Data, AI, & Analytics

Insights

Marlowe Dazley

Predicting Denials to Improve the Healthcare Revenue Cycle and Maximize Operating Margins

Healthcare financial leaders are constantly brainstorming ways to increase operating margins through better revenue cycle performance. These efforts often lead revenue cycle leaders to denied claims—when a payer doesn’t reimburse a health system for a service rendered. Although denials are a common reason for lost revenue, experts deem nearly 90 percent avoidable.
Effective denials management starts with prevention. Organizations can use revenue cycle performance data, combined with artificial intelligence, to predict areas within each claim’s lifecycle that are likely to result in a denial. With denial insight, health systems can optimize revenue cycle processes to prevent denials and increase operating margins.

Health Catalyst

AI-Powered Benchmarking Transforms Data into Insight, Improving Organizational Effectiveness

Healthcare organizations have a vast amount of data available. Data need to be converted into better decisions regarding organizational focus, resource allocation, and setting and driving toward appropriate targets to optimize performance. Beyond significant data integration and transformation, optimal leadership decisions require a broad perspective and cutting-edge analytics.
Previously, INTEGRIS Health had high volumes of data but lacked the insight it wanted to drive performance. By leveraging a robust analytics platform, INTEGRIS Health now has more comprehensive and better-integrated data coupled with the cutting-edge analytics it needs to make critical leadership decisions and drive daily performance.

Taylor Larsen

Optimize Your Healthcare Data Quality Investment: Three Ways to Accelerate Time to Value and Prepare for the Future

Healthcare organizations increasingly rely on data to inform strategic decisions. This growing dependence makes ensuring data across the organization is fit for purpose more critical than ever. Decision-making challenges associated with pandemic-driven urgency, variety of data, and lack of resources have further highlighted the critical importance of healthcare data quality and prompted more focus and investment. However, many data quality initiatives are too narrow in focus and reactive in nature or take longer than expected to demonstrate value. This leaves organizations unprepared for future events, like COVID-19, that require a rapid enterprisewide analytic response.
What are some actionable ways you can help your organization guard against the data quality challenges uncovered this past year and better prepare to respond in the future? Join our live webinar with Taylor Larsen, Director of Data Quality for Health Catalyst, to learn more.
What You’ll Learn

How data profiling and data quality assessments, in combination with your data catalog, can increase data quality transparency, expedite root cause analysis, and close data quality monitoring gaps.
How to leverage AI to reduce data quality monitoring configuration and maintenance time and improve accuracy.
How defining data quality based on its measurable utility (i.e., data represents information that supports better decisions) can provide a scalable way to ensure data are fit for purpose and avoid cost outstripping return.

Health Catalyst Editors

The Top Four Skills of an Effective Healthcare Data Analyst

As health systems experience more pressure to deliver quality care with limited resources during a pandemic, data analysts play a vital role in helping organizations overcome new COVID-19-induced challenges. Data analysts provide direction about the best way to dissect data, identify areas for improvement, and solve complex problems that stand in the way of better healthcare delivery. However, by developing four specific skills, data analysts can optimize their work and help leaders make sound operational, clinical, and financial decisions:

Begin with the end in mind.
Focus on problem solving.
Master the foundational competencies.
Play the data detective.

Jason Jones, PhD

Expanding AI in Healthcare: Introducing the New Healthcare.AI™ by Health Catalyst

The New Healthcare.AI offering from Health Catalyst is a transformational suite of products and expert services that address the wider array of critical business issues. Healthcare.AI dramatically broadens the use and uses cases for effective AI within your organization. Join Jason Jones, Chief Analytics and Data Science Officer, as he shares tools and approaches to serve a growing breadth of stakeholders needing faster turnaround and smaller margins for error.
What You’ll Learn

How to expand the use cases where AI is applied.
How to integrate AI into everyday workflow and decisions.
How to increase your success rate in AI adoption.

Daniel Orenstein, JD
Stephen Grossbart, PhD

2021 Healthcare Trends: What Leaders Need to Know from COVID-19 to New Administration Policies (White Paper)

While much of the healthcare industry was eager to put 2020 behind it, the new year brings its own challenges, concerns, and promises. Trends in the three main categories of new Biden administration policy, care delivery, and healthcare technology will shape 2021, with key issues including the long-term effects of COVID-19, future emergency preparedness, and the outlook for the Affordable Care Act (ACA). Healthcare leaders can prepare for this pivotal year by understanding critical areas to watch within these categories and how events, activities, and political appointments will affect the healthcare ecosystem.

Health Catalyst

COVID-19 Capacity Planning Tool Provides Advanced Analytics and Improved Operational Effectiveness

COVID-19 is causing many hospitals and health systems to face resource and capacity restrictions, making the accurate estimation of COVID-19 requirements crucial. Carle Health System needed the ability to anticipate the impact COVID-19 would have on its organization and community. After analyzing national COVID-19 capacity planning resources, Carle chose a model that was customized for its organization. Carle leveraged its analytics platform and data science tools, using local data and infection rates to forecast the impact of COVID-19 locally. The organization now has critical insight into when surges will occur and can determine if it has enough available resources.

Health Catalyst Editors

Deliver Data to Decision Makers: Two Important Strategies for Success

Surviving on thin operating margins underscores the need for all end users at a health system to make decisions based on comprehensive data sets. This data-centered approach to decision making allows team members to take the right course of action the first time and avoid making decisions based on fragmented data that exclude key pieces of information.
To promote data-driven decision making and a data-centric culture, healthcare organizations should increase data access and availability across the institution. With easy access to complete data, end users rely on the same data to make decisions, no matter where they work within the health system.
Two strategies can help organizations integrate and deliver data to end users when they need it:

Select infrastructure that fits most people’s needs.
Ask the right questions.

Health Catalyst Editors

The Right Way to Build Predictive Models for the Most Vulnerable Patient Populations

Predictive artificial intelligence (AI) models can help health systems manage population health initiatives by identifying the organization’s most vulnerable patient populations. With these patients identified, organizations can perform outreach and interventions to maximize the quality of patient care and further enhance the AI model’s effectiveness.
The most successful models leverage a mix of technology, data, and human intervention. However, assembling the appropriate resources can be challenging. Barriers include multiple technology solutions that don’t share information, hundreds of possible, often disparate, data points, and the need to appropriately allocate resources and plan the correct interventions. When it comes to predictive AI for population health, simple models may harness the most predictive power, which allows for more informed risk stratification and identifies opportunities for patient engagement.

Tarah Neujahr Bryan

Expanding AI in Healthcare: Introducing the New Healthcare.AI™ by Health Catalyst

As healthcare leaders continue to face unprecedented decisions around revenue, cost, and quality, they turn to augmented intelligence (AI) to maximize their analytics. However, leaders struggle to implement AI into existing business intelligence workflows, demonstrate ROI, and move AI efforts beyond predictive models.
Health systems can overcome AI’s implementation challenges with the New Healthcare.AI™ offering by Health Catalyst. As a suite of AI products and expert services, Heatlhcare.AI integrates transparent, cutting-edge technology into existing workflows, allowing analysts to produce high-quality insights in minutes. The AI offering dramatically broadens the use and use cases of AI for any healthcare organization with a mix of self-service products and expert services:

Analytics integration.
Choosing/building predictive models.
Optimizing predictive models.
Retrospective comparisons.
Prescriptive optimization.

Kyle Salyers

Innovative Healthcare Partnerships: Making the Most of Merging Resources and Capabilities

Healthcare mergers and acquisitions performed solidly in 2020, despite the downturn in the U.S. economy and healthcare in general. Organizations responded to new challenges by partnering with each other to build core business strengths, address gaps in care delivery the pandemic exposed, and enhance their resources to navigate current and future crises.
Realizing the potential of emerging healthcare partnerships requires an open and scalable analytics infrastructure plus a cultural and contractual openness to allow innovation to flourish. Organizations that have adopted an open analytics platform have the data operating advantage to form partnerships, efficiently and smoothly bring best-of-breed solutions to market, and enable the innovative potential of collaborations.

Health Catalyst

Using Analytics to Automate Heart Failure Data Aggregation

For each heart failure admission, registered nurses at Guy’s and St Thomas’ NHS Foundation Trust collected data from five different sources, and then filled out a 10-page form for each patient. Information from the forms was then manually entered into the National Institute for Cardiovascular Outcomes Research (NICOR) web portal. This manual process for data collection and reporting was not only time-consuming and resource-intensive—but was also highly susceptible to error. To address these challenges, the organization leveraged the Health Catalyst® Data Operating System (DOS™) to integrate the data from the five source systems and extract data for nearly all of the elements required for heart failure readmissions—streamlining the NICOR submission process and improving data quality and accuracy.

Health Catalyst

Analytics Reveal AAA Programme Improvement Success

As part of its efforts to improve the timeliness of care for patients undergoing abdominal aortic aneurysm (AAA) repair, Guy’s and St Thomas’ NHS Foundation Trust needed to collect data to guide care redesign, help assess the impact of specific interventions, and gauge progress toward desired outcomes. Guy’s and St Thomas’ implemented the Health Catalyst® Data Operating System (DOS™) platform, including a Referral Pathway analytics application, allowing the organization to aggregate and standardize data across source systems. Improved data and analytics have enabled Guy’s and St Thomas’ to analyze, evaluate, and monitor outcomes for the entire AAA cohort and evaluate operational performance and associated patient outcomes.

Daniel Orenstein, JD
Stephen Grossbart, PhD

2021 Healthcare Trends: What Leaders Need to Know from COVID-19 to New Administration Policies

While much of the healthcare industry was eager to put 2020 behind it, the new year brings its own challenges, concerns, and promises. Trends in the three main categories of new Biden administration policy, care delivery, and healthcare technology will shape 2021, with key issues including the long-term effects of COVID-19, future emergency preparedness, and the outlook for the Affordable Care Act (ACA). Healthcare leaders can prepare for this pivotal year by understanding critical areas to watch within these categories and how events, activities, and political appointments will affect the healthcare ecosystem.

Health Catalyst

Using Analytics to Improve Clinical Coding

Responsible for coding approximately 380,000 episodes annually, clinical coders at Guy’s and St Thomas’ NHS Foundation Trust review documentation across several systems. The overwhelming amount of data, burdensome manual review processes, and limited coding resources made reviewing all data unfeasible. To address its coding challenges, Guy’s and St Thomas’ leveraged its data platform to combine and standardise data across disparate source systems. The organization now has access to data and technology that can be used to augment coders’ work, automating data gathering to better identify patients whose diagnostic coding could be improved.

Taylor Larsen

How to Build a Healthcare Data Quality Coalition to Optimize Decision Making

Healthcare data-informed decision making’s complexity and consequences demand the highest-quality data—a relationship that COVID-19 has amplified. Decision-making challenges associated with pandemic-driven urgency, variety of data, and a lack of resources have made it more critical than ever that organization’s build a data quality coalition and strategy to ensure systemwide data is fit for purpose. Having the people, processes, and technology necessary to define, evaluate, and monitor data quality allows for a quick, effective, and sustained response at an organizational scale. The coalition keeps all resources working together on the task at hand within a well-defined structure.

Health Catalyst

Data and Analytics Helps Deliver Safe Care During the Pandemic

Renown Health was prepared to safely provide care to patients with COVID-19. As the pandemic emerged, the organization used data in its EMR to monitor COVID-19 activity, but quickly identified that it needed more robust disease surveillance and reporting. By leveraging the Health Catalyst® Data Operating System (DOS™) platform and Rapid Response Analytics Solution, Renown Health expanded its COVID-19 data beyond the data in the EMR. It now has the integrated data and analytics required to plan and manage a comprehensive COVID-19 response effectively.

Health Catalyst Editors

The Key to Better Healthcare Decision Making

When healthcare leaders make data-driven decisions, they often think they see the same thing in the data and assume they’re drawing the same conclusions. However, decision makers often discover later that they were looking at the data differently and didn’t derive the same insights, leading to ineffective and unsustainable choices. Healthcare leaders can manage differing data interpretations by using statistical process control (SPC) methodology to find focus, avoid divergent data interpretations, make better decisions, and monitor change for a sustainable future. By deriving concise insights, SPC separates the signal from the noise, augmenting leaders’ decision-making capabilities.

Health Catalyst Editors

Artificial Intelligence and Machine Learning in Healthcare: Four Real-World Improvements

As COVID-19 has strained health systems clinically, operationally, and financially, advanced data science capabilities have emerged as highly valuable pandemic resources. Organizations use artificial intelligence (AI) and machine learning (ML) to better understand COVID-19 and other health conditions, patient populations, operational and financial challenges, and more—insights that are supporting pandemic response and recovery as well as ongoing healthcare delivery. Meanwhile, improved data science adoption guidelines are making implementation of capabilities such as AI and ML more accessible and actionable, allowing organizations to achieve meaningful short-term improvements and prepare for an emergency-ready future.

Health Catalyst

Integrated Data and Analytics Critical for COVID-19 Response and Recovery Planning

Allina Health needed integrated data and analytics to manage its organizational response and recovery to COVID-19. The organization had a substantial amount of actionable information in its EMR, but some data like supply and equipment were not available. Allina Health leveraged the Health Catalyst® Data Operating System (DOS™) platform and Instant Data Entry Application to capture and visualize the data required to respond to COVID-19 effectively.

Cathy Menkiena, MBA, BSN, RN-BC

The Three Essential Responsibilities of a Nurse Informaticist

With data driving decisions at every level of a health system, healthcare organizations must have data experts who can understand and communicate the technological processes and the reasons behind them to clinical staff. Nurse informaticists bridge the gap between data and nursing practice by combining clinical experience and data expertise. They fulfill three pivotal responsibilities:

Understand and communicate the “why” behind new processes.
Implement new processes.
Validate data quality.

With a nurse informaticist guiding data-driven processes, educating nurses, and validating data quality, health systems advance data beyond the data platform so it reaches the nursing workforce to inform decisions at the frontlines of healthcare delivery.