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.
This group paper, written as a graduate student at CMU, attempts to define and summarize the huge challenge ahead of North American healthcare providers by illuminating current and future trends of healthcare business intelligence (BI); ramifications of EMR; the pros and cons of BI and analytics; the myriad ethical and privacy issues of big data’s role (normally associated with market share and profits); and lastly provide an industry overview of BI and analytics solutions specific to healthcare.
To view the 30+ page paper for which this presentation summarizes, please contact James Young via LinkedIn: https://www.linkedin.com/in/jamesyoung007
Preparing for the Coming Change: An Overview of the Healthcare Analytics MarketHealth Catalyst
Jim Adams, Executive Director, The Advisory Board, discusses the two market forces in particular, population health management and the retail revolution, that are driving the need for new applications of analytics and business intelligence (BI).
Attendees will learn:
The role of analytics in population health and the growing retail market
The key challenges provider organizations are facing in developing analytics capabilities
The pros and cons of the core strategies providers are utilizing to develop analytics capabilities and the vendors that map to those strategies
Bring your most pressing healthcare problems and spend an hour listening to one of the most seasoned industry analysts talking through the top forces shifting the landscape of the healthcare market in 2015.
We hope you'll come away with some insight and refined thinking about solutions that will drive your work forward. Please do join us.
4 Best Practices for Analyzing Healthcare DataHealth Catalyst
Meaningful healthcare analytics today generally need data from multiple source systems to help address the triple aim cost, quality, and patient satisfaction. Once appropriate data has been captured, pulled into a single place, and tied together, then data analysis can begin. In this article I share 4 ways to enable your analyst including providing them with
1) a data warehouse
2) a sandbox
3) a set of discovery tools
4) the right kind of direction.
Clinical Data Repository vs. A Data Warehouse - Which Do You Need?Health Catalyst
It can be confusing to know whether or not your health system needs to add a data warehouse unless you understand how it’s different from a clinical data repository. A clinical data repository consolidates data from various clinical sources, such as an EMR, to provide a clinical view of patients. A data warehouse, in comparison, provides a single source of truth for all types of data pulled in from the many source systems across the enterprise. The data warehouse also has these benefits: a faster time to value, flexible architecture to make easy adjustments, reduction in waste and inefficiencies, reduced errors, standardized reports, decreased wait times for reports, data governance and security.
Healthcare Visualizations: Are You Getting the Entire StoryHealth Catalyst
The emergence of powerful and user-friendly healthcare data visualization programs has transformed analytical reporting. The amount of information conveyed by all types of graphs, symbols, sizes, and colors is staggering. The ability to “drill down” in real-time with increasing levels of granularity enables all manner of analyses. The downside of this data hunger is the creation of simplified, context-free visualizations which may inadvertently lead to misinterpretations, most often in the form of a false positive (believing a change has occurred that really hasn’t). This often leads to knee-jerk reactions to correct the “change” and unnecessary actions being taken that waste time, effort, and money. Avoiding the most common pitfalls will ensure your organization has the most complete picture to drive meaningful change.
This group paper, written as a graduate student at CMU, attempts to define and summarize the huge challenge ahead of North American healthcare providers by illuminating current and future trends of healthcare business intelligence (BI); ramifications of EMR; the pros and cons of BI and analytics; the myriad ethical and privacy issues of big data’s role (normally associated with market share and profits); and lastly provide an industry overview of BI and analytics solutions specific to healthcare.
To view the 30+ page paper for which this presentation summarizes, please contact James Young via LinkedIn: https://www.linkedin.com/in/jamesyoung007
Preparing for the Coming Change: An Overview of the Healthcare Analytics MarketHealth Catalyst
Jim Adams, Executive Director, The Advisory Board, discusses the two market forces in particular, population health management and the retail revolution, that are driving the need for new applications of analytics and business intelligence (BI).
Attendees will learn:
The role of analytics in population health and the growing retail market
The key challenges provider organizations are facing in developing analytics capabilities
The pros and cons of the core strategies providers are utilizing to develop analytics capabilities and the vendors that map to those strategies
Bring your most pressing healthcare problems and spend an hour listening to one of the most seasoned industry analysts talking through the top forces shifting the landscape of the healthcare market in 2015.
We hope you'll come away with some insight and refined thinking about solutions that will drive your work forward. Please do join us.
4 Best Practices for Analyzing Healthcare DataHealth Catalyst
Meaningful healthcare analytics today generally need data from multiple source systems to help address the triple aim cost, quality, and patient satisfaction. Once appropriate data has been captured, pulled into a single place, and tied together, then data analysis can begin. In this article I share 4 ways to enable your analyst including providing them with
1) a data warehouse
2) a sandbox
3) a set of discovery tools
4) the right kind of direction.
Clinical Data Repository vs. A Data Warehouse - Which Do You Need?Health Catalyst
It can be confusing to know whether or not your health system needs to add a data warehouse unless you understand how it’s different from a clinical data repository. A clinical data repository consolidates data from various clinical sources, such as an EMR, to provide a clinical view of patients. A data warehouse, in comparison, provides a single source of truth for all types of data pulled in from the many source systems across the enterprise. The data warehouse also has these benefits: a faster time to value, flexible architecture to make easy adjustments, reduction in waste and inefficiencies, reduced errors, standardized reports, decreased wait times for reports, data governance and security.
Healthcare Visualizations: Are You Getting the Entire StoryHealth Catalyst
The emergence of powerful and user-friendly healthcare data visualization programs has transformed analytical reporting. The amount of information conveyed by all types of graphs, symbols, sizes, and colors is staggering. The ability to “drill down” in real-time with increasing levels of granularity enables all manner of analyses. The downside of this data hunger is the creation of simplified, context-free visualizations which may inadvertently lead to misinterpretations, most often in the form of a false positive (believing a change has occurred that really hasn’t). This often leads to knee-jerk reactions to correct the “change” and unnecessary actions being taken that waste time, effort, and money. Avoiding the most common pitfalls will ensure your organization has the most complete picture to drive meaningful change.
6 Essential Data Analyst Skills for Your Healthcare OrganizationHealth Catalyst
Healthcare organizations are turning to the enterprise data warehouse (EDW) as the foundation of their analytics strategy. But simply implementing an EDW doesn’t guarantee an organization’s success. One obstacle organizations come up against is that their analytics team members don’t have the right skills to maximize the effectiveness of the EDW. The following six skills are essential for analytics team members: structured query language (SQL); the ability to perform export, transform, and load (ETL) processes; data modeling; data analysis; business intelligence (BI) reporting; and the ability to tell a story with data.
Healthcare Total Cost of Care Analysis: A Vital ToolHealth Catalyst
How can healthcare organizations set themselves up for success as the industry shifts from fee-for-service to value-based reimbursement? They need to understand risk of their patients and population to identify ways to reduce healthcare costs and improve quality of care. This makes total cost of care (TCOC) analysis a necessary skillset in this time of transition.
TCOC analysis leverages key elements of the healthcare analytics infrastructure to understand how money is being spent at the organization and identify the drivers of high cost:
An integrated EDW.
Payer reporting tools.
Claims and membership data.
Predictive capabilities.
Risk scores.
Scorecards and dashboards.
Analyst support.
In this webinar, Dale Sanders will provide a pragmatic, step-by-step, and measurable roadmap for the adoption of analytics in healthcare-- a roadmap that organizations can use to plot their strategy and evaluate vendors; and that vendors can use to develop their products. Attendees will have a chance to learn about:
1) The details of his eight-level model, 2) A brief introduction to the HIMSS/IIA DELTA Model, 3) The importance of permanent organizational teams to sustain improvements from analytic investments, 4) The process of curating and maturing data governance, and 5) The coordination of a data acquisition strategy with payment and reimbursement strategies
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.
Reducing Unwanted Variation in Healthcare Clears the Way for Outcomes Improve...Health Catalyst
According to statistician W. Edwards Deming, “Uncontrolled variation is the enemy of quality.” The statement is particularly true of outcomes improvement in healthcare, where variation threatens quality across processes and outcomes. To improve outcomes, health systems must recognize where and how inconsistency impacts their outcomes and reduce unwanted variation.
There are three key steps to reducing unwanted variation:
Remove obstacles to success on a communitywide level.
Maintain open lines of communication and share lessons learned.
Decrease the magnitude of variation.
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.
Health Catalyst Overview: A Platform Approach For Transforming HealthcareHealth Catalyst
Please join us as Jared Crapo, Vice President, covers important basics including who Health Catalyst is, what we provide and how we deliver our products.
We’ll still make it education-oriented as we just aren’t a pushy, salesy company. We’ll orient around the basics of who we are and what we do.
Jared will provide an easy-to-understand discussion regarding the key analytic principles of adaptive data architecture.
Some specific items he will cover are:
The industry challenges that warranted the creation of Health Catalyst.
The use of Health Catalyst’s data analysis tools and applications that enable organizations to quickly uncover care improvement and cost reduction opportunities.
Implementation best practices including how the Health Catalyst Platform is delivered, installed, and typical implementation schedules.
Attendees will understand who in your organization needs to be involved and the secrets to success and pitfalls to avoid.
The discussion will include the key analytic principles of an adaptive data architecture including data aggregation, normalization, security, and governance. He will also address the basic requirements for implementation of the measurement platform of a data warehouse, such as team creation, roles, and reporting.
Finally, Jared will demonstrate several of the key tools necessary to move the analytics strategy forward including applications used to organize patient populations, others used to monitor and measure care results and still others that are specific to advanced areas of care.
We hope you’ll join us.
Business Intelligence & Analytics solutions enable healthcare service providers to build sustainable competitive advantage with the help of insights derived from their existing operations and patient data.
Preparing for the Future: How one ACO is Using Analytics to Drive Clinical & ...Health Catalyst
Crystal Run Healthcare — a physician-led Accountable Care Organization (ACO) and one of the first ACOs to participate in the Medicare Shared Savings Program — is experiencing the long-anticipated shift toward more value-based reimbursement.
To ensure financial stability as they assume more risk, Crystal Run is implementing a strategy focused on rapid growth and aligning physician reimbursement with favorable patient outcomes. To effectively execute on this strategy they knew they needed to become more data-driven. Webinar attendees will learn how this ACO is using advanced analytics to execute on their population management and growth strategies with a focus on continuous improvement in the following areas:
Ensuring patient care aligns with evidence based practices
Reducing inappropriate clinical variation
Enhancing operational efficiency
Analyzing data from a “single source of truth” integrated from their EMR, billing, costing, patient satisfaction and other operational systems
Making “self-service analytics” available to decision-makers to decrease time to decision
Please join Greg Spencer, MD, Chief Medical & Chief Medical Information Officer and Scott Hines, MD, Chief Quality Officer and Medical Specialties Medical Director, Crystal Run, as they discuss how advanced analytics is helping position the ACO for continued success in an increasingly value-based reimbursement environment.
Platforms and Partnerships: The Building Blocks for Digital InnovationHealth Catalyst
Virtually all service-oriented industries have experienced massive disruption and transformation, resulting from the confluence of digital, mobile, cloud, data, and consumerization. And then there’s healthcare…
In this webinar Ryan Smith, executive advisor at Health Catalyst, shares practical insights gained from his combined 25 years of IT and digital leadership roles at Banner Health and Intermountain Healthcare. He explores why our industry is struggling to provide the tools and self-service experiences that patients and consumers have come to expect in every other aspect of their lives. To attract and retain patients and members, healthcare organizations need to “shift gears” and go on the digital offensive to sustain brand loyalty; however, decades of siloed, monolithic approaches to implementing technology and managing data continue to hamper industry progress.
During this session, Ryan shares his approach for building business support to enable digital transformation.
By viewing this webinar, you will learn key digitization concepts:
- How to conceptualize a digital enablement framework.
- Ten strategic guiding principles for technology leaders.
- Why it’s vital to create business-driven technology governance.
- Why building strategic vendor partnerships really matters.
- How to apply case studies to bolster digital investments.
Exploring How to Use Hadoop in your Healthcare Big Data StrategyHealth Catalyst
Big Data, Big Data, Big Data – everybody is talking about it, but what is it, why are people talking about it, and how is it being done? Come ready to talk about emerging healthcare big data use cases that are pleading for the help of practical and powerful technologies like Spark, Hive, and others. If applied appropriately, these technologies can rev up your data warehouse and help you to address evolving data-driven healthcare needs around unstructured data, real-time data feeds, and machine learning.
Sean Stohl, SVP in Product Development at Health Catalyst, will give you a practical understanding of where to get started with these technologies. Sean will also give you a glimpse how he thinks these technologies will evolve over time in this technically focused webinar.
Attendees will be able to explain:
What Big Data and Hadoop are
Why Big Data and Hadoop are needed in healthcare
What the challenges to adoption are
How to get started
Attendees will also get to see Big Data in action. We look forward to you joining us.
3 Perspectives to Better Apply Predictive & Prescriptive Models in HealthcareHealth Catalyst
In healthcare we tend to think of predictive or prescriptive model building and deployment as technical challenges. We do not put enough emphasis on the importance of change management. This disorientation leads to uneven adoption and results. In this webinar Jason Jones discusses and demonstrates three perspectives, accompanied by tools, to help you drive action and deliver better outcomes.
We develop predictive and prescriptive models in healthcare to improve Quadruple Aim outcomes—population health, patient experience, reduced cost, and positive provider work life. Successful adoption of predictive and prescriptive models heavily depends upon behavior change. This requires more than technical accuracy. While prediction algorithms abound, tools to facilitate change management remain scarce. During this webinar, we will discuss how to achieve model understanding using three perspectives: functional, contextual, and operational.
View the webinar to learn:
- Why a predictive or prescriptive model endeavor is more a change management challenge than a technical one
- How to apply three types of model understanding to a use case in your own organization
In this webinar, Jason Jones, PhD, Chief Data Scientist at Health Catalyst discusses and provides examples of our work using three perspectives of understanding to help clinical and operational leaders achieve value from predictive and prescriptive models. Investing time and effort to ensure model understanding is necessary for broad scale adoption.
Healthcare Business Intelligence for Power UsersPerficient, Inc.
The Healthcare industry is accustomed to volumes of clinical and administrative data. Business intelligence helps convert these large amounts of data into actionable insights to reduce costs, streamline processes, and improve healthcare delivery. Our first webinar, “An Introduction to Business Intelligence for Healthcare,” introduces business intelligence in healthcare and common concepts.
In the second of this series of two webinars, Health BI Practice Manager, Mike Jenkins addresses:
- The BI Maturity Level
- Examples of Levels 3 and 4
- Attaining Level 5
The Four Balancing Acts Involved with Healthcare Data Security FrameworksHealth Catalyst
There’s a lot at stake for healthcare organizations when it comes to securing data. A primary concern is to protect privacy and avoid costly breaches or leaks, but at the same time, data must be accessible if it’s to be used for actionable insights. This executive report introduces four balancing acts that organizations must maintain to build an ideal data security framework:
Monitoring
Data de-identification
Cloud environments
User access
This can be a tug-of-war between IT and security, two groups that often have divergent interests, however well-meaning they may be. Healthcare systems that build bridges between these interests and strike the crucial balance between data utilization and security can dial in on long-term goals, like better care at a lower cost and overall outcomes improvement.
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
Database vs Data Warehouse: A Comparative ReviewHealth Catalyst
What are the differences between a database and a data warehouse? A database is any collection of data organized for storage, accessibility, and retrieval. A data warehouse is a type of database the integrates copies of transaction data from disparate source systems and provisions them for analytical use. The important distinction is that data warehouses are designed to handle analytics required for improving quality and costs in the new healthcare environment. A transactional database, like an EHR, doesn’t lend itself to analytics.
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.
6 Essential Data Analyst Skills for Your Healthcare OrganizationHealth Catalyst
Healthcare organizations are turning to the enterprise data warehouse (EDW) as the foundation of their analytics strategy. But simply implementing an EDW doesn’t guarantee an organization’s success. One obstacle organizations come up against is that their analytics team members don’t have the right skills to maximize the effectiveness of the EDW. The following six skills are essential for analytics team members: structured query language (SQL); the ability to perform export, transform, and load (ETL) processes; data modeling; data analysis; business intelligence (BI) reporting; and the ability to tell a story with data.
Healthcare Total Cost of Care Analysis: A Vital ToolHealth Catalyst
How can healthcare organizations set themselves up for success as the industry shifts from fee-for-service to value-based reimbursement? They need to understand risk of their patients and population to identify ways to reduce healthcare costs and improve quality of care. This makes total cost of care (TCOC) analysis a necessary skillset in this time of transition.
TCOC analysis leverages key elements of the healthcare analytics infrastructure to understand how money is being spent at the organization and identify the drivers of high cost:
An integrated EDW.
Payer reporting tools.
Claims and membership data.
Predictive capabilities.
Risk scores.
Scorecards and dashboards.
Analyst support.
In this webinar, Dale Sanders will provide a pragmatic, step-by-step, and measurable roadmap for the adoption of analytics in healthcare-- a roadmap that organizations can use to plot their strategy and evaluate vendors; and that vendors can use to develop their products. Attendees will have a chance to learn about:
1) The details of his eight-level model, 2) A brief introduction to the HIMSS/IIA DELTA Model, 3) The importance of permanent organizational teams to sustain improvements from analytic investments, 4) The process of curating and maturing data governance, and 5) The coordination of a data acquisition strategy with payment and reimbursement strategies
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.
Reducing Unwanted Variation in Healthcare Clears the Way for Outcomes Improve...Health Catalyst
According to statistician W. Edwards Deming, “Uncontrolled variation is the enemy of quality.” The statement is particularly true of outcomes improvement in healthcare, where variation threatens quality across processes and outcomes. To improve outcomes, health systems must recognize where and how inconsistency impacts their outcomes and reduce unwanted variation.
There are three key steps to reducing unwanted variation:
Remove obstacles to success on a communitywide level.
Maintain open lines of communication and share lessons learned.
Decrease the magnitude of variation.
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.
Health Catalyst Overview: A Platform Approach For Transforming HealthcareHealth Catalyst
Please join us as Jared Crapo, Vice President, covers important basics including who Health Catalyst is, what we provide and how we deliver our products.
We’ll still make it education-oriented as we just aren’t a pushy, salesy company. We’ll orient around the basics of who we are and what we do.
Jared will provide an easy-to-understand discussion regarding the key analytic principles of adaptive data architecture.
Some specific items he will cover are:
The industry challenges that warranted the creation of Health Catalyst.
The use of Health Catalyst’s data analysis tools and applications that enable organizations to quickly uncover care improvement and cost reduction opportunities.
Implementation best practices including how the Health Catalyst Platform is delivered, installed, and typical implementation schedules.
Attendees will understand who in your organization needs to be involved and the secrets to success and pitfalls to avoid.
The discussion will include the key analytic principles of an adaptive data architecture including data aggregation, normalization, security, and governance. He will also address the basic requirements for implementation of the measurement platform of a data warehouse, such as team creation, roles, and reporting.
Finally, Jared will demonstrate several of the key tools necessary to move the analytics strategy forward including applications used to organize patient populations, others used to monitor and measure care results and still others that are specific to advanced areas of care.
We hope you’ll join us.
Business Intelligence & Analytics solutions enable healthcare service providers to build sustainable competitive advantage with the help of insights derived from their existing operations and patient data.
Preparing for the Future: How one ACO is Using Analytics to Drive Clinical & ...Health Catalyst
Crystal Run Healthcare — a physician-led Accountable Care Organization (ACO) and one of the first ACOs to participate in the Medicare Shared Savings Program — is experiencing the long-anticipated shift toward more value-based reimbursement.
To ensure financial stability as they assume more risk, Crystal Run is implementing a strategy focused on rapid growth and aligning physician reimbursement with favorable patient outcomes. To effectively execute on this strategy they knew they needed to become more data-driven. Webinar attendees will learn how this ACO is using advanced analytics to execute on their population management and growth strategies with a focus on continuous improvement in the following areas:
Ensuring patient care aligns with evidence based practices
Reducing inappropriate clinical variation
Enhancing operational efficiency
Analyzing data from a “single source of truth” integrated from their EMR, billing, costing, patient satisfaction and other operational systems
Making “self-service analytics” available to decision-makers to decrease time to decision
Please join Greg Spencer, MD, Chief Medical & Chief Medical Information Officer and Scott Hines, MD, Chief Quality Officer and Medical Specialties Medical Director, Crystal Run, as they discuss how advanced analytics is helping position the ACO for continued success in an increasingly value-based reimbursement environment.
Platforms and Partnerships: The Building Blocks for Digital InnovationHealth Catalyst
Virtually all service-oriented industries have experienced massive disruption and transformation, resulting from the confluence of digital, mobile, cloud, data, and consumerization. And then there’s healthcare…
In this webinar Ryan Smith, executive advisor at Health Catalyst, shares practical insights gained from his combined 25 years of IT and digital leadership roles at Banner Health and Intermountain Healthcare. He explores why our industry is struggling to provide the tools and self-service experiences that patients and consumers have come to expect in every other aspect of their lives. To attract and retain patients and members, healthcare organizations need to “shift gears” and go on the digital offensive to sustain brand loyalty; however, decades of siloed, monolithic approaches to implementing technology and managing data continue to hamper industry progress.
During this session, Ryan shares his approach for building business support to enable digital transformation.
By viewing this webinar, you will learn key digitization concepts:
- How to conceptualize a digital enablement framework.
- Ten strategic guiding principles for technology leaders.
- Why it’s vital to create business-driven technology governance.
- Why building strategic vendor partnerships really matters.
- How to apply case studies to bolster digital investments.
Exploring How to Use Hadoop in your Healthcare Big Data StrategyHealth Catalyst
Big Data, Big Data, Big Data – everybody is talking about it, but what is it, why are people talking about it, and how is it being done? Come ready to talk about emerging healthcare big data use cases that are pleading for the help of practical and powerful technologies like Spark, Hive, and others. If applied appropriately, these technologies can rev up your data warehouse and help you to address evolving data-driven healthcare needs around unstructured data, real-time data feeds, and machine learning.
Sean Stohl, SVP in Product Development at Health Catalyst, will give you a practical understanding of where to get started with these technologies. Sean will also give you a glimpse how he thinks these technologies will evolve over time in this technically focused webinar.
Attendees will be able to explain:
What Big Data and Hadoop are
Why Big Data and Hadoop are needed in healthcare
What the challenges to adoption are
How to get started
Attendees will also get to see Big Data in action. We look forward to you joining us.
3 Perspectives to Better Apply Predictive & Prescriptive Models in HealthcareHealth Catalyst
In healthcare we tend to think of predictive or prescriptive model building and deployment as technical challenges. We do not put enough emphasis on the importance of change management. This disorientation leads to uneven adoption and results. In this webinar Jason Jones discusses and demonstrates three perspectives, accompanied by tools, to help you drive action and deliver better outcomes.
We develop predictive and prescriptive models in healthcare to improve Quadruple Aim outcomes—population health, patient experience, reduced cost, and positive provider work life. Successful adoption of predictive and prescriptive models heavily depends upon behavior change. This requires more than technical accuracy. While prediction algorithms abound, tools to facilitate change management remain scarce. During this webinar, we will discuss how to achieve model understanding using three perspectives: functional, contextual, and operational.
View the webinar to learn:
- Why a predictive or prescriptive model endeavor is more a change management challenge than a technical one
- How to apply three types of model understanding to a use case in your own organization
In this webinar, Jason Jones, PhD, Chief Data Scientist at Health Catalyst discusses and provides examples of our work using three perspectives of understanding to help clinical and operational leaders achieve value from predictive and prescriptive models. Investing time and effort to ensure model understanding is necessary for broad scale adoption.
Healthcare Business Intelligence for Power UsersPerficient, Inc.
The Healthcare industry is accustomed to volumes of clinical and administrative data. Business intelligence helps convert these large amounts of data into actionable insights to reduce costs, streamline processes, and improve healthcare delivery. Our first webinar, “An Introduction to Business Intelligence for Healthcare,” introduces business intelligence in healthcare and common concepts.
In the second of this series of two webinars, Health BI Practice Manager, Mike Jenkins addresses:
- The BI Maturity Level
- Examples of Levels 3 and 4
- Attaining Level 5
The Four Balancing Acts Involved with Healthcare Data Security FrameworksHealth Catalyst
There’s a lot at stake for healthcare organizations when it comes to securing data. A primary concern is to protect privacy and avoid costly breaches or leaks, but at the same time, data must be accessible if it’s to be used for actionable insights. This executive report introduces four balancing acts that organizations must maintain to build an ideal data security framework:
Monitoring
Data de-identification
Cloud environments
User access
This can be a tug-of-war between IT and security, two groups that often have divergent interests, however well-meaning they may be. Healthcare systems that build bridges between these interests and strike the crucial balance between data utilization and security can dial in on long-term goals, like better care at a lower cost and overall outcomes improvement.
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
Database vs Data Warehouse: A Comparative ReviewHealth Catalyst
What are the differences between a database and a data warehouse? A database is any collection of data organized for storage, accessibility, and retrieval. A data warehouse is a type of database the integrates copies of transaction data from disparate source systems and provisions them for analytical use. The important distinction is that data warehouses are designed to handle analytics required for improving quality and costs in the new healthcare environment. A transactional database, like an EHR, doesn’t lend itself to analytics.
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.
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.
What Is the ROI of Investing in a Healthcare Data AnalystHealth Catalyst
Making the most of a healthcare data analyst’s knowledge is a key component to getting the best ROI from a hospital improvement project. But all too often, analysts serve merely as data validators — they justify the data that leadership wants validated. Because analysts aren’t decision makers, they don’t have the authority to ask the questions that can save a health system millions. Empowering analysts, however, enables them to ask the right questions — and find the right answers — that will lead to significant savings.
5 Reasons Why Healthcare Data is Unique and Difficult to MeasureHealth Catalyst
Healthcare data is not linear. It is a complex, diverse beast unlike the data of any other industry. There are five ways in particular that make healthcare data unique:
1. Much of the data is in multiple places.
2. The data is structured and unstructured.
3. It has inconsistent and variable definitions; evidence-based practice and new research is coming out every day. 4. The data is complex.
5. Changing regulatory requirements.
The answer for this unpredictability and complexity is the agility of a late-binding Data Warehouse.
What is the best Healthcare Data Warehouse Model for Your Organization?Health Catalyst
Join Steve Barlow as he addresses the strengths and weaknesses of each of the following three primary Data Model approaches for data warehousing in healthcare:
1. Enterprise Data Model
2. Independent Data Marts
3. Late-binding Solutions
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
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.
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.
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
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.
These slides were presentet at Munich Meetup of April 18th. They present the reco4j project, its high view and it vision.
See the project site for more details here: http://www.reco4j.org
The social graph of Facebook is the most popular application for a graph database. In addition, there are far more exciting applications, such as spatial data, financial trail, indexing, and others. If you combine different graphs, you are able to evaluate those together with the algorithms known from the graph theory. As a graph, a domain can often be easier and more natural designed. This talk introduces the topic of graph databases and shows how to implement mediated models with large, complex and highly connected data with Neo4j. Subsequently, topics like querying, indexing, import / export are considered as well.
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?
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.
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.
Targeted Analytics: Using Core Measures to Jump-Start Enterprise AnalyticsPerficient, Inc.
How top healthcare organizations are realizing the benefits of data analytics in such core areas as core measures, clinical alerting, surgical analytics, service line profitability, diabetes management, revenue cycle management, claims management and utilization.
Purchasing Metrics: One Size Does Not Fit AllBill Kohnen
A top reason for failure of Purchasing Managers, especially joining an organization from outside, is misunderstanding actual expectations and setting wrong metrics. What metrics you use needs to be based on what are the perceptions and expectations of purchasing in your organization. Updated from a presentation at PASIA Supply Chain Conference Manila PI 2011. Includes how to determine measures and operational benchmark methodology.
Learn how insurance organizations can leverage FastTrack Analytics to improve financial, underwriting, agent & customer service operations. Use your data to gain competitive advantage in challenging economic times.
Customized Consulting and Decision Support for Pharma and Biotech ExecutivesCutting Edge Information
Cutting Edge Information Capabilities presentation - Consulting firm providing decision support and implementation to companies in pharmaceutical, biotechnology and other life science sectors.
Gain valuable insight into how to leverage complex dimensional analytics for improved clinical, operational & financial outcomes across your surgical operations, including: best practices for implementing a robust surgical analytics platform and approach, success stories of leading healthcare organizations that are using analytics effectively in various settings, how analytics informs and enables strategic decision-making in challenging economic times, and how implementing an analytics solution can be affordable, fast & customizable to your business.
The decision of selecting the right analytics service provider can’t be made lightly as the consequences can easily make or break a critical initiative, or career.
In this paper, you will learn how to choose the right analytics partner by defining:
1.The complexity of your business problem(s)
2.The nature of the desired solution
3.How you are currently addressing the issue
4.The depth and level of service desired
Similar to Strategic Options for Analytics in Healthcare (20)
The Philosophy, Psychology, and Technology of Data in HealthcareDale Sanders
Over-application of data and analytics in healthcare is alienating clinicians and, for the most part, not bending the cost-quality curves. This lecture spends 60% of the time on the softer issues, 40% on the technology.
Healthcare Analytics Summit Keynote Fall 2017Dale Sanders
The Data Operating System. Changing the Digital Trajectory of Healthcare. Why do we need to change the current digital trajectory? What’s the business case for a Data Operating System? What is a Data Operating System and how did we get here? What difference will DOS make? What should we do with it and what should we expect?
Why should we care about integrating data? What should we be trying to achieve? Population Health. The Softer, Human Side of Being “Data Driven” not “Driven By Data." The New Era of Decision Support in Healthcare. Top 10 Challenges To Integrating External Data.
The Data Operating System: Changing the Digital Trajectory of HealthcareDale Sanders
This is the next evolution in health information exchanges and data warehouses, specifically designed to support analytics, transaction processing, and third party application development, in one platform, the Data Operating System.
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.
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.
Data Driven Clinical Quality and Decision SupportDale Sanders
From a lecture about the use of data warehousing, analytics, and point of care clinical decision support to improve the quality and reduce the cost of healthcare.
1. Strategic Analytic Options in Healthcare
Category
Pros & Cons
Example Vendors
Buy & Build from an
Analytics Platform
Vendor
Highest degree of analytic flexibility and adaptability
Requires a data driven culture with high aspirations that views analytics as a clear
business differentiator
Best suited for a culture with a higher degree of data literacy and data management skills
Slow initial time-to-value plagues some vendors
Inconsistent ROI track record, but when ROI occurs, it’s big
Caradigm Intelligence Platform
Buy from an Analytics
Service Provider
Best suited for cultures that want to avoid the details of analytics and data management,
but aspire to improve basic internal and external reporting
Inter-organizational benchmarking and comparative analytics is a natural part of the
business model and service
Limited analytic flexibility and adaptability
Substantive ROI is not well-documented nor widely acknowledged
Explorys
Lumeris
Optum
Premier Alliance
Truven Analytics Suite
Buy “Best of Breed”
Point Solutions
Leverages expertise and very specific analytics applications in business and clinical areas
that are not always available in other options
Does not facilitate data integration; i.e., does not provide a single analytic perspective on
patient care and costs
Costly and complicated to maintain
AltaSoft
Crimson Suite
EPSI
MedeAnalytics
Medventive
Midas
Omincell
Buy from your EMR
Vendor
Offers the possibility of “closed loop analytics” driving analytics back to the point of care,
in the EMR and clinical workflow
No proven track record with analytics to date from the EMR vendors
Tend to be very focused on analytics that are specific to the EMR vendor’s data
Less flexible and adaptable to new sources of data and analytic use cases, especially
complex ones
Allscripts Sunrise
Cerner PowerInsight
Epic Clarity & Cogito
McKesson Horizon
Meditech Data Repository
Siemens Decision Support
Dale Sanders, Creative Commons Copyright, 2013
1
Health Catalyst
Healthcare Data Works
IBM Healthcare Data Model
Oracle Healthcare Data Model
Recombinant (Deloitte)