands out: designating an executive sponsor to oversee the program. This sponsor is a C-suite level leader who’s committed to championing analytics throughout the organization and has the influence and relationships to drive widespread outcomes improvement.
Healthcare executives can use four criteria to identify a great executive sponsor for their analytics programs:

Have a single accountable leader.
Find a sponsor with passion for and knowledge about data.
Choose organizational clout and a vision for analytics over a specific title.
Build a partnership with the CIO.

Imran Qureshi

How to Turn Data Analysts into Data Scientists

Healthcare data scientists are in high demand. This shortage limits the ability of healthcare organizations to leverage the power of artificial intelligence (AI). Health systems must better utilize their data analysts, and, where possible, turn some data analysts into data scientists.
This report covers the following:

Healthcare use cases and which ones data analysts can take the lead on.
Specific steps for turning data analysts into data scientists.
How to identify the best candidates among your data analysts.
Recommended resources to get started on an AI journey.

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.