Webinars

Webinars & Events

The Why And How Of Machine Learning And AI: An Implementation Guide For Healthcare Leaders

Join Kenneth Kleinberg, Health IT Strategist, and Eric Just, Senior Vice President, Health Catalyst, as they discuss the What, Why, and How of Machine Learning and AI for healthcare leaders.

Attendees will learn:

Practical steps, timeframes and skills as well as real-time data and moving targets associated with the Implementation of ML and AI
How to deal with challenges inherent in ML and AI implementation
What the future holds for ML and AI

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Care Management - Critical Component Of Effective Population Health

In this first webinar, of a two-part series, Dr. Kathleen Clary will share how analytics can be used to answer these questions to ensure delivery of a well-organized and effective care management program.
Dr. Clary will discuss how analytics can enable:

Data integration from multiple EMRs and data sources
Patient stratification and intake
Care coordination
Patient engagement
Performance measurement

We look forward to you joining us!

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Demystifying Text Analytics and NLP in Healthcare

Leading the discussion, we have two exceptional thinkers in this space, Mike Dow, a former CIO and current Health Catalyst product manager and software developer, and Dr. Carolyn Simpkins, Health Catalyst’s Chief Medical Informatics Officer.
They will share thoughts on the challenges of text in clinical analytics as well as demonstrate:

Why text is an important part of clinical analytics
Why a text search is not enough
How clinical text search can be refined with NLP techniques

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Using Data To Improve Patient Safety: Part 2 — Real Life Use Cases

Stan and Valere will discuss how using an automated trigger tool for all-cause harm reviews will provide timely, real-time patient safety data useful to drive down harm rates with earlier interventions. Additional benefits of this approach include having a more accurate and robust source of data for identifying harm trends to then be able to integrate the findings into existing quality improvement processes for further quality improvement efforts.

Attendees will learn how to:

Understand the importance of dedicating resources to impact downstream costs
Identify their key sources of Patient Safety data
Integrate Patient Safety data in to existing Quality Improvement Processes
Learn and improve from real-time safety analytics combined with a Culture of Safety

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Introducing The Next-Generation Cost Management Suite To Help Hospitals Thrive Amid Unprecedented Market Disruption

Learn how CORUS is enabling these significant improvements:

Integration of EHR data, including patient-level clinical and operational data, as well as departmental and equipment resource-utilization data, delivering the first truly comprehensive view of the true cost of patient care
Manufacturing-style activity-based costing that is scalable and maintainable, freeing analysts to focus on identifying variation and cost-saving opportunities
Embedded costing knowledge including best practices, rules, and algorithms from world-renowned academic healthcare institutions, accelerating cost management transformation
Dramatically more timely and actionable cost data based on an analytics platform that supports over 160 source systems including EHR, claims, General Ledger, payroll, supply chain, and patient satisfaction systems

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Using Data To Improve Patient Safety: A Two-Part Discussion

As healthcare organizations continue to experience expenses growing faster than revenues, value based care, and consumer transparency of costs and quality, patient safety will be an important determinant of success. This session will describe the sociotechnical attributes of a safe system, the challenges, the barriers and opportunities, and how to use data and your culture of safety as a powerful tool to drive down adverse events.
 
Attendees will learn:
 

Why patient safety and quality are important.
How data can help improve patient safety.
The history of patient safety and where we are today.
What components make up a safety analytics culture.
How the internal safety culture directly impacts patient safety metrics.
To describe basic guidelines for improving a safety culture with analytics.

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The Data Operating System: Changing The Digital Trajectory Of Healthcare

In 1989, John Reed, the CEO of Citibank and the early pioneer for ATMs, said, “I can see a future in which the data and information that is exchanged in our transactions are worth more than the transactions themselves.” We are at an interesting digital nexus in healthcare. Few of us would argue against the notion that data and digital health will play a bigger and bigger role in the future. But, are we on the right track to deliver on that future? It required $30B in federal incentive money to subsidize the uptake of Electronic Health Records (EHRs). You could argue that the federal incentives stimulated the first major step towards the digitization of health, but few physicians would celebrate its value in comparison to its expense. As the healthcare market consolidates through mergers and acquisitions (M&A), patching disparate EHRs and other information systems together becomes even more important, and challenging. An organization is not integrated until its data is integrated, but costly forklift replacements of these transaction information systems and consolidating them with a single EHR solution is not a viable financial solution.
At Health Catalyst, we’re not satisfied with the current trajectory of digital healthcare. Health information exchanges (HIEs) have struggled technically and economically. Interoperability is lagging and remains one of the top concerns of clinicians and executives today.  EHRs are now widely available, but physician burnout is the highest on record– they now spend over 50% of their day interacting with a computer, not patients. Only 8% of the data required for precision medicine and population health currently resides in today’s EHRs. Clinical and financial decision support at the point of care is almost nonexistent, restricted to a few pioneering organizations who can afford the engineering and informatics staff to implement and maintain it.  The traditional design of batch oriented healthcare data warehouses based on monolithic, early binding data models, focused almost exclusively on conference room analytics, cannot keep up with the rapid reporting and data analysis changes in the industry, or the pace of decision making that requires the right data, at the right time, delivered to the right person. According to the Center for Medical Interoperability, “Health information is trapped.” Yet, while healthcare lags behind in the digital world, the technology and the engineering patterns in data and software have never been better, thanks to the contributions to open source technology by the five largest listed companies in the world–Amazon, Apple, Google, Facebook, and Microsoft.
The time is right for a major change in healthcare.
At Health Catalyst, we are disrupting ourselves and the industry by developing a FHIR-based healthcare Data Operating System (DOS) which leverages the technology and engineering patterns of Lambda architectures in Silicon Valley, enabling real-time data exchange and interoperability along with decision support and analytics in the same environment, at a fraction of the cost invested in HIEs, EHRs, and traditional data warehouses. Within the DOS, Health Catalyst will open source the APIs within a layer called the Fabric, encouraging 3rd party software vendors to leverage the DOS infrastructure for our clients, much like typical, modern Operating Systems—Android, Windows and iOS—but at the data level. By using the FHIR framework, the Health Catalyst DOS can push the output of machine learning models and other analytic algorithms into the workflow of existing EHRs, turning them from clinical data entry terminals into clinical decision support tools. The Fabric can lay over the top of existing data warehouses, as well, leveraging those investments in data aggregation. The DOS concept will become the digital backbone for healthcare and other industries, as society moves from transaction-oriented information technology to intelligence-oriented information technology. For those who might think this is a conceptual dream, Health Catalyst is already well on its way of implementing the DOS with off-the-shelf APIs that integrate data from 154 different healthcare data sources; 212 reusable healthcare data bindings, and 24 healthcare-specific machine learning models… and adding to each of these libraries, everyday.

Please join Dale Sanders, Executive Vice President, Product Development, for a webinar on this topic as he discusses Health Catalyst’s plans to change the digital trajectory of healthcare through the development of the Data Operating System. Dale has a diverse background in complex data environments and decision support, spanning three decades in the US Air Force, National Security Agency, and a CIO in healthcare. You can learn more about his background, here: https://www.linkedin.com/in/dalersanders/.

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Patient-Centered Care Requires Patient-Centered Insight: What We Can Do To Complete The Picture

Health systems and providers are inundated with measurement systems and reporting. Why would we want to add to the measurement mayhem? The real question is, “Are we measuring what matters?”
Carolyn Simpkins MD, PhD, chief medical informatics officer, will discuss how putting the patient at the center of the measurement matrix can bring coherence and completeness to the picture of care delivery performance across the patient journey, and therefore the performance of the healthcare ecosystem.
She will describe the building blocks for patient-centered measurement and how other metrics, patient-reported outcomes, and patient satisfaction fit into this approach. Carolyn will also review the challenges that have kept health systems from completing a patient-centered outcomes approach and why we are poised to break through. Finally, she will share case studies of organizations who have begun to pioneer the use of patient centered metrics to improve care and outcomes.

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Machine Learning Misconceptions

With all the buzz around machine learning, predictive analytics, and artificial intelligence (AI) there are a lot of misconceptions and misunderstandings surrounding the optimal use of modern machine learning tools. Healthcare.ai, a free software package developed by the Health Catalyst data science team, was recently released to help hospitals gain valuable insights and advance outcomes improvements from their immense data sets. The software automates machine learning tasks and democratizes machine learning by making it accessible to ‘citizen data scientists’. We have received several questions about machine learning in healthcare, such as how do you define machine learning, how is it different than AI, what are some common uses cases for machine learning in healthcare, and what are the pitfalls. This webinar will develop a common vocabulary around these ideas. We’ll cover the differences between the most cutting-edge predictive techniques, how a model can be improved over time, and use case vignettes to understand and avoid typical machine learning pitfalls. In today’s healthcare industry, the fastest path to healthcare outcomes is often achieved using the simplest predictive tools.

Mike Mastanduno, PhD, data scientist, and Levi Thatcher, PhD, director of data science, will discuss the landscape of healthcare-specific machine learning. Mike and Levi have extensive experience building and deploying impactful machine learning models using healthcare.ai and have worked at the cutting edge of medical research. During and after the discussion, they will answer viewer-submitted questions. This webinar will:
 

Compare and contrast machine learning and AI.
Discuss techniques that offer feedback into the system and when it’s necessary to retrain a model.
Give advice on how to avoid common pitfalls in machine learning implementation.
Provide use case example and vignette examples on how to apply the different classes of machine learning techniques.

 

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Introducing Leading Wisely™: The Next-Generation of Executive Decision Support

Healthcare leaders are tasked with managing the most complex and changeable industry on Earth, one in which access to the right information at the right time is mission critical. Yet many are almost literally drowning in information as they struggle to collect and interpret data from dozens of IT systems and hundreds of reports in competing formats.
To help, Health Catalyst has announced a breakthrough technology marking the long-awaited next step in the evolution of executive decision support. The web-based solution automatically transforms data, key measures and goals from multiple business units into the fundamental insights critical to leadership. This product combines and analyzes near real-time data from every available IT system and software program, and then enables users to customize information, share it with others, and set their own alerts and notifications. As a result, leaders are empowered to take control of the data deluge to more effectively plan, prioritize improvement projects, create alignment among groups, strategize best solutions, and communicate decisions effectively.
Dorian DiNardo, Senior Vice President of Product Development, discusses this new product with the following primary benefits:

Visibility across all of your key measures and goals to strategize, balance, and optimize your performance
Provides a single source of cross-organizational truth via the Health Catalyst data warehouse
Gives real-time ability to slice-and-dice different vertical or horizontal views with additional drill-down capabilities
Prioritize and communicate with proactive notifications, alerts, and social interactions

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Will New Healthcare Policy Impact Value-Based Healthcare?

The final days of 2016 were fraught with uncertainty about what Congress and the new Trump Administration would do to the Affordable Care Act (ACA) and the healthcare regulatory landscape overall. So far, in 2017, we do not have much more clarity. Repeal, repeal and replace, repeal and delay, modify without repeal—there are now even more questions than answers and still no consensus Republican plan in sight. Yet healthcare executives would certainly appreciate some modicum of clarity, at least on the narrower topic of whether the shift to value-based healthcare models will continue under whatever new system is coming. This webinar attempts to add clarity by analyzing what we know so far, as reflected in the limited actual evidence that is available.

Join Dan Orenstein, General Counsel, Health Catalyst, as he analyzes these three key pieces of information:

The 21st Century Cures Act (Cures)
The Executive Order on reducing the “burden” of the Affordable Care Act (ACA)
Tom Price’s comments at his confirmation hearings

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KLAS: The Population Health Management Journey

As population health management goes mainstream, providers need robust, integrated software solutions to aggregate and analyze data, coordinate care, engage patients and clinicians, and provide full administrative and financial functionality. Population Health Management is a journey, and the number of approaches to population health are varied.
Join Bradley Hunter, Research Director over Population Health at KLAS as he addresses these key questions:

How are providers looking to tackle population health?
What are the challenges facing providers today?
Which vendors are meeting the needs or are poised the meet the needs of providers in the future?

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The MD Anderson / IBM Watson Announcement: What Does It Mean For Machine Learning In Healthcare?

It’s been over six years since IBM’s Watson amazed all of us on Jeopardy, but it has yet to deliver similar breakthroughs in healthcare. The headlines in last week’s Forbes article read, “MD Anderson Benches IBM Watson In Setback For Artificial Intelligence In Medicine.” Is it really a setback for the entire industry or not? Health Catalyst’s EVP for Product Development, Dale Sanders, believes that the challenges are unique to IBM’s machine learning strategy in healthcare. If they adjust that strategy and better manage expectations about what’s possible for machine learning in medicine, the future will be brighter for Watson, their clients, and AI in healthcare, in general. Watson’s success is good for all of us, but it’s failure is bad for all of us, too.

Join Dale as he discusses:

The good news: Machine learning technology is accelerating at a rate beyond Moore’s Law. Dale believes that machine learning algorithms and models are doubling in capability every six months.
The bad news: The healthcare data ecosystem is not nearly as rich as many would believe, and certainly not as rich as that used to train Watson for Jeopardy. Without high-volume, high-quality data, Watson’s potential and the constant advances in machine learning algorithms will hit a glass ceiling in healthcare.
The best news: By adjusting strategy and expectations, there are still plenty of opportunities to do great things with machine learning by using the current data content in healthcare, while we build out the volume and breadth of data we need to truly understand the patient at the center of the healthcare picture… and you don’t need an army of PhD data scientists to do it.

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Introducing Two New Products From Health Catalyst

Join Eric Just, Senior Vice President of Product Development, as he will discuss:

How machine learning is now included into our analytics platform and being built into all our applications.
The toolsets we have developed to automate and democratize machine learning tasks both within Health Catalyst clients and to the broader healthcare industry.
Processes to gain clinician buy-in, and engage the best machine learning engine in the world.
Demonstrations and examples of this life-saving technology.

Dorian DiNardo, Vice President, will share how the Health Catalyst® MACRA Measures & Insights product can help you:

Integrate hundreds of measures across financial, regulatory, and quality departments.
Monitor the behavior, activities, and other changing information needed to influence, manage, or change outcomes.
Tactically and strategically identify measures to take on risk in multi-year value-based care contracts.

The webinar begins with Eric Just presenting catalyst.ai and is followed by Dorian DiNardo presenting  MACRA Measures & Insights starting at the 35:57 mark.

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Machine Learning Using Healthcare.ai

Levi Thatcher, Health Catalyst Director of Data Science and his team provide a live demonstration using healthcare.ai to implement a healthcare-specific machine learning model from data source to patient impact. Levi goes through a hands-on coding example while sharing his insights on the value of predictive analytics, the best path towards implementation, and avoiding common pitfalls. Frequently asked questions are answered during the session.
During the webinar, we will:
Describe and install healthcare.ai
Build and evaluate a machine learning model
Deploy interpretable predictions to SQL Server
Discuss the process of deploying into a live analytics environment.
If you’d like to follow along, you should download and install R and RStudio prior to the event. We look forward to you joining us!

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