Truven Health Analytics is a healthcare data and analytics company with over 2,300 employees and 9,000 customers worldwide. It has a large collection of healthcare data from over 1,000 data suppliers and affects healthcare benefit decisions for 1 in 3 Americans. Truven Health works with hospitals, physicians, government agencies, payers, employers, and life sciences companies to help improve healthcare through data-driven analytics and services.
Improving Clinical and Operational Outcomes by Leveraging Healthcare Data Ana...NUS-ISS
Presented by Mr. Sandeep Makhijani, Regional Director for Asia Pacific (APAC), Truven Health Analytics at ISS Seminar: How Analytics is Transforming Healthcare on 31 Oct 2014.
Medisolv offers comprehensive Quality Reporting and Management software that assists Eligible Hospitals and Professionals in addressing their electronic and abstracted measure needs. Our software solution, paired with our expert consultants, assist clients with their quality reporting requirements. As a part of our quality solution we offer submission services to CMS and The Joint Commission. Our Quality Reporting and Management solution is exclusively endorsed by the American Hospital Association.
Medisolv also offers Business Analytics solutions that feature automated daily EHR data extracts. Our platform provides management with the tools and analytics to improve performance.
Powering Medical Research With Data: The Research Analytics Adoption ModelHealth Catalyst
Analytics are becoming imperative to researchers in recruiting patients into studies, making breakthrough discoveries, as well as monitoring the clinical implementation of these discoveries. This webinar will be for organizations that want to leverage their enterprise data to power more effective research.
Join Eric Just, Vice President of Technology at Health Catalyst, as he presents a Research Analytics Adoption Model that outlines ways that a research organization can leverage data and analytics to achieve greater speed and ROI on research.The Adoption Model walks through analytics competencies starting with basic data usage and culminating with using analytics to incorporate the latest research discoveries into clinical practice.
Content presented and discussed:
A summary of some of the challenges in using data and analytics for research
A research analytics adoption framework for all organizations interested in using clinical data for research
What is needed from a workflow and organizational perspective to power research with data
We hope you enjoy.
Healthcare Interoperability: New Tactics and TechnologyHealth Catalyst
Every provider agrees on the need for healthcare interoperability to achieve clinical data insights at the point of care. The question is how to get there from the myriad technologies and the volumes of data that comprise electronic medical records. It’s been difficult to organize among participants that have had little incentive to cooperate. And standards for sending and receiving data have been slow to develop. This is changing, but the key components that are still vital to realizing insights are closed-loop analytics and its accompanying tools, an enterprise data warehouse and analytics applications. This article defines the problems and explores the solutions to optimizing clinical decision making where it’s needed most.
Unlike few can do, Dr. David Burton has simplified these complex topics into a simple construct of four population health management building blocks. By acquiring proficiency in each of these four dimensions, healthcare delivery systems can create an asset which can be marketed to various types of governmental and commercial payers, which sponsor health benefit plans and offer shared accountability contracts (i.e. accountable care) into which these population health management sponsors can enter.
The key learning points of the webinar include:
The four building blocks of population health management (provider network, population(s), quality/safety outcomes, and cost outcomes)
The central role patient registries play in success in population health management
Pragmatic tools and methodologies to help healthcare delivery systems become proficient in each of the four dimensions of the framework
A discussion of the categories of governmental and commercial sponsors of shared accountability solutions, including the potential impact of the shift from defined benefit to defined contribution health benefit programs
Looking Back on Clinical Decision Support and Data WarehousingHealth Catalyst
Dale will take a slide deck previously prepared in 2006, from a lecture entitled, "The Power of an Enterprise Data Warehouse in Clinical Decision Support", presented to several informatics masters classes at Northwestern University and the University of Victoria. He won’t change anything about the slide deck, including the content and the old school graphics. The concept with this webinar is to give a “time capsule” perspective on past thinking and contrast that against current thoughts and trends in the market. Some of the information will be laughably wrong and naive, and some of the information will still be relevant. The hope is, by regularly reviewing our past, we will better inform our future.
How to Use Data to Improve Patient Safety: Part 2Health Catalyst
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
Improving Clinical and Operational Outcomes by Leveraging Healthcare Data Ana...NUS-ISS
Presented by Mr. Sandeep Makhijani, Regional Director for Asia Pacific (APAC), Truven Health Analytics at ISS Seminar: How Analytics is Transforming Healthcare on 31 Oct 2014.
Medisolv offers comprehensive Quality Reporting and Management software that assists Eligible Hospitals and Professionals in addressing their electronic and abstracted measure needs. Our software solution, paired with our expert consultants, assist clients with their quality reporting requirements. As a part of our quality solution we offer submission services to CMS and The Joint Commission. Our Quality Reporting and Management solution is exclusively endorsed by the American Hospital Association.
Medisolv also offers Business Analytics solutions that feature automated daily EHR data extracts. Our platform provides management with the tools and analytics to improve performance.
Powering Medical Research With Data: The Research Analytics Adoption ModelHealth Catalyst
Analytics are becoming imperative to researchers in recruiting patients into studies, making breakthrough discoveries, as well as monitoring the clinical implementation of these discoveries. This webinar will be for organizations that want to leverage their enterprise data to power more effective research.
Join Eric Just, Vice President of Technology at Health Catalyst, as he presents a Research Analytics Adoption Model that outlines ways that a research organization can leverage data and analytics to achieve greater speed and ROI on research.The Adoption Model walks through analytics competencies starting with basic data usage and culminating with using analytics to incorporate the latest research discoveries into clinical practice.
Content presented and discussed:
A summary of some of the challenges in using data and analytics for research
A research analytics adoption framework for all organizations interested in using clinical data for research
What is needed from a workflow and organizational perspective to power research with data
We hope you enjoy.
Healthcare Interoperability: New Tactics and TechnologyHealth Catalyst
Every provider agrees on the need for healthcare interoperability to achieve clinical data insights at the point of care. The question is how to get there from the myriad technologies and the volumes of data that comprise electronic medical records. It’s been difficult to organize among participants that have had little incentive to cooperate. And standards for sending and receiving data have been slow to develop. This is changing, but the key components that are still vital to realizing insights are closed-loop analytics and its accompanying tools, an enterprise data warehouse and analytics applications. This article defines the problems and explores the solutions to optimizing clinical decision making where it’s needed most.
Unlike few can do, Dr. David Burton has simplified these complex topics into a simple construct of four population health management building blocks. By acquiring proficiency in each of these four dimensions, healthcare delivery systems can create an asset which can be marketed to various types of governmental and commercial payers, which sponsor health benefit plans and offer shared accountability contracts (i.e. accountable care) into which these population health management sponsors can enter.
The key learning points of the webinar include:
The four building blocks of population health management (provider network, population(s), quality/safety outcomes, and cost outcomes)
The central role patient registries play in success in population health management
Pragmatic tools and methodologies to help healthcare delivery systems become proficient in each of the four dimensions of the framework
A discussion of the categories of governmental and commercial sponsors of shared accountability solutions, including the potential impact of the shift from defined benefit to defined contribution health benefit programs
Looking Back on Clinical Decision Support and Data WarehousingHealth Catalyst
Dale will take a slide deck previously prepared in 2006, from a lecture entitled, "The Power of an Enterprise Data Warehouse in Clinical Decision Support", presented to several informatics masters classes at Northwestern University and the University of Victoria. He won’t change anything about the slide deck, including the content and the old school graphics. The concept with this webinar is to give a “time capsule” perspective on past thinking and contrast that against current thoughts and trends in the market. Some of the information will be laughably wrong and naive, and some of the information will still be relevant. The hope is, by regularly reviewing our past, we will better inform our future.
How to Use Data to Improve Patient Safety: Part 2Health Catalyst
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
How To Avoid The 3 Most Common Healthcare Analytics Pitfalls And Related Inef...Health Catalyst
Analytics are supposed to provide data-driven solutions, not additional healthcare analytics pitfalls and other related inefficiencies. Yet such issues are quite common. Becoming familiar with potential problems will help health systems avoid them in the future. The three common analytics pitfalls are point solutions, EHRs, and independent data marts located in many different databases. An EDW will counter all three of these problems. The two inefficiencies include report factories and flavor of the month projects. The solution that best overcomes these inefficiencies is a robust deployment system.
User Group Kickoff and New Product Roadmap - HAS Session 12Health Catalyst
This session will be highly interactive, targeted primarily at existing Health Catalyst clients. First, our “three amigos” will introduce the concept of three user groups focused around analytics, deployment, and clinical knowledge assets, and solicit your feedback and input on the best way to collaborate and share best practices. Then we will introduce our new product category offerings, and solicit your interactive input and priorities as a guide to our future product roadmap.
Three Approaches to Predictive Analytics in HealthcareHealth Catalyst
Predictive analytics in healthcare must be timely, role-specific, and actionable to be successful. There are also three common types of healthcare predictive analytics: Risk scores (risk stratification using CMS-HCC or other models), What-if scenarios (simulations of specific outcomes given a certain combination of events, and Geo-spatial analytics (mapping a geographical location’s patient disease burden). The common thread in all of these is the element of action, or specifically, the intervention that really matters in healthcare predictive analytics.
Predictive Analytics: It's The Intervention That MattersHealth Catalyst
In this two-part webinar, get the detailed knowledge you need to make informed decisions about adopting predictive analytics in healthcare so you can separate today's hype from reality. In part 1, you'll learn key learnings from Dale Sanders including 1) our fixation on predictive analytics in readmissions, 2) the common trap of predictions without interventions, 3) the common misconceptions of correlations verses causation, 4) examples of predictions without algorithms, and 5) the importance of putting the basics first.
In part 2, you'll hear from industry expert David Crockett, PhD in a "graduate level" crash course cover key concepts such as machine learning, algorithms, feature selection, classification, tools and more.
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.
4 Essential Lessons for Adopting Predictive Analytics in HealthcareHealth Catalyst
Predictive analytics is quite a popular current topic. Unfortunately, there are many potential side tracks or pit falls for those that do not approach this carefully. Fortunately for healthcare, there are numerous existing models from other industries that are very efficient at risk stratification in the realm of population management. David Crocket, PhD shares 4 key pitfalls to avoid for those beginning predictive analytics. These include
1) confusing data with insight
2) confusing insight with value
3) overestimating the ability to interpret the data
4) underestimating the challenge of implementation.
Against the Odds: How this Small Community Hospital Used Six Strategies to Su...Health Catalyst
The constant thread weaving through every healthcare organizational strategy should be adherence to the Triple Aim. But with uncertainty generated by the changes at the federal level, healthcare organizations may be tempted to put their value-based care plans on hold. This article explains why that’s not necessary and lists six strategies for thriving under a fee-for-value model: 1.) Use Leadership and Team Structure to Support Improvement 2.) Drive Down Costs 3.) Reduce Unnecessary Waste 4.) Encourage the Learning Organization 5.) Prioritize Patient Education 6.) Track Data and Outcomes This blog cites one small medical center with odds stacked against it, and how it is managing to not only weather the changes, but also distinguish itself by staying true to the values of the Triple Aim.
How to Use Text Analytics in Healthcare to Improve Outcomes: Why You Need Mor...Health Catalyst
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.
Demystifying Text Analytics and NLP in HealthcareHealth Catalyst
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
From Installed to Stalled: Why Sustaining Outcomes Improvement Requires More ...Health Catalyst
The big first step toward building an outcomes improvement program is installing the analytics platform. But it’s certainly not the only step. Sustaining healthcare outcomes improvement is a triathlon, and the three legs are:
Installing an analytics platform
Gaining adoption
Implementing best practices
The program requires buy-in, enthusiasm, even evangelizing of analytics and its tools throughout the organization. It also requires that learnings from analysis translate into best practices, otherwise the program fails to produce results and will eventually fade away. Equally important is that top-level leadership across the organization, not just IT, supports and promotes the program ongoing. We explore each of the elements and how they come together to create successful and sustainable outcomes improvement that defines leading healthcare organizations.
The Four Balancing Acts Involved with Healthcare Data Security FrameworksHealth Catalyst
There’s a lot at stake for healthcare organizations when it comes to securing data. A primary concern is to protect privacy and avoid costly breaches or leaks, but at the same time, data must be accessible if it’s to be used for actionable insights. This executive report introduces four balancing acts that organizations must maintain to build an ideal data security framework:
Monitoring
Data de-identification
Cloud environments
User access
This can be a tug-of-war between IT and security, two groups that often have divergent interests, however well-meaning they may be. Healthcare systems that build bridges between these interests and strike the crucial balance between data utilization and security can dial in on long-term goals, like better care at a lower cost and overall outcomes improvement.
The Changing Role of Healthcare Data AnalystsHealth Catalyst
The healthcare industry is undergoing a sea change, and healthcare data analysts will play a central role in this transformation. This report explores how the evolution to value-based care is changing the role of healthcare data analysts, how data analysts’ skills can best be applied to achieve value-based objectives and, finally, how Health Catalyst’s most successful health system clients are making this cultural transformation happen in the real world.
Five Strategies for Easing the Burden of Clinical Quality MeasuresHealth Catalyst
Healthcare systems need to view regulatory measures in a different light. Rather than approaching them as required processes that burden the system, they should be viewed as quality improvement opportunities that lead to best practices. It helps to have a strategy to get there:
Prioritize measures that truly impact patient care
Have a line-of-sight to reimbursement
Understand measure alignment across programs
Involve the right people
Get involved in measure development upstream
The right tools also help, but a plan for success is advised for healthcare system administrators and clinicians who need to ease the reporting burden and take advantage of every measure in a positive way.
The Why And How Of Machine Learning And AI: An Implementation Guide For Healt...Health Catalyst
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
Data Mining in Healthcare: How Health Systems Can Improve Quality and Reduce...Health Catalyst
This is the complete 4-part series demonstrating real-world examples of the power of data mining in healthcare. Effective data mining requires a three-system approach: the analytics system (including an EDW), the content system (and systematically applying evidence-based best practices to care delivery), and the deployment system (driving change management throughout the organization and implementing a dedicated team structure). Here, we also show organizations with successful data-mining-application in critical areas such as: tracking fee-for-service and value-based payer contracts, population health management initiatives involving primary care reporting, and reducing hospital readmissions. Having the data and tools to use data mining and predict trends is giving these health systems a big advantage.
Quality Improvement In Healthcare: Where Is The Best Place To Start?Health Catalyst
One of the biggest challenges providers face in their quality improvement efforts is knowing where to get started. In my experience, one of the best ways to overcome that “where do we begin?” factor is by using data from an enterprise data warehouse to look for high-cost areas where there are large variations in how health care is delivered. Variation found through the KPA is an indicator of opportunity. The more avoidable variation that is reflected in a particular care process, the more opportunity there is to reduce that variation and standardize the process. Suppose after performing a KPA you discover three areas of opportunity. How do you determine which one to pursue, especially if it’s your first journey into process improvement? The most obvious answer would seem to be the one with the largest potential ROI. That may not always be the best course to pursue, however. You will also want to take into consideration the readiness/openness to change in each of those areas.
Precise Patient Registries: The Foundation for Clinical Research & Population...Health Catalyst
Join Dale Sanders as he shares his experience in developing disease registries, the history of patient registries, and the current design patterns in data engineering to create highly precise registries to support clinical research and population health management.
Topics:
*How the definition of the term “patient registry" has evolved from being associated with a federal- or state-mandated reporting requirement to a hospital or health system’s own population of patients, including device registries, drug registries, and procedure registries.
*Why engaging certain populations via group registries allows them to better understand their conditions and reach out for support from others who share their condition.
*Several untapped benefits of registries for disease and quality management.
*When to utilize patient registries to guide decision-making and drive change, especially at the point of care.
*Which of the critical steps to building a disease registry is most important.
*The keys to winning organizational support in order to implement a successful registry initiative.
*Precise patient registries play a significant role in the management of a broad variety of healthcare processes, including chronic diseases and conditions, as well as clinical research.
Understanding how registries are currently built vs. how they should be built is critical to the future of healthcare outcomes improvement, cost reduction, and translational research.
How To Avoid The 3 Most Common Healthcare Analytics Pitfalls And Related Inef...Health Catalyst
Analytics are supposed to provide data-driven solutions, not additional healthcare analytics pitfalls and other related inefficiencies. Yet such issues are quite common. Becoming familiar with potential problems will help health systems avoid them in the future. The three common analytics pitfalls are point solutions, EHRs, and independent data marts located in many different databases. An EDW will counter all three of these problems. The two inefficiencies include report factories and flavor of the month projects. The solution that best overcomes these inefficiencies is a robust deployment system.
User Group Kickoff and New Product Roadmap - HAS Session 12Health Catalyst
This session will be highly interactive, targeted primarily at existing Health Catalyst clients. First, our “three amigos” will introduce the concept of three user groups focused around analytics, deployment, and clinical knowledge assets, and solicit your feedback and input on the best way to collaborate and share best practices. Then we will introduce our new product category offerings, and solicit your interactive input and priorities as a guide to our future product roadmap.
Three Approaches to Predictive Analytics in HealthcareHealth Catalyst
Predictive analytics in healthcare must be timely, role-specific, and actionable to be successful. There are also three common types of healthcare predictive analytics: Risk scores (risk stratification using CMS-HCC or other models), What-if scenarios (simulations of specific outcomes given a certain combination of events, and Geo-spatial analytics (mapping a geographical location’s patient disease burden). The common thread in all of these is the element of action, or specifically, the intervention that really matters in healthcare predictive analytics.
Predictive Analytics: It's The Intervention That MattersHealth Catalyst
In this two-part webinar, get the detailed knowledge you need to make informed decisions about adopting predictive analytics in healthcare so you can separate today's hype from reality. In part 1, you'll learn key learnings from Dale Sanders including 1) our fixation on predictive analytics in readmissions, 2) the common trap of predictions without interventions, 3) the common misconceptions of correlations verses causation, 4) examples of predictions without algorithms, and 5) the importance of putting the basics first.
In part 2, you'll hear from industry expert David Crockett, PhD in a "graduate level" crash course cover key concepts such as machine learning, algorithms, feature selection, classification, tools and more.
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.
4 Essential Lessons for Adopting Predictive Analytics in HealthcareHealth Catalyst
Predictive analytics is quite a popular current topic. Unfortunately, there are many potential side tracks or pit falls for those that do not approach this carefully. Fortunately for healthcare, there are numerous existing models from other industries that are very efficient at risk stratification in the realm of population management. David Crocket, PhD shares 4 key pitfalls to avoid for those beginning predictive analytics. These include
1) confusing data with insight
2) confusing insight with value
3) overestimating the ability to interpret the data
4) underestimating the challenge of implementation.
Against the Odds: How this Small Community Hospital Used Six Strategies to Su...Health Catalyst
The constant thread weaving through every healthcare organizational strategy should be adherence to the Triple Aim. But with uncertainty generated by the changes at the federal level, healthcare organizations may be tempted to put their value-based care plans on hold. This article explains why that’s not necessary and lists six strategies for thriving under a fee-for-value model: 1.) Use Leadership and Team Structure to Support Improvement 2.) Drive Down Costs 3.) Reduce Unnecessary Waste 4.) Encourage the Learning Organization 5.) Prioritize Patient Education 6.) Track Data and Outcomes This blog cites one small medical center with odds stacked against it, and how it is managing to not only weather the changes, but also distinguish itself by staying true to the values of the Triple Aim.
How to Use Text Analytics in Healthcare to Improve Outcomes: Why You Need Mor...Health Catalyst
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.
Demystifying Text Analytics and NLP in HealthcareHealth Catalyst
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
From Installed to Stalled: Why Sustaining Outcomes Improvement Requires More ...Health Catalyst
The big first step toward building an outcomes improvement program is installing the analytics platform. But it’s certainly not the only step. Sustaining healthcare outcomes improvement is a triathlon, and the three legs are:
Installing an analytics platform
Gaining adoption
Implementing best practices
The program requires buy-in, enthusiasm, even evangelizing of analytics and its tools throughout the organization. It also requires that learnings from analysis translate into best practices, otherwise the program fails to produce results and will eventually fade away. Equally important is that top-level leadership across the organization, not just IT, supports and promotes the program ongoing. We explore each of the elements and how they come together to create successful and sustainable outcomes improvement that defines leading healthcare organizations.
The Four Balancing Acts Involved with Healthcare Data Security FrameworksHealth Catalyst
There’s a lot at stake for healthcare organizations when it comes to securing data. A primary concern is to protect privacy and avoid costly breaches or leaks, but at the same time, data must be accessible if it’s to be used for actionable insights. This executive report introduces four balancing acts that organizations must maintain to build an ideal data security framework:
Monitoring
Data de-identification
Cloud environments
User access
This can be a tug-of-war between IT and security, two groups that often have divergent interests, however well-meaning they may be. Healthcare systems that build bridges between these interests and strike the crucial balance between data utilization and security can dial in on long-term goals, like better care at a lower cost and overall outcomes improvement.
The Changing Role of Healthcare Data AnalystsHealth Catalyst
The healthcare industry is undergoing a sea change, and healthcare data analysts will play a central role in this transformation. This report explores how the evolution to value-based care is changing the role of healthcare data analysts, how data analysts’ skills can best be applied to achieve value-based objectives and, finally, how Health Catalyst’s most successful health system clients are making this cultural transformation happen in the real world.
Five Strategies for Easing the Burden of Clinical Quality MeasuresHealth Catalyst
Healthcare systems need to view regulatory measures in a different light. Rather than approaching them as required processes that burden the system, they should be viewed as quality improvement opportunities that lead to best practices. It helps to have a strategy to get there:
Prioritize measures that truly impact patient care
Have a line-of-sight to reimbursement
Understand measure alignment across programs
Involve the right people
Get involved in measure development upstream
The right tools also help, but a plan for success is advised for healthcare system administrators and clinicians who need to ease the reporting burden and take advantage of every measure in a positive way.
The Why And How Of Machine Learning And AI: An Implementation Guide For Healt...Health Catalyst
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
Data Mining in Healthcare: How Health Systems Can Improve Quality and Reduce...Health Catalyst
This is the complete 4-part series demonstrating real-world examples of the power of data mining in healthcare. Effective data mining requires a three-system approach: the analytics system (including an EDW), the content system (and systematically applying evidence-based best practices to care delivery), and the deployment system (driving change management throughout the organization and implementing a dedicated team structure). Here, we also show organizations with successful data-mining-application in critical areas such as: tracking fee-for-service and value-based payer contracts, population health management initiatives involving primary care reporting, and reducing hospital readmissions. Having the data and tools to use data mining and predict trends is giving these health systems a big advantage.
Quality Improvement In Healthcare: Where Is The Best Place To Start?Health Catalyst
One of the biggest challenges providers face in their quality improvement efforts is knowing where to get started. In my experience, one of the best ways to overcome that “where do we begin?” factor is by using data from an enterprise data warehouse to look for high-cost areas where there are large variations in how health care is delivered. Variation found through the KPA is an indicator of opportunity. The more avoidable variation that is reflected in a particular care process, the more opportunity there is to reduce that variation and standardize the process. Suppose after performing a KPA you discover three areas of opportunity. How do you determine which one to pursue, especially if it’s your first journey into process improvement? The most obvious answer would seem to be the one with the largest potential ROI. That may not always be the best course to pursue, however. You will also want to take into consideration the readiness/openness to change in each of those areas.
Precise Patient Registries: The Foundation for Clinical Research & Population...Health Catalyst
Join Dale Sanders as he shares his experience in developing disease registries, the history of patient registries, and the current design patterns in data engineering to create highly precise registries to support clinical research and population health management.
Topics:
*How the definition of the term “patient registry" has evolved from being associated with a federal- or state-mandated reporting requirement to a hospital or health system’s own population of patients, including device registries, drug registries, and procedure registries.
*Why engaging certain populations via group registries allows them to better understand their conditions and reach out for support from others who share their condition.
*Several untapped benefits of registries for disease and quality management.
*When to utilize patient registries to guide decision-making and drive change, especially at the point of care.
*Which of the critical steps to building a disease registry is most important.
*The keys to winning organizational support in order to implement a successful registry initiative.
*Precise patient registries play a significant role in the management of a broad variety of healthcare processes, including chronic diseases and conditions, as well as clinical research.
Understanding how registries are currently built vs. how they should be built is critical to the future of healthcare outcomes improvement, cost reduction, and translational research.
Introduction to Population Health Analytics, Predictive Analytics, Big Data a...Frank Wang
UNH HCAD 6635 Healthcare Analytics Session 12, the last session of Health Information Analytics. Details of the topics of this session will be covered in HCAD 6637 "Advanced Analytics and Health Data Mining"
GraphX: Graph Analytics in Apache Spark (AMPCamp 5, 2014-11-20)Ankur Dave
GraphX is a graph processing framework built into Apache Spark. This talk introduces GraphX, describes key features of its API, and gives an update on its status.
Healthcare Analytics Adoption Model -- UpdatedHealth Catalyst
The Healthcare Analytics Adoption Model is the result of a collaboration of healthcare industry veterans over the last 15 years. The model borrows lessons learned from the HIMSS EMR Adoption Model, and describes an analogous approach for assessing the adoption of analytics in healthcare.
The Healthcare Analytics Adoption Model provides:
1) A framework for evaluating the industry’s adoption of analytics
2) A roadmap for organizations to measure their own progress toward analytic adoption
3) A framework for evaluating vendor products
This Analytics Adoption Model will enable healthcare organizations to fully understand and leverage the capabilities of analytics and so achieve the ultimate goal that has eluded most provider organizations – that of improving the quality of care while lowering costs and enhancing clinician and patient satisfaction.
Predictive Analytics: Context and Use Cases
Historical context for successful implementation of predictive analytic techniques and examples of implementation of successful use cases.
This presentation introduces big data and explains how to generate actionable insights using analytics techniques. The deck explains general steps involved in a typical analytics project and provides a brief overview of the most commonly used predictive analytics methods and their business applications.
Vijay Adamapure is a Data Science Enthusiast with extensive experience in the field of data mining, predictive modeling and machine learning. He has worked on numerous analytics projects ranging from healthcare, business analytics, renewable energy to IoT.
Vijay presented these slides during the Internet of Everything Meetup event 'Predictive Analytics - An Overview' that took place on Jan. 9, 2015 in Mumbai. To join the Meetup group, register here: http://bit.ly/1A7T0A1
Big Data in Healthcare Made Simple: Where It Stands Today and Where It’s GoingHealth Catalyst
Health system leaders have questions about big data: When will I need it? How should I prepare? What’s the best way to use it? It’s important to separate the hype of big data from the reality. Where big data stands in healthcare today is a far cry from where it will be in the future. Right now, the best use cases are in academic- or research-focused healthcare institutions. Most healthcare organizations are still tackling issues with their transactional databases and learning how to use those databases effectively. But soon—once the issues of expertise and security have been addressed—big data will play a huge role in care management, predictive analytics, prescriptive analytics, and genomics for everyday patients. The transition to big data will be easier if health systems adopt a late-binding approach to the data now.
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
Harness Your Clinical and Financial Data with an Enterprise Health Informat...Perficient, Inc.
The importance of Enterprise Health Information Exchange (EHIE) as a key way to empower your physicians and patients and demonstrate meaningful use of electronic health records:
- Present the business case for EHIE as an important architecture that matters to progressive health systems
- Take a look at some of the market-leading EHIE architectures and products
- Provide real exam...ples of organizations that are using EHIE to improve their operations
While Healthcare 1.0 was broadly defined by a focus on defensive medicine, billing, and fee-for-service, culminating in the mass adoption of EMRs, Healthcare 2.0 is a new wave focused on improving clinical efficiency, quality of care, affordability, and fee-for-value; culminating in a new age of healthcare analytics. This new age of analytics will require a new set of organizational skills and a foundational set of analytic information systems that many executives have not anticipated.
Join Dale Sanders, a 20-year healthcare CIO veteran and the industry's leading analytics expert, as he discusses his lessons learned, best practices in analytics, and what the C-level suite needs to know about this topic, now. Listen to Dale discuss 1) A step-by-step curriculum for analytic adoption and maturity in healthcare organizations, 2) the basic approach to a late-binding data warehouse, 3) pros and cons of early versus late binding, 4) the volatility in vocabulary and business rules in healthcare, 5) how to engineer your data to accommodate volatility in the future
The 10th Annual Utah Health Services Research Conference: Data: What's available and how we are use it is changing. By: Danielle A. Lloyd, MPH - Premier
Health Services Research Conference: March 16, 2015
Patient Centered Research Methods Core, University of Utah, CCTS
Align Patient Outcomes with Financial Data: a Formula for Correlating Cost an...Perficient, Inc.
This slideshare discusses the cost crisis in healthcare, challenges healthcare organizations are facing, and how to:
Uncover true patient costs and value based purchasing
Understand quality and cost outcomes by aligning clinical and financial data
Identify trends and opportunities, and create actionable steps to improve business
Accelerate data integration with Perficient's High-Performance Costing Expressway
Leverage actionable visuals via dashboards with Oracle Business Intelligence tools
Evaluate patient complications, outcomes and detailed costs with Oracle’s Enterprise Healthcare Analytics Data Model
Supporting Individuals with Intellectual and Developmental Disability During the First 100 Days of the COVID-19 Outbreak in the U.S.
BrightSpring Health Services Chief Medical Officer Dr. William Mills presents on BrightSpring's ongoing response to COVID-19 and how the organization is mitigating risks for our patients, clients, and team members.
How Northwestern Medicine is Leveraging Epic to Enable Value-Based CarePerficient, Inc.
Value-based care and payment reform are prompting hospitals and healthcare providers to more closely manage population health. Hospitals and health systems rely on technology and data to outline the characteristics of their population and identify high-risk patients in order to manage chronic diseases and deliver enhanced preventative care.
Our webinar covered how Cadence Health, now part of Northwestern Medicine, is leveraging the native capabilities of Epic to manage their population health initiatives and value-based care relationships across the continuum of care.
Our speakers:
-Analyzed how Epic’s Healthy Planet and Cogito platforms can be used to manage value-based care initiatives.
-Examined the three steps for effective population health management: Collect data, analyze data and engage with patients.
-Covered how access to analytics allows physicians at Northwestern Medicine to deliver enhanced preventive care and better manage chronic diseases.
-Discussed Northwestern Medicine’s strategy to integrate data from Epic and other data sources.
HealthSaaS Overview Deck October 2014 (RPM, Home Health)HealthSaaS, Inc.
The HealthSaaS Connected Outcomes Platform removes silo barriers to connect, aggregate and integrate disparate data from mHealth applications and Remote Patient Monitoring (RPM) devices.
Our services provide HIPAA secure data to the “point of care” wherever the clinician is located. Enabling clinicians to rapidly respond to clinically relevant patient health information can facilitate early interventions, reduce hospital admissions, improve outcomes and lower costs.
Our passion empowers us to create eHealth collaboration tools that enhance provider efficiencies, track outcomes and improve the quality of life for patients throughout the continuum of care.
As the author of “Big Data in Healthcare Hype and Hope,” Dr. Feldman has interviewed over 180 emerging tech and healthcare companies, always asking, “How can your new approach help patients?” Her research shows that data, as an enabling tool, has the power to give us critical new insights into not only what causes disease, but what comprises normal. Despite this promise, few patients have reaped the benefits of personalized medicine. A panel of leading big data innovators will discuss the evolving health data ecosystem and how big data is being leveraged for research, discovery, clinical trials, genomics, and cancer care. Case studies and real-life examples of what’s working, what’s not working, and how we can help speed up progress to get patients the right care at the right time will be explored and debated.
• Bonnie Feldman, DDS, MBA - Chief Growth Officer, @DrBonnie360
• Colin Hill - CEO, GNS Healthcare
• Jonathan Hirsch - Founder & President, Syapse
• Andrew Kasarskis, PhD - Co-Director, Icahn Institute for Genomics & Multiscale Biology; Associate Professor, Genetics & Genomic Studies, Icaahn School of Medicine at Mt. Sinai
• William King - CEO, Zephyr Health
New York eHealth Collaborative Digital Health Conference
November 18, 2014
"Healthcare Services at Merck & Co". Presentation by Guy Eiferman, President of Healthcare Services and Solutions, Merck & Co., made at the mHealth Israel Investors Summit, June 29, 2015, in Jerusalem
DATA-DRIVEN CARE: THE KEY TO ACCOUNTABLE CARE DELIVERY FROM A PHYSICIAN GROUP...Health Catalyst
Hospitals, payers and physician groups alike are facing changes in healthcare that require their attention. These changes are a result of financial forces that are changing the ways healthcare services are paid, cost of care pressures, ever-changing patient population behaviors, improvements in the science of health care and federal regulations tied to incentives that are soon turning to penalties. Anyone in health care is grappling to understand these changes and chart their strategies to be prepared for the future.
The presenters have proven expertise developing their strategies to care for patients in an accountable care model using data to drive their strategies. The presenting organizations will talk through their strategy including their future expectations and early results using data to identify improvement opportunities and to shift the clinical approach to health care. In addition to strategy, they will share solutions and analytic applications critical to the current and future expected results of their strategy.
Want to move your career forward? Looking to build your leadership skills while helping others learn, grow, and improve their skills? Seeking someone who can guide you in achieving these goals?
You can accomplish this through a mentoring partnership. Learn more about the PMISSC Mentoring Program, where you’ll discover the incredible benefits of becoming a mentor or mentee. This program is designed to foster professional growth, enhance skills, and build a strong network within the project management community. Whether you're looking to share your expertise or seeking guidance to advance your career, the PMI Mentoring Program offers valuable opportunities for personal and professional development.
Watch this to learn:
* Overview of the PMISSC Mentoring Program: Mission, vision, and objectives.
* Benefits for Volunteer Mentors: Professional development, networking, personal satisfaction, and recognition.
* Advantages for Mentees: Career advancement, skill development, networking, and confidence building.
* Program Structure and Expectations: Mentor-mentee matching process, program phases, and time commitment.
* Success Stories and Testimonials: Inspiring examples from past participants.
* How to Get Involved: Steps to participate and resources available for support throughout the program.
Learn how you can make a difference in the project management community and take the next step in your professional journey.
About Hector Del Castillo
Hector is VP of Professional Development at the PMI Silver Spring Chapter, and CEO of Bold PM. He's a mid-market growth product executive and changemaker. He works with mid-market product-driven software executives to solve their biggest growth problems. He scales product growth, optimizes ops and builds loyal customers. He has reduced customer churn 33%, and boosted sales 47% for clients. He makes a significant impact by building and launching world-changing AI-powered products. If you're looking for an engaging and inspiring speaker to spark creativity and innovation within your organization, set up an appointment to discuss your specific needs and identify a suitable topic to inspire your audience at your next corporate conference, symposium, executive summit, or planning retreat.
About PMI Silver Spring Chapter
We are a branch of the Project Management Institute. We offer a platform for project management professionals in Silver Spring, MD, and the DC/Baltimore metro area. Monthly meetings facilitate networking, knowledge sharing, and professional development. For event details, visit pmissc.org.
The Impact of Artificial Intelligence on Modern Society.pdfssuser3e63fc
Just a game Assignment 3
1. What has made Louis Vuitton's business model successful in the Japanese luxury market?
2. What are the opportunities and challenges for Louis Vuitton in Japan?
3. What are the specifics of the Japanese fashion luxury market?
4. How did Louis Vuitton enter into the Japanese market originally? What were the other entry strategies it adopted later to strengthen its presence?
5. Will Louis Vuitton have any new challenges arise due to the global financial crisis? How does it overcome the new challenges?Assignment 3
1. What has made Louis Vuitton's business model successful in the Japanese luxury market?
2. What are the opportunities and challenges for Louis Vuitton in Japan?
3. What are the specifics of the Japanese fashion luxury market?
4. How did Louis Vuitton enter into the Japanese market originally? What were the other entry strategies it adopted later to strengthen its presence?
5. Will Louis Vuitton have any new challenges arise due to the global financial crisis? How does it overcome the new challenges?Assignment 3
1. What has made Louis Vuitton's business model successful in the Japanese luxury market?
2. What are the opportunities and challenges for Louis Vuitton in Japan?
3. What are the specifics of the Japanese fashion luxury market?
4. How did Louis Vuitton enter into the Japanese market originally? What were the other entry strategies it adopted later to strengthen its presence?
5. Will Louis Vuitton have any new challenges arise due to the global financial crisis? How does it overcome the new challenges?
New Explore Careers and College Majors 2024.pdfDr. Mary Askew
Explore Careers and College Majors is a new online, interactive, self-guided career, major and college planning system.
The career system works on all devices!
For more Information, go to https://bit.ly/3SW5w8W