Unlike few can do, Dr. David Burton has simplified these complex topics into a simple construct of four population health management building blocks. By acquiring proficiency in each of these four dimensions, healthcare delivery systems can create an asset which can be marketed to various types of governmental and commercial payers, which sponsor health benefit plans and offer shared accountability contracts (i.e. accountable care) into which these population health management sponsors can enter.
The key learning points of the webinar include:
The four building blocks of population health management (provider network, population(s), quality/safety outcomes, and cost outcomes)
The central role patient registries play in success in population health management
Pragmatic tools and methodologies to help healthcare delivery systems become proficient in each of the four dimensions of the framework
A discussion of the categories of governmental and commercial sponsors of shared accountability solutions, including the potential impact of the shift from defined benefit to defined contribution health benefit programs
Looking Back on Clinical Decision Support and Data WarehousingHealth Catalyst
Dale will take a slide deck previously prepared in 2006, from a lecture entitled, "The Power of an Enterprise Data Warehouse in Clinical Decision Support", presented to several informatics masters classes at Northwestern University and the University of Victoria. He won’t change anything about the slide deck, including the content and the old school graphics. The concept with this webinar is to give a “time capsule” perspective on past thinking and contrast that against current thoughts and trends in the market. Some of the information will be laughably wrong and naive, and some of the information will still be relevant. The hope is, by regularly reviewing our past, we will better inform our future.
Powering Medical Research With Data: The Research Analytics Adoption ModelHealth Catalyst
Analytics are becoming imperative to researchers in recruiting patients into studies, making breakthrough discoveries, as well as monitoring the clinical implementation of these discoveries. This webinar will be for organizations that want to leverage their enterprise data to power more effective research.
Join Eric Just, Vice President of Technology at Health Catalyst, as he presents a Research Analytics Adoption Model that outlines ways that a research organization can leverage data and analytics to achieve greater speed and ROI on research.The Adoption Model walks through analytics competencies starting with basic data usage and culminating with using analytics to incorporate the latest research discoveries into clinical practice.
Content presented and discussed:
A summary of some of the challenges in using data and analytics for research
A research analytics adoption framework for all organizations interested in using clinical data for research
What is needed from a workflow and organizational perspective to power research with data
We hope you enjoy.
Late-Binding Data Warehouse - An Update on the Fastest Growing Trend in Healt...Health Catalyst
This document discusses the concept of late binding in data warehousing and analytics. It defines late binding as binding data elements, rules, and vocabularies together as late as practical for analytic purposes. This allows for more flexibility and agility to address new use cases compared to early binding. The document also presents an eight level model of analytic adoption in healthcare and discusses how data governance and analytic complexity increase with each level of maturity. Late binding is presented as an important design principle to enable progression through the levels of analytic maturity.
Predictive Analytics: It's The Intervention That MattersHealth Catalyst
In this two-part webinar, get the detailed knowledge you need to make informed decisions about adopting predictive analytics in healthcare so you can separate today's hype from reality. In part 1, you'll learn key learnings from Dale Sanders including 1) our fixation on predictive analytics in readmissions, 2) the common trap of predictions without interventions, 3) the common misconceptions of correlations verses causation, 4) examples of predictions without algorithms, and 5) the importance of putting the basics first.
In part 2, you'll hear from industry expert David Crockett, PhD in a "graduate level" crash course cover key concepts such as machine learning, algorithms, feature selection, classification, tools and more.
Best Practices in Implementing Population Health Health Catalyst
To manage population health, one needs to intimately understand the anatomy of healthcare and model how healthcare is delivered, in order to systematically improve healthcare outcomes. In this webinar, Dr. Burton draws on his 26-year executive career at Intermountain, Select Health, and Health Catalyst. He emphasizes the importance of linking administrative data (e.g., billing codes) to processes of clinical care to use the 80/20 principle to prioritize care processes within each venue to focus improvement initiatives on the things that matter most. He will also discuss a Clinical Integration framework to use in driving out waste by reducing variation in the ordering of care, the efficiency with which the care that is ordered is delivered and reducing defects in care delivery to make it safer.
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.
4 Essential Lessons for Adopting Predictive Analytics in HealthcareHealth Catalyst
Predictive analytics is quite a popular current topic. Unfortunately, there are many potential side tracks or pit falls for those that do not approach this carefully. Fortunately for healthcare, there are numerous existing models from other industries that are very efficient at risk stratification in the realm of population management. David Crocket, PhD shares 4 key pitfalls to avoid for those beginning predictive analytics. These include
1) confusing data with insight
2) confusing insight with value
3) overestimating the ability to interpret the data
4) underestimating the challenge of implementation.
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.
Looking Back on Clinical Decision Support and Data WarehousingHealth Catalyst
Dale will take a slide deck previously prepared in 2006, from a lecture entitled, "The Power of an Enterprise Data Warehouse in Clinical Decision Support", presented to several informatics masters classes at Northwestern University and the University of Victoria. He won’t change anything about the slide deck, including the content and the old school graphics. The concept with this webinar is to give a “time capsule” perspective on past thinking and contrast that against current thoughts and trends in the market. Some of the information will be laughably wrong and naive, and some of the information will still be relevant. The hope is, by regularly reviewing our past, we will better inform our future.
Powering Medical Research With Data: The Research Analytics Adoption ModelHealth Catalyst
Analytics are becoming imperative to researchers in recruiting patients into studies, making breakthrough discoveries, as well as monitoring the clinical implementation of these discoveries. This webinar will be for organizations that want to leverage their enterprise data to power more effective research.
Join Eric Just, Vice President of Technology at Health Catalyst, as he presents a Research Analytics Adoption Model that outlines ways that a research organization can leverage data and analytics to achieve greater speed and ROI on research.The Adoption Model walks through analytics competencies starting with basic data usage and culminating with using analytics to incorporate the latest research discoveries into clinical practice.
Content presented and discussed:
A summary of some of the challenges in using data and analytics for research
A research analytics adoption framework for all organizations interested in using clinical data for research
What is needed from a workflow and organizational perspective to power research with data
We hope you enjoy.
Late-Binding Data Warehouse - An Update on the Fastest Growing Trend in Healt...Health Catalyst
This document discusses the concept of late binding in data warehousing and analytics. It defines late binding as binding data elements, rules, and vocabularies together as late as practical for analytic purposes. This allows for more flexibility and agility to address new use cases compared to early binding. The document also presents an eight level model of analytic adoption in healthcare and discusses how data governance and analytic complexity increase with each level of maturity. Late binding is presented as an important design principle to enable progression through the levels of analytic maturity.
Predictive Analytics: It's The Intervention That MattersHealth Catalyst
In this two-part webinar, get the detailed knowledge you need to make informed decisions about adopting predictive analytics in healthcare so you can separate today's hype from reality. In part 1, you'll learn key learnings from Dale Sanders including 1) our fixation on predictive analytics in readmissions, 2) the common trap of predictions without interventions, 3) the common misconceptions of correlations verses causation, 4) examples of predictions without algorithms, and 5) the importance of putting the basics first.
In part 2, you'll hear from industry expert David Crockett, PhD in a "graduate level" crash course cover key concepts such as machine learning, algorithms, feature selection, classification, tools and more.
Best Practices in Implementing Population Health Health Catalyst
To manage population health, one needs to intimately understand the anatomy of healthcare and model how healthcare is delivered, in order to systematically improve healthcare outcomes. In this webinar, Dr. Burton draws on his 26-year executive career at Intermountain, Select Health, and Health Catalyst. He emphasizes the importance of linking administrative data (e.g., billing codes) to processes of clinical care to use the 80/20 principle to prioritize care processes within each venue to focus improvement initiatives on the things that matter most. He will also discuss a Clinical Integration framework to use in driving out waste by reducing variation in the ordering of care, the efficiency with which the care that is ordered is delivered and reducing defects in care delivery to make it safer.
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.
4 Essential Lessons for Adopting Predictive Analytics in HealthcareHealth Catalyst
Predictive analytics is quite a popular current topic. Unfortunately, there are many potential side tracks or pit falls for those that do not approach this carefully. Fortunately for healthcare, there are numerous existing models from other industries that are very efficient at risk stratification in the realm of population management. David Crocket, PhD shares 4 key pitfalls to avoid for those beginning predictive analytics. These include
1) confusing data with insight
2) confusing insight with value
3) overestimating the ability to interpret the data
4) underestimating the challenge of implementation.
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.
Turn Research Into Care Delivery Improvements Using the Research Analytics Ad...Health Catalyst
Research is a complex yet vital component of improving care delivery, and it can be hindered by a variety of organizational and technical roadblocks:
Insufficient tools and processes
Poor infrastructure
No single source of truth for data
Health systems can overcome these common research roadblocks and turn analytics-powered research into care delivery improvements by using the Research Analytics Adoption model as a strategic roadmap.
The model consists of 8 levels designed to align operations and research priorities:
De-identified tools and data marts
Delivery of customized data sets
EDW-facilitated study recruitment
Centralized, research-specific data collection
Automated research operations reporting
Biobank/genomic data integration
Multi-site data sharing
Translational Analytics
Patient Flight Path Analytics: From Airline Operations to Healthcare OutcomesHealth Catalyst
We developed a predictive analytics framework for patient care based upon concepts from airline operations. Using the idea of an aircraft turnaround time where the airline wants to put the aircraft back into operation as soon as possible, we’ve created a way to help patients headed toward poor outcomes, along with their providers, “turnaround” and get the best possible, most cost-effective outcome. For example, in a diabetes patient, we might use variables such as: age, alcohol use, annual eye/foot exam, BMI, etc. to look for patterns that might influence two outcomes: 1) Diabetic control and 2) The absence of progression toward diabetic complications. The notion of our Patient Flight Path is useful at both the conceptual level, as well as the predictive algorithm implementation level.
How to Use Data to Improve Patient Safety: Part 2Health Catalyst
Stan and Valere will discuss how using an automated trigger tool for all-cause harm reviews will provide timely, real-time patient safety data useful to drive down harm rates with earlier interventions. Additional benefits of this approach include having a more accurate and robust source of data for identifying harm trends to then be able to integrate the findings into existing quality improvement processes for further quality improvement efforts.
Attendees will learn how to:
Understand the importance of dedicating resources to impact downstream costs
Identify their key sources of Patient Safety data
Integrate Patient Safety data in to existing Quality Improvement Processes
Learn and improve from real-time safety analytics combined with a Culture of Safety
Becoming the Change Agent Your Healthcare System NeedsHealth Catalyst
I’ve met many clinical and operational leaders across the U.S. and seen how many have become progressively cynical and disengaged when faced with important healthcare reform issues like cost cutting and tight budgets. These clinicians would agree that equally important are quality and safety issues. However, most don’t have the tools available to actually measure that quality or patient outcomes. When clinicians do have access to the ability to measure, and the work together, I’ve seen enormous energy arise as they ask questions they really care about: What is quality? What do we measure? How do we achieve the best outcome?
Leveraging Healthcare Analytics to Reduce Heart Failure Readmission Rates Health Catalyst
Heart failure patients are adding an enormous strain to the US healthcare system. In addition, readmission rates for these diseases are adding to the burden. Healthcare analytics can play a key role. By following these 4 steps, all of which include data analytics, health systems can begin to reduce readmission rates: 1) Understand your true admission rates. 2) Establish reliable baseline measures. 3) Be aware of balance measures. 4) Establish an EDW.
Why Process Measures Are Often More Important Than Outcome Measures in Health...Health Catalyst
The healthcare industry is currently obsessed with outcome measures — and for good reason. But tracking outcome measures alone is insufficient to reach the goals of better quality and reduced costs. Instead, health systems must get more granular with their data by tracking process measures. Process measures make it possible to identify the root cause of a health system’s failures. They’re the checklists of systematically guaranteeing that the right care will be delivered to every patient, every time. By using these checklists, organizations will be able to improve quality and cost by reducing the amount of variation in care delivery.
Improving Patient Safety and Quality Through Culture, Clinical Analytics, Evi...Health Catalyst
According to the Centers of Disease Control (CDC), an estimated 70,000 patients die each year from hospital-associated infections (HAIs): contrast the CDC statistic with the fact that only 35,000 people die each year in the U.S. from motor vehicle accidents. Learn key best practices in patient safety and quality including: patient safety as a team sport, the added challenges of healthcare being the most complex, adaptive system, and how culture, analytics, and content contribute to improve outcomes and lower costs.
How To Avoid The 3 Most Common Healthcare Analytics Pitfalls And Related Inef...Health Catalyst
The document discusses three common pitfalls in healthcare analytics: 1) using point solutions that focus on single goals and data slices, 2) relying solely on electronic health record systems, and 3) having independent data marts in different databases. It recommends using an enterprise data warehouse to aggregate data from multiple sources into a single system of truth. The document also describes two common sources of inefficiency: the report factory approach and flavor-of-the-month approach, and recommends a robust deployment system to address these.
The Power of Geo Analytics (and maps) to Improve Predictive Analytics in Heal...Health Catalyst
As far back as the 1840s, clinicians have been using maps to inform them about population health trends. Today, the geo-analytics industry is well-developed in almost every application, with the exception of healthcare and medicine. There is potential to use mapping technologies to show patient disease burden in geographic form, map locations of health care facilities, and a plethora of accountable care population health initiatives would benefit from geo-analysis. Health Catalyst is working to integrate inputs into analysis like maps that can show geographic care boundaries, population health demographics, and more.
Healthcare Interoperability: New Tactics and TechnologyHealth Catalyst
Every provider agrees on the need for healthcare interoperability to achieve clinical data insights at the point of care. The question is how to get there from the myriad technologies and the volumes of data that comprise electronic medical records. It’s been difficult to organize among participants that have had little incentive to cooperate. And standards for sending and receiving data have been slow to develop. This is changing, but the key components that are still vital to realizing insights are closed-loop analytics and its accompanying tools, an enterprise data warehouse and analytics applications. This article defines the problems and explores the solutions to optimizing clinical decision making where it’s needed most.
The 3 Must-Have Qualities of a Care Management SystemHealth Catalyst
Care management systems are defined in many ways, but the only effective system comprises three qualities:
1.) It’s comprehensive and includes a suite of tools to address all five core competencies of care management.
2.) It’s inclusive of all EMRs and other data sources to enable thorough communication and analysis.
3.) It’s analytics-driven design facilitates clinical decision making and workflow.
Ultimately, an effective system improves outcomes and becomes an indispensable tool for managing population health.
This article describes what drives successful care management, and reveals a suite of applications that aid care team members and patients through advanced algorithms and embedded analytics. Learn how technology is helping to develop appropriate interventions and improve clinical and financial outcomes.
Use Well-Crafted Aim Statements To Achieve Clinical Quality ImprovementsHealth Catalyst
Too often, hospitals and health systems stop at developing broad clinical quality improvement statements that come up short of achieving their desired goals. What’s missing are clearly defined improvement objectives in the form of aim statements that take into account the effects on other areas of the organization: patient safety and satisfaction, physician engagement, and financial contribution. Aim statements help articulate the problems that add value for patients and the organization, but good data, and the analytics tools required to understand the data, are essential to illuminating high-value problem areas. Additionally, aim statements must stick to the SMART guidelines: Specific, Measureable, Achievable, Relevant, and Time-bound.
Disease Surveillance Monitoring and Reacting to Outbreaks (like Ebola) with a...Health Catalyst
The current options for monitoring data to help identify disease outbreaks like Ebola are not great. These are: 1) Monitoring chief complaint/reason for admission data in ADT data streams. Although this is a real-time approach, the data is not codified and would require some degree of NLP. 2) Monitoring coded data collected in EHRs. The most precise option available, but the data is not available until after the patient encounter is closed, which would be too late in most cases. And 3) Monitoring billing data. This approach has the same problems as the two listed above, but it’s better than nothing in the absence of an EMR. All of these weaknesses can be solved with the use of a data warehouse.
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.
The Analytic System: Finding Patterns in the DataHealth Catalyst
Dr. Haughom set the stage for this upcoming discussion in his previous webinar, explaining the key components of an effective analytical system that enables self-exploration and learning. In this session Attendees will learn:
How the distinction between random variation and assignable cause variation is critically important to patient care
Creation and application of Statistical Process Control (SPC) charts to:
Monitor process variation over time
Differentiate between assignable cause and random cause variation
Assess effectiveness of change on a given process
Achieve and maintain process stability
How implementing inlier management and creating a collaborative environment will drive continuous improvement
How to identify patterns in data using a live demonstration of advanced analytical tools.
How to Improve Clinical Programs by Breaking the Cycle of Waste in HealthcareHealth Catalyst
To succeed with value-based care, health systems must demonstrate to CMS they operate more effectively, efficiently, and safely. This requires organizations to identify and improve three types of waste commonly found in clinical programs: ordering waste, workflow and operational variations waste, and defect waste. Finding these areas, however, requires three critical solutions: an EDW, a KPA Application, and organizational readiness assessments.
The Changing Role of Healthcare Data AnalystsHealth Catalyst
The healthcare industry is undergoing a sea change, and healthcare data analysts will play a central role in this transformation. This report explores how the evolution to value-based care is changing the role of healthcare data analysts, how data analysts’ skills can best be applied to achieve value-based objectives and, finally, how Health Catalyst’s most successful health system clients are making this cultural transformation happen in the real world.
Population Health Management, Predictive Analytics, Big Data and Text AnalyticsFrank Wang
HCAD 6635 Health Information Analytics session 12
Population Health Management Analytics
Predictive Analytics
Big Data and its potential applications in Healthcare
Text Analytics
Public Health Analytics
Outcomes improvement: what you get when you mix good data with physician enga...Health Catalyst
The prescription for improving healthcare outcomes is pretty straightforward: improve quality by working with good data that’s based on patient perceptions of quality, as well as functional health outcomes. Then make that data accessible and actionable among your physicians and give them the leeway they need to reduce variation and, ultimately, improve outcomes. As simple as this may seem, it’s been complicated by an inefficient data infrastructure with non-standardized components (EHRs) and the inability to distribute analyses and visualizations where they are needed most (at the point of care). Dale Sanders explains these issues in detail and outlines solutions in this article published in the April 2015 edition of BMJ Outcomes.
What can healthcare executives learn from military decision-making, as it relates to predictiveanalytics in healthcare? As it turns out, quite a lot. Dale Sanders, senior vice president for strategy at Salt Lake City, Utah-based Health Catalyst, drew some surprising parallels between these two vital sectors of the economy during a concluding session at the Plante Moran Healthcare Executive Summit on June 5 in Chicago. His main theme was to remember that in predictive analytic analytics, it's the intervention that matters, noting that much of the industry is seduced by flashy predictive analytics "objects" without thinking through the needed interventions which are needed to get the proper ROI.
To lower health costs, physician networks and medical homes must employ a closed loop population management program that focus on patient SOH stratification, chronic disease management, care coordination and incentive management. This approach will enable them to consistently reduce ER and inpatient admissions, which are the greatest expenditures in health care today.
The Path to Shared Savings With Population Health Management ApplicationsHealth Catalyst
Eric Just, Vice President of Technology and Kathleen Merkley, Clinical Engagement Executive and Vice President at Health Catalyst, will demonstrate live several advanced applications built on a Late-Binding Catalyst data warehouse. Attendees will better understand how to:
Identify variability in care
Define accurate populations
Report on key health indicators across the continuum of care
Apply flexible models for risk stratification
Measure detailed process metrics spanning transitions of care for HF patients
Next generation health systems and Accountable Care Organizations will be paid based on an evolving model that rewards healthcare providers through ‘shared savings.’ Those savings must be achieved through systematic cost reductions while still improving quality of care. For most, this dual focus will prove to be the most critical and difficult part of realizing success.
Turn Research Into Care Delivery Improvements Using the Research Analytics Ad...Health Catalyst
Research is a complex yet vital component of improving care delivery, and it can be hindered by a variety of organizational and technical roadblocks:
Insufficient tools and processes
Poor infrastructure
No single source of truth for data
Health systems can overcome these common research roadblocks and turn analytics-powered research into care delivery improvements by using the Research Analytics Adoption model as a strategic roadmap.
The model consists of 8 levels designed to align operations and research priorities:
De-identified tools and data marts
Delivery of customized data sets
EDW-facilitated study recruitment
Centralized, research-specific data collection
Automated research operations reporting
Biobank/genomic data integration
Multi-site data sharing
Translational Analytics
Patient Flight Path Analytics: From Airline Operations to Healthcare OutcomesHealth Catalyst
We developed a predictive analytics framework for patient care based upon concepts from airline operations. Using the idea of an aircraft turnaround time where the airline wants to put the aircraft back into operation as soon as possible, we’ve created a way to help patients headed toward poor outcomes, along with their providers, “turnaround” and get the best possible, most cost-effective outcome. For example, in a diabetes patient, we might use variables such as: age, alcohol use, annual eye/foot exam, BMI, etc. to look for patterns that might influence two outcomes: 1) Diabetic control and 2) The absence of progression toward diabetic complications. The notion of our Patient Flight Path is useful at both the conceptual level, as well as the predictive algorithm implementation level.
How to Use Data to Improve Patient Safety: Part 2Health Catalyst
Stan and Valere will discuss how using an automated trigger tool for all-cause harm reviews will provide timely, real-time patient safety data useful to drive down harm rates with earlier interventions. Additional benefits of this approach include having a more accurate and robust source of data for identifying harm trends to then be able to integrate the findings into existing quality improvement processes for further quality improvement efforts.
Attendees will learn how to:
Understand the importance of dedicating resources to impact downstream costs
Identify their key sources of Patient Safety data
Integrate Patient Safety data in to existing Quality Improvement Processes
Learn and improve from real-time safety analytics combined with a Culture of Safety
Becoming the Change Agent Your Healthcare System NeedsHealth Catalyst
I’ve met many clinical and operational leaders across the U.S. and seen how many have become progressively cynical and disengaged when faced with important healthcare reform issues like cost cutting and tight budgets. These clinicians would agree that equally important are quality and safety issues. However, most don’t have the tools available to actually measure that quality or patient outcomes. When clinicians do have access to the ability to measure, and the work together, I’ve seen enormous energy arise as they ask questions they really care about: What is quality? What do we measure? How do we achieve the best outcome?
Leveraging Healthcare Analytics to Reduce Heart Failure Readmission Rates Health Catalyst
Heart failure patients are adding an enormous strain to the US healthcare system. In addition, readmission rates for these diseases are adding to the burden. Healthcare analytics can play a key role. By following these 4 steps, all of which include data analytics, health systems can begin to reduce readmission rates: 1) Understand your true admission rates. 2) Establish reliable baseline measures. 3) Be aware of balance measures. 4) Establish an EDW.
Why Process Measures Are Often More Important Than Outcome Measures in Health...Health Catalyst
The healthcare industry is currently obsessed with outcome measures — and for good reason. But tracking outcome measures alone is insufficient to reach the goals of better quality and reduced costs. Instead, health systems must get more granular with their data by tracking process measures. Process measures make it possible to identify the root cause of a health system’s failures. They’re the checklists of systematically guaranteeing that the right care will be delivered to every patient, every time. By using these checklists, organizations will be able to improve quality and cost by reducing the amount of variation in care delivery.
Improving Patient Safety and Quality Through Culture, Clinical Analytics, Evi...Health Catalyst
According to the Centers of Disease Control (CDC), an estimated 70,000 patients die each year from hospital-associated infections (HAIs): contrast the CDC statistic with the fact that only 35,000 people die each year in the U.S. from motor vehicle accidents. Learn key best practices in patient safety and quality including: patient safety as a team sport, the added challenges of healthcare being the most complex, adaptive system, and how culture, analytics, and content contribute to improve outcomes and lower costs.
How To Avoid The 3 Most Common Healthcare Analytics Pitfalls And Related Inef...Health Catalyst
The document discusses three common pitfalls in healthcare analytics: 1) using point solutions that focus on single goals and data slices, 2) relying solely on electronic health record systems, and 3) having independent data marts in different databases. It recommends using an enterprise data warehouse to aggregate data from multiple sources into a single system of truth. The document also describes two common sources of inefficiency: the report factory approach and flavor-of-the-month approach, and recommends a robust deployment system to address these.
The Power of Geo Analytics (and maps) to Improve Predictive Analytics in Heal...Health Catalyst
As far back as the 1840s, clinicians have been using maps to inform them about population health trends. Today, the geo-analytics industry is well-developed in almost every application, with the exception of healthcare and medicine. There is potential to use mapping technologies to show patient disease burden in geographic form, map locations of health care facilities, and a plethora of accountable care population health initiatives would benefit from geo-analysis. Health Catalyst is working to integrate inputs into analysis like maps that can show geographic care boundaries, population health demographics, and more.
Healthcare Interoperability: New Tactics and TechnologyHealth Catalyst
Every provider agrees on the need for healthcare interoperability to achieve clinical data insights at the point of care. The question is how to get there from the myriad technologies and the volumes of data that comprise electronic medical records. It’s been difficult to organize among participants that have had little incentive to cooperate. And standards for sending and receiving data have been slow to develop. This is changing, but the key components that are still vital to realizing insights are closed-loop analytics and its accompanying tools, an enterprise data warehouse and analytics applications. This article defines the problems and explores the solutions to optimizing clinical decision making where it’s needed most.
The 3 Must-Have Qualities of a Care Management SystemHealth Catalyst
Care management systems are defined in many ways, but the only effective system comprises three qualities:
1.) It’s comprehensive and includes a suite of tools to address all five core competencies of care management.
2.) It’s inclusive of all EMRs and other data sources to enable thorough communication and analysis.
3.) It’s analytics-driven design facilitates clinical decision making and workflow.
Ultimately, an effective system improves outcomes and becomes an indispensable tool for managing population health.
This article describes what drives successful care management, and reveals a suite of applications that aid care team members and patients through advanced algorithms and embedded analytics. Learn how technology is helping to develop appropriate interventions and improve clinical and financial outcomes.
Use Well-Crafted Aim Statements To Achieve Clinical Quality ImprovementsHealth Catalyst
Too often, hospitals and health systems stop at developing broad clinical quality improvement statements that come up short of achieving their desired goals. What’s missing are clearly defined improvement objectives in the form of aim statements that take into account the effects on other areas of the organization: patient safety and satisfaction, physician engagement, and financial contribution. Aim statements help articulate the problems that add value for patients and the organization, but good data, and the analytics tools required to understand the data, are essential to illuminating high-value problem areas. Additionally, aim statements must stick to the SMART guidelines: Specific, Measureable, Achievable, Relevant, and Time-bound.
Disease Surveillance Monitoring and Reacting to Outbreaks (like Ebola) with a...Health Catalyst
The current options for monitoring data to help identify disease outbreaks like Ebola are not great. These are: 1) Monitoring chief complaint/reason for admission data in ADT data streams. Although this is a real-time approach, the data is not codified and would require some degree of NLP. 2) Monitoring coded data collected in EHRs. The most precise option available, but the data is not available until after the patient encounter is closed, which would be too late in most cases. And 3) Monitoring billing data. This approach has the same problems as the two listed above, but it’s better than nothing in the absence of an EMR. All of these weaknesses can be solved with the use of a data warehouse.
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.
The Analytic System: Finding Patterns in the DataHealth Catalyst
Dr. Haughom set the stage for this upcoming discussion in his previous webinar, explaining the key components of an effective analytical system that enables self-exploration and learning. In this session Attendees will learn:
How the distinction between random variation and assignable cause variation is critically important to patient care
Creation and application of Statistical Process Control (SPC) charts to:
Monitor process variation over time
Differentiate between assignable cause and random cause variation
Assess effectiveness of change on a given process
Achieve and maintain process stability
How implementing inlier management and creating a collaborative environment will drive continuous improvement
How to identify patterns in data using a live demonstration of advanced analytical tools.
How to Improve Clinical Programs by Breaking the Cycle of Waste in HealthcareHealth Catalyst
To succeed with value-based care, health systems must demonstrate to CMS they operate more effectively, efficiently, and safely. This requires organizations to identify and improve three types of waste commonly found in clinical programs: ordering waste, workflow and operational variations waste, and defect waste. Finding these areas, however, requires three critical solutions: an EDW, a KPA Application, and organizational readiness assessments.
The Changing Role of Healthcare Data AnalystsHealth Catalyst
The healthcare industry is undergoing a sea change, and healthcare data analysts will play a central role in this transformation. This report explores how the evolution to value-based care is changing the role of healthcare data analysts, how data analysts’ skills can best be applied to achieve value-based objectives and, finally, how Health Catalyst’s most successful health system clients are making this cultural transformation happen in the real world.
Population Health Management, Predictive Analytics, Big Data and Text AnalyticsFrank Wang
HCAD 6635 Health Information Analytics session 12
Population Health Management Analytics
Predictive Analytics
Big Data and its potential applications in Healthcare
Text Analytics
Public Health Analytics
Outcomes improvement: what you get when you mix good data with physician enga...Health Catalyst
The prescription for improving healthcare outcomes is pretty straightforward: improve quality by working with good data that’s based on patient perceptions of quality, as well as functional health outcomes. Then make that data accessible and actionable among your physicians and give them the leeway they need to reduce variation and, ultimately, improve outcomes. As simple as this may seem, it’s been complicated by an inefficient data infrastructure with non-standardized components (EHRs) and the inability to distribute analyses and visualizations where they are needed most (at the point of care). Dale Sanders explains these issues in detail and outlines solutions in this article published in the April 2015 edition of BMJ Outcomes.
What can healthcare executives learn from military decision-making, as it relates to predictiveanalytics in healthcare? As it turns out, quite a lot. Dale Sanders, senior vice president for strategy at Salt Lake City, Utah-based Health Catalyst, drew some surprising parallels between these two vital sectors of the economy during a concluding session at the Plante Moran Healthcare Executive Summit on June 5 in Chicago. His main theme was to remember that in predictive analytic analytics, it's the intervention that matters, noting that much of the industry is seduced by flashy predictive analytics "objects" without thinking through the needed interventions which are needed to get the proper ROI.
To lower health costs, physician networks and medical homes must employ a closed loop population management program that focus on patient SOH stratification, chronic disease management, care coordination and incentive management. This approach will enable them to consistently reduce ER and inpatient admissions, which are the greatest expenditures in health care today.
The Path to Shared Savings With Population Health Management ApplicationsHealth Catalyst
Eric Just, Vice President of Technology and Kathleen Merkley, Clinical Engagement Executive and Vice President at Health Catalyst, will demonstrate live several advanced applications built on a Late-Binding Catalyst data warehouse. Attendees will better understand how to:
Identify variability in care
Define accurate populations
Report on key health indicators across the continuum of care
Apply flexible models for risk stratification
Measure detailed process metrics spanning transitions of care for HF patients
Next generation health systems and Accountable Care Organizations will be paid based on an evolving model that rewards healthcare providers through ‘shared savings.’ Those savings must be achieved through systematic cost reductions while still improving quality of care. For most, this dual focus will prove to be the most critical and difficult part of realizing success.
Provider Based Patient Engagement - An Essential Strategy for Population HealthPhytel
As the healthcare industry starts to re-engineer care delivery to accommodate new reimbursement models, providers on the front lines of change recognize the need for population health management and for increasing patients’ engagement in their own care. These two approaches are inextricably bound together, because it is impossible to manage the health of a population without getting patients more involved in self-management and the modification of their own risk factors. This paper discusses the fundamentals of patient engagement and shows how automation tools and web-based care management can facilitate this key process.
The Next Generation ACO Model team hosted an open door forum on Tuesday, February 28, 2017. During this open door forum Model team members provided a deep dive presentation examining details of financial aspects relating to the model.
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Using Patient Registries and Evidence-Based Guidelines to Overcome Declining ...Phytel
Mankato Clinic implemented automated patient outreach to improve quality of care and address declining visit trends. Using patient registries and evidence-based guidelines, the outreach identified care gaps and engaged patients to schedule recommended visits. Patients responded quickly to the outreach, with 27% scheduling visits within 5 days. Following the outreach implementation, outpatient visits increased by 22%, demonstrating the program's ability to motivate patients and improve compliance with guidelines.
Population Health Management: Where are YOU?Phytel
This presentation explains how population health is fundamental to value-based delivery models, including key principles and definitions of PHM, as well as how to assess your organization’s “population health readiness.”
Precise Patient Registries: The Foundation for Clinical Research & Population...Health Catalyst
This document discusses the importance and design of precise patient registries. It asserts that without precise definitions and registries of patient types, organizations cannot achieve precise clinical research, comparisons, financial management, or personalized healthcare. The document discusses different types of registries and provides an example of how Northwestern University Medicine developed registries for various diseases and conditions. It emphasizes that precise inclusion and exclusion criteria are needed for clinical registries to be useful.
What Is Population Health And How Does It Compare to Public HealthHealth Catalyst
Master data management is key for healthcare organizations looks to integrate different systems. The two types of master data are identity data and reference data. Master data management is the process of linking identity data and reference data. MDM is important for mergers and acquisitions and health information exchanges. The three approaches for MDM are: IT system consolidation, Upstream MDM implementation, and Downstream master data reconciliation in an enterprise data warehouse.
This document provides an overview of TOGAF 9.1, including:
- TOGAF is an enterprise architecture framework developed by The Open Group to help design, plan, implement, and govern an enterprise information technology architecture.
- The key component of TOGAF is the Architecture Development Method (ADM), which provides a process for developing enterprise architectures in a standardized and systematic way.
- The ADM supports iteration across its nine phases: preliminary, architecture vision, business architecture, data architecture, application architecture, technology architecture, opportunities & solutions, migration planning, and implementation governance.
Landmark Review of Population Health ManagementHealth Catalyst
Population health management (PHM) is in its early stages of maturity, suffering from inconsistent definitions and understanding, overhyped by vendors and ill-defined by the industry. Healthcare IT vendors are labeling themselves with this new and popular term, quite often simply re-branding their old-school, fee-for-service, and encounter-based analytic solutions. Even the analysts —KLAS, Chilmark, IDC, and others—are also having a difficult time classifying the market. In this paper, I identify and define 12 criteria that any health system will want to consider in evaluating population health management companies. The reality of the market is that there is no single vendor that can provide a complete PHM solution today. However there are a group of vendors that provide a subset of capabilities that are certainly useful for the next three years. In this paper, I discuss the criteria and try my best to share an unbiased evaluation of sample of the PHM companies in this space.
The document provides an overview of accountable care organizations (ACOs) including:
1) ACOs aim to tie provider reimbursements to quality and reduce total cost of care for assigned patients.
2) Key stakeholders include providers, payers (primarily Medicare), and patients (primarily Medicare beneficiaries).
3) The concept of ACOs originated in 2006 but builds on prior models. Successful implementation remains challenging.
4) The Patient Protection and Affordable Care Act supports the development of ACOs and other innovative models.
Data Driven Healthcare That Work: A Physician Group PerspectiveHealth Catalyst
Crystal Run Healthcare shares their story about using proven strategies to care for patients in an accountable care model by using data to drive those strategies. Gregory A. Spencer, MD, FACP, CMO, and CMIO at Crystal Run Healthcare discusses why they moved towards analytics and data warehousing as well as the 6 requirements their health system had as they searched for a partner: 1) The solution needed to hit the ground running. 2) The solution needed to provide quick, actionable data. 3) There needed to be a library of analytical applications. 4) The healthcare data model needed to be able to evolve. 5) They needed to be taught how to fish for the data. 6) A long-term relationship with the vendor was important
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?”
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
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?
The Deployment System: Creating the Organizational Infrastructure to Support ...Health Catalyst
Join Dr. Haughom as he continues the next installment in his webinar series. He will help participants to better understand the key components of an effective deployment system that supports sustainable large-scale improvements in quality, safety and efficiency. He will also continue his live demonstration of the power of modern analytics in managing the health of populations.
Attendees will learn:
Through a live demonstration, the use of analytics to identify potential risk by understanding the size of disease populations and their risk profiles
How to effectively engage opinion leaders in quality improvement and move the entire organization’s workforce forward
How to organize teams that take ownership of the organization’s quality, cost and patient satisfaction improvement strategy
The elements of an effective team structure and governance model for quality improvement
The implementation of an agile, or iterative, approach that fosters continuous improvement
The integration of Lean process improvements with the measurement system to achieve and sustain improvement gains
A CEO's Keys to Continuous Quality ImprovementHealth Catalyst
The document discusses keys to continuous quality improvement at Thibodaux Regional Medical Center. It describes how the CEO Greg Stock engaged physicians by establishing a steering team led by physicians to drive care transformation. Analytics from their data warehouse and applications helped identify opportunities for improvement and support data-driven decisions. Driving organizational competence involved training staff on quality improvement methods and holding people accountable for ongoing monitoring of projects to ensure gains were maintained. Their sepsis improvement project alone saved 16 lives per year. The Joint Commission praised their methodology as a best practice for physician engagement and using data to drive improvement.
This document provides an overview of Health Catalyst, a company that provides healthcare data warehousing and analytics solutions. It discusses Health Catalyst's mission to transform US healthcare using data-driven insights. The company offers an enterprise data warehouse, data integration tools, reports/dashboards, and applications for quality improvement, population health and predictive analytics. Case studies demonstrate successes in cost savings, core measure performance, and clinical outcomes.
- HealthTech innovation is disrupting healthcare and its established players
- Technology is driving a new paradigm to create better health care
- Developing markets can leapfrog their healthcare infrastructure limitations
- New opportunities are opening to shape the new paradigm
Introducing the Health Catalyst Monitor™ Patient Safety Suite Surveillance Mo...Health Catalyst
Unlike the standard post-event reporting process, the Patient Safety Monitor Suite: Surveillance Module is a trigger-based surveillance system, enabled by the unique industry-first technological capabilities of the Health Catalyst Data Operating System platform, including predictive analytic models and AI. Additionally, once listed, the Health Catalyst PSO will create a secure and safe environment where clients can collect and analyze patient safety events to learn and improve, free from fear of litigation. Coupled with patient safety services, an organization’s active all-cause harm patient safety system is fully enabled to deliver measurable and meaningful improvements.
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.
Data Mining in Healthcare: How Health Systems Can Improve Quality and Reduce...Health Catalyst
This is the complete 4-part series demonstrating real-world examples of the power of data mining in healthcare. Effective data mining requires a three-system approach: the analytics system (including an EDW), the content system (and systematically applying evidence-based best practices to care delivery), and the deployment system (driving change management throughout the organization and implementing a dedicated team structure). Here, we also show organizations with successful data-mining-application in critical areas such as: tracking fee-for-service and value-based payer contracts, population health management initiatives involving primary care reporting, and reducing hospital readmissions. Having the data and tools to use data mining and predict trends is giving these health systems a big advantage.
Transforming Healthcare: The Promise of InnovationHealth Catalyst
A number of powerful technologies are on the verge of producing dramatic change in how, when and where care is delivered, including artificial intelligence, genomics, monitoring sensors, robotics, nanotechnology, 3D printing, mobile computing technologies and others. This technology-driven change will dramatically impact all healthcare providers, and it will propel healthcare into the realm of Big Data.
Participants will:
Appreciate the role of innovation in healthcare's future.
Understand the classes of technology that will foster innovation and drive change.
Learn how technology-driven change will support data-driven improvement and population health management.
Know how these technologies will impact analytics.
Understand the application of transformational principles in light of the many engaging practitioner discussions at the recently concluded Health Analytics Summit.
The future is becoming clearer and it promises to be exciting, impactful, and powerful for patients and healthcare providers alike.
Three Approaches to Predictive Analytics in HealthcareHealth Catalyst
Predictive analytics in healthcare must be timely, role-specific, and actionable to be successful. There are also three common types of healthcare predictive analytics: Risk scores (risk stratification using CMS-HCC or other models), What-if scenarios (simulations of specific outcomes given a certain combination of events, and Geo-spatial analytics (mapping a geographical location’s patient disease burden). The common thread in all of these is the element of action, or specifically, the intervention that really matters in healthcare predictive analytics.
Virtual Care Reality & Accelerators to Scale - James R. Mault, MD, Qualcomm -...VSee
An exploration of the digital health landscape - what are "snake oils" and the accelerators from the Telehealth Failures & Secrets To Success Conference:
vsee.com/telehealth-failures-conference
David Albert presented on the potential of mobile health (mHealth) technologies. He discussed how mHealth can help address global healthcare issues by improving access and shifting care from reactive to proactive. Smartphones are poised to become the primary healthcare portal worldwide. Physicians and patients support remote monitoring technologies. The mHealth market is projected to grow significantly in coming years as these tools empower consumers, improve existing systems, and create new infrastructure in developing areas. Connecting data and apps has the potential to greatly enhance outcomes through patient self-management of conditions like diabetes, hypertension, and atrial fibrillation.
A Health Catalyst Overview: A Platform Approach for Transforming HealthcareHealth Catalyst
Join two of Health Catalyst’s best, Vice President Dan Soule, and Senior Consultant Sam Turman, as they cover 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.
Dan and Sam will provide an easy-to-understand discussion regarding the key analytic principles of adaptive data architecture.
Some specific items they 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. They will also address the basic requirements for implementation of the measurement platform of a data warehouse, such as team creation, roles, and reporting.
Finally, they 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.
Laying the Foundation for Sustainable Change and SuccessHealth Catalyst
In the initial presentation in this series, we examined the historical, cultural, financial and social forces that have defined and shaped the healthcare system and the dynamics that are irreversibly driving the need for change. Now, it is time to focus on a review of emerging concepts and methods that will allow healthcare organizations to adapt to a rapidly changing future. This slides will focus on the improvement concepts, methods and tools that individuals and organizations need to consider in order to address the challenges facing healthcare and successfully ride the wave of change sweeping across the industry.
HealthSaaS Overview Deck October 2014 (RPM, Home Health)HealthSaaS, Inc.
The HealthSaaS Connected Outcomes Platform removes silo barriers to connect, aggregate and integrate disparate data from mHealth applications and Remote Patient Monitoring (RPM) devices.
Our services provide HIPAA secure data to the “point of care” wherever the clinician is located. Enabling clinicians to rapidly respond to clinically relevant patient health information can facilitate early interventions, reduce hospital admissions, improve outcomes and lower costs.
Our passion empowers us to create eHealth collaboration tools that enhance provider efficiencies, track outcomes and improve the quality of life for patients throughout the continuum of care.
Presentation given at the Garage Start Digital Health Startup Workshop sponsored by ABRT Venture Capital. Content focuses on the healthtech investment landscape.
Similar to Accountable Care Transformation Framework (20)
Empowering ACOs: Leveraging Quality Management Tools for MIPS and BeyondHealth Catalyst
Join us as we delve into the crucial realm of quality reporting for MSSP (Medicare Shared Savings Program) Accountable Care Organizations (ACOs).
In this session, we will explore how a robust quality management solution can empower your organization to meet regulatory requirements and improve processes for MIPS reporting and internal quality programs. Learn how our MeasureAble application enables compliance and fosters continuous improvement.
Unlock the Secrets to Optimizing Ambulatory Operations Efficiency and Change ...Health Catalyst
Today’s healthcare leaders are seeking technology solutions to optimize efficiencies and improve patient care. However, without effective change management and strategies in place, healthcare leaders struggle to strategically improve patient flow, space, to strategically improve patient flow, space, and schedule management, and implement daily huddles. The role of technology in supporting operational efficiency and change management initiatives is inevitable.
During this webinar, attendees will learn how to optimize Ambulatory Operational Efficiencies and Change Management. Attendees will also learn about the importance of visual management boards in enhancing clinic performance and insights into effective change management approaches.
Patient expectations are rising, and organizations are continuously being asked to do more with less.
Additionally, the convergence of several significant emerging market and policy trends, economic uncertainty, labor force shortages, and the end of the COVID-19 public health emergency has created a unique set of challenges for healthcare organizations.
Attend this timely webinar to learn about new trends and their impact on key healthcare issues, such as patient engagement, migration to value-based care, analytics adoption, the use of alternative care sites, and data governance and management challenges.
During this webinar, we will discuss the complexities of AI, trends, and platforms in the industry. Dive deep into understanding the true essence of AI, exploring its potential, real-world use cases, and common misconceptions. Gain valuable insights into the latest technology trends impacting healthcare and discover strategies for maximizing ROI in your technology investments.
Explore the profound impact of data literacy on healthcare organizations and how it shapes the utilization of data and technology for transformative outcomes. Understand the top technology priorities for healthcare organizations and learn how to navigate the digital landscape effectively. Furthermore, simplify industry jargon by defining common data elements, fostering clearer communication and collaboration across stakeholders.
Finally, uncover the transformative potentials of platforms in healthcare and how they can revolutionize scalability, interoperability, and innovation within your organization. Don't miss this opportunity to gain invaluable insights from industry experts and stay ahead in the ever-evolving healthcare landscape. Reserve your spot now for an enlightening journey into the future of healthcare technology!
Three Keys to a Successful Margin: Charges, Costs, and LaborHealth Catalyst
How can cost management and complete charge capture protect and enhance the margin?
In this webinar, we will look at 2024 margin pressures likely to impact your organization’s financial resiliency. This presentation will also share how organizations can move from Fee-for-Service to Value; bringing Cost to the forefront.
2024 CPT® Updates (Professional Services Focused) - Part 3Health Catalyst
Each year the CPT code set undergoes significant changes. Physicians and their office staff need to be aware of the changes in order to ensure a smooth transition into 2024. Join us for a discussion of the new, deleted and revised CPT codes and associated guidelines for 2024. This presentation will focus on the changes to the CPT dataset and the associated work RVU value changes that impact professional service reporting.
During this complimentary webinar, we will empower you to correctly apply the new and revised codes and discuss the rationale behind this year’s changes. You will leave with an understanding of the financial implications of the changes on your practice.
2024 CPT® Code Updates (HIM Focused) - Part 2Health Catalyst
Each year the CPT code set and the HCPCS code set undergo significant changes, and your coding staff needs to be aware of the changes in order to ensure a smooth transition into 2024. Join us for a discussion of the new, deleted and revised CPT codes and associated guidelines for 2024. This is part two in a three-part series.
During these complimentary webinars, we will empower you to correctly apply the new and revised codes and discuss the rationale behind this year’s changes. This presentation will be geared towards hospital staff with a focus on the surgical section of the CPT book in addition to surgical Category III codes.
2024 CPT® Code Updates (CDM Focused) - Part 1Health Catalyst
The document provides an overview of changes to CPT codes that will take effect in 2024, with a focus on changes relevant to clinical documentation. Key points include:
- There are 145 total codes added, 34 deleted, and 55 revised across various sections.
- Changes are provided for the Radiology, Laboratory/Pathology, and Category III sections. New codes are added for things like non-invasive coronary FFR estimation using AI and various intraoperative ultrasound exams.
- Guidelines are established for new genomic sequencing procedures codes focusing on solid organ and hematolymphoid neoplasms. Definitions are also provided for various genomic analysis techniques.
- Several Tier I and Tier II molecular
What’s Next for Hospital Price Transparency in 2024 and BeyondHealth Catalyst
The Centers for Medicare & Medicaid Services (CMS) published updates to the hospital price transparency requirements in the CY 2024 Outpatient Prospective Payment System (OPPS) Final Rule. The updates will be phased in over the next 14 months and include several significant changes including the use of a CMS-mandated template, a requirement for an affirmation statement from the hospital, and several new data elements. Join us to discover what changes are scheduled for implementation in 2024 and 2025 and how they’ll impact your facility.
During this complimentary 60-minute webinar, we’ll analyze the key provisions of the Price Transparency regulations and provide insights to help you prepare for the upcoming changes.
Automated Patient Reported Outcomes (PROs) for Hip & Knee ReplacementHealth Catalyst
What was once voluntary reporting will soon be made mandatory with penalties.
On July 1, 2024, all health systems will be required to collect Patient Reported Outcome Measures (PROM) as part of the Centers for Medicare & Medicaid Services (CMS) regulation for the following measures:
Hospital-Level, Risk Standardized Patient-Reported Outcomes Performance Measure (PRO-PM) Following Elective Primary Total Hip Arthroplasty (THA) and/or Total Knee Arthroplasty (TKA)
Hospital-Level Risk-Standardized Complication Rate (RSCR) Following Elective Primary THA/TKA
Are you equipped to handle these new requirements?
Mandatory data collection begins April 1, 2024, and failure to submit timely data can result in a 25 percent reduction in payments by Medicare.
Attend this webinar to learn how mobile engagement can empower your organization to meet this requirement.
2024 Medicare Physician Fee Schedule (MPFS) Final Rule UpdatesHealth Catalyst
According to the Centers for Medicare & Medicaid Services (CMS), the calendar year (CY) 2024 MPFS final rule was created to advance health equity and improve access to affordable healthcare. This webinar will cover the major policy updates of the MPFS final rule including updates to the telehealth services policy and remote monitoring services and enrollment of MFTs and MHCs as Medicare providers. The conversation will also cover policy changes on split (or shared) evaluation and management (E/M) visits, and the Appropriate Use Criteria (AUC) for Advanced Diagnostic Imaging.
What's Next for OPPS: A Look at the 2024 Final RuleHealth Catalyst
During this webinar, we’ll analyze the key provisions of the OPPS final rule and identify the significant changes for the coming year to help prepare your staff for compliance with the 2024 Medicare outpatient billing guidelines.
Insight into the 2024 ICD-10 PCS Updates - Part 2Health Catalyst
Three new codes were added to describe procedures involving a short-term external heart assist system inserted into the descending thoracic aorta. Codes were also added for fluorescence guided procedures of the female reproductive system and trunk region using pafolacianine. Additionally, new technology codes were introduced for insertion of intraluminal devices such as venous valves, leadless pacemakers, and artery bypass procedures.
Vitalware Insight Into the 2024 ICD10 CM Updates.pdfHealth Catalyst
This document provides an overview of upcoming changes to ICD-10-CM codes for fiscal year 2024. It notes that there will be 395 new codes, 13 revisions, and 25 deletions. Specific changes include 18 new major complication or comorbidity (MCC) codes, 3 deleted MCC codes, 79 new CC codes, and 8 deleted CC codes. The presentation reviews code additions, deletions, and revisions for various body systems and disease chapters. It also outlines changes to the MCC and CC lists as well as Medicare Severity Diagnosis Related Groups (MS-DRG) updates.
Driving Value: Boosting Clinical Registry Value Using ARMUS SolutionsHealth Catalyst
Many hospitals today face a perfect storm of operational and financial challenges. With increasing competition from outpatient facilities and rising care costs negatively impacting budgets, now is the time to boost your clinical registry’s value. However, collecting and analyzing data can be time-consuming and costly without the right tools. During this webinar, we will share insights and best practices for increasing the value of registry participation and how it’s possible to reduce costs while improving outcomes using the ARMUS Product Suite.
Tech-Enabled Managed Services: Not Your Average OutsourcingHealth Catalyst
The document discusses tech-enabled managed services (TEMS) as an alternative to traditional outsourcing. TEMS aims to reduce costs for health systems while maintaining performance, employees, and culture. It achieves this through specialized partnering, alleviating financial pressures, and ensuring dependable performance using a combination of people, processes, technology, and data. TEMS rebadges existing employees and takes on open positions to prevent workforce reductions. It also maintains existing processes while implementing new technology. This model is said to create wins for Health Catalyst through new employees, the health system through reduced costs and governed performance, and employees through continued work and an improved experience.
This webinar will provide an in-depth review of the CPT/HCPCS code set changes that will be effective on July 1, 2023. The review will include additions and deletions to the CPT/HCPCS code set, revisions of code descriptors, payment changes, and rationale behind the changes.
How Managing Chronic Conditions Is Streamlined with Digital TechnologyHealth Catalyst
Chronic conditions across the United States are prevalent and continue to rise. Managing one or more chronic diseases can be very challenging for patients who may be overwhelmed or confused about their care plan and may not have access to the resources they need. At the same time, care teams are overburdened, making it difficult to provide the support these patients require to stay as healthy as possible. A new approach to chronic condition management leverages technology to enable organizations to scale high-quality care, identify gaps in care, provide personalized support, and monitor patients on an ongoing basis. Such streamlined management will result in better outcomes, reduced costs, and more satisfied patients.
COVID-19: After the Public Health Emergency EndsHealth Catalyst
In this fast-paced webinar, we will discuss the impact of the end of the public health emergency (PHE), including upcoming changes to the different flexibilities allowed during the PHE and the timeline for when these flexibilities will end. We’ll also cover coding changes and reimbursement updates.
Automated Medication Compliance Tools for the Provider and PatientHealth Catalyst
When it comes to sustaining patient health outcomes, compliance and adherence to medication regimens are critically important, especially as providers manage patients with complex care needs and multiple medications. But, with provider burnout and staffing shortages at an all-time high, an efficient solution is critical. The use of automated medication management workflows to decrease provider burnout, while improving both medication compliance and patient engagement, is the way forward.
Allopurinol, a uric acid synthesis inhibitor acts by inhibiting Xanthine oxidase competitively as well as non- competitively, Whereas Oxypurinol is a non-competitive inhibitor of xanthine oxidase.
Predictabilty and Preventability Assessment, Management of ADR, Terminologies...Kshama Mundokar
The predictability and preventability assesment of ADR are explained with the information related with the management of ADR as well as the various terminologies which are used to study and better understand ADR are also described.
CLASSIFICATION OF H1 ANTIHISTAMINICS-
FIRST GENERATION ANTIHISTAMINICS-
1)HIGHLY SEDATIVE-DIPHENHYDRAMINE,DIMENHYDRINATE,PROMETHAZINE,HYDROXYZINE 2)MODERATELY SEDATIVE- PHENARIMINE,CYPROHEPTADINE, MECLIZINE,CINNARIZINE
3)MILD SEDATIVE-CHLORPHENIRAMINE,DEXCHLORPHENIRAMINE
TRIPROLIDINE,CLEMASTINE
SECOND GENERATION ANTIHISTAMINICS-FEXOFENADINE,
LORATADINE,DESLORATADINE,CETIRIZINE,LEVOCETIRIZINE,
AZELASTINE,MIZOLASTINE,EBASTINE,RUPATADINE. Mechanism of action of 2nd generation antihistaminics-
These drugs competitively antagonize actions of
histamine at the H1 receptors.
Pharmacological actions-
Antagonism of histamine-The H1 antagonists effectively block histamine induced bronchoconstriction, contraction of intestinal and other smooth muscle and triple response especially wheal, flare and itch. Constriction of larger blood vessel by histamine is also antagonized.
2) Antiallergic actions-Many manifestations of immediate hypersensitivity (type I reactions)are suppressed. Urticaria, itching and angioedema are well controlled.3) CNS action-The older antihistamines produce variable degree of CNS depression.But in case of 2nd gen antihistaminics there is less CNS depressant property as these cross BBB to significantly lesser extent.
4) Anticholinergic action- many H1 blockers
in addition antagonize muscarinic actions of ACh. BUT IN 2ND gen histaminics there is Higher H1 selectivitiy : no anticholinergic side effects
Understanding Atherosclerosis Causes, Symptoms, Complications, and Preventionrealmbeats0
Definition: Atherosclerosis is a condition characterized by the buildup of plaques, which are made up of fat, cholesterol, calcium, and other substances, in the walls of arteries. Over time, these plaques harden and narrow the arteries, restricting blood flow.
Importance: This condition is a major contributor to cardiovascular diseases, including coronary artery disease, carotid artery disease, and peripheral artery disease. Understanding atherosclerosis is crucial for preventing these serious health issues.
Overview: We will cover the aims and objectives of this presentation, delve into the signs and symptoms of atherosclerosis, discuss its complications, and explore preventive measures and lifestyle changes that can mitigate risk.
Aim: To provide a detailed understanding of atherosclerosis, encompassing its pathophysiology, risk factors, clinical manifestations, and strategies for prevention and management.
Purpose: The primary purpose of this presentation is to raise awareness about atherosclerosis, highlight its impact on public health, and educate individuals on how they can reduce their risk through lifestyle changes and medical interventions.
Educational Goals:
Explain the pathophysiology of atherosclerosis, including the processes of plaque formation and arterial hardening.
Identify the risk factors associated with atherosclerosis, such as high cholesterol, hypertension, smoking, diabetes, and sedentary lifestyle.
Discuss the clinical signs and symptoms that may indicate the presence of atherosclerosis.
Highlight the potential complications arising from untreated atherosclerosis, including heart attack, stroke, and peripheral artery disease.
Provide practical advice on preventive measures, including dietary recommendations, exercise guidelines, and the importance of regular medical check-ups.
Selective alpha1 blockers are Prazosin, Terazosin, Doxazosin, Tamsulosin and Silodosin majorly used to treat BPH, also hypertension, PTSD, Raynaud's phenomenon, CHF
Storyboard on Skin- Innovative Learning (M-pharm) 2nd sem. (Cosmetics)MuskanShingari
Skin is the largest organ of the human body, serving crucial functions that include protection, sensation, regulation, and synthesis. Structurally, it consists of three main layers: the epidermis, dermis, and hypodermis (subcutaneous layer).
1. **Epidermis**: The outermost layer primarily composed of epithelial cells called keratinocytes. It provides a protective barrier against environmental factors, pathogens, and UV radiation.
2. **Dermis**: Located beneath the epidermis, the dermis contains connective tissue, blood vessels, hair follicles, and sweat glands. It plays a vital role in supporting and nourishing the epidermis, regulating body temperature, and housing sensory receptors for touch, pressure, temperature, and pain.
3. **Hypodermis**: Also known as the subcutaneous layer, it consists of fat and connective tissue that anchors the skin to underlying structures like muscles and bones. It provides insulation, cushioning, and energy storage.
Skin performs essential functions such as regulating body temperature through sweat production and blood flow control, synthesizing vitamin D when exposed to sunlight, and serving as a sensory interface with the external environment.
Maintaining skin health is crucial for overall well-being, involving proper hygiene, hydration, protection from sun exposure, and avoiding harmful substances. Skin conditions and diseases range from minor irritations to chronic disorders, emphasizing the importance of regular care and medical attention when needed.
Breast cancer :Receptor (ER/PR/HER2 NEU) Discordance.pptxDr. Sumit KUMAR
Receptor Discordance in Breast Carcinoma During the Course of Life
Definition:
Receptor discordance refers to changes in the status of hormone receptors (estrogen receptor ERα, progesterone receptor PgR, and HER2) in breast cancer tumors over time or between primary and metastatic sites.
Causes:
Tumor Evolution:
Genetic and epigenetic changes during tumor progression can lead to alterations in receptor status.
Treatment Effects:
Therapies, especially endocrine and targeted therapies, can selectively pressure tumor cells, causing shifts in receptor expression.
Heterogeneity:
Inherent heterogeneity within the tumor can result in subpopulations of cells with different receptor statuses.
Impact on Treatment:
Therapeutic Resistance:
Loss of ERα or PgR can lead to resistance to endocrine therapies.
HER2 discordance affects the efficacy of HER2-targeted treatments.
Treatment Adjustment:
Regular reassessment of receptor status may be necessary to adjust treatment strategies appropriately.
Clinical Implications:
Prognosis:
Receptor discordance is often associated with a poorer prognosis.
Biopsies:
Obtaining biopsies from metastatic sites is crucial for accurate receptor status assessment and effective treatment planning.
Monitoring:
Continuous monitoring of receptor status throughout the disease course can guide personalized therapy adjustments.
Understanding and managing receptor discordance is essential for optimizing treatment outcomes and improving the prognosis for breast cancer patients.
Phosphorus, is intensely sensitive to ‘other worlds’ and lacks the personal boundaries at every level. A Phosphorus personality is susceptible to all external impressions; light, sound, odour, touch, electrical changes, etc. Just like a match, he is easily excitable, anxious, fears being alone at twilight, ghosts, about future. Desires sympathy and has the tendency to kiss everyone who comes near him. An insane person with the exaggerated idea of one’s own importance.
Storyboard on Acne-Innovative Learning-M. pharm. (2nd sem.) CosmeticsMuskanShingari
Acne is a common skin condition that occurs when hair follicles become clogged with oil and dead skin cells. It typically manifests as pimples, blackheads, or whiteheads, often on the face, chest, shoulders, or back. Acne can range from mild to severe and may cause emotional distress and scarring in some cases.
**Causes:**
1. **Excess Oil Production:** Hormonal changes during adolescence or certain times in adulthood can increase sebum (oil) production, leading to clogged pores.
2. **Clogged Pores:** When dead skin cells and oil block hair follicles, bacteria (usually Propionibacterium acnes) can thrive, causing inflammation and acne lesions.
3. **Hormonal Factors:** Fluctuations in hormone levels, such as during puberty, menstrual cycles, pregnancy, or certain medical conditions, can contribute to acne.
4. **Genetics:** A family history of acne can increase the likelihood of developing the condition.
**Types of Acne:**
- **Whiteheads:** Closed plugged pores.
- **Blackheads:** Open plugged pores with a dark surface.
- **Papules:** Small red, tender bumps.
- **Pustules:** Pimples with pus at their tips.
- **Nodules:** Large, solid, painful lumps beneath the surface.
- **Cysts:** Painful, pus-filled lumps beneath the surface that can cause scarring.
**Treatment:**
Treatment depends on the severity and type of acne but may include:
- **Topical Treatments:** Such as benzoyl peroxide, salicylic acid, or retinoids to reduce bacteria and unclog pores.
- **Oral Medications:** Antibiotics or oral contraceptives for hormonal acne.
- **Procedures:** Such as chemical peels, extraction of comedones, or light therapy for more severe cases.
**Prevention and Management:**
- **Cleanse:** Regularly wash skin with a gentle cleanser.
- **Moisturize:** Use non-comedogenic moisturizers to keep skin hydrated without clogging pores.
- **Avoid Irritants:** Such as harsh cosmetics or excessive scrubbing.
- **Sun Protection:** Use sunscreen to prevent exacerbation of acne scars and inflammation.
Acne treatment can take time, and consistency in skincare routines and treatments is crucial. Consulting a dermatologist can help tailor a treatment plan that suits individual needs and reduces the risk of scarring or long-term skin damage.