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.
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.
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?
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
Predicting the Future of Predictive Analytics in HealthcareDale Sanders
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 DataHealth Catalyst
In 2005, Northwestern Memorial Healthcare embarked upon a strategic Enterprise Data Warehousing (EDW) initiative with the Microsoft technology platform as the foundation. Dale Sanders was CIO at Northwestern and led the development of Northwestern’s Microsoft-based EDW. At that time, Microsoft as an EDW platform was not en vogue and there were many who doubted the success of the Northwestern project. While other organizations were spending millions of dollars and years developing EDW’s and analytics on other platforms, Northwestern achieved great and rapid value at a fraction of the cost of the more typical technology platforms. Now, there are more healthcare data warehouses built around Microsoft products than any other vendor. The risky bet on Microsoft in 2005 paid off.
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.
In this context, Dale will talk about:
His up and down journey with Microsoft as an Air Force and healthcare CIO, and why he is now more bullish on Microsoft like never before
A quick review of the Healthcare Analytics Adoption Model and Closed Loop Analytics in healthcare, and how Microsoft products relate to both
The rise of highly specialized, cloud-based analytic services and their value to healthcare organizations’ analytics strategies
Microsoft’s transformation from a closed-system, desktop PC company to an open-system consumer and business infrastructure company
The current transition period of enterprise data warehouses between the decline of relational databases and the rise of non-relational databases, and the new Microsoft products, notably Azure and the Analytic Platform System (APS), that bridge the transition of skills and technology while still integrating with core products like Office, Active Directory, and System Center
Microsoft’s strategy with its PowerX product line, and geospatial analysis and machine learning visualization tools
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?
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.
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.
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?
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
Predicting the Future of Predictive Analytics in HealthcareDale Sanders
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 DataHealth Catalyst
In 2005, Northwestern Memorial Healthcare embarked upon a strategic Enterprise Data Warehousing (EDW) initiative with the Microsoft technology platform as the foundation. Dale Sanders was CIO at Northwestern and led the development of Northwestern’s Microsoft-based EDW. At that time, Microsoft as an EDW platform was not en vogue and there were many who doubted the success of the Northwestern project. While other organizations were spending millions of dollars and years developing EDW’s and analytics on other platforms, Northwestern achieved great and rapid value at a fraction of the cost of the more typical technology platforms. Now, there are more healthcare data warehouses built around Microsoft products than any other vendor. The risky bet on Microsoft in 2005 paid off.
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.
In this context, Dale will talk about:
His up and down journey with Microsoft as an Air Force and healthcare CIO, and why he is now more bullish on Microsoft like never before
A quick review of the Healthcare Analytics Adoption Model and Closed Loop Analytics in healthcare, and how Microsoft products relate to both
The rise of highly specialized, cloud-based analytic services and their value to healthcare organizations’ analytics strategies
Microsoft’s transformation from a closed-system, desktop PC company to an open-system consumer and business infrastructure company
The current transition period of enterprise data warehouses between the decline of relational databases and the rise of non-relational databases, and the new Microsoft products, notably Azure and the Analytic Platform System (APS), that bridge the transition of skills and technology while still integrating with core products like Office, Active Directory, and System Center
Microsoft’s strategy with its PowerX product line, and geospatial analysis and machine learning visualization tools
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?
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.
A Reference Architecture for Digital Health: The Health Catalyst Data Operati...Health Catalyst
There are essentially four strategic options to address the enterprise data platform requirements of today’s healthcare systems: (1) build your own, (2) buy from EHR vendors, (3) look to a Silicon Valley high-tech startup, and (4) partner with Health Catalyst or a handful of similar companies.
In this webinar, Health Catalyst’s CTO, Dale Sanders, comments on all four approaches, hoping to help you to assess your organization’s strategy against the options and vendors in each category.
It’s been exactly three years since Health Catalyst embarked on a major investment in its next-generation technology, the Data Operating System (DOS™) and its applications. This webinar is an update on the progress, less about marketing the technology, but rather offering DOS as a reference architecture that can support analytics, AI, text processing, data-first application development, and interoperability, as an all-in-one agile cost-savings architecture.
In addition to the successes, Dale comments on the challenges that Health Catalyst has faced under a very ambitious DOS development plan. In its current state, DOS has made some significant improvements to overcome early mistakes, and is now a very solid enterprise data platform. In the interests of industry-wide learning, Sanders will talk transparently about those mistakes and how those learnings are being applied to the DOS platform, positioning it to evolve gracefully over the next 25 years.
View the webinar to learn how the DOS reference architecture:
- Helps manage the 2,000+ compulsory measures in US healthcare
- Enables applications as varied as a real-time patient safety surveillance system, and an activity-based costing system in one platform
- Can ingest data of any type or velocity from over 300 healthcare source systems and growing
- Bundles tools, applications, and analytics that would cost 3-6x more to build on your own
- Compares to EHR vendors as an option to serve as an enterprise data and analytics platform
- Is a performant, sustainable, and maintainable platform for deploying AI models in the natural flow of the healthcare data pipeline
- Provides curated data content and models while still allowing for the agility of a late binding design option
- Functions as a reference architecture that all healthcare organizations and vendors will ultimately have to build in their pursuit of digital health
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?”
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.
Growing amounts of data can be overwhelming for healthcare entities to organize, manage, and distribute effectively, sometimes making data more of a burden than a benefit. However, if organizations adopt the right data mentality, they can gain insight into performance, track an intervention’s success, and improve outcomes. According to data experts, Bryan Hinton, our Chief Technology officer, and TJ Elbert, our SVP and General Manager of Data, organizations can apply five mindset changes to avoid data overload and achieve data-driven improvement:
1. Focus on data orchestration, not data computing.
2. Leverage real-time data, especially in a pandemic.
3. Prioritize data democratization over data control.
4. Use AI, if you’re not already.
5. Change current care models to fit the data.
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.
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
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.
A hybrid approach to data management is emerging in healthcare as organizations recognize the value of an enterprise data warehouse in combination with a data lake.
In this SlideShare, we discuss data lakes in healthcare and we:
Provide an overview of a Hadoop-based data lake architecture and integration platform, and its application in machine learning, predictive modeling, and data discovery
Discuss several key use cases driving the adoption of data lakes for both providers and health plans
Discuss available data storage forms and the required tools for a data lake environment
Detail best practices for conducting data lake assessments and review key implementation considerations for healthcare
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.
Leveraging Predictive Models to Reduce ReadmissionsHealth Catalyst
Far too often analytics efforts have fallen short of making a tangible impact on outcomes because they haven’t been successfully implemented in real workflows. Predictive models remain at risk of becoming isolated in their use along the continuum of care where their integration may provide benefits larger than the sum of each silo.
To combat this, UnityPoint Health (UPH) focused on integrating analytical models within the same readmission reduction strategy and coaching the care team to facilitate their adoption. Using this approach, one of UPH hospital’s risk-adjusted readmission indexes improved 40 percent over three years, surpassing internal system targets in performance and becoming the top performer in the health system.
Learning Objectives:
- Describe applicable predictive models useful in reducing 30-day readmissions.
- Learn the elements of a successful readmissions reduction strategy in an integrated health system.
- Understand common obstacles faced in the adoption of analytical tools and how to overcome them.
View this webinar to gain knowledge of the analytics tools and methods UPH used, including innovative individualized risk heat-maps generated for each patient, strategies for analytics adoption, and lessons learned along the way.
The Case for Healthcare Data Literacy: It's Not About Big DataHealth Catalyst
While many people are looking to Big Data to solve a lot of healthcare’s data problems, Big Data won’t offer a lot of solutions for a while to come. For one, healthcare doesn’t have “Big” data; there just isn’t the volume, velocity, or variety seen in other industries such as banking where Big Data has been used successfully. For another, Big Data seems to be the answer to almost every question from cancer to Alzheimer’s, and that’s blinding us to the reality of healthcare analytics. A big way toward answering healthcare’s problems would be to improve data literacy among not only consumers, but physicians and administrators as well. Learning to ask the right questions about the data and learning how to read data correctly will get us further down the road to improvement than the latest buzzword (in this case, “Big Data”) ever will.
A Health Catalyst Overview: Learn How a Data First Strategy Can Drive Increas...Health Catalyst
Without the pressure of a one-on-one demo, you can join a crowd of peers to ‘kick the tires’ if you will, as you listen to Jared Crapo—a sought after healthcare strategist—talk about what a data-first strategy is, and the strategic components to a data-first strategy employing a data operating system, a breakthrough engineering approach that combines the features of data warehousing, clinical data repositories, and health information exchanges in a single, common-sense technology platform that turns data into actionable assets used for all types of outcomes improvements.
Lest you worry about too much ‘pie in the sky’ strategy talk with few results to show, Sam Turman, Senior Solution Architect, will provide tangible solution demonstrations that are driving material results. Even if you aren’t in the market for Health Catalyst solutions and services, you will be able to:
Think with more clarity through your approach to overcoming the current market challenges.
Reconsider the strategy you are employing to build cross-organizational awareness and support to put a data-first plan at the center of your plan.
Define action you can take today to assess your gaps, understand your options, and accelerate your progress to drive outcomes improvements.
Join us and you won’t be disappointed. Jared is one of those types of thinkers that many pay big money to listen to and it is our fortune to have 60 minutes with him to think deeply about moving healthcare forward, one patient at a time. We hope you can join us.
Because everyone matters.
IBM Health and Social Programs Summit, October 2014
Stephen Morgan
Senior Vice President and Chief Medical Officer
Carilion Clinic
Jianying Hu
Research Staff Member and Manager of Healthcare Analytics Research
IBM
Paul Grundy
Global Director of Healthcare Transformation
IBM
How to Achieve the Competencies of Successful Value-based Contracting Delive...Health Catalyst
This webinar will review the evolution of the value-based contracting world, identifying key insights into impactable contract levers, and delineating systematic steps that lead to sustainable value-based contracting success. Health Catalyst team members Bobbi Brown, SVP, a healthcare finance executive with over 40 years’ experience, and Jonas Varnum, a population health and value-based care strategic consultant expert, will present on many of their battle-scarred experiences working with the financial, clinical, analytical, and operational components of value-based contracting delivery models including: 1) Shared qualities of successful value-based contracting delivery systems.
2) The intensifying need for robust data to drive success.
3) Refining and optimizing core competencies.
4) Increasing sustainability by impacting key contract levers.
The Power and Promise of Unstructured Patient Data Healthline
Unstructured search capabilities, superior natural language processing, and healthcare ontology capabilities will help distinguish the leading products in the category (information and data-driven decision making).
Network, Technology, and Data: Missing Pieces of the Puzzle for Clinical Tria...Health Catalyst
There is a massive shortfall in the enrollment and accrual of patients for clinical trials. Identifying the “right patients for the right trials at the right time” is a growing concern for providers, pharmaceutical companies, and clinical research organizations. In this webinar, we will discuss the evolution of clinical trials, including how to break barriers to enable successful clinical research as a care option, how clinical research impacts patient satisfaction and revenue, and more.
The Future of Data: High-Value Data is the Next Big ThingHealth Catalyst
The data today’s healthcare leaders need to drive decisions is locked away in siloed source systems. As a result, health systems must devote significant time and expense to access the information they need to empower decision makers.
In today’s rapidly changing healthcare environment, leaders need to make decisions in hours and days— not weeks and months. And too often the requisite data to drive fully informed decisions is unavailable or inaccurate. Meanwhile, over the past 18 months, COVID-19 has increased the urgency for high-value data in decision making. In this webinar, TJ Elbert, Senior Vice President and General Manager of Data, will outline Health Catalyst’s strategy to empower leaders with the trusted, timely, high-value data assets the need to drive decision making and maximize value.
You’ll Learn How to:
• Reduce data acquisition time and cost with DOS Marts™.
• Lower barriers to access through data integration and normalization.
• Create high-value, reusable data assets with Expert Data Collections™.
• Drive insights into clinical workflows and analytics applications through DOS™ data-sharing capabilities.
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.
Data Lake vs. Data Warehouse: Which is Right for Healthcare?Health Catalyst
The data lake style of a data warehouse architecture is a flexible alternative to a traditional data warehouse. It allows for unstructured data. When a warehousing approach requires that the data be in a structured format, there are constraints on the analyses that can be performed because not all of the data can be structured early. The data lake concept is very similar to our Late-Binding approach in that data lakes are our source marts. We increase the efficiency and effectiveness of these through: 1. Metadata, 2. Source Mart Designer, and 3. Subject Area Mart Designer.
Optimize Your Healthcare Data Quality Investment: Three Ways to Accelerate Ti...Health Catalyst
Healthcare organizations increasingly rely on data to inform strategic decisions. This growing dependence makes ensuring data across the organization is fit for purpose more critical than ever. Decision-making challenges associated with pandemic-driven urgency, variety of data, and lack of resources have further highlighted the critical importance of healthcare data quality and prompted more focus and investment. However, many data quality initiatives are too narrow in focus and reactive in nature or take longer than expected to demonstrate value. This leaves organizations unprepared for future events, like COVID-19, that require a rapid enterprise-wide analytic response.
What are some actionable ways you can help your organization guard against the data quality challenges uncovered this past year and better prepare to respond in the future? Join Taylor Larsen, Director of Data Quality for Health Catalyst, to learn more.
What You’ll Learn
- How data profiling and data quality assessments, in combination with your data catalog, can increase data quality transparency, expedite root cause analysis, and close data quality monitoring gaps.
- How to leverage AI to reduce data quality monitoring configuration and maintenance time and improve accuracy.
- How defining data quality based on its measurable utility (i.e., data represents information that supports better decisions) can provide a scalable way to ensure data are fit for purpose and avoid cost outstripping return.
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
A Reference Architecture for Digital Health: The Health Catalyst Data Operati...Health Catalyst
There are essentially four strategic options to address the enterprise data platform requirements of today’s healthcare systems: (1) build your own, (2) buy from EHR vendors, (3) look to a Silicon Valley high-tech startup, and (4) partner with Health Catalyst or a handful of similar companies.
In this webinar, Health Catalyst’s CTO, Dale Sanders, comments on all four approaches, hoping to help you to assess your organization’s strategy against the options and vendors in each category.
It’s been exactly three years since Health Catalyst embarked on a major investment in its next-generation technology, the Data Operating System (DOS™) and its applications. This webinar is an update on the progress, less about marketing the technology, but rather offering DOS as a reference architecture that can support analytics, AI, text processing, data-first application development, and interoperability, as an all-in-one agile cost-savings architecture.
In addition to the successes, Dale comments on the challenges that Health Catalyst has faced under a very ambitious DOS development plan. In its current state, DOS has made some significant improvements to overcome early mistakes, and is now a very solid enterprise data platform. In the interests of industry-wide learning, Sanders will talk transparently about those mistakes and how those learnings are being applied to the DOS platform, positioning it to evolve gracefully over the next 25 years.
View the webinar to learn how the DOS reference architecture:
- Helps manage the 2,000+ compulsory measures in US healthcare
- Enables applications as varied as a real-time patient safety surveillance system, and an activity-based costing system in one platform
- Can ingest data of any type or velocity from over 300 healthcare source systems and growing
- Bundles tools, applications, and analytics that would cost 3-6x more to build on your own
- Compares to EHR vendors as an option to serve as an enterprise data and analytics platform
- Is a performant, sustainable, and maintainable platform for deploying AI models in the natural flow of the healthcare data pipeline
- Provides curated data content and models while still allowing for the agility of a late binding design option
- Functions as a reference architecture that all healthcare organizations and vendors will ultimately have to build in their pursuit of digital health
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?”
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.
Growing amounts of data can be overwhelming for healthcare entities to organize, manage, and distribute effectively, sometimes making data more of a burden than a benefit. However, if organizations adopt the right data mentality, they can gain insight into performance, track an intervention’s success, and improve outcomes. According to data experts, Bryan Hinton, our Chief Technology officer, and TJ Elbert, our SVP and General Manager of Data, organizations can apply five mindset changes to avoid data overload and achieve data-driven improvement:
1. Focus on data orchestration, not data computing.
2. Leverage real-time data, especially in a pandemic.
3. Prioritize data democratization over data control.
4. Use AI, if you’re not already.
5. Change current care models to fit the data.
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.
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
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.
A hybrid approach to data management is emerging in healthcare as organizations recognize the value of an enterprise data warehouse in combination with a data lake.
In this SlideShare, we discuss data lakes in healthcare and we:
Provide an overview of a Hadoop-based data lake architecture and integration platform, and its application in machine learning, predictive modeling, and data discovery
Discuss several key use cases driving the adoption of data lakes for both providers and health plans
Discuss available data storage forms and the required tools for a data lake environment
Detail best practices for conducting data lake assessments and review key implementation considerations for healthcare
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.
Leveraging Predictive Models to Reduce ReadmissionsHealth Catalyst
Far too often analytics efforts have fallen short of making a tangible impact on outcomes because they haven’t been successfully implemented in real workflows. Predictive models remain at risk of becoming isolated in their use along the continuum of care where their integration may provide benefits larger than the sum of each silo.
To combat this, UnityPoint Health (UPH) focused on integrating analytical models within the same readmission reduction strategy and coaching the care team to facilitate their adoption. Using this approach, one of UPH hospital’s risk-adjusted readmission indexes improved 40 percent over three years, surpassing internal system targets in performance and becoming the top performer in the health system.
Learning Objectives:
- Describe applicable predictive models useful in reducing 30-day readmissions.
- Learn the elements of a successful readmissions reduction strategy in an integrated health system.
- Understand common obstacles faced in the adoption of analytical tools and how to overcome them.
View this webinar to gain knowledge of the analytics tools and methods UPH used, including innovative individualized risk heat-maps generated for each patient, strategies for analytics adoption, and lessons learned along the way.
The Case for Healthcare Data Literacy: It's Not About Big DataHealth Catalyst
While many people are looking to Big Data to solve a lot of healthcare’s data problems, Big Data won’t offer a lot of solutions for a while to come. For one, healthcare doesn’t have “Big” data; there just isn’t the volume, velocity, or variety seen in other industries such as banking where Big Data has been used successfully. For another, Big Data seems to be the answer to almost every question from cancer to Alzheimer’s, and that’s blinding us to the reality of healthcare analytics. A big way toward answering healthcare’s problems would be to improve data literacy among not only consumers, but physicians and administrators as well. Learning to ask the right questions about the data and learning how to read data correctly will get us further down the road to improvement than the latest buzzword (in this case, “Big Data”) ever will.
A Health Catalyst Overview: Learn How a Data First Strategy Can Drive Increas...Health Catalyst
Without the pressure of a one-on-one demo, you can join a crowd of peers to ‘kick the tires’ if you will, as you listen to Jared Crapo—a sought after healthcare strategist—talk about what a data-first strategy is, and the strategic components to a data-first strategy employing a data operating system, a breakthrough engineering approach that combines the features of data warehousing, clinical data repositories, and health information exchanges in a single, common-sense technology platform that turns data into actionable assets used for all types of outcomes improvements.
Lest you worry about too much ‘pie in the sky’ strategy talk with few results to show, Sam Turman, Senior Solution Architect, will provide tangible solution demonstrations that are driving material results. Even if you aren’t in the market for Health Catalyst solutions and services, you will be able to:
Think with more clarity through your approach to overcoming the current market challenges.
Reconsider the strategy you are employing to build cross-organizational awareness and support to put a data-first plan at the center of your plan.
Define action you can take today to assess your gaps, understand your options, and accelerate your progress to drive outcomes improvements.
Join us and you won’t be disappointed. Jared is one of those types of thinkers that many pay big money to listen to and it is our fortune to have 60 minutes with him to think deeply about moving healthcare forward, one patient at a time. We hope you can join us.
Because everyone matters.
IBM Health and Social Programs Summit, October 2014
Stephen Morgan
Senior Vice President and Chief Medical Officer
Carilion Clinic
Jianying Hu
Research Staff Member and Manager of Healthcare Analytics Research
IBM
Paul Grundy
Global Director of Healthcare Transformation
IBM
How to Achieve the Competencies of Successful Value-based Contracting Delive...Health Catalyst
This webinar will review the evolution of the value-based contracting world, identifying key insights into impactable contract levers, and delineating systematic steps that lead to sustainable value-based contracting success. Health Catalyst team members Bobbi Brown, SVP, a healthcare finance executive with over 40 years’ experience, and Jonas Varnum, a population health and value-based care strategic consultant expert, will present on many of their battle-scarred experiences working with the financial, clinical, analytical, and operational components of value-based contracting delivery models including: 1) Shared qualities of successful value-based contracting delivery systems.
2) The intensifying need for robust data to drive success.
3) Refining and optimizing core competencies.
4) Increasing sustainability by impacting key contract levers.
The Power and Promise of Unstructured Patient Data Healthline
Unstructured search capabilities, superior natural language processing, and healthcare ontology capabilities will help distinguish the leading products in the category (information and data-driven decision making).
Network, Technology, and Data: Missing Pieces of the Puzzle for Clinical Tria...Health Catalyst
There is a massive shortfall in the enrollment and accrual of patients for clinical trials. Identifying the “right patients for the right trials at the right time” is a growing concern for providers, pharmaceutical companies, and clinical research organizations. In this webinar, we will discuss the evolution of clinical trials, including how to break barriers to enable successful clinical research as a care option, how clinical research impacts patient satisfaction and revenue, and more.
The Future of Data: High-Value Data is the Next Big ThingHealth Catalyst
The data today’s healthcare leaders need to drive decisions is locked away in siloed source systems. As a result, health systems must devote significant time and expense to access the information they need to empower decision makers.
In today’s rapidly changing healthcare environment, leaders need to make decisions in hours and days— not weeks and months. And too often the requisite data to drive fully informed decisions is unavailable or inaccurate. Meanwhile, over the past 18 months, COVID-19 has increased the urgency for high-value data in decision making. In this webinar, TJ Elbert, Senior Vice President and General Manager of Data, will outline Health Catalyst’s strategy to empower leaders with the trusted, timely, high-value data assets the need to drive decision making and maximize value.
You’ll Learn How to:
• Reduce data acquisition time and cost with DOS Marts™.
• Lower barriers to access through data integration and normalization.
• Create high-value, reusable data assets with Expert Data Collections™.
• Drive insights into clinical workflows and analytics applications through DOS™ data-sharing capabilities.
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.
Data Lake vs. Data Warehouse: Which is Right for Healthcare?Health Catalyst
The data lake style of a data warehouse architecture is a flexible alternative to a traditional data warehouse. It allows for unstructured data. When a warehousing approach requires that the data be in a structured format, there are constraints on the analyses that can be performed because not all of the data can be structured early. The data lake concept is very similar to our Late-Binding approach in that data lakes are our source marts. We increase the efficiency and effectiveness of these through: 1. Metadata, 2. Source Mart Designer, and 3. Subject Area Mart Designer.
Optimize Your Healthcare Data Quality Investment: Three Ways to Accelerate Ti...Health Catalyst
Healthcare organizations increasingly rely on data to inform strategic decisions. This growing dependence makes ensuring data across the organization is fit for purpose more critical than ever. Decision-making challenges associated with pandemic-driven urgency, variety of data, and lack of resources have further highlighted the critical importance of healthcare data quality and prompted more focus and investment. However, many data quality initiatives are too narrow in focus and reactive in nature or take longer than expected to demonstrate value. This leaves organizations unprepared for future events, like COVID-19, that require a rapid enterprise-wide analytic response.
What are some actionable ways you can help your organization guard against the data quality challenges uncovered this past year and better prepare to respond in the future? Join Taylor Larsen, Director of Data Quality for Health Catalyst, to learn more.
What You’ll Learn
- How data profiling and data quality assessments, in combination with your data catalog, can increase data quality transparency, expedite root cause analysis, and close data quality monitoring gaps.
- How to leverage AI to reduce data quality monitoring configuration and maintenance time and improve accuracy.
- How defining data quality based on its measurable utility (i.e., data represents information that supports better decisions) can provide a scalable way to ensure data are fit for purpose and avoid cost outstripping return.
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
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
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
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.
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.
Using data relationships to make connections between individual data records transforms the data you already have into something much more powerful. This webinar will explain how both young and established companies have adopted graph thinking - and how they’ve risen to dominate their fields.
Azure Machine Learning 101 slides which I used on Advanced Technology Days conference, held in Zagreb (Croatia) on November 12th and 13th.
Slides are divided into 2 parts. First part is introducing machine learning in a simple way with some basic definitions and basic examples. Second part is introducing Azure Machine Learning service including main features and workflow.
Slides are used only 30% of the presentation time so there is no much detailed information on them regarding machine learning. Rest of the time I did live demos on Azure Machine Learning portal which is probably more interesting to the audience.
Presentation can be useful as a concept for similar topics or to combine it some other resource. If you need access to the demos just send me a message so I will grant you access to Azure ML workspace where are all experiments used in this session.
Better Insights from Your Master Data - Graph Database LA MeetupBenjamin Nussbaum
Master Data Management, is a practice that involves discovering, cleaning, housing, and governing data. Data architects for enterprises require a data model that offers ad hoc, variable, and flexible structures as business needs are constantly changing.
We'll be discussing the benefits of using the Neo4j graph database for Master Data Management including the flexible schema free data model, concepts of layering in data, keeping your data current and flowing and then the benefits of connected data analytics and real-time recommendations that can result.
An overview of MDM with Neo4j https://www.graphgrid.com/graph-advantage-master-data-management/
The demo portion of the presentation is here: https://youtu.be/_GnDiwngnXk
Knowledge Graphs - Journey to the Connected Enterprise - Data Strategy and An...Benjamin Nussbaum
We live in an era where the world is more connected than ever before and the trajectory is such that data relationships will only continue to increase with no signs of slowing down.
Connected data is the key to your business succeeding and growing in today’s connected world.
Leading enterprises will be the ones that utilize relationship-centric technologies to leverage connections from their internal operations and supply chain to their customer and user interactions. This ability to utilize connected data to understand all the nuanced relationships within their organization will propel them forward as they act on more holistic insights.
Every organization needs a knowledge graph because connected data is an essential foundation to advancing business. Knowledge graphs provide:
- Increased visibility between internal groups
- Efficiency gains
- Cross-functional data collaboration
- Core complete and reliable business insights
- Better customer engagement
The live presentation and discussion can be found here: https://youtu.be/7vBdlXzhs_4
Additional reading on why connected data is beneficial: https://www.graphgrid.com/why-connected-data-is-more-useful/
Connected data solutions available by Benjamin and his team via GraphGrid and AtomRain: https://www.graphgrid.com and https://www.atomrain.com
The goal of this webinar is to help healthcare professionals improve care coordination for patients with advanced illness and to reduce hospital readmissions and length of stay (LOS).
The goal of this webinar was to help healthcare professionals improve care coordination for patients with advanced illness and to reduce hospital readmissions and length of stay (LOS).
The clinical case study of a patient with advanced COPD who has multiple comorbid conditions and develops sepsis provides the backdrop for two potential clinical pathways—sepsis and post-sepsis syndrome—and explores the natural history and indicators of poor prognosis in both conditions.
The clinical case study of a patient with advanced COPD who has multiple comorbid conditions and develops sepsis provideD the backdrop for two potential clinical pathways—sepsis and post-sepsis syndrome.
The goal of this webinar is to help healthcare professionals improve care coordination for patients with advanced illness and to reduce hospital readmissions and length of stay (LOS).
Barbados 2012-13 Health Accounts ReportHFG Project
This report presents the findings and policy implications of Barbados’ first Health Accounts estimation, conducted for the year April 2012 to March 2013. It captures spending from all sources: the government, non-governmental organizations, external donors, private employers, private insurance companies and households. The analysis presented breaks down spending to the standard classifications, as defined by the System of Health Accounts 2011 framework, namely sources of financing, financing schemes, type of provider, type of activity and disease/health condition.
Peter L. Slavin, M.D., 2015 Leadership in Academic Medicine Lectureuabsom
Peter L. Slavin, M.D., president of Massachusetts General Hospital, presented “The Future of Academic Medicine” on Thursday, Aug. 6 as the featured speaker for the 2015 Leadership in Academic Medicine Lecture, sponsored by UAB Medicine.
Dr Ashish Jha: lessons from organisational changeNuffield Trust
Dr Ashish Jha, Harvard School of Public Health, presenting at the Nuffield Trust Health Policy Summit, explores how change happens, drawing on examples from Accountable Care Organisations in the USA.
The goal of this webinar was to help healthcare professionals improve care coordination for patients with advanced illness and to reduce hospital readmissions and length of stay.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
Anti ulcer drugs and their Advance pharmacology ||
Anti-ulcer drugs are medications used to prevent and treat ulcers in the stomach and upper part of the small intestine (duodenal ulcers). These ulcers are often caused by an imbalance between stomach acid and the mucosal lining, which protects the stomach lining.
||Scope: Overview of various classes of anti-ulcer drugs, their mechanisms of action, indications, side effects, and clinical considerations.
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
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The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
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We specializes in exporting high quality Research chemical, medical intermediate, Pharmaceutical chemicals and so on. Products are exported to USA, Canada, France, Korea, Japan,Russia, Southeast Asia and other countries.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Managing National Health: An Overview of Metrics & Options
1. Elements of a National Health Strategy:
Key Metrics and Performance
Indicators
Healthcare 20/20: Cayman Islands International Healthcare Conference 2011
2. Introduction
Dale Sanders
Twitter: @drsanders
Text: 1-345-925-8329
Email: dale.sanders@hsa.ky
LinkedIn: http://www.linkedin.com/in/dalersanders
Blogs
http://callitanything.blogspot.com/
http://healthsystemcio.com/tag/dale-sanders/
2
Text, email, or tweet your questions during the presentation
3. Overview
National Health Accounts: What Are They?
Sources of Benchmarks for National Healthcare
Policy and Management
Related Health Services Authority Metrics
All HSA/Cayman metrics are preliminary and need
refinement
3
4. Key Messages
The old but true cliché: You can’t manage what you can’t
measure
An effective national health plan requires national healthcare
indicators and metrics
We have some Cayman Islands metrics already and our
“data momentum” is increasing
But we should probably increase our focus and efforts
Becoming a “data driven culture” in healthcare is not easy
There will soon be three major skills shortages in healthcare
Physicians, nurses and…
Data analysts
4
6. If I had kids, I can imagine this would
be the conversation…
6
7. National Health Accounts (NHA)
WHO, OECD, World Bank and Gates Foundation
In lay terms- NHAs are an international standard for
healthcare accounting at the national policy level
“NHA constitute a systematic, comprehensive and consistent
monitoring of resource flows in a country’s health system
financing.”
“…helps in developing national strategies for effective health
financing and in raising additional funds for health.”
“…concerns itself primarily with the health care goods and
services consumed by residents, irrespective of where that
consumption takes place.”
Healthcare IT vendors should support NHA standards
7
8. +
NHA Healthcare Financing
Schemes
Government
Compulsory social insurance
Voluntary health insurance
Out-of-pocket payments
Foreign aid programs
Charitable programs
Agents
Government departments
Social insurance funds
Insurance companies
Households
Foreign countries
Charities & foundations
8
10. 10
Progression of Disease and the Provision of Healthcare Goods and Services
Adapted from Norman, 2003
11. + Sources of Metrics &
Benchmarks for National
Healthcare Policy
11
12. World Health Organization, Pan American Health
Organization
Somewhat focused on the issues of Third World countries
TB, HIV, Drinking Water, Malaria, Improved Sanitation Utilization,
parasites, etc.
Sometimes tainted by political agendas in the United Nations
Organization of Economic Cooperation and Development
Focused on the 34-member organizations
Very thorough, very focused on the ratio of Cost-per-Outcome
The Commonwealth Fund
Private US foundation, less prone to politics
Motivated to change the US healthcare system
Benchmarks against major democracies
All of these ignore mental and spiritual health measurement
12
13. WHO/PAHO Categories
1. Life Expectancy And Mortality
2. Cause Specific Mortality And Morbidity
3. Selected Infectious Diseases
4. Health Service Coverage
5. Risk Factors
6. Health Workforce, Infrastructure, And Essential Medicines
7. Health Expenditure
8. Health Inequities
9. Demographic And Socioeconomic Statistics
13
14. Deaths By 19 Leading Factors,
By Country Income Level, 2004
15. OECD Categories
1. Health Status: Life Expectancy, Mortality, Chronic
Conditions
2. Risk Factors
3. Health Workforce: Number Of Physicians, Nurses, etc.;
Remuneration Of Physicians And Nurses
4. Consumption Of Healthcare: Diagnostics, Treatments,
Pharmaceuticals
5. Quality Of Care: Life Threatening Acute Care; Chronic
Disease
6. Healthcare Expenditure: Costs And Financing
15
16. Public Spending on Health Care per Capita, 2006
Adjusted for Differences in Cost of Living
$1,906
$2,011$2,027
$2,408
$2,591$2,591$2,597
$2,750
$3,074
$0
$500
$1,000
$1,500
$2,000
$2,500
$3,000
$3,500
United
States
France Switzerland Canada Germany United
Kingdom
OECD
Median
Australia* New
Zealand
Source: OECD Health Data 2008, “June 2008.”
Cayman Islands?
$150M/50K = $3,000 per capita
17. Source: OECD and International Diabetes Federation (IDF) (2009), “Diabetes Atlas, 4th edition”.
Prevalence Of Diabetes
Adults Aged 20-79 Years, 2010
5.8% of HSA patients have a
diabetes diagnosis…but this
number is probably low.
Further analysis required.
18. The Number Of Physicians Per Capita
Source: OECD Health Data 2009, OECD (http://www.oecd.org/health/healthdata).
Cayman Islands:
About 4
physicians per
1,000 population
19. Commonwealth Fund Categories
1. Quality Care
2. Access
3. Efficiency
4. Equity
5. Long, Healthy, Productive Lives
6. Health Expenditures per Capita
19
22. Obesity (BMI>30) Prevalence in 2004
30.6%
23.0%
22.4%
20.9%
13.0%
12.9%
10.9%
9.5%
3.2%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
United
States
United
Kingdom
Canada New
Zealand
OECD
Median
Germany Netherlands France Japan
a
a
a2003
b2002
a
b
Source: The Commonwealth Fund, calculated from OECD Health Data 2006.
Cayman Islands…?
23. 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
U.S. Population Health Expenditures
Health Care Costs Concentrated in Sick Few—
Sickest 10 Percent Account for 64 Percent of Expenses
1%
5%
10%
49%
64%
24%
Source: The Commonwealth Fund. Data from S. H. Zuvekas and J. W. Cohen, “Prescription Drugs and the
Changing Concentration of Health Care Expenditures,” Health Affairs, Jan./Feb. 2007 26(1):249–57.
50%
97%
$36,280
$12,046
$6,992
$715
Distribution of health expenditures for the U.S. population,
by magnitude of expenditure, 2003
Expenditure
threshold
(2003 dollars)
25. Perspective
Director of Public Health (Dr. Kumar) and team have been
reporting PH metrics to PAHO for a number of years
Challenged by the technology for data extraction from our
Cerner system– the data is there, but wrapped in a
proprietary programming language
Last two years at HSA – Financial Survivability & Stability
Next two years– Chronic Disease Management & Patient
Satisfaction
Slowly gathering “Metrics Momentum”
25
26. As Reported in the Compass
CI Government spend $93.4 million on health care in the last
financial year
17.5% of the national budget
22% (about $20.5 million) was spent on overseas referrals
for 2,500 persons;
More than $12 million per year on overseas referrals for the
indigent population alone
CINICO budget: $59M for 13,000 covered lives
26
30. Leading Causes of Death in CI
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
31. Top Ten Procedures (CPT) at HSA
1. Supplies and materials provided by a physician
2. Level 3 office or other outpatient visit
3. Blood glucose monitoring
4. Therapeutic, prophylactic or diagnostic injection
5. Level 1 office or other outpatient visit
6. Comprehensive metabolic panel
7. Emergency department visit
8. Therapeutic, prophylactic or diagnostic injection
9. Physical therapy re-evaluation
10. Level 2 office or other outpatient visit
31
32. Top Ten HSA ICD9 Diagnosis
1. Hypertension
2. Diabetes
3. Routine health check for child
4. Upper respiratory infection
5. Change or removal of non-surgical wound dressing
6. Care involving physical therapy
7. High cholesterol (hyperlipidemia)
8. Pregnant
9. Long term anticoagulant use
10. Observation for unspecified conditions
32
33. Other Key Performance Indicators
Metric 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11
Total Admissions 4,766 4,889 5,071 5,718 5,574
Bed Occupancy Rate 65% 68.6% 68.8% 73.5% 63.7% 59.4%
Average Length of Stay 4 4 4 4 4 4
Total ER Visits 25,302 26,901 27,041 29,859 34,032 33,172
Total Outpatient visits including
Pharmacy
158,364 150,632 155,162 186,460 180,030 181,947
Total Surgeries 2,509 2,660 2,726 2,912 3,101 2,977
Total deliveries 628 634 647 664 691 695
Total C- Sections 228 252 247 270 281 280
Total Specialist Clinic visits 24,036 23,973 24,445 25,614 32,038 30,869
34. HSA Patients (Cayman
Islands Residents) with
an encounter during
Oct 2010 – Sep 2011
These reported rates
are artificially low,
probably because the
data relies only on
diagnosis code
(ICD9). Further
analysis required.
Chronic Disease Rates for HSA Patients
Chronic Disease
Patient
Count
Percent of
all Patients US Rate
Hypertension 3571 10.80% 33%
Diabetes 1903 5.80% 10%
Mental Disorders 1619 4.90% 3.30%
Respiratory Disease 539 2.50% 4%
Cardiovascular Disease 407 3.00% 12%
Cancer 356 1.10% 4%
Kidney Disease 235 0.70% 2%
Osteoarthritis 208 0.60% 23%
Chronic liver disease 142 0.40% ?
Osteoporosis 94 0.30% 3%
Paralysis and cerebral
palsy 46 0.10% ?
AIDS/HIV 16 0.00% 0.40%
35. HSA Patients with Cancer
Cancer Type Patient Count
Percent of all
Patients
Breast 93 0.28%
Prostate 83 0.25%
Leukemia and Myeloma 30 0.09%
Other Non-Melanomatous Skin Cancer 28 0.08%
Colon 26 0.08%
Gynecologic 25 0.08%
Urinary Tract 19 0.06%
Non-colon GI 14 0.04%
Head and Neck 12 0.04%
Melanoma 11 0.03%
Lung 11 0.03%
Non-Hodgkin Lymphoma 9 0.03%
Lymphosarcoma and Reticulosarcoma 6 0.02%
Bones/Soft Tissue 5 0.02%
Hodgkin Lymphoma 4 0.01%
Brain 3 0.01%
Endocrine 3 0.01%
Carcinoid Tumors 2 0.01%
Pleural Mesothelioma 1 0.00%
All Cancer patients (patient can have two
cancer types) 365 1.10%
HSA Patients
(Cayman Islands
Residents) with an
encounter Sep10 -
Aug11 with a
cancer diagnosis
These reported rates
are artificially low,
probably because the
data relies only on
diagnosis code
(ICD9). Further
analysis required.
4% of the US population
has some form of cancer.
37. Keeping It All In Perspective
What surprises you most about humanity?
Dalai Lama:
"Man. He sacrifices his health in order to make money. Then
he sacrifices money to recuperate his health. And then he is
so anxious about the future that he does not enjoy the
present; the result being that he does not live in the present
or the future. He lives as if he is never going to die, and
then dies having never really lived."
37
38. We are entering the “Data Driven Age” of
healthcare that informs…
Government leaders and national policy
Physicians and patient management
Healthcare leadership and administration
Patients and healthcare consumers
Employers
Is it time for a national initiative to define and
baseline our key performance indicators?
38