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.
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.
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.
Healthcare Best Practices in Data Warehousing & AnalyticsDale Sanders
The document discusses the history and evolution of data warehousing. It notes that data warehousing emerged due to technological limitations that prevented transactional and analytical uses of data on the same platform. Early stages included departments storing unused data to avoid tape changes and government projects consolidating databases. Factors like business reengineering and a focus on continuous improvement drove more analytical uses of data. Key lessons discussed include the importance of business culture supportive of fact-based decision making and managing political issues raised by data warehouses. The document advocates for keeping metadata simple and focused on understandability and findability of data.
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?
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.
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?”
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?
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
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.
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.
Healthcare Best Practices in Data Warehousing & AnalyticsDale Sanders
The document discusses the history and evolution of data warehousing. It notes that data warehousing emerged due to technological limitations that prevented transactional and analytical uses of data on the same platform. Early stages included departments storing unused data to avoid tape changes and government projects consolidating databases. Factors like business reengineering and a focus on continuous improvement drove more analytical uses of data. Key lessons discussed include the importance of business culture supportive of fact-based decision making and managing political issues raised by data warehouses. The document advocates for keeping metadata simple and focused on understandability and findability of data.
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?
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.
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?”
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?
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
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
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.
Have Data—Need Analysts. Lessons Learned From The Woodworking IndustryHealth Catalyst
The document discusses lessons learned from the woodworking industry that can be applied to healthcare analytics. It notes that both require the right tools, skilled people, and analytic workflows. The woodshop layout places importance on how stations are arranged to efficiently flow materials through the process. Similarly, the layout of analytic work streams should reflect skills, tools, and experience to optimize analytic workflow. It also stresses the importance of data analysis skills over other technical skills and of having the right skills matched to descriptive and prescriptive analytic work.
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.
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.
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.
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.
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.
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.
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.
This document summarizes a presentation about setting vision and strategy for health IT leaders in dynamic times. It discusses exploring new leadership skills required for effective collaboration. It also addresses aligning technology strategies with organizational services and objectives. Additionally, it covers representing the organization to external partners to achieve business goals while leveraging technology. The presentation provides approaches for health IT leaders to develop an organizational vision and strategy that can adapt to changing conditions.
This document is a presentation by Raymond Gensinger on data analytics in healthcare. It discusses examples of analytics used in baseball to improve performance, the different types of analytics including descriptive, predictive, and prescriptive. It also covers how analytics have evolved, organizational readiness for analytics, and key factors for analytics success including data, enterprise integration, leadership, targets, and having the right analysts. The presentation provides a framework for healthcare to apply analytics and examples of how different types of analytics could be used.
The document discusses strategies and solutions for accountable care organizations (ACOs) to improve patient outcomes and lower costs. It proposes an ACO Optimization Toolkit and ACO-ASK decision support system that use techniques like predictive modeling, text mining, and machine learning. Revenue projections show consulting revenues growing from $53 million in 2012 to over $200 million by 2018 as more ACOs adopt the solutions. The goal is to help providers succeed under new reimbursement models that emphasize quality and value over volume.
Organizing for Analytics Success - HAS Session 7Health Catalyst
This document discusses organizing for analytics success through establishing three core systems: the analytic system, content system, and deployment system. It provides examples of activities and components for each system, such as automating data visualization in the analytic system, standardizing care delivery through shared baselines in the content system, and applying agile principles to care improvement in the deployment system. The document also includes examples of exercises and poll questions to aid discussion around each system.
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
Getting The Most Out of Your Data Analyst - HAS Session 9Health Catalyst
Many analysts spend 90% of their time managing rather than analyzing data. How do we enable analysts to do what they were hired to do? In this session, you will learn best practices on helping your analyst focus more on analytics and less on data capture and provisioning, as well as how to create sustainable and meaningful analytics. We will show best practices and common pitfalls to avoid. This will be a fun and interactive session with many hands-on examples and exercises.
The presentation discusses how big data and population health management tools can help reduce healthcare costs and improve outcomes. It explains that big data allows for deeper analysis of existing data to make better business decisions. Advanced analytics can help identify opportunities to improve clinical quality and financial performance. With proper outreach and lifestyle changes, big data tools may enable fewer hospital visits.
This document discusses how big data can be used in the healthcare sector to improve outcomes and reduce costs. It begins by defining big data and describing how large corporations have been using big data for years. It then draws a parallel between how big data helped answer what advertising worked for companies like Google, and how big data can help determine which medical treatments are effective. The document outlines some key characteristics of big data in healthcare, such as different types of data silos and the 4 Vs of big data. It also discusses drivers for adoption of big data in healthcare and provides examples of how big data can enable quality improvement and cost cutting. Challenges to adoption are outlined as well as some leading big data companies in healthcare. The document
The document discusses using big data and Hadoop in healthcare. It outlines challenges in healthcare like a lack of continuous observation and data storage. Hadoop can help address this by making large amounts of healthcare data less expensive and more available. This would allow doctors more insight into patient conditions. The Internet of Things is also discussed where devices can collect patient readings and send them to remote hospitals. The presentation concludes with a demo of Hadoop used with a healthcare dataset.
The 12 Criteria of Population Health ManagementDale Sanders
This document outlines 12 criteria for population health data management and describes each criterion in detail. It also evaluates several population health management vendors against these criteria, providing a score from 1-10 for each vendor's capabilities in meeting each criterion. The purpose is to help healthcare organizations evaluate vendors and develop strategies for accountable care. No single vendor meets all criteria today, so a combination of solutions may be needed. The document emphasizes starting with internal data and processes before acquiring external data.
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
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
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.
Have Data—Need Analysts. Lessons Learned From The Woodworking IndustryHealth Catalyst
The document discusses lessons learned from the woodworking industry that can be applied to healthcare analytics. It notes that both require the right tools, skilled people, and analytic workflows. The woodshop layout places importance on how stations are arranged to efficiently flow materials through the process. Similarly, the layout of analytic work streams should reflect skills, tools, and experience to optimize analytic workflow. It also stresses the importance of data analysis skills over other technical skills and of having the right skills matched to descriptive and prescriptive analytic work.
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.
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.
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.
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.
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.
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.
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.
This document summarizes a presentation about setting vision and strategy for health IT leaders in dynamic times. It discusses exploring new leadership skills required for effective collaboration. It also addresses aligning technology strategies with organizational services and objectives. Additionally, it covers representing the organization to external partners to achieve business goals while leveraging technology. The presentation provides approaches for health IT leaders to develop an organizational vision and strategy that can adapt to changing conditions.
This document is a presentation by Raymond Gensinger on data analytics in healthcare. It discusses examples of analytics used in baseball to improve performance, the different types of analytics including descriptive, predictive, and prescriptive. It also covers how analytics have evolved, organizational readiness for analytics, and key factors for analytics success including data, enterprise integration, leadership, targets, and having the right analysts. The presentation provides a framework for healthcare to apply analytics and examples of how different types of analytics could be used.
The document discusses strategies and solutions for accountable care organizations (ACOs) to improve patient outcomes and lower costs. It proposes an ACO Optimization Toolkit and ACO-ASK decision support system that use techniques like predictive modeling, text mining, and machine learning. Revenue projections show consulting revenues growing from $53 million in 2012 to over $200 million by 2018 as more ACOs adopt the solutions. The goal is to help providers succeed under new reimbursement models that emphasize quality and value over volume.
Organizing for Analytics Success - HAS Session 7Health Catalyst
This document discusses organizing for analytics success through establishing three core systems: the analytic system, content system, and deployment system. It provides examples of activities and components for each system, such as automating data visualization in the analytic system, standardizing care delivery through shared baselines in the content system, and applying agile principles to care improvement in the deployment system. The document also includes examples of exercises and poll questions to aid discussion around each system.
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
Getting The Most Out of Your Data Analyst - HAS Session 9Health Catalyst
Many analysts spend 90% of their time managing rather than analyzing data. How do we enable analysts to do what they were hired to do? In this session, you will learn best practices on helping your analyst focus more on analytics and less on data capture and provisioning, as well as how to create sustainable and meaningful analytics. We will show best practices and common pitfalls to avoid. This will be a fun and interactive session with many hands-on examples and exercises.
The presentation discusses how big data and population health management tools can help reduce healthcare costs and improve outcomes. It explains that big data allows for deeper analysis of existing data to make better business decisions. Advanced analytics can help identify opportunities to improve clinical quality and financial performance. With proper outreach and lifestyle changes, big data tools may enable fewer hospital visits.
This document discusses how big data can be used in the healthcare sector to improve outcomes and reduce costs. It begins by defining big data and describing how large corporations have been using big data for years. It then draws a parallel between how big data helped answer what advertising worked for companies like Google, and how big data can help determine which medical treatments are effective. The document outlines some key characteristics of big data in healthcare, such as different types of data silos and the 4 Vs of big data. It also discusses drivers for adoption of big data in healthcare and provides examples of how big data can enable quality improvement and cost cutting. Challenges to adoption are outlined as well as some leading big data companies in healthcare. The document
The document discusses using big data and Hadoop in healthcare. It outlines challenges in healthcare like a lack of continuous observation and data storage. Hadoop can help address this by making large amounts of healthcare data less expensive and more available. This would allow doctors more insight into patient conditions. The Internet of Things is also discussed where devices can collect patient readings and send them to remote hospitals. The presentation concludes with a demo of Hadoop used with a healthcare dataset.
The 12 Criteria of Population Health ManagementDale Sanders
This document outlines 12 criteria for population health data management and describes each criterion in detail. It also evaluates several population health management vendors against these criteria, providing a score from 1-10 for each vendor's capabilities in meeting each criterion. The purpose is to help healthcare organizations evaluate vendors and develop strategies for accountable care. No single vendor meets all criteria today, so a combination of solutions may be needed. The document emphasizes starting with internal data and processes before acquiring external data.
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. 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
1) Life expectancy has increased over 10 years in OECD countries since 1960 due to lower mortality rates. However, chronic diseases like diabetes are rising due to aging populations and lifestyle changes.
2) Health systems face challenges like obesity, lack of exercise among youth, and improving treatment for chronic conditions to reduce hospitalizations. Access to care issues also remain regarding cost and geographic barriers.
3) Health workforces have expanded in most OECD countries, but challenges with rural distribution of physicians and improving primary care for conditions like diabetes persist. Health spending varies widely between countries from 6-16% of GDP.
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.
HIMSS National Data Warehousing WebinarDale Sanders
BMJ and other sources
• Integrated into Cerner EMR
• Action sets, order sets, reference
• Chronic condition management
• Population health monitoring
• Local quality improvement
• Data analytics and reporting
• Continuous improvement
• Outcomes and process measures
• Cost and utilization measures
• Staff education and training
• Governance and oversight
• Continuous refinement
• Continuous expansion of content
• Continuous expansion of use
• Continuous expansion of benefits
• Continuous expansion of users
• Continuous expansion of evidence
• Continuous expansion of data
• Continuous expansion of analytics
• Continuous expansion of improvement
• Continuous
The document discusses the history and use of patient registries, including definitions, types of registries like product, health services, and disease registries, and provides an overview of Northwestern Medical Faculty Foundation's experience implementing Epic and building disease registries to translate quality measures into their electronic health record system. It also reviews NMFF's implementation of Epic over time and in different specialties across their large academic medical group.
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.
Choosing an Analytics Solution in HealthcareDale Sanders
This document provides guidance on evaluating and choosing an analytics solution for healthcare. It discusses general criteria for assessment, including completeness of vision, ability to execute, culture and values alignment, technology adaptability, total cost of ownership, and company viability. It also frames the analytic environment and needs in healthcare. Key factors are the evolving data ecosystem, analytic motives shifting from billing to quality and prevention, and lessons from EMR adoption. The best solutions will provide a closed-loop analytic experience with integrated knowledge systems, deployment processes, and analytic capabilities.
This document discusses late binding in data warehousing and its importance for analytic agility. Late binding means delaying the binding of data to rules and vocabularies for as long as possible. This allows data to be used flexibly for different analyses without being rigidly structured early on. It also discusses the progression of analytic sophistication in healthcare and how late binding is needed to support more advanced predictive and prescriptive analytics. Maintaining a record of changes to data bindings over time helps enable retracing of analytic steps. While early binding may be suitable when rules/vocabularies are stable, late binding is generally preferable to maximize flexibility and adaptability for analytics.
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.
The document discusses healthcare analytics and data management. It begins by outlining the typical evolution of data collection, sharing, and analysis that occurs in industries. It then discusses key principles for healthcare analytics including regularly evaluating goals, measures, and how to achieve them. The remainder of the document discusses challenges around data binding, governance, and adoption models for healthcare analytics. It emphasizes the importance of analytics for return on investment and outlines strategic options and considerations for healthcare organizations evaluating their analytic capabilities.
The document categorizes and lists vendors in the healthcare analytics market. It identifies categories such as healthcare enterprise data warehouse platforms, healthcare analytics as a service, domain specific healthcare analytics point solutions, EMR-centric analytics, cross-industry development platforms, cross-industry visualization and exploration tools, and big data/Hadoop development platforms. Numerous vendors are listed within each category as providing notable solutions, though the lists are not exhaustive.
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
Breaking All the Rules: What the Leading Health Systems Do Differently with A...Health Catalyst
Voluntarily or not, we are entering the Age of Analytics in healthcare. As the healthcare industry emerges from the deployment of EMR’s and health information exchanges, enterprise data warehouses represent the next significant opportunity in information technology.
However, the meaningful use of an enterprise data warehouse is much more difficult to achieve than the meaningful use of an EMR. There are scant few organizations in healthcare that have achieved excellence in the “meaningful use” of an enterprise data warehouse.
Fortunate to see both failings and successes, Dale Sanders has spent the last 18 years analyzing the characteristics of healthcare analytics and data warehousing leadership. Join him as he shares his observations and lessons to help you and your organization become one of the success stories.
Presentation Covers:
Why C-level involvement is important, but not a guarantee of success, and can sometimes be a hindrance
The pivotal characteristics of culture, strategy, and execution that are critical to data warehousing and analytics success
How to balance tactical analytic victories without sacrificing strategic adaptability and scalability
Finding the perfect data governance environment is an elusive target. It’s important to govern to the least extent necessary in order to achieve the greatest common good. With the three data governance cultures, authoritarian, tribal, and democratic, the latter is best for a balanced, productive governance strategy.
The Triple Aim of data governance is: 1) ensuring data quality, 2) building data literacy, and 3) maximizing data exploitation for the organization’s benefit. The overall strategy should be guided by these three principles under the guidance of the data governance committee.
Data governance committees need to be sponsored at the executive board and leadership level, with supporting roles defined for data stewards, data architects, database and systems administrators, and data analysts. Data governance committees need to avoid the most common failure modes: wandering, technical overkill, political infighting, and bureaucratic red tape.
Healthcare organizations that are undergoing analytics adoption will also go through six phases of data governance including: 1) establishing the tone for becoming a data-driven organization, 2) providing access to data, 3) establishing data stewards, 4) establishing a data quality program, 5) exploiting data for the benefit of the organization, 6) the strategic acquisition of data to benefit the organization.
As U.S. healthcare moves into its next stage of evolution, the organizations that will survive and thrive will be those who most effectively acquire, analyze, and utilize their data to its fullest extent. Such is the mission of data governance.
Outsourced vs. In-house Healthcare Analytics: Pros and ConsHealth Catalyst
Healthcare analytics are essential for organizations to thrive in the new healthcare environment. Using analytics, systems can evaluate efficiency, effectiveness, and find improvement opportunities. There are two principal approaches: outsourcing the analytics function to benchmarking companies and providers of software-as-a-service; and doing analytics in-house with a system’s own data warehouse. The pros of outsourcing include gaining benchmarking access to how health system peers are performing. The cons to outsourcing include focusing too much high-level outcomes with no insight in how to effect change. The pros of in-house analytics include having quick access to fine-grained details of the data and being able to include clinicians in the implementation and development of the analytics process. A con is that in-house analytics can require significant resources – an investment in the right personnel and right technology.
3 Phases of Healthcare Data Governance in AnalyticsHealth Catalyst
Healthcare data governance is a broad topic and covers more than data stewardship, storage, and technical roles and responsibilities. And it’s not easy to implement. It’s necessary, though, for health systems that are entering the world of analytics because the governance structure will enable the organizations to drive higher-quality, low cost care. In order for healthcare data governance to be most effective however, it needs to be adaptive because real healthcare data governance is much more fluid than any plan laid out on paper. Typically there are three phases that characterize successful analytics implementations: the early stage, the mid-term stage, and the steady state. As health systems begin to determine the effectiveness of their data governance strategy, it’s important to look at key metrics from their analytics implementations that will either trend up, remain solid, or trend down.
Why Your Healthcare Business Intelligence Strategy Can't WinHealth Catalyst
Business intelligence may hold tremendous promise but it can’t answer healthcare’s challenges unless it’s built on the solid foundation of a clinical data warehouse. Learn the definition of business intelligence, why a clinical data warehouse is needed for any healthcare BI strategy, the various options in data warehousing, which one is most effective for hospitals and the industry and why.
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.
7 Essential Practices for Data Governance in HealthcareHealth Catalyst
This document outlines 7 essential practices for data governance in healthcare. It discusses the growing value of healthcare data and importance of data governance. Effective data governance requires balancing broad vision with limited application and expanding only as needed. The key functions of data governance include enhancing data quality, increasing data content, encouraging data access, promoting data literacy, establishing standards for master reference data, prioritizing analytics, and managing master data. Maintaining high data quality, access, and literacy are crucial.
How to Evaluate a Clinical Analytics Vendor: A ChecklistHealth Catalyst
Based on 25 years of healthcare IT experience, Dale outlines a detailed set of criteria for evaluating clinical analytic vendors. These criteria include 1) completeness of vision, 2) culture and values of senior leadership, 3) ability to execute, 4) technology adaptability and supportability, 5) total cost of ownership, 6) company viability, and 7) nine elements of technical specificity including data modeling, master data management, metadata, white space data, visualization, security, ETL, performance and utilization metrics, hardware and software infrastructure.
This document discusses building champions for new ideas to enable innovation within organizations. It introduces Marla Hetzel from AARP Services and Jennifer Draklellis from United Healthcare, who are collaborating on an innovation initiative. Their collaboration leverages each organization's strengths and capabilities to develop new product and service concepts for AARP members. The document emphasizes that innovation requires investing in building leadership engagement and overcoming challenges like risk aversion. It provides tips for persuading others to support new ideas, such as telling human-centered stories and finding common goals between initiatives.
Think Tank V Key Takeaways & Best PracticesJustin Barnes
Care Strategy, Care Collaboration, Innovation, Industry Disruptors & Social Determinants of Health best practices directly and unscripted from thought leaders on the front lines of healthcare
Is That Data Valid? Getting Accurate Financial Data in HealthcareHealth Catalyst
A consolidated EDW is not a replacement or threat to the individual financial systems and reporting tools employed for general ledger, billing, payroll, or supply management. On the contrary, each of those systems is designed with sophisticated functionality that drives organizational efficiency. But alone, these systems realize only a portion of their true return on investment for the enterprise. As a consolidated data resource, these systems provide untold potential to address the underlying challenges to efficient, cost-effective health care.
Sutherland and International Institute for Analytics HIStalk Webinar - Charti...Sutherland Healthcare
The digital era is disrupting every industry and healthcare is no exception. Emerging technologies will introduce challenges and opportunities to transform operations and raise the bar of consumer experience. Success in this new era requires a new way of thinking, new skills, and new technologies to help your organization embrace digital health. This presentation demonstrates how to measure your organization's analytics maturity and design a strategy to digital transformation.
The three main objectives of this presentation are to show how to:
1) Leverage transformational design thinking methodologies to discover new opportunities, optimize existing operations, and improve experiences.
2) Measure and compare their organization's analytics maturity.
3) Develop a strategy for leveraging analytics and design thinking as a competitive differentiator
Healthcare Analytics: Right-Brain Advice in a Left-Brain WorldHealth Catalyst
U.S. healthcare is badly missing the soft, human side of healthcare analytics, especially as it impacts clinicians. How do we fix that? This webinar explores those ideas.
You won’t hear Dale talk about SQL, inner joins, outer joins, R, Python, logistic regression, random forest, or convolutional neural networks but instead, in this webinar he talks about the principles and philosophy of analytics.
For the most part, we’ve figured out the technology of analytics. That is all left-brain thinking—analytical, logical and methodical in nature—and it is literally getting easier every day with new data technology. But, in healthcare, we’re missing the right-brain thinking—creative and artistic in nature—that has almost nothing to do with technology but has everything to do with the human side of pursuing “data driven healthcare.”
Right-brain thinking is required for the oddities and shortcomings of healthcare data, and how to manage those shortcomings in the context of delivering data to the humans who we hope will consume it. The right-brain relates to the personality characteristics of the people who are leading your analytics strategy. It relates to the leadership culture of the organization and where that culture resides on a scale of transparency, internally and externally. The right-brain relates to behavioral economics, evolutionary psychology, human decision making theories, and the fundamental factors that motivate or demotivate human behavior. The right-brain relates to concepts like experimental design and PICO—patients, interventions, comparisons, and outcomes—that, if followed, can make your analytics more truthful and believable. It has to do with the way we negotiate and structure performance-based contracts that are loaded with quality metrics that either measure things that can’t be measured accurately or may measure the wrong thing, altogether.
You see, right-brained thinking in this left-brain world of analytics relates to a bunch of things, but mostly it relates to the Golden Rule of Data. Do unto others with data as you would have them do unto you.
These slides were used for a invited presentation @ Patheon Seminar – Bridgewater, NJ, 31 July 2014.
Some modification have been made to connect the dots for the audience who will review this slide-deck on the internet.
This presentation provides a very brief snap-shot of a day long training program conducted recently at a company in India. In preparing the day long training session I had asked the following questions; (1) How to effectively communicate to an audience of a group of young and bright Indian professionals in any company in India and their supervisors/management about the importance of cGMPs and QbD? (2) How do I understand their challenges, perspectives and biases? (3) How do I connect with them to share the joy of Quality by Design?
The response received has been overwhelming from the audiences in India and yesterday at the Patheon Seminar in Bridgewater, NJ. I hope you will also the see some of the important dots and the connections. How this content connects to regulatory requirements is not covered in this slide deck – it connects via ‘A, B, C, D’ to 21 CFR, Quality Systems Approach to cGMP, ICH 7, 8, 9, 10, and 11.
Innovative Talent Solutions to Drive Healthcare ResultsCielo
Pinstripe Healthcare provides talent solutions and recruitment process outsourcing (RPO) services to drive results for healthcare organizations. The document discusses Pinstripe's partnership models, solutions, and technology platform that provide a customized, end-to-end talent acquisition process. It also shares perspectives from healthcare CEOs on challenges around financial pressures, industry changes, and talent needs. Examples are given of Pinstripe partnerships with organizations like Cone Health and SSM Health Care that improved quality measures, satisfaction scores, and reduced costs.
AHRQ’s Health Care Innovations Exchange held a Web event on Promoting the Spread of Health Care Innovations on April 9, 2013. For more information, visit https://innovations.ahrq.gov/events/2013/04/promoting-spread-health-care-innovations.
Analytics and Small Hospitals: Embracing Data to Thrive in the New Era of Val...Health Catalyst
Value-based care has remade the healthcare landscape for small hospitals. Many are struggling to compete with the larger, better-funded medical centers in the communities they serve. Embracing data and analytics is no longer a luxury for these organizations if they are to succeed and remain competitive. Data analysis can assist senior leaders in identifying opportunities for improvement while balancing long-term goals with short-term pressures. Incorporating data in to the culture and making it a part of everyday decision making will enable smaller hospitals to not only survive, but thrive in the new era of value-based care.
Analytics and Small Hospitals: Embracing Data to Thrive in the New Era of Val...Health Catalyst
Value-based care has remade the healthcare landscape for small hospitals. Many are struggling to compete with the larger, better-funded medical centers in the communities they serve. Embracing data and analytics is no longer a luxury for these organizations if they are to succeed and remain competitive. Data analysis can assist senior leaders in identifying opportunities for improvement while balancing long-term goals with short-term pressures. Incorporating data in to the culture and making it a part of everyday decision making will enable smaller hospitals to not only survive, but thrive in the new era of value-based care.
Artificial Intelligence and Machine Learning in Healthcare: Four Real-World I...Health Catalyst
This document discusses how artificial intelligence (AI) and machine learning (ML) are improving healthcare outcomes in four key areas:
1) Augmenting leadership decisions by helping identify issues and make future-oriented decisions
2) Overcoming data security challenges by detecting potential privacy violations or attacks
3) Resolving uncompensated care costs by using propensity-to-pay tools to target unpaid accounts
4) Improving patient flow by reducing wait times and avoiding delays through predictive models
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?
This webinar is intended to be valuable to both technical and non-technical audiences, as we explore the convergence of Big Data technology and Healthcare’s Age of Analytics.
Similar to Break All The Rules: What the Leading Health Systems Do Differently with Analytics and Data Warehousing (20)
Fit to Fly PCR Covid Testing at our Clinic Near YouNX Healthcare
A Fit-to-Fly PCR Test is a crucial service for travelers needing to meet the entry requirements of various countries or airlines. This test involves a polymerase chain reaction (PCR) test for COVID-19, which is considered the gold standard for detecting active infections. At our travel clinic in Leeds, we offer fast and reliable Fit to Fly PCR testing, providing you with an official certificate verifying your negative COVID-19 status. Our process is designed for convenience and accuracy, with quick turnaround times to ensure you receive your results and certificate in time for your departure. Trust our professional and experienced medical team to help you travel safely and compliantly, giving you peace of mind for your journey.www.nxhealthcare.co.uk
Joker Wigs has been a one-stop-shop for hair products for over 26 years. We provide high-quality hair wigs, hair extensions, hair toppers, hair patch, and more for both men and women.
Health Tech Market Intelligence Prelim Questions -Gokul Rangarajan
The Ultimate Guide to Setting up Market Research in Health Tech part -1
How to effectively start market research in the health tech industry by defining objectives, crafting problem statements, selecting methods, identifying data collection sources, and setting clear timelines. This guide covers all the preliminary steps needed to lay a strong foundation for your research.
This lays foundation of scoping research project what are the
Before embarking on a research project, especially one aimed at scoping and defining parameters like the one described for health tech IT, several crucial considerations should be addressed. Here’s a comprehensive guide covering key aspects to ensure a well-structured and successful research initiative:
1. Define Research Objectives and Scope
Clear Objectives: Define specific goals such as understanding market needs, identifying new opportunities, assessing risks, or refining pricing strategies.
Scope Definition: Clearly outline the boundaries of the research in terms of geographical focus, target demographics (e.g., age, socio-economic status), and industry sectors (e.g., healthcare IT).
3. Review Existing Literature and Resources
Literature Review: Conduct a thorough review of existing research, market reports, and relevant literature to build foundational knowledge.
Gap Analysis: Identify gaps in existing knowledge or areas where further exploration is needed.
4. Select Research Methodology and Tools
Methodological Approach: Choose appropriate research methods such as surveys, interviews, focus groups, or data analytics.
Tools and Resources: Select tools like Google Forms for surveys, analytics platforms (e.g., SimilarWeb, Statista), and expert consultations.
5. Ethical Considerations and Compliance
Ethical Approval: Ensure compliance with ethical guidelines for research involving human subjects.
Data Privacy: Implement measures to protect participant confidentiality and adhere to data protection regulations (e.g., GDPR, HIPAA).
6. Budget and Resource Allocation
Resource Planning: Allocate resources including time, budget, and personnel required for each phase of the research.
Contingency Planning: Anticipate and plan for unforeseen challenges or adjustments to the research plan.
7. Develop Research Instruments
Survey Design: Create well-structured surveys using tools like Google Forms to gather quantitative data.
Interview and Focus Group Guides: Prepare detailed scripts and discussion points for qualitative data collection.
8. Sampling Strategy
Sampling Design: Define the sampling frame, size, and method (e.g., random sampling, stratified sampling) to ensure representation of target demographics.
Participant Recruitment: Plan recruitment strategies to reach and engage the intended participant groups effectively.
9. Data Collection and Analysis Plan
Data Collection: Implement methods for data gathering, ensuring consistency and validity.
Analysis Techniques: Decide on analytical approaches (e.g., statistical
English Drug and Alcohol Commissioners June 2024.pptxMatSouthwell1
Presentation made by Mat Southwell to the Harm Reduction Working Group of the English Drug and Alcohol Commissioners. Discuss stimulants, OAMT, NSP coverage and community-led approach to DCRs. Focussing on active drug user perspectives and interests
India Medical Devices Market: Size, Share, and In-Depth Competitive Analysis ...Kumar Satyam
According to TechSci Research report, “India Medical Devices Market Industry Size, Share, Trends, Competition, Opportunity and Forecast, 2019-2029,” the India Medical Devices Market was valued at USD 15.35 billion in 2023 and is anticipated to witness impressive growth in the forecast period, with a Compound Annual Growth Rate (CAGR) of 5.35% through 2029. This growth is driven by various factors, including strategic collaborations and partnerships among leading companies, a growing population, and the increasing demand for advanced healthcare solutions.
Recent Trends
Strategic Collaborations and Partnerships
One of the most significant trends driving the India Medical Devices Market is the increasing number of collaborations and partnerships among leading companies. These alliances aim to merge the expertise of individual companies to strengthen their market position and enhance their product offerings. For instance, partnerships between local manufacturers and international companies bring advanced technologies and manufacturing techniques to the Indian market, fostering innovation and improving product quality.
Browse over XX market data Figures and spread through XX Pages and an in-depth TOC on " India Medical Devices Market.” - https://www.techsciresearch.com/report/india-medical-devices-market/8161.html
Test bank clinical nursing skills a concept based approach 4e pearson educati...rightmanforbloodline
Test bank clinical nursing skills a concept based approach 4e pearson education
Test bank clinical nursing skills a concept based approach 4e pearson education
Test bank clinical nursing skills a concept based approach 4e pearson education
The facial nerve, also known as cranial nerve VII, is one of the 12 cranial nerves originating from the brain. It's a mixed nerve, meaning it contains both sensory and motor fibres, and it plays a crucial role in controlling various facial muscles, as well as conveying sensory information from the taste buds on the anterior two-thirds of the tongue.
Ensure the highest quality care for your patients with Cardiac Registry Support's cancer registry services. We support accreditation efforts and quality improvement initiatives, allowing you to benchmark performance and demonstrate adherence to best practices. Confidence starts with data. Partner with Cardiac Registry Support. For more details visit https://cardiacregistrysupport.com/cancer-registry-services/
India Home Healthcare Market: Driving Forces and Disruptive Trends [2029]Kumar Satyam
According to the TechSci Research report titled "India Home Healthcare Market - By Region, Competition, Forecast and Opportunities, 2029," the India home healthcare market is anticipated to grow at an impressive rate during the forecast period. This growth can be attributed to several factors, including the rising demand for managing health issues such as chronic diseases, post-operative care, elderly care, palliative care, and mental health. The growing preference for personalized healthcare among people is also a significant driver. Additionally, rapid advancements in science and technology, increasing healthcare costs, changes in food laws affecting label and product claims, a burgeoning aging population, and a rising interest in attaining wellness through diet are expected to escalate the growth of the India home healthcare market in the coming years.
Browse over XX market data Figures spread through 70 Pages and an in-depth TOC on "India Home Healthcare Market”
https://www.techsciresearch.com/report/india-home-healthcare-market/15508.html
This particular slides consist of- what is Pneumothorax,what are it's causes and it's effect on body, risk factors, symptoms,complications, diagnosis and role of physiotherapy in it.
This slide is very helpful for physiotherapy students and also for other medical and healthcare students.
Here is a summary of Pneumothorax:
Pneumothorax, also known as a collapsed lung, is a condition that occurs when air leaks into the space between the lung and chest wall. This air buildup puts pressure on the lung, preventing it from expanding fully when you breathe. A pneumothorax can cause a complete or partial collapse of the lung.
The story of Dr. Ranjit Jagtap's daughters is more than a tale of inherited responsibility; it's a narrative of passion, innovation, and unwavering commitment to a cause greater than oneself. In Poulami and Aditi Jagtap, we see the beautiful continuum of a father's dream and the limitless potential of compassion-driven healthcare.
As Mumbai's premier kidney transplant and donation center, L H Hiranandani Hospital Powai is not just a medical facility; it's a beacon of hope where cutting-edge science meets compassionate care, transforming lives and redefining the standards of kidney health in India.
Solution manual for managerial accounting 18th edition by ray garrison eric n...rightmanforbloodline
Solution manual for managerial accounting 18th edition by ray garrison eric noreen and peter brewer_compressed
Solution manual for managerial accounting 18th edition by ray garrison eric noreen and peter brewer_compressed
24. Small Business Checklist
1. Have you clearly documented your products and services AND the customers that
want/need them?
2. Do you have product line managers and teams?
3. Do you track who uses which of your products and how often?
4. Do you have a strategic product roadmap?
5. Do you have a web site that makes it easy…
‒ For new customers to find you, understand your products, and consume them?
‒ For existing customers to get the most out of your products and encourage those customers to collaborate?
6. Do you have a team member that leads and executes your marketing strategy to
attract new clients?
7. Are you operating with financial efficiency?
8. Do you regularly poll your customers for their satisfaction and suggestions for product
enhancements?
9. Do you regularly poll your team members to track their satisfaction?
10. Do you have a Board of Directors (i.e., Data Governance Committee)?
24
28. Leaders Are
Covering The
Continuum
Of Analytic
Use Cases
28 Robert Wood Johnson Foundation, 2014
Requires a collaborative
strategy between leaders
in healthcare, politics,
charity, education, and
business
Risk Takers
Consistently Principle-Driven
Data Trumps Ego & Anecdote
Lead Past Competing Interests
Mastery, Autonomy, Purpose
29. They Invest In Analytics
• This is not an “additional duty”
• EMRs required investments in technology and
people… so do enterprise data warehouses
(EDW). But, luckily, a small fraction of an EMR.
29
Risk Takers
Consistently Principle-Driven
Data Trumps Ego & Anecdote
Lead Past Competing Interests
Mastery, Autonomy, Purpose
32. Aligning Strategy: Data to Monitor Variation
Range
85% - 95%
Range
94% - 98%
The Joint Commission Index Across Hospitals:
Demonstrated Progress in Reducing Variation
Thank you, Lisa
Shilling, Kaiser
Permanente
42. Geisinger
Managing gaps in care
Lipid Panel, HgbA1c
Lab Result
Imaging Result
Clinical Goal BP <140/90, LDL <100
Aspirin for CAD
42
42
Care Gap Examples
Advance Directives
Depression and Asthma
Active Medication
Scanned Document
Patient Reported Data
Mammogram, DXA
Nephrology for CKD Stage 4
Referral
Diagnosis Coding HCCs
Patient Education Heart Failure
Asthma Action Plan
Clinical Documentation
Thank you, Dr. Fred Bloom
43. Geisinger
Redesign and Care Coordination Delivers Rapid Impact
Thank you, Dr. Fred Bloom
43
43
44. Care Gap Programs
Thank you, Dr. Fred Bloom
AAA Screening Malignant Melanoma
Adolescent Well Visits—
Birthday Model
Medical Weight
Management
Adult Preventive Care—
Birthday Model
Osteoporosis Program
Colonoscopy Screening
Program—Birthday Model
Peds Transition to Primary
Care (after age 18)
CKD Stage 4 Sleep Medicine Outreach
(BMI 35-39)
Dexa Scans Women’s Health: Annual
GYN Exams—Birthday
Model
Emergency Department
Transition to Primary Care
Provider
Influenza, Pertussis &
Pneumococcal
44
45. Proven Health Navigator
Results for Medicare
Thank you, Dr. Fred Bloom
45 (Am J Manag Care. 2010;16(8):607-614)
46. PHN Return On Investment
Thank you, Dr. Fred Bloom
46
47. Three-Year Results in 25,000
DM Patients
Thank you, Dr. Fred Bloom
47
305 MIs
Prevented
NNT to prevent
one (1) MI
=
82 patients
140 Strokes
Prevented
NNT to prevent
one (1) Stroke
=
170 patients
166 Cases of
Retinopathy
Prevented
NNT to prevent one
(1) case of
Retinopathy
=
152 patients
48. Kaiser’s Quality Goals Timeline – 2011 – 2013
Domain 2011 2012 2013
Population Health
Self perceived health status data
for 15% of members
Self perceived health status data
for 20% of members
Self perceived health status data
for 25% of members
Population Care
Management - Chronic
Conditions
Medicare Stars Part C 4 Stars
HEDIS composite at 90th percentile
All CV, diabetes, and cancer screening
metrics at 90th percentile
Behaviorial Health, Musculoskeletal 90%
Medication Management 75%
Medicare Stars Part C 4 Stars
All CV, diabetes, and cancer screening
metrics at 90th percentile
Behaviorial Health, Musculoskeletal and
respiratory @ 90%
Medication Management 75%
Medicare Stars Part C 4 Stars
All CV, diabetes, and cancer screening
metrics at 90th percentile
Behaviorial Health, Musculoskeletal and
respiratory @ 90%
Medication Management 90%
Inpatient
HSMR
TJC Composite
Reduce HSMR: Below US Medicare
average, crude mortality 10% from 2010
baseline
TJC Composite at national 90th percentile
Readmit rate<15% of all cause
readmissions
Reduce HSMR: Below US Medicare
TBD - May shift to inpatient outcomes
Readmit rate<10% of all cause
readmissions
TJC Composite at national 90th
percentile
Reduce HSMR: Below US Medicare
TJC Composite at national 90th percentile
Patient Safety Never
Events
10% less events than 2010 10% less events than 2011 10% less events than 2012
Workplace Safety Per regional targets Per regional targets Per regional targets
Clinical Risk
Management
0 to 5% reduction in lawsuits
with a payout from 2010
0 to 5% reduction in lawsuits
with a payout from 2011
0 to 5% reduction in lawsuits
with a payout from 2012
Service
Hospital
Outpatient
HealthPlan
Medicare Stars
At National 75th percentile (final
quarter)
75th percentile in local or national
in 3 of 8 regions
75th percentile in local or national
in 6 of 8 regions
4 Stars on Overall CAHPS
At national 75th percentile (rolling
12 months)
75th percentile in local or national
in 5 of 8 regions
75th percentile in local or national 7
of 8 regions
4+ Stars on Overall CAHPS
Above National 75th percentile (rolling
12 months)
8 of 8 Regions at goal
8 of 8 regions at goal
4+ Stars on Overall CAHPS
Equitable Care
Identify interventions to
reduce the gap
Decrease the gap by x%
Decrease by x% more over
2012
Thank you, Lisa
Shilling, Kaiser
Permanente
50. Intermountain’s Clinical Excellence Board Goals
1. Behavioral Health Clinical Program: Decrease inpatient psychiatric 30-day readmission rate for the Intermountain system.
2. Cardiovascular Clinical Program: Integrate the treatment of heart failure patients across the continuum to improve care and reduce
hospital readmissions.
3. Intermountain Homecare: Improve care transitions to and from Homecare through effective communication, collaboration, and
coordination among care providers.
4. Intensive Medicine Clinical Program: Decrease mortality in patients diagnosed during their hospital stay with severe sepsis.
5. Oncology Clinical Program: Improve the appropriate utilization of genetic screening to determine if families are at higher risk for
colon and endometrial cancer.
6. Patient Safety: Reduce the system rate of catheter-associated urinary tract infections.
7. Pediatric Specialties Clinical Program: Build a care model for children with type 1 diabetes.
8. Primary Care Clinical Program: Establish an individualized approach to diabetic care by engaging patients in self-management,
primary care visits, and specialty consultations.
9. SelectHealth: Increase the percentage of SelectHealth members with diabetes who meet four measures of diabetes care: blood sugar
control, cholesterol control, kidney function, and eye exam.
10. Surgical Services: A three-part goal that reduces blood utilization, defines clinical outcome measures for specific development teams,
and develops and implements a standardized process to decrease intracase supply utilization. Intracase utilization means the
processes or units of care used within each health episode.
11. Women and Newborns: Improve care, cost efficiency, and resource utilization in the neonatal intensive care unit (NICU), and
accurately estimate the number of babies with early onset bacterial infection.
12. Primary Children’s Hospital: Increase the involvement of infectious disease specialists in decisions to use outpatient antibiotic
therapy via infusion, injection, or implantation.
13. Rural Facilities: Implement electronic physician orders to guide evidence-based care for patients with the primary diagnosis of
pneumonia, labor induction, pancreatitis, and sepsis.
14. CMS Value-Based Purchasing: The Hospital Value-Based Purchasing (VBP) Program is a Centers for Medicare & Medicaid Services
(CMS) initiative that rewards hospitals with incentive payments for the quality of care they provide to people covered by Medicare. The
focuses for Intermountain’s goal in this area are to:
● Attain a significant improvement in the value-based purchasing process and outcome domains for select measures.
● Sustain progress for those hospitals that already meet or exceed national benchmarks.
Thank you, Int5e0rmountain web site