This is the latest version of a slide deck that discusses some of the less technical, but very important issues, related to the effective use of predictive analytics in healthcare.
Microsoft: A Waking Giant in Healthcare Analytics and Big DataDale Sanders
Ten years ago, critics didn’t believe that Microsoft could scale in the second generation of relational data warehouses, but they did. More recently, many of these same pundits have criticized Microsoft for missing the technology wave du jour in cloud offerings, mobile technology, and big data. But, once again, Microsoft has been quietly reengineering its culture and products, and as a result, they now offer the best value and most visionary platform for cloud services, big data, and analytics in healthcare.
Break All The Rules: What the Leading Health Systems Do Differently with Anal...Dale Sanders
This was my attempt to capture the intangible differences between leaders and followers in data driven healthcare. It should be noted that the organizations listed are not necessarily Health Catalyst clients. This slide deck is not intended to market or advertise Health Catalyst, but rather highlight leadership in analytics, wherever it exists.
Healthcare Best Practices in Data Warehousing & AnalyticsDale Sanders
This is from a class lecture that I gave in 2005. Rather dated, but 95% of content is still very relevant today, which is a bit unfortunate. That's an indication of how little we've progressed in the healthcare domain.
The term “Big Data” emerged from Silicon Valley in 2003 to describe the unprecedented volume and velocity of data that was being collected and analyzed by Yahoo, Google, eBay, and others. They had reached an affordability, scalability and performance ceiling with traditional relational database technology that required the development of a new solution, not being met by the relational data base vendors. Through the Apache Open Source consortium, Hadoop was that new solution. Since then, Hadoop has become the most powerful and popular technology platform for data analysis in the world. But, healthcare being the information technology culture that it is, Hadoop’s adoption in healthcare operations has been slow. In this webinar, Dale Sanders, Executive Vice President of Product Development will explore several questions:
Why should healthcare leaders and executives care about this technology?
What makes Hadoop so attractive and rapidly adopted in other industries but not in healthcare?
Why is Big Data a bigger deal to them than healthcare?
What do they see that we don’t and are we missing the IT boat again?
How is the cloud reducing the barriers to adoption by commoditizing the skilled labor impact at the local healthcare organizational level?
There Is A 90% Probability That Your Son Is Pregnant: Predicting the Future ...Health Catalyst
In this webinar, which is geared for managers and executives, Dale Sanders provides a new version of a very popular lecture he presented at this year’s Health Analytics Summit in Salt Lake City. Attendees will gain an understanding of:
What to expect from predictive analytics as it relates to human behavior
A general overview of predictive analytics models, and the contexts in which those various models should and should not be used
The scenarios in which predictive models in healthcare are effective and when they are not, given that 80% of population health outcomes are determined by socio-econonic factors, not healthcare delivery
The relationship between predictive analytic accuracy and topics of data management such as data quality, data volume and patient outcomes data
The use of predictive analytics to identify patients who are on a trajectory for poor, as well as good, outcomes
How current predictive analytics strategies are overlookng the cost of intervention and “Return on Engagement”, ROE— the cost per unit of healthcare improvement for patient populations
The cultural, philosophical, and legal conundrums that predictive analytics will create for healthcare, notably healthcare rationing
The success of predictive analytics will not be defined by the simple risk stratification of patient populations for care management teams. Success will depend on the costs of intervention to reduce the risks that are identified by predictive analytics, which boils down to this two-part question: Now that we can predict a patient’s risk for a bad healthcare outcome, “What’s the probability of influencing this patient’s behavior towards a better outcome?” And, “How much effort and cost will be required for that influence?”
These are the slides from the workshop I delivered at the Healthcare Analytics Symposium in July 2014. This 3-hour workshop walked the attendees step-by-step through the requirements to start a healthcare predictive analytics program and some of the areas already showing progress.
Managing National Health: An Overview of Metrics & OptionsDale Sanders
This is a presentation that I gave at the annual international healthcare conference hosted by the Cayman Islands government. It summarizes the international standards and frameworks for planning and managing the health of a nation. One of the most fun parts of a very fun career was the time that I spent working and living in the Cayman Islands and serving as the CIO of the national health system. The Cayman Islands national health system sat at the intersection of three very influential healthcare ecosystems-- the United States, United Kingdom, and the Pan-American Healthcare Organization. As a result, I was fortunate enough to learn from these international settings and contrast that to the US healthcare system. Other healthcare systems tend to benchmark themselves internationally more so than the United States, where we tend to benchmark ourselves internally. Unfortunately, those internal US benchmarks are the lowest in the developed world by almost every measure of national health.
Develop Your Analysts and They'll Pay for ThemselvesHealth Catalyst
Healthcare organizations dream of analysts who can quickly build powerful reports and dashboards that turn simple data into insights. However, those technical and communication skills take years to develop and longer to refine. How do you make sure your organization is able to develop analysts into critical team members who can deliver outcomes improvement? In this webinar, you will learn what it takes to grow the analytical skills—including technical prowess and adaptive leadership—that are critical for transforming healthcare.
Russ and Peter will discuss:
The culture that leaders need to cultivate to foster improvement.
The skill sets that leaders need to encourage in their analysts.
The barriers that leaders should focus on eliminating to make it easier for analysts to succeed.
We look forward to you joining us.
Microsoft: A Waking Giant in Healthcare Analytics and Big DataDale Sanders
Ten years ago, critics didn’t believe that Microsoft could scale in the second generation of relational data warehouses, but they did. More recently, many of these same pundits have criticized Microsoft for missing the technology wave du jour in cloud offerings, mobile technology, and big data. But, once again, Microsoft has been quietly reengineering its culture and products, and as a result, they now offer the best value and most visionary platform for cloud services, big data, and analytics in healthcare.
Break All The Rules: What the Leading Health Systems Do Differently with Anal...Dale Sanders
This was my attempt to capture the intangible differences between leaders and followers in data driven healthcare. It should be noted that the organizations listed are not necessarily Health Catalyst clients. This slide deck is not intended to market or advertise Health Catalyst, but rather highlight leadership in analytics, wherever it exists.
Healthcare Best Practices in Data Warehousing & AnalyticsDale Sanders
This is from a class lecture that I gave in 2005. Rather dated, but 95% of content is still very relevant today, which is a bit unfortunate. That's an indication of how little we've progressed in the healthcare domain.
The term “Big Data” emerged from Silicon Valley in 2003 to describe the unprecedented volume and velocity of data that was being collected and analyzed by Yahoo, Google, eBay, and others. They had reached an affordability, scalability and performance ceiling with traditional relational database technology that required the development of a new solution, not being met by the relational data base vendors. Through the Apache Open Source consortium, Hadoop was that new solution. Since then, Hadoop has become the most powerful and popular technology platform for data analysis in the world. But, healthcare being the information technology culture that it is, Hadoop’s adoption in healthcare operations has been slow. In this webinar, Dale Sanders, Executive Vice President of Product Development will explore several questions:
Why should healthcare leaders and executives care about this technology?
What makes Hadoop so attractive and rapidly adopted in other industries but not in healthcare?
Why is Big Data a bigger deal to them than healthcare?
What do they see that we don’t and are we missing the IT boat again?
How is the cloud reducing the barriers to adoption by commoditizing the skilled labor impact at the local healthcare organizational level?
There Is A 90% Probability That Your Son Is Pregnant: Predicting the Future ...Health Catalyst
In this webinar, which is geared for managers and executives, Dale Sanders provides a new version of a very popular lecture he presented at this year’s Health Analytics Summit in Salt Lake City. Attendees will gain an understanding of:
What to expect from predictive analytics as it relates to human behavior
A general overview of predictive analytics models, and the contexts in which those various models should and should not be used
The scenarios in which predictive models in healthcare are effective and when they are not, given that 80% of population health outcomes are determined by socio-econonic factors, not healthcare delivery
The relationship between predictive analytic accuracy and topics of data management such as data quality, data volume and patient outcomes data
The use of predictive analytics to identify patients who are on a trajectory for poor, as well as good, outcomes
How current predictive analytics strategies are overlookng the cost of intervention and “Return on Engagement”, ROE— the cost per unit of healthcare improvement for patient populations
The cultural, philosophical, and legal conundrums that predictive analytics will create for healthcare, notably healthcare rationing
The success of predictive analytics will not be defined by the simple risk stratification of patient populations for care management teams. Success will depend on the costs of intervention to reduce the risks that are identified by predictive analytics, which boils down to this two-part question: Now that we can predict a patient’s risk for a bad healthcare outcome, “What’s the probability of influencing this patient’s behavior towards a better outcome?” And, “How much effort and cost will be required for that influence?”
These are the slides from the workshop I delivered at the Healthcare Analytics Symposium in July 2014. This 3-hour workshop walked the attendees step-by-step through the requirements to start a healthcare predictive analytics program and some of the areas already showing progress.
Managing National Health: An Overview of Metrics & OptionsDale Sanders
This is a presentation that I gave at the annual international healthcare conference hosted by the Cayman Islands government. It summarizes the international standards and frameworks for planning and managing the health of a nation. One of the most fun parts of a very fun career was the time that I spent working and living in the Cayman Islands and serving as the CIO of the national health system. The Cayman Islands national health system sat at the intersection of three very influential healthcare ecosystems-- the United States, United Kingdom, and the Pan-American Healthcare Organization. As a result, I was fortunate enough to learn from these international settings and contrast that to the US healthcare system. Other healthcare systems tend to benchmark themselves internationally more so than the United States, where we tend to benchmark ourselves internally. Unfortunately, those internal US benchmarks are the lowest in the developed world by almost every measure of national health.
Develop Your Analysts and They'll Pay for ThemselvesHealth Catalyst
Healthcare organizations dream of analysts who can quickly build powerful reports and dashboards that turn simple data into insights. However, those technical and communication skills take years to develop and longer to refine. How do you make sure your organization is able to develop analysts into critical team members who can deliver outcomes improvement? In this webinar, you will learn what it takes to grow the analytical skills—including technical prowess and adaptive leadership—that are critical for transforming healthcare.
Russ and Peter will discuss:
The culture that leaders need to cultivate to foster improvement.
The skill sets that leaders need to encourage in their analysts.
The barriers that leaders should focus on eliminating to make it easier for analysts to succeed.
We look forward to you joining us.
How to Choose the Best Healthcare Analytics Software Solution in a Crowded Ma...Health Catalyst
There’s a new trend in the healthcare industry to adopt analytics software solutions to help organizations achieve clinical and financial success. Because of the high demand for analytics, there are many players touting their ability to delivery comprehensive solutions. With so many options available, health systems need to be able to cut through the marketing hype to find tools that provide the best value for their needs. Key solutions include an enterprise data warehouse and analytics software applications (from foundational to discovery to advanced). Other considerations include the organization’s readiness for cultural change, the total cost of ownership required, and the viability of the company providing the technology.
20 Years in Healthcare Analytics & Data Warehousing: What did we learn? What'...Health Catalyst
The enterprise data warehouse (EDW) at Intermountain Healthcare went live in 1998. The EDW at Northwestern Medicine went live in 2006. Dale Sanders was the chief architect and strategist for both. The business inspiration behind Health Catalyst was, in essence, to create the commercial availability of the technology, analytics, and data utilization skills associated with these systems at Intermountain and Northwestern. Lee Pierce assumed leadership of the Intermountain EDW in 2008. Andrew Winter assumed leadership of the Northwestern EDW in 2009, and transitioned leadership of the EDW to Shakeeb Akhter in 2016. This webinar is a fireside chat among friends and colleagues as they look back across their healthcare IT decisions to answer these questions:
What did we do right and what did we do wrong?
What advice do we have for others in this emerging era of Big Data?
What does the future of analytics and Big Data look like in healthcare?
Precision medicine has profound implications for patient care and clinical outcomes, and is already beginning to impact everyday medical practice. However, implementation faces several obstacles, including overstated claims, resistance among clinical medicine thought leaders and providers, and concerns about costs, data overload, and interoperability. This webinar will address five key concerns, challenges, and barriers among clinicians and IT professionals struggling to determine the value and limitations of implementing precision medicine, and offer tangible recommendations to help drive toward precision medicine adoption.
Learning Objectives:
Identify obstacles that impede the implementation of precision medicine in clinical practice.
Contrast population-based medicine and precision medicine.
Demonstrate the real world benefits of precision medicine in today's healthcare setting.
Have Data—Need Analysts. Lessons Learned From The Woodworking IndustryHealth Catalyst
Recent advances in healthcare technology are designed to help organizations achieve the Quadruple Aim. While IT vendors tout better tools as the solution for healthcare woes, all data analysts and architects know this isn’t true. John Wadsworth, senior vice president, Health Catalyst, will share valuable, practical learnings from his 20 plus years of experience as a data analyst in this hands-on session that will explore the rich analytic ecosystem found in health care.
John will share:
Healthcare industry examples that highlight the required synergy of technology (e.g. EHRs, EDWs, ancillary health and reporting systems, etc.)
Data analyst skill proficiencies that help drive sustained clinical, financial, and operational improvements.
John’s unique presentation style leverages simple and fun analogies to galvanize key concepts for technology, clinical, and executive attendees. In this webinar, he will bring lessons learned from the woodworking industry and demonstrate how they apply to healthcare analytics. Come prepared to engage, laugh, and learn. You’ll leave with a deeper understanding of the skills needed to fully leverage your analytic ecosystem.
As the Age of Analytics emerges in healthcare, health system executives are increasingly challenged to define a data governance strategy that maximizes the value of data to the mission of their organizations.
Adding to that challenge, the competitive nature of the data warehouse and analytics market place has resulted in significant noise from vendors and consultants alike who promise to help health systems develop their data governance strategy. Having gone on his own turbulent data governance ride as a CIO in the US Air Force and healthcare, Dale Sanders, Senior Vice President at Health Catalyst will cut through the market noise to cover the following topics:
General concepts of data governance, regardless of industry
Unique aspects of data governance in healthcare
Data governance in a “Late Binding” data warehouse
The layers and roles in data governance
The four “Closed Loops” of healthcare analytics and data governance
Why the Data Steward’s Role is Critical to Sustained Outcomes Improvement in ...Health Catalyst
The data steward is critical to sustained outcomes improvement, yet they tend to be underappreciated members of the healthcare analytics family. Combining the invaluable technical expertise of a data analyst with the vital clinical knowledge of an experienced caregiver, the data steward’s skills and proficiency at both positions brings value beyond measure to any outcomes improvement project. Unfortunately, all too often, their role is non-existent even though potential candidates for the job are located in multiple data sources throughout the organization. Among other responsibilities, the data steward:
Reinforces the global data governance principles.
Helps develop and refine details of local data governance practices.
Is the eyes and ears of the organization with respect to data governance and the governance committee.
Provides direction to peers regarding appropriate data definitions, usage, and access.
Anticipates local consequences of global changes
For innovative health system leaders who have specifically recognized this emerging role, the ROI of data stewards who help achieve improved outcomes is very worthwhile.
There Is A 90% Probability That Your Son Is Pregnant: Predicting The Future ...Health Catalyst
Predictive: Relating to or having the effect of predicting an event or result. Analytics: The systematic computational analysis of data or statistics. Together they make up one of the most popular topics in healthcare today. But predictive analytics is a means to an ends, and there is little good in predicting an event or result without a strategy for acting upon that event, when it happens. If, as the Robert Wood Johnson Foundation recently published, 80% of healthcare determinants fall outside of the healthcare delivery system as we traditionally define it, should we focus our predictive analytics on the traditional 20% of traditional healthcare delivery, or broaden our focus to the 80% that includes social and economic factors, physical environment, and lifestyle behaviors? What if our predictive models reveal to us that the highest risk variable to a patient’s length of life and quality of life is their economic status? Can an accountable care organization and patient centered medical home realistically do anything to reduce that risk, in reaction to the predictive model? Given the current availability and type of data in the healthcare ecosystem, and our organizational ability or inability to realistically intervene, where should we focus our predictive and interventional risk management strategies? There is enormous potential value in the application of predictive analytics to healthcare, but, in contrast to predicting the weather, credit risk, consumer purchasing habits, or college dropout rates, the data collection, and social and ethical complexities of applying predictive analytics in healthcare are significantly higher. This session will explore some of the less technical, more human interest and philosophical issues, associated with predictive analytics in healthcare, including the speaker’s experience prior to healthcare, in the US Air Force, National Security Agency, and manufacturing.
Healthcare Analytics Careers: New Roles for the Brave, New World of Value-bas...Health Catalyst
Job titles can be leading indicators of the direction an industry is moving and the same holds true for healthcare. The new healthcare economic model—from fee-for-service (FFS) to value-based—is driving a change in roles and responsibilities for professionals seeking healthcare analytics careers. Motivated by CMS and commercial payers, healthcare organizations are realizing the need to find and hire new types of healthcare professionals, a Chief Population Health Officer or Vice President of Clinical Informatics, who are focused on value. Senior leaders are seeking to build teams that have the ability to bring together analytics, best-practice clinical content, and process improvement to create long-term, sustainable change across their healthcare systems.
The Philosophy, Psychology, and Technology of Data in HealthcareDale Sanders
Over-application of data and analytics in healthcare is alienating clinicians and, for the most part, not bending the cost-quality curves. This lecture spends 60% of the time on the softer issues, 40% on the technology.
Gain insights from data analytics and take action! Learn why everyone is making a big deal about big data in healthcare and how data analytics creates action.
Part 2 - 20 Years in Healthcare Analytics & Data Warehousing: What did we lea...Health Catalyst
Lessons learned over 20 years. This time we focus on technology lessons learned from experience at Intermountain Healthcare, Northwestern Medicine and Cayman Islands Health Authority
Developing a Strategic Analytics Framework that Drives Healthcare TransformationTrevor Strome
About the presentation.
Based on Chapter 3 of my book "Healthcare Analytics for Quality and Performance Improvement", this presentation describes the key components of a strategic analytics framework that can enable your healthcare organization to leverage data from source-systems to achieve its quality, safety, and performance improvement goals.
What is an analytics strategy?
Analytics is currently a very “trendy” topic. The internet is scattered with many buzzwords, marketing angles, white papers, and opinions on the topic of healthcare analytics. With all this “noise”, it is easy to get distracted from what is actually required, from an analytics perspective, by your organization. An analytics strategy helps cut through the noise and keep focus on what is important for the organization. Regardless of what the latest “buzz” is, your analytics strategy will enable your organization to Invest now for what is required now, and invest later for what is required in the future.
An analytics strategy helps ensure that analytics development and capabilities are in alignment with enterprise quality and performance goals and helps avoids the “all dashboard, no improvement” syndrome. Furthermore, a well formed strategy document helps to achieve optimal use of analytics within a healthcare organization and can mean the difference between a “collection of reports” versus a high-value information resource.
An analytics strategy can rarely stand on its own. In general, the analytics strategy should use as input an organization’s Quality Improvement (QI) strategy and should be used to inform an organization’s Business Intelligence (BI) or Information Technology (IT) strategy. The analytics strategy is an important input to technical strategies because analytics, after all, can involve a sophisticated use of data and technology. Requirements for analytics may trigger a cascade of enhancements throughout other components of IT and BI (i.e., reporting, data storage, ETL, etc)
The document is intended to accompany Chapter 3, “Developing an Analytics Strategy to Drive Change”, so please refer to the chapter for further information about developing an analytics strategy.
Organizing for Analytics Success - HAS Session 7Health Catalyst
Many organizations underestimate the need for organizational shifts and changes required for successful data-driven decision making. In this session, we will explain the three types of ongoing systems that are needed for sustainable analytics improvement and implementation. We will share best practices in how organizations can structure executive teams, clinical integration and guidance teams, and workgroup teams, as well as share examples of successes and setbacks when these principles are implemented or missed. We will also describe key roles and responsibilities and charters, show sample meeting agendas and recommended frequencies, and give you a set of tools that you can leverage for your initiatives.
The MD Anderson / IBM Watson Announcement: What does it mean for machine lear...Health Catalyst
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.
How to Assess the ROI of Your Population Health InitiativeHealth Catalyst
In the brave new world of value-based healthcare, investing in population health management (PHM) is a requirement for success. Defining PHM isn’t easy, but there is one common term that appears among all the diverse interpretations—outcomes. Assessing the potential ROI for investments in PHM using a clear, understandable framework, can help organizations methodically identify and prioritize their PHM investments. While not every PHM intervention makes sense for every situation, it is important to determine which programs provide the most benefit, as well as determining when the investment will begin paying dividends, to achieve success in the era of PHM.
Delivering the Healthcare Pricing Transparency That Consumers Are DemandingHealth Catalyst
Can you imagine having your detailed healthcare pricing published in the Wall Street Journal? The thought makes most health systems cringe with concern that they’d lose money on the unknown. And yet every other major consumer category includes pricing up front. Amazingly, one health system has developed just such a care model for most major specialties that is predictable and completely transparent. Join us in this webinar to learn how they did it. You’ll get amazing insight into the importance of their quality measures and actual, daily costing for each procedure, not just allocated costs.
Improving Healthcare Operations Using Process Data MiningSplunk
It’s estimated that 80% of healthcare data is unstructured, which makes it challenging to do any sort of analytics to drive improvements in population health, patient care and operational efficiency. Machine learning techniques can be utilized to predict future events from similar past events, anticipate resource capacity issues and proactively identify bottlenecks and patient outcome risks. This session will provide an overview of how process data mining can be applied to healthcare and provide real-world examples of process data mining in action.
BIG Data & Hadoop Applications in HealthcareSkillspeed
Explore the applications of BIG Data & Hadoop in Healthcare via Skillspeed.
BIG Data & Hadoop in Healthcare is a key differentiator, especially in terms of providing superior patient care. They are used for optimizing clinical trials, disease detection & boosting healthcare profitability.
To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com
Machine learning and artificial intelligence already are and will continue to be beneficial in health care. However, serious pitfalls dot the health care landscape — while failed experiments like Twitter chatbots may be amusing, mistakes in a medical context can have serious consequences. This talk will demystify the basic concepts of machine learning and several of the freely available tools that are making this technology more and more accessible, as well as explore the pros and cons of machine learning’s expansion across the health care industry.
How to Choose the Best Healthcare Analytics Software Solution in a Crowded Ma...Health Catalyst
There’s a new trend in the healthcare industry to adopt analytics software solutions to help organizations achieve clinical and financial success. Because of the high demand for analytics, there are many players touting their ability to delivery comprehensive solutions. With so many options available, health systems need to be able to cut through the marketing hype to find tools that provide the best value for their needs. Key solutions include an enterprise data warehouse and analytics software applications (from foundational to discovery to advanced). Other considerations include the organization’s readiness for cultural change, the total cost of ownership required, and the viability of the company providing the technology.
20 Years in Healthcare Analytics & Data Warehousing: What did we learn? What'...Health Catalyst
The enterprise data warehouse (EDW) at Intermountain Healthcare went live in 1998. The EDW at Northwestern Medicine went live in 2006. Dale Sanders was the chief architect and strategist for both. The business inspiration behind Health Catalyst was, in essence, to create the commercial availability of the technology, analytics, and data utilization skills associated with these systems at Intermountain and Northwestern. Lee Pierce assumed leadership of the Intermountain EDW in 2008. Andrew Winter assumed leadership of the Northwestern EDW in 2009, and transitioned leadership of the EDW to Shakeeb Akhter in 2016. This webinar is a fireside chat among friends and colleagues as they look back across their healthcare IT decisions to answer these questions:
What did we do right and what did we do wrong?
What advice do we have for others in this emerging era of Big Data?
What does the future of analytics and Big Data look like in healthcare?
Precision medicine has profound implications for patient care and clinical outcomes, and is already beginning to impact everyday medical practice. However, implementation faces several obstacles, including overstated claims, resistance among clinical medicine thought leaders and providers, and concerns about costs, data overload, and interoperability. This webinar will address five key concerns, challenges, and barriers among clinicians and IT professionals struggling to determine the value and limitations of implementing precision medicine, and offer tangible recommendations to help drive toward precision medicine adoption.
Learning Objectives:
Identify obstacles that impede the implementation of precision medicine in clinical practice.
Contrast population-based medicine and precision medicine.
Demonstrate the real world benefits of precision medicine in today's healthcare setting.
Have Data—Need Analysts. Lessons Learned From The Woodworking IndustryHealth Catalyst
Recent advances in healthcare technology are designed to help organizations achieve the Quadruple Aim. While IT vendors tout better tools as the solution for healthcare woes, all data analysts and architects know this isn’t true. John Wadsworth, senior vice president, Health Catalyst, will share valuable, practical learnings from his 20 plus years of experience as a data analyst in this hands-on session that will explore the rich analytic ecosystem found in health care.
John will share:
Healthcare industry examples that highlight the required synergy of technology (e.g. EHRs, EDWs, ancillary health and reporting systems, etc.)
Data analyst skill proficiencies that help drive sustained clinical, financial, and operational improvements.
John’s unique presentation style leverages simple and fun analogies to galvanize key concepts for technology, clinical, and executive attendees. In this webinar, he will bring lessons learned from the woodworking industry and demonstrate how they apply to healthcare analytics. Come prepared to engage, laugh, and learn. You’ll leave with a deeper understanding of the skills needed to fully leverage your analytic ecosystem.
As the Age of Analytics emerges in healthcare, health system executives are increasingly challenged to define a data governance strategy that maximizes the value of data to the mission of their organizations.
Adding to that challenge, the competitive nature of the data warehouse and analytics market place has resulted in significant noise from vendors and consultants alike who promise to help health systems develop their data governance strategy. Having gone on his own turbulent data governance ride as a CIO in the US Air Force and healthcare, Dale Sanders, Senior Vice President at Health Catalyst will cut through the market noise to cover the following topics:
General concepts of data governance, regardless of industry
Unique aspects of data governance in healthcare
Data governance in a “Late Binding” data warehouse
The layers and roles in data governance
The four “Closed Loops” of healthcare analytics and data governance
Why the Data Steward’s Role is Critical to Sustained Outcomes Improvement in ...Health Catalyst
The data steward is critical to sustained outcomes improvement, yet they tend to be underappreciated members of the healthcare analytics family. Combining the invaluable technical expertise of a data analyst with the vital clinical knowledge of an experienced caregiver, the data steward’s skills and proficiency at both positions brings value beyond measure to any outcomes improvement project. Unfortunately, all too often, their role is non-existent even though potential candidates for the job are located in multiple data sources throughout the organization. Among other responsibilities, the data steward:
Reinforces the global data governance principles.
Helps develop and refine details of local data governance practices.
Is the eyes and ears of the organization with respect to data governance and the governance committee.
Provides direction to peers regarding appropriate data definitions, usage, and access.
Anticipates local consequences of global changes
For innovative health system leaders who have specifically recognized this emerging role, the ROI of data stewards who help achieve improved outcomes is very worthwhile.
There Is A 90% Probability That Your Son Is Pregnant: Predicting The Future ...Health Catalyst
Predictive: Relating to or having the effect of predicting an event or result. Analytics: The systematic computational analysis of data or statistics. Together they make up one of the most popular topics in healthcare today. But predictive analytics is a means to an ends, and there is little good in predicting an event or result without a strategy for acting upon that event, when it happens. If, as the Robert Wood Johnson Foundation recently published, 80% of healthcare determinants fall outside of the healthcare delivery system as we traditionally define it, should we focus our predictive analytics on the traditional 20% of traditional healthcare delivery, or broaden our focus to the 80% that includes social and economic factors, physical environment, and lifestyle behaviors? What if our predictive models reveal to us that the highest risk variable to a patient’s length of life and quality of life is their economic status? Can an accountable care organization and patient centered medical home realistically do anything to reduce that risk, in reaction to the predictive model? Given the current availability and type of data in the healthcare ecosystem, and our organizational ability or inability to realistically intervene, where should we focus our predictive and interventional risk management strategies? There is enormous potential value in the application of predictive analytics to healthcare, but, in contrast to predicting the weather, credit risk, consumer purchasing habits, or college dropout rates, the data collection, and social and ethical complexities of applying predictive analytics in healthcare are significantly higher. This session will explore some of the less technical, more human interest and philosophical issues, associated with predictive analytics in healthcare, including the speaker’s experience prior to healthcare, in the US Air Force, National Security Agency, and manufacturing.
Healthcare Analytics Careers: New Roles for the Brave, New World of Value-bas...Health Catalyst
Job titles can be leading indicators of the direction an industry is moving and the same holds true for healthcare. The new healthcare economic model—from fee-for-service (FFS) to value-based—is driving a change in roles and responsibilities for professionals seeking healthcare analytics careers. Motivated by CMS and commercial payers, healthcare organizations are realizing the need to find and hire new types of healthcare professionals, a Chief Population Health Officer or Vice President of Clinical Informatics, who are focused on value. Senior leaders are seeking to build teams that have the ability to bring together analytics, best-practice clinical content, and process improvement to create long-term, sustainable change across their healthcare systems.
The Philosophy, Psychology, and Technology of Data in HealthcareDale Sanders
Over-application of data and analytics in healthcare is alienating clinicians and, for the most part, not bending the cost-quality curves. This lecture spends 60% of the time on the softer issues, 40% on the technology.
Gain insights from data analytics and take action! Learn why everyone is making a big deal about big data in healthcare and how data analytics creates action.
Part 2 - 20 Years in Healthcare Analytics & Data Warehousing: What did we lea...Health Catalyst
Lessons learned over 20 years. This time we focus on technology lessons learned from experience at Intermountain Healthcare, Northwestern Medicine and Cayman Islands Health Authority
Developing a Strategic Analytics Framework that Drives Healthcare TransformationTrevor Strome
About the presentation.
Based on Chapter 3 of my book "Healthcare Analytics for Quality and Performance Improvement", this presentation describes the key components of a strategic analytics framework that can enable your healthcare organization to leverage data from source-systems to achieve its quality, safety, and performance improvement goals.
What is an analytics strategy?
Analytics is currently a very “trendy” topic. The internet is scattered with many buzzwords, marketing angles, white papers, and opinions on the topic of healthcare analytics. With all this “noise”, it is easy to get distracted from what is actually required, from an analytics perspective, by your organization. An analytics strategy helps cut through the noise and keep focus on what is important for the organization. Regardless of what the latest “buzz” is, your analytics strategy will enable your organization to Invest now for what is required now, and invest later for what is required in the future.
An analytics strategy helps ensure that analytics development and capabilities are in alignment with enterprise quality and performance goals and helps avoids the “all dashboard, no improvement” syndrome. Furthermore, a well formed strategy document helps to achieve optimal use of analytics within a healthcare organization and can mean the difference between a “collection of reports” versus a high-value information resource.
An analytics strategy can rarely stand on its own. In general, the analytics strategy should use as input an organization’s Quality Improvement (QI) strategy and should be used to inform an organization’s Business Intelligence (BI) or Information Technology (IT) strategy. The analytics strategy is an important input to technical strategies because analytics, after all, can involve a sophisticated use of data and technology. Requirements for analytics may trigger a cascade of enhancements throughout other components of IT and BI (i.e., reporting, data storage, ETL, etc)
The document is intended to accompany Chapter 3, “Developing an Analytics Strategy to Drive Change”, so please refer to the chapter for further information about developing an analytics strategy.
Organizing for Analytics Success - HAS Session 7Health Catalyst
Many organizations underestimate the need for organizational shifts and changes required for successful data-driven decision making. In this session, we will explain the three types of ongoing systems that are needed for sustainable analytics improvement and implementation. We will share best practices in how organizations can structure executive teams, clinical integration and guidance teams, and workgroup teams, as well as share examples of successes and setbacks when these principles are implemented or missed. We will also describe key roles and responsibilities and charters, show sample meeting agendas and recommended frequencies, and give you a set of tools that you can leverage for your initiatives.
The MD Anderson / IBM Watson Announcement: What does it mean for machine lear...Health Catalyst
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.
How to Assess the ROI of Your Population Health InitiativeHealth Catalyst
In the brave new world of value-based healthcare, investing in population health management (PHM) is a requirement for success. Defining PHM isn’t easy, but there is one common term that appears among all the diverse interpretations—outcomes. Assessing the potential ROI for investments in PHM using a clear, understandable framework, can help organizations methodically identify and prioritize their PHM investments. While not every PHM intervention makes sense for every situation, it is important to determine which programs provide the most benefit, as well as determining when the investment will begin paying dividends, to achieve success in the era of PHM.
Delivering the Healthcare Pricing Transparency That Consumers Are DemandingHealth Catalyst
Can you imagine having your detailed healthcare pricing published in the Wall Street Journal? The thought makes most health systems cringe with concern that they’d lose money on the unknown. And yet every other major consumer category includes pricing up front. Amazingly, one health system has developed just such a care model for most major specialties that is predictable and completely transparent. Join us in this webinar to learn how they did it. You’ll get amazing insight into the importance of their quality measures and actual, daily costing for each procedure, not just allocated costs.
Improving Healthcare Operations Using Process Data MiningSplunk
It’s estimated that 80% of healthcare data is unstructured, which makes it challenging to do any sort of analytics to drive improvements in population health, patient care and operational efficiency. Machine learning techniques can be utilized to predict future events from similar past events, anticipate resource capacity issues and proactively identify bottlenecks and patient outcome risks. This session will provide an overview of how process data mining can be applied to healthcare and provide real-world examples of process data mining in action.
BIG Data & Hadoop Applications in HealthcareSkillspeed
Explore the applications of BIG Data & Hadoop in Healthcare via Skillspeed.
BIG Data & Hadoop in Healthcare is a key differentiator, especially in terms of providing superior patient care. They are used for optimizing clinical trials, disease detection & boosting healthcare profitability.
To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com
Machine learning and artificial intelligence already are and will continue to be beneficial in health care. However, serious pitfalls dot the health care landscape — while failed experiments like Twitter chatbots may be amusing, mistakes in a medical context can have serious consequences. This talk will demystify the basic concepts of machine learning and several of the freely available tools that are making this technology more and more accessible, as well as explore the pros and cons of machine learning’s expansion across the health care industry.
Abstractions Conference 2016 - Machine Learning in Healthcare – ML for the Re...Mohinder Dick, PMP
This talk was given at Abstractions Pittsburgh on August 18, 2016. It is an introduction to the basics of machine learning and how they can be applied to healthcare.
This presentation shows reco4j features and vision. In particular we add the new concept of context aware recommendation and how we integrate it into reco4j. See the project site for more details here: http://www.reco4j.org
This presentation shows reco4j features and vision. In particular we add the new concept of context aware recommendation and how we integrate it into reco4j. In this new presentation there is also some piece of code that show how simple is integrate our software. See the project site for more details here: http://www.reco4j.org
These slides were presentet at Munich Meetup of April 18th. They present the reco4j project, its high view and it vision.
See the project site for more details here: http://www.reco4j.org
The social graph of Facebook is the most popular application for a graph database. In addition, there are far more exciting applications, such as spatial data, financial trail, indexing, and others. If you combine different graphs, you are able to evaluate those together with the algorithms known from the graph theory. As a graph, a domain can often be easier and more natural designed. This talk introduces the topic of graph databases and shows how to implement mediated models with large, complex and highly connected data with Neo4j. Subsequently, topics like querying, indexing, import / export are considered as well.
Precise Patient Registries for Clinical Research and Population ManagementDale Sanders
Patient registries have evolved from external, mandatory reporting databases to playing a critical role in internal clinical research, clinical quality, cost reduction, and population health management. This slide deck describes how to design those precise registries.
Strategic Options for Analytics in HealthcareDale Sanders
There are essentially four analytic strategies available in the healthcare IT market at present. This slide summarizes those options, the pros and cons, and vendors in the space.
Machine learning and Internet of Things, the future of medical preventionPierre Gutierrez
Title:
"Machine learning and Internet of Things, the future of medical prevention"
Abstract:
In this talk, Pierre Gutierrez, a data scientist at Dataiku, will discuss Dataiku's experiences using machine learning on IOT data. We will talk about the challenges processing and cleaning IoT data, and how to successfully train a model that can be deployed in production. We will illustrate our talk with two examples from our previous work. Creating algorithm for early epilepsy seizure detection based on wearable tech and Detecting people activity through sensor data.
Healthcare Billing and Reimbursement: Starting from ScratchDale Sanders
The healthcare billing environment in the US is a disaster. It creates huge waste in care and cost. As presented at the Cayman Islands International Healthcare Conference in October 2010, this slide deck suggests what the billing system might look like, if we could start over.
Clinical Decision Support: Driving the Last MileHealth Catalyst
Self-driving cars have become the most visible form of computer-aided decision support in society. What can we learn from these innovations—both good and bad, technically and culturally—about computer-aided decision support for clinicians? The adoption of EHRs provided a foundation; what and how do we build on that foundation to help clinicians, and patients, benefit from meaningful, precise decision support?
Scott Weingarten, MD, MPH, and Dale Sanders explore clinical decision support in a joint webinar. Dr. Weingarten is recognized throughout the U.S. and international healthcare space as a physician and for his contributions to decision support, including his role in founding Zynx and Stanson Health. Dale brings a technologist’s viewpoint to the conversation, informed by his background in computer-aided decision support in the healthcare, military, and national intelligence sectors.
During this webinar, learn more about the following topics:
-How clinical decision support can improve the quality, safety, and value of care.
-How developments in the field of artificial intelligence will impact clinical decision support.
-The conceptual framework for digitizing an industry.Tradeoffs in artificial intelligence models between data volume and algorithm complexity.
-The approach to digitization in the automobile and aerospace industries.
-Shortcomings in current healthcare data.Future aspirations and plans for further digitization of healthcare.
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.
Disrupting Conventional Therapies with Digital TherapiesMedullan
Join this webinar to learn about:
Bio 2.0 - what it is, what problems it aims to address, why it's important now, and what leading indicators of its potential have we seen in the market.
The new clinical development patterns that will emerge to help Life Science companies bring digital therapies and solutions to market in a predictable and sustainable way.
The potential roadblocks, the regulatory ambiguity, and its potential for clarity as these patterns take hold.
Many will still find it hard to decide what initiatives to scale up and how to determine which pathways to follow, as it’s unclear what digital success will look like five years from now. This series aims to bring innovators and industry leaders together to imagine what success could be and discuss ways to get there.
Big Data Provides Opportunities, Challenges and a Better Future in Health and...Cirdan
This presentation was delivered by Ashraf Mina, NSW Pathology at the Pathology Horizons 2017 Conference in Cairns, Australia.
Pathology Horizons 2017 is an annual CPD conference organised by Cirdan on the future of pathology. You can access more information about the event at www.pathologyhorizons.com
The company was founded in 2010 and is headquartered in Lisburn, Northern Ireland and has additional offices in Canada and Australia.
Cirdan is also responsible for organising Pathology Horizons, an annual and open CPD conference on the future of pathology. For more information visit - www.pathologyhorizons.com
Algorithmic Bias: Challenges and Opportunities for AI in HealthcareGregory Nelson
Gregory S. Nelson, VP, Analytics and Strategy – Vidant Health | Adjunct Faculty Duke University
The promise of AI is quickly becoming a reality for a number of industries including healthcare. For example, we have seen early successes in the augmenting clinical intelligence for diagnostic imaging and in early detection of pneumonia and sepsis. But what happens when the algorithms are biased? In this presentation, we will outline a framework for AI governance and discuss ways in which we can address algorithmic bias in machine learning.
Objective 1: Illustrate the issues of bias in AI through examples specific to healthcare.
Objective 2: Summarize the growing body of work in the legal, regulatory, and ethical oversight of AI models and the implications for healthcare.
Objective 3: Outline steps that we can take to establish an AI governance strategy for our organizations.
Big data, RWE and AI in Clinical Trials made simpleHadas Jacoby
Technology is slowly but surely penetrating the healthcare industry in general and the clinical trials sector in particular. New and advanced solutions offer a variety of possibilities aimed to both improving existing processes and creating new and more efficient ones. And on top of all stands the desire to make clinical trials more patient centric.
In all of this, even though some of the technologies have yet to mature enough to meet the high quality standards necessary, it is important to know them and begin imagining the promise they hold for clinical trials.
The Future of Personalized Health Care: Predictive Analytics by @Rock_HealthRock Health
View the archived webinar here: https://www.youtube.com/watch?v=UJak41hIDWc
How can we use new and existing sources of data to deliver better, personalized care? Predictive analytics underlies what has always been conducted by doctors through their training, experience, and decision-making. Dozens of new digital products have hit the market and $1.9B has flowed into the space since 2011—but what does it take for an algorithm to accurately and reliably impact care?
Purchase the report here: https://gumroad.com/l/gzbzV
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Reforming Medical Device approval processes especially in software requires careful consideration of shifting risks to patients without adequate protections.
In an age where uncertainty seems to be the only certainty, the desire to foresee what lies ahead is a fundamental human instinct. From ancient oracles to modern-day algorithms, humanity has always sought methods to predict the future. Today, the digital era has ushered in a new era of prognostication through prediction sites, harnessing the power of data and machine learning to offer glimpses into tomorrow. But what exactly are prediction sites, and how do they work?
What are Prediction Sites?
Prediction sites are online platforms that utilize various techniques, including statistical analysis, artificial intelligence, and crowdsourcing, to forecast future events across a wide array of domains. These domains can range from sports outcomes and financial markets to geopolitical developments and entertainment industry trends. The core premise behind these sites is to aggregate and analyze relevant data to generate insights that can inform individuals and organizations in making informed decisions.
How Do Prediction Sites Work?
The operation of prediction sites can vary depending on their focus and methodology. However, they typically follow a few common steps:
Data Collection: Prediction sites gather data from diverse sources, including historical trends, current events, expert opinions, and user-generated predictions.
Analysis: Advanced algorithms process the collected data, identifying patterns, correlations, and trends that could indicate future outcomes. Machine learning techniques play a crucial role in refining predictive models over time.
Prediction Generation: Based on the analysis, prediction sites generate forecasts for specific events or phenomena. These predictions are often presented with associated probabilities or confidence levels to indicate the level of certainty.
Feedback Loop: As events unfold, prediction sites continuously update their models based on real-world outcomes. This feedback loop allows the systems to learn from their predictions' successes and failures, improving their accuracy over time.
Applications and Impact
The applications of prediction sites are vast and diverse, impacting various aspects of society and industry:
Financial Markets: Investors and traders utilize prediction sites to gauge market sentiment, forecast asset prices, and identify potential investment opportunities.
Sports Betting: Enthusiasts leverage prediction platforms to make informed bets on sports events, leveraging predictive analytics to increase their chances of success.
Politics and Elections: Prediction sites offer insights into election outcomes, political trends, and public opinion, influencing campaign strategies and voter perceptions.
Business Decision-Making: Companies use prediction sites to anticipate market trends, consumer behavior, and competitive dynamics, aiding strategic planning and risk management.
Healthcare and Science: Predictive modeling contributes to disease surveillance, epidemiological forecasting, dr
Data Science for Healthcare: What Today’s Leaders Must KnowHealth Catalyst
Healthcare leaders who understand data science can embrace the significant improvement potential of the industry’s vast data stores, including an estimated $300 billion in annual costs savings. Executives must know the value of data science to understand the urgency in investing and supporting the technology and data scientists to fully leverage data’s capabilities. Today’s data science-savvy executives will lead the healthcare transformation by enabling faster, more accurate diagnoses and more effective, lower-risk treatments.
Introducing catalyst.ai and MACRA Measures & InsightsHealth 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.
CORD Rare Drug Conference, June 8 - 9, 2022
Opportunities and Challenges for Data Management Real-World Data and Real-World Evidence
• Patient support programs: Sandra Anderson, Innomar Strategies
• AI for Data Management and Enhancement: Aaron Leibtag, Pentavere
• Patient Support and RWE: Laurie Lambert, CADTH
Attached is a view on the importance of Automation & appropriate technology in the collection of Adverse Event data, and how this data can be used to the benefit of the patient population.
Similar to Predicting the Future of Predictive Analytics in Healthcare (20)
The dimensions of healthcare quality refer to various attributes or aspects that define the standard of healthcare services. These dimensions are used to evaluate, measure, and improve the quality of care provided to patients. A comprehensive understanding of these dimensions ensures that healthcare systems can address various aspects of patient care effectively and holistically. Dimensions of Healthcare Quality and Performance of care include the following; Appropriateness, Availability, Competence, Continuity, Effectiveness, Efficiency, Efficacy, Prevention, Respect and Care, Safety as well as Timeliness.
Rate Controlled Drug Delivery Systems, Activation Modulated Drug Delivery Systems, Mechanically activated, pH activated, Enzyme activated, Osmotic activated Drug Delivery Systems, Feedback regulated Drug Delivery Systems systems are discussed here.
CHAPTER 1 SEMESTER V PREVENTIVE-PEDIATRICS.pdfSachin Sharma
This content provides an overview of preventive pediatrics. It defines preventive pediatrics as preventing disease and promoting children's physical, mental, and social well-being to achieve positive health. It discusses antenatal, postnatal, and social preventive pediatrics. It also covers various child health programs like immunization, breastfeeding, ICDS, and the roles of organizations like WHO, UNICEF, and nurses in preventive pediatrics.
Veterinary Diagnostics Market PPT 2024: Size, Growth, Demand and Forecast til...IMARC Group
The global veterinary diagnostics market size reached US$ 6.6 Billion in 2023. Looking forward, IMARC Group expects the market to reach US$ 12.6 Billion by 2032, exhibiting a growth rate (CAGR) of 7.3% during 2024-2032.
More Info:- https://www.imarcgroup.com/veterinary-diagnostics-market
Chandrima Spa Ajman is one of the leading Massage Center in Ajman, which is open 24 hours exclusively for men. Being one of the most affordable Spa in Ajman, we offer Body to Body massage, Kerala Massage, Malayali Massage, Indian Massage, Pakistani Massage Russian massage, Thai massage, Swedish massage, Hot Stone Massage, Deep Tissue Massage, and many more. Indulge in the ultimate massage experience and book your appointment today. We are confident that you will leave our Massage spa feeling refreshed, rejuvenated, and ready to take on the world.
Visit : https://massagespaajman.com/
Call : 052 987 1315
LGBTQ+ Adults: Unique Opportunities and Inclusive Approaches to CareVITASAuthor
This webinar helps clinicians understand the unique healthcare needs of the LGBTQ+ community, primarily in relation to end-of-life care. Topics include social and cultural background and challenges, healthcare disparities, advanced care planning, and strategies for reaching the community and improving quality of care.
Letter to MREC - application to conduct studyAzreen Aj
Application to conduct study on research title 'Awareness and knowledge of oral cancer and precancer among dental outpatient in Klinik Pergigian Merlimau, Melaka'
ICH Guidelines for Pharmacovigilance.pdfNEHA GUPTA
The "ICH Guidelines for Pharmacovigilance" PDF provides a comprehensive overview of the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines related to pharmacovigilance. These guidelines aim to ensure that drugs are safe and effective for patients by monitoring and assessing adverse effects, ensuring proper reporting systems, and improving risk management practices. The document is essential for professionals in the pharmaceutical industry, regulatory authorities, and healthcare providers, offering detailed procedures and standards for pharmacovigilance activities to enhance drug safety and protect public health.
Empowering ACOs: Leveraging Quality Management Tools for MIPS and BeyondHealth Catalyst
Join us as we delve into the crucial realm of quality reporting for MSSP (Medicare Shared Savings Program) Accountable Care Organizations (ACOs).
In this session, we will explore how a robust quality management solution can empower your organization to meet regulatory requirements and improve processes for MIPS reporting and internal quality programs. Learn how our MeasureAble application enables compliance and fosters continuous improvement.
Global launch of the Healthy Ageing and Prevention Index 2nd wave – alongside...ILC- UK
The Healthy Ageing and Prevention Index is an online tool created by ILC that ranks countries on six metrics including, life span, health span, work span, income, environmental performance, and happiness. The Index helps us understand how well countries have adapted to longevity and inform decision makers on what must be done to maximise the economic benefits that comes with living well for longer.
Alongside the 77th World Health Assembly in Geneva on 28 May 2024, we launched the second version of our Index, allowing us to track progress and give new insights into what needs to be done to keep populations healthier for longer.
The speakers included:
Professor Orazio Schillaci, Minister of Health, Italy
Dr Hans Groth, Chairman of the Board, World Demographic & Ageing Forum
Professor Ilona Kickbusch, Founder and Chair, Global Health Centre, Geneva Graduate Institute and co-chair, World Health Summit Council
Dr Natasha Azzopardi Muscat, Director, Country Health Policies and Systems Division, World Health Organisation EURO
Dr Marta Lomazzi, Executive Manager, World Federation of Public Health Associations
Dr Shyam Bishen, Head, Centre for Health and Healthcare and Member of the Executive Committee, World Economic Forum
Dr Karin Tegmark Wisell, Director General, Public Health Agency of Sweden
About this webinar: This talk will introduce what cancer rehabilitation is, where it fits into the cancer trajectory, and who can benefit from it. In addition, the current landscape of cancer rehabilitation in Canada will be discussed and the need for advocacy to increase access to this essential component of cancer care.
DECODING THE RISKS - ALCOHOL, TOBACCO & DRUGS.pdfDr Rachana Gujar
Introduction: Substance use education is crucial due to its prevalence and societal impact.
Alcohol Use: Immediate and long-term risks include impaired judgment, health issues, and social consequences.
Tobacco Use: Immediate effects include increased heart rate, while long-term risks encompass cancer and heart disease.
Drug Use: Risks vary depending on the drug type, including health and psychological implications.
Prevention Strategies: Education, healthy coping mechanisms, community support, and policies are vital in preventing substance use.
Harm Reduction Strategies: Safe use practices, medication-assisted treatment, and naloxone availability aim to reduce harm.
Seeking Help for Addiction: Recognizing signs, available treatments, support systems, and resources are essential for recovery.
Personal Stories: Real stories of recovery emphasize hope and resilience.
Interactive Q&A: Engage the audience and encourage discussion.
Conclusion: Recap key points and emphasize the importance of awareness, prevention, and seeking help.
Resources: Provide contact information and links for further support.
Cold Sores: Causes, Treatments, and Prevention Strategies | The Lifesciences ...The Lifesciences Magazine
Cold Sores, medically known as herpes labialis, are caused by the herpes simplex virus (HSV). HSV-1 is primarily responsible for cold sores, although HSV-2 can also contribute in some cases.
2. www.healthcatalyst.comCreative Commons Copyright 2014
Presenter and Contact Information
2
Dale Sanders
Senior Vice President, Strategy, Health Catalyst
801-708-6800
dale.sanders@healthcatalyst.com
@drsanders
www.linkedin.com/in/dalersanders/
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Acknowledgements
David Crockett, PhD, Health Catalyst
Eric Siegel, PhD, Columbia University
Ron Gault, Aerospace Corporation, Northrup-Grumman, TRW
Wikipedia
3
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The Goal Today
I hope you leave this webinar with…
Informed Expectations and Opinions: Be generally aware of the
realistic possibilities for predictive analytics in healthcare, over the next
few years
The Right Questions: To be conversant in the concepts of predictive
analytics and be able to ask reasonably well-informed questions of
your analytics teams, especially vendors, during the strategic process
of developing your organization’s predictive analytics strategy
4
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5
Agenda
5
Basic Concepts, Fundamental Assertions
Predictive Analytics Outside Healthcare
Predictive Analytics Inside Healthcare
Key Questions To Ask Vendors And Your Analytics Teams
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What Should We Expect In Healthcare?
Machines are predictable; humans aren’t
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“People are influenced by their environment in innumerable ways. Trying to
understand what people will do next, assumes that all the influential
variables can be known and measured accurately. People's environments
change even more quickly than they themselves do. Everything from the
weather to their relationship with their mother can change the way people
think and act. All of those variables are unpredictable. How they will impact a
person is even less predictable. If put in the exact same situation tomorrow,
they may make a completely different decision. This means that a statistical
prediction is only valid in sterile laboratory conditions, which suddenly isn't
as useful as it seemed before.”
Gary King, Harvard University and the Director of the Institute for
Quantitative Social Science
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Semantics, Ssschmantics
Predictive Analytics and Predictive Models: These terms have their origins in
statisticians; e.g., understanding real-world phenomena such as healthcare, retail
sales, customer relationship management, voting preferences, etc.
Machine Learning Algorithms: This term has its origins in computer scientists;
e.g., natural language processing, speech recognition, image recognition, adaptive
control systems in manufacturing, robots, satellites, automobiles and aircraft, etc.
13
As it turns out, the latter can be applied to the former, so the two schools
of thought are now generally interchangeable. Don’t let vendors fool
you into thinking that “machine learning” is more sophisticated or better
than predictive modeling.
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A Big & Common Mistake: Over Fitting
17
You train the model to be very specific on a given data set, but the model
cannot adapt to a new, unknown data set
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Specificity vs. Sensitivity:
Trading One For Another
Specificity:
The true negative rate. For example, the percentage of diabetic
patients identified who will not have a myocardial infarction
Sensitivity:
The true positive rate. For example, the percentage of diabetic
patients that will have a myocardial infarction
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Receiver Operating
Characteristic (ROC) Plot
19
• Tuning radar receivers in WWII
• Maximum radar receiver sensitivity led to
many false positives… too many alarms
• Lower radar receiver sensitivity led to
many false negatives… missed threats
• Same challenge in airport security
screening systems and spam filters
• Concept has been applied heavily in
diagnostic medicine
• True Positive Rate vs. False Positive Rate
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Data Volume vs. Predictive Model
“But invariably, simple models and a lot of data trump more
elaborate models based on less data.”
“The Unreasonable Effectiveness of Data”, March 2009, IEEE Computer Society;
Alon Halevy, Peter Norvig, and Fernando Pereira, Google
24. www.healthcatalyst.comCreative Commons Copyright 2014Thank you for the graphs, PreSonus
Healthcare and
patients are continuous
flow, analog process
and beings
But, if we sample that
analog process
enough, we can
approximately recreate
it with digital data
24
Remember Your Calculus
Digital Sampling Theory?
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“Mr. Sanders, while your 9-year tenure as an inmate has been stellar,
our analytics models predict that you are 87% likely to become a repeat
offender if you are granted parole. Therefore, your parole is denied.”
- 2014, 80% of parole boards now use predictive analytics for case
management*
* The Economist, “Big data can help states decide whom to release from prison” April 19, 2014
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Thank you Sonja Star, New York Times
“Evidence Based” Sentencing
20 states use predictive analytics risk assessments
to inform criminal sentencing.
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“Evidence Based” Sentencing
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Recidivism Risk Assessment:
Level of Service/Case Management Inventory (LS/CMI)*
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15 different scales feed the PA algorithm
1. Criminal history
2. Education/employment
3. Family/marital
4. Leisure/recreation
5. Companions
6. Alcohol/drug problems
7. Antisocial patterns
8. Pro-criminal attitude orientation
9. Barriers to release
10.Case management plan
11.Progress record
12.Discharge summary
13.Specific risk/needs factors
14.Prison experience - institutional
factors
15.Special responsivity
consideration
42.2% of high-risk offenders recidivate within 3 years
*Nov. 2012, Hennepin County, Minn. Department of Community Corrections and Rehabilitation
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“Since the publishing of Lewis' book,
there has been an explosion in the
use of data analytics to identify
patterns of human behavior and
experience and bring new insights to
fields of nearly every kind.”
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eHarmony Predictions
“Heart” of the system: Compatibility Match Processor
(CMP)
• 320 profiling questions/attributes per user
• 29 dimensions of compatibility
• ~75TB
• 20M users
• 3B potential matches daily
• 60M+ queries per day, 250 attributes
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Thank you, Thod Nugyen, eHarmony CTO
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What Are We Trying to Predict?
Common applications being marketed today
• Identifying preventable re-admissions: COPD, MI/CHF,
Pneumonia, et al
• Sepsis
• Risk of decubitus ulcers
• LOS predictions in hospital and ICU
• Cost-per-patient per inpatient stay
• Cost-per-patient per year by disease and comorbidity
• Risk of ICU mortality
• Risk of ICU admission
• Appropriateness of C-section
• Emerging: Genomic phenotyping
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True Population Predictive
Risk Management
Thank you, for the diagram, Robert Wood
Johnson Foundation, 2014
Very Little ACO
Influence
Very Little ACO
Influence
>/=30% Waste*
100% ACO Influence
*Congressional Budget Office, IOM,
“Best Care at Lower Cost”, 2013
True Population
Health Management
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Not all patients can functionally participate in a protocol
At Northwestern (2007-2009), we found that 30% of patients fell into one
or more of these categories:
• Cognitive inability
• Economic inability
• Physical inability
• Geographic inability
• Religious beliefs
• Contraindications to the protocol
• Voluntarily non-compliant
Socioeconomic Data Matters
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The key to predictive analytics in the future of health care will be the
ability to answer this two-part question:
What’s the probability of influencing this patient’s
behavior towards our desired outcome and how much
effort (cost) will be required for that influence?
Return on Engagement (ROE)
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Flight Path “Outcomes”
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Examples
Diabetes
Cohort
1. A1c < 7
2. LDL < 100
3. BP < 130/80
1. A1c > 7
2. LDL > 100
3. BP > 130/80
$ COST Per Member Per Year (Charges)
For > 1 year of encounters
(~5 yrs and 26k patients)
These aren’t really outcomes… they are proxies for outcomes
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Allina’s Intervention To Reduce Risk
Transition of Care “Conferences”
• Patients, families, care givers
• 15% reduction in readmissions
• 100+ APR-DRGs affected
• More patients utilizing post-acute care
• Skilled Nursing Facility
• Home Health
• TCU
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Antibiotic
Protocol
Dosage Route Interval Predicted
Efficacy
Average
Cost/Patient
Option 1 500mg IV Q12 98% $7,256
Option 2 300mg IV Q24 96% $1,236
Option 3 40mg IV Q6 90% $1,759
• Predictive and prescriptive (suggestive) analytics in the same
user interface
• The efficacy and costs of antibiotic protocols for inpatients
Thank you, Dave Claussen, Scott Evans, et al, Intermountain Healthcare
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The Antibiotic Assistant
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The Antibiotic Assistant Impact
• Complications declined 50%
• Avg. number of doses declined from 19 to 5.3
• The replicable and bigger story
• Antibiotic cost per treated patient: $123 to $52
• By simply displaying the cost to physicians
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Key Questions To Ask
Of Vendors and Your Analytics Teams
51
1. What is your formal training, education, and practical
experience in this field?
2. What are the input variables to the model?
3. What model and/or algorithms are you using and why?
4. How are you going to train the model?
5. Are you using our data or other organizations’ data for
training? Why?
6. If you are using other organizations’ data, how are you going
to customize the model to our specific data environment?
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1. Action matters: What is the return in investment for intervention?
Are we prepared to invest more... or say “no”… to patients who
score low on predicted engagement?
2. Human unpredictability: The mathematical models of human
behavior are relatively immature.
3. Socio-economics: Can today’s healthcare ecosystem expand to
make a difference?
4. Missing data: Without patient outcomes, the PA models are open
loop.
5. Social controversy: How much do we want to know about the
future of our health, especially when the predictive models are
uncertain?
6. Wisdom of crowds: Suggestive analytics from “wise crowds”
might be easier and more reliable than predictive analytics, until
our data content improves
Closing Thoughts and Questions
54. Thank You
For questions and follow-up, please contact me
• dale.sanders@healthcatalyst.com
• @drsanders
Upcoming Educational Opportunities
An Overview of the Healthcare Analytics Market
Date: January 21, 2015, 1-2pm, EST
Host: Jim Adams, Executive Director, The Advisory Board
A Pioneer ACO Case Study: Quality Improvement in Healthcare
Date: January 28, 2015, 1-2pm, EST
Hosts:
Robert Sawicki, MD, Senior Vice President of Supportive Care, OSF HealthCare
Roopa Foulger, Executive Director Data Delivery, OSF HealthCare
Linda Fehr, RN, Division Director of Supportive Care, OSF HealthCare
Editor's Notes
Intermountain’s ability to extend the boundaries and achieve success is in part due to the communal nature of Utah and the lifestyle choices that Utah citizens choose