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
Want to know the best healthcare data warehouse for your organization? You’ll need to start first by modeling the data, because the data model used to build your healthcare enterprise data warehouse (EDW) will have a significant effect on both the time-to-value and the adaptability of your system going forward. Each of the models I describe below bind data at different times in the design process, some earlier, some later. As you’ll see, we believe that binding data later is better. The three approaches are 1) the enterprise data model, 2) the independent data model, and 3) the Health Catalyst Late-Binding™ approach.
Five Practical Steps Towards Healthcare Data GovernanceHealth Catalyst
Health systems increasingly recognize data as one of their top strategic assets, but how many organization have the processes and frameworks in place to protect their data? Without effective data governance, organizations risk losing trust in their data and its value in process and outcomes improvement; a 2018 survey indicated less than half of healthcare CIOs have strong trust in their data.
By following five steps towards data governance, health systems can effectively steward data and grow and maintain trust in it as a critical asset:
Identify the organizational priorities.
Identify the data governance priorities.
Identify and recruit the early adopters.
Identify the scope of the opportunity appropriately.
Enable early adopters to become enterprise data governance leaders and mentors.
A hybrid approach to data management is emerging in healthcare as organizations recognize the value of an enterprise data warehouse in combination with a data lake.
In this SlideShare, we discuss data lakes in healthcare and we:
Provide an overview of a Hadoop-based data lake architecture and integration platform, and its application in machine learning, predictive modeling, and data discovery
Discuss several key use cases driving the adoption of data lakes for both providers and health plans
Discuss available data storage forms and the required tools for a data lake environment
Detail best practices for conducting data lake assessments and review key implementation considerations for healthcare
The Microsoft Fabric is the one stop for all the data and AI workloads in an organisation. I found its easy and more effective, data engineers, data scientists and business analytics can work seamlessly in a unified platform.
A dive into Microsoft Fabric/AI Solutions offering. For the event: AI, Data, and CRM: Shaping Business through Unique Experiences. By D. Koutsanastasis, Microsoft
Data governance with Unity Catalog PresentationKnoldus Inc.
Databricks Unity Catalog is the industry’s first unified governance solution for data and AI on the lakehouse. With Unity Catalog, organizations can seamlessly govern their structured and unstructured data, machine learning models, notebooks, dashboards and files on any cloud or platform. Data scientists, analysts and engineers can use Unity Catalog to securely discover, access and collaborate on trusted data and AI assets, leveraging AI to boost productivity and unlock the full potential of the lakehouse environment. This session will cover the potential of unity catalog to achieve a flexible and scalable governance implementation without sacrificing the ability to manage and share data effectively.
6 Steps for Implementing Successful Performance Improvement Initiatives in He...Health Catalyst
A systematic approach to performance improvement initiative includes three components: analytics, content, and deployment. Taking six steps will help an organization to effectively cover all three components of success. Step 1: Integrate performance improvement into your strategic objectives. Step 2: Use analytics to unlock data and identity areas of opportunity. Step 3: Prioritize programs using a combination of analytics and a deployment system. Step 4: Define the performance improvement program’s permanent teams. Step 5: Use a content system to define program outcomes and define interventions. Step 6: Estimate the ROI.
Want to know the best healthcare data warehouse for your organization? You’ll need to start first by modeling the data, because the data model used to build your healthcare enterprise data warehouse (EDW) will have a significant effect on both the time-to-value and the adaptability of your system going forward. Each of the models I describe below bind data at different times in the design process, some earlier, some later. As you’ll see, we believe that binding data later is better. The three approaches are 1) the enterprise data model, 2) the independent data model, and 3) the Health Catalyst Late-Binding™ approach.
Five Practical Steps Towards Healthcare Data GovernanceHealth Catalyst
Health systems increasingly recognize data as one of their top strategic assets, but how many organization have the processes and frameworks in place to protect their data? Without effective data governance, organizations risk losing trust in their data and its value in process and outcomes improvement; a 2018 survey indicated less than half of healthcare CIOs have strong trust in their data.
By following five steps towards data governance, health systems can effectively steward data and grow and maintain trust in it as a critical asset:
Identify the organizational priorities.
Identify the data governance priorities.
Identify and recruit the early adopters.
Identify the scope of the opportunity appropriately.
Enable early adopters to become enterprise data governance leaders and mentors.
A hybrid approach to data management is emerging in healthcare as organizations recognize the value of an enterprise data warehouse in combination with a data lake.
In this SlideShare, we discuss data lakes in healthcare and we:
Provide an overview of a Hadoop-based data lake architecture and integration platform, and its application in machine learning, predictive modeling, and data discovery
Discuss several key use cases driving the adoption of data lakes for both providers and health plans
Discuss available data storage forms and the required tools for a data lake environment
Detail best practices for conducting data lake assessments and review key implementation considerations for healthcare
The Microsoft Fabric is the one stop for all the data and AI workloads in an organisation. I found its easy and more effective, data engineers, data scientists and business analytics can work seamlessly in a unified platform.
A dive into Microsoft Fabric/AI Solutions offering. For the event: AI, Data, and CRM: Shaping Business through Unique Experiences. By D. Koutsanastasis, Microsoft
Data governance with Unity Catalog PresentationKnoldus Inc.
Databricks Unity Catalog is the industry’s first unified governance solution for data and AI on the lakehouse. With Unity Catalog, organizations can seamlessly govern their structured and unstructured data, machine learning models, notebooks, dashboards and files on any cloud or platform. Data scientists, analysts and engineers can use Unity Catalog to securely discover, access and collaborate on trusted data and AI assets, leveraging AI to boost productivity and unlock the full potential of the lakehouse environment. This session will cover the potential of unity catalog to achieve a flexible and scalable governance implementation without sacrificing the ability to manage and share data effectively.
6 Steps for Implementing Successful Performance Improvement Initiatives in He...Health Catalyst
A systematic approach to performance improvement initiative includes three components: analytics, content, and deployment. Taking six steps will help an organization to effectively cover all three components of success. Step 1: Integrate performance improvement into your strategic objectives. Step 2: Use analytics to unlock data and identity areas of opportunity. Step 3: Prioritize programs using a combination of analytics and a deployment system. Step 4: Define the performance improvement program’s permanent teams. Step 5: Use a content system to define program outcomes and define interventions. Step 6: Estimate the ROI.
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.
Henry Peyret Presentation - Data Governance 2.0.
Based on the analysis of Digital Transformation and Values Transformation, Forrester gives its insight and orientations in terms of Data Governance 2.0 and Data Citizenship.
Intuit Data Ecosystem supports unique consumer and small business assets at scale, and handle petabytes of customer data. We have 8M active small business customers and 16M paid workers that uses Intuit Quick Books and Quick Books Payroll Products. Huge customer base and large volumes of data always challenges the data teams in terms of freshness of data, correctness of data etc. This presentation is intended to cover such problems we faced at Intuit along with the data observability model we follow to cure, detect and prevent data Issues. We would like to provide deep insights into the implementations and the impact of some of the great work done by Intuit in this direction.
Key Elements of a Successful Data Governance ProgramDATAVERSITY
At its core, Data Governance (DG) is all about managing data with guidance. This immediately provokes the question: Would you tolerate any of your assets to be managed without guidance? (In all likelihood, your organization has been managing data without adequate guidance and this accounts for its current, less-than-optimal state.) This program provides a practical guide to implementing DG or recharging your existing program. It provides an understanding of what Data Governance functions are required and how they fit with other Data Management disciplines. Understanding these aspects is a prerequisite to eliminate the ambiguity that often surrounds initial discussions and implement effective Data Governance/Stewardship programs that manage data in support of organizational strategy. Delegates will understand why Data Governance can be tricky for organizations due to data’s confounding characteristics. This webinar will focus on four key DG elements:
- Keeping DG practically focused
- DG must exist at the same level as HR
- Gradually add ingredients (practicing and getting better)
- Data Governance in action: storytelling
An overview of clinical healthcare data analytics from the perspective of an interventional cardiology registry. This was initially presented as part of a workshop at the University of Illinois College of Computer Science on April 20, 2017.
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.
Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...DATAVERSITY
With the explosive growth of DataOps to drive faster and more confident business decisions, proactively understanding the quality and health of your data is more important than ever. Data observability is an emerging discipline within data quality used to expose anomalies in data by continuously monitoring and testing data using artificial intelligence and machine learning to trigger alerts when issues are discovered.
Join Julie Skeen and Shalaish Koul from Precisely, to learn how data observability can be used as part of a DataOps strategy to improve data quality and reliability and to prevent data issues from wreaking havoc on your analytics and ensure that your organization can confidently rely on the data used for advanced analytics and business intelligence.
Topics you will hear addressed in this webinar:
Data observability – what is it and how it can complement your data quality strategy
Why now is the time to incorporate data observability into your DataOps strategy
How data observability helps prevent data issues from impacting downstream analytics
Examples of how data observability can be used to prevent real-world issues
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.
These are the slides from the workshop I delivered at the Healthcare Analytics Symposium in July 2014. This 3-hour workshop walked the attendees step-by-step through the requirements to start a healthcare predictive analytics program and some of the areas already showing progress.
All about Informatica PowerCenter features for both Business and Technical staff, it illustrates how Informatica PowerCenter solves core business challenges in Data Integration projects.
Slides: Taking an Active Approach to Data GovernanceDATAVERSITY
A Look at How Riot Games Implemented Non-Invasive Data Governance
Riot Games created and runs “League of Legends,” the world’s most-played PC game and most viewed eSport — and is now transforming to become a multi-title publisher. To keep pace with this transformation and support a growing player base of millions, Riot Games is taking a page from Bob Seiner’s book, “Non-Invasive Data Governance: The Path of Least Resistance and Greatest Success” and leveraging the Alation Data Catalog to help guide accurate, well-governed analysis.
Bob Seiner will join Riot Games’ Chris Kudelka, Technical Product Manager, and Michael Leslie, Senior Data Governance Architect, and Alation’s John Wills, VP of Professional Service, for an inside look at Data Governance at one of the world’s leading gaming companies.
Join this webinar to learn:
• How Riot Games is implementing Non-Invasive Data Governance
• How this new approach to Data Governance helps to drive the business
• How the Alation Data Catalog helps Riot Games create the foundation for guiding accurate, well-governed data use
The path to a Modern Data Architecture in Financial ServicesHortonworks
Delivering Data-Driven Applications at the Speed of Business: Global Banking AML use case.
Chief Data Officers in financial services have unique challenges: they need to establish an effective data ecosystem under strict governance and regulatory requirements. They need to build the data-driven applications that enable risk and compliance initiatives to run efficiently. In this webinar, we will discuss the case of a global banking leader and the anti-money laundering solution they built on the data lake. With a single platform to aggregate structured and unstructured information essential to determine and document AML case disposition, they reduced mean time for case resolution by 75%. They have a roadmap for building over 150 data-driven applications on the same search-based data discovery platform so they can mitigate risks and seize opportunities, at the speed of business.
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.
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.
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.
Henry Peyret Presentation - Data Governance 2.0.
Based on the analysis of Digital Transformation and Values Transformation, Forrester gives its insight and orientations in terms of Data Governance 2.0 and Data Citizenship.
Intuit Data Ecosystem supports unique consumer and small business assets at scale, and handle petabytes of customer data. We have 8M active small business customers and 16M paid workers that uses Intuit Quick Books and Quick Books Payroll Products. Huge customer base and large volumes of data always challenges the data teams in terms of freshness of data, correctness of data etc. This presentation is intended to cover such problems we faced at Intuit along with the data observability model we follow to cure, detect and prevent data Issues. We would like to provide deep insights into the implementations and the impact of some of the great work done by Intuit in this direction.
Key Elements of a Successful Data Governance ProgramDATAVERSITY
At its core, Data Governance (DG) is all about managing data with guidance. This immediately provokes the question: Would you tolerate any of your assets to be managed without guidance? (In all likelihood, your organization has been managing data without adequate guidance and this accounts for its current, less-than-optimal state.) This program provides a practical guide to implementing DG or recharging your existing program. It provides an understanding of what Data Governance functions are required and how they fit with other Data Management disciplines. Understanding these aspects is a prerequisite to eliminate the ambiguity that often surrounds initial discussions and implement effective Data Governance/Stewardship programs that manage data in support of organizational strategy. Delegates will understand why Data Governance can be tricky for organizations due to data’s confounding characteristics. This webinar will focus on four key DG elements:
- Keeping DG practically focused
- DG must exist at the same level as HR
- Gradually add ingredients (practicing and getting better)
- Data Governance in action: storytelling
An overview of clinical healthcare data analytics from the perspective of an interventional cardiology registry. This was initially presented as part of a workshop at the University of Illinois College of Computer Science on April 20, 2017.
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.
Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...DATAVERSITY
With the explosive growth of DataOps to drive faster and more confident business decisions, proactively understanding the quality and health of your data is more important than ever. Data observability is an emerging discipline within data quality used to expose anomalies in data by continuously monitoring and testing data using artificial intelligence and machine learning to trigger alerts when issues are discovered.
Join Julie Skeen and Shalaish Koul from Precisely, to learn how data observability can be used as part of a DataOps strategy to improve data quality and reliability and to prevent data issues from wreaking havoc on your analytics and ensure that your organization can confidently rely on the data used for advanced analytics and business intelligence.
Topics you will hear addressed in this webinar:
Data observability – what is it and how it can complement your data quality strategy
Why now is the time to incorporate data observability into your DataOps strategy
How data observability helps prevent data issues from impacting downstream analytics
Examples of how data observability can be used to prevent real-world issues
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.
These are the slides from the workshop I delivered at the Healthcare Analytics Symposium in July 2014. This 3-hour workshop walked the attendees step-by-step through the requirements to start a healthcare predictive analytics program and some of the areas already showing progress.
All about Informatica PowerCenter features for both Business and Technical staff, it illustrates how Informatica PowerCenter solves core business challenges in Data Integration projects.
Slides: Taking an Active Approach to Data GovernanceDATAVERSITY
A Look at How Riot Games Implemented Non-Invasive Data Governance
Riot Games created and runs “League of Legends,” the world’s most-played PC game and most viewed eSport — and is now transforming to become a multi-title publisher. To keep pace with this transformation and support a growing player base of millions, Riot Games is taking a page from Bob Seiner’s book, “Non-Invasive Data Governance: The Path of Least Resistance and Greatest Success” and leveraging the Alation Data Catalog to help guide accurate, well-governed analysis.
Bob Seiner will join Riot Games’ Chris Kudelka, Technical Product Manager, and Michael Leslie, Senior Data Governance Architect, and Alation’s John Wills, VP of Professional Service, for an inside look at Data Governance at one of the world’s leading gaming companies.
Join this webinar to learn:
• How Riot Games is implementing Non-Invasive Data Governance
• How this new approach to Data Governance helps to drive the business
• How the Alation Data Catalog helps Riot Games create the foundation for guiding accurate, well-governed data use
The path to a Modern Data Architecture in Financial ServicesHortonworks
Delivering Data-Driven Applications at the Speed of Business: Global Banking AML use case.
Chief Data Officers in financial services have unique challenges: they need to establish an effective data ecosystem under strict governance and regulatory requirements. They need to build the data-driven applications that enable risk and compliance initiatives to run efficiently. In this webinar, we will discuss the case of a global banking leader and the anti-money laundering solution they built on the data lake. With a single platform to aggregate structured and unstructured information essential to determine and document AML case disposition, they reduced mean time for case resolution by 75%. They have a roadmap for building over 150 data-driven applications on the same search-based data discovery platform so they can mitigate risks and seize opportunities, at the speed of business.
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.
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.
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.
How to Choose the Best Healthcare Analytics Software Solution in a Crowded Ma...Health Catalyst
There’s a new trend in the healthcare industry to adopt analytics software solutions to help organizations achieve clinical and financial success. Because of the high demand for analytics, there are many players touting their ability to delivery comprehensive solutions. With so many options available, health systems need to be able to cut through the marketing hype to find tools that provide the best value for their needs. Key solutions include an enterprise data warehouse and analytics software applications (from foundational to discovery to advanced). Other considerations include the organization’s readiness for cultural change, the total cost of ownership required, and the viability of the company providing the technology.
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.
The Medicare Access and CHIP Reauthorization Act (MACRA) overhauls the payment system for Medicare providers. It’s a complex program that requires careful study so physicians can make the best choice for how they want to report. This choice ultimately impacts reimbursement and the potential bonuses or penalties associated with each reporting option.
This FAQ covers both tracks of the new rule, the Merit-based Incentive Payment System (MIPS), and the Advanced Alternative Payment Model (APM), with a background review and a comprehensive list of questions and answers.
It’s a practical guide complete with next steps for strategic and tactical planning.
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.
Surviving Value-Based Purchasing in Healthcare: Connecting Your Clinical and ...Health Catalyst
Reducing healthcare costs is a major driving force in bundled payments, home-centered medical care, and accountable care organizations. But each new delivery model is built on the premise of reducing revenue per patient. So how can a health system win? Find out what you can do financially survive in today’s environment.
Linking Clinical And Financial Data: The Key To Real Quality And Cost OutHealth Catalyst
Since accountable care took the healthcare industry by a storm in 2010, health systems have had to move from their predictable revenue streams based on volume to a model that includes quality measures. While the switch will ultimately improve both quality and cost outcomes, health systems now need the capability of tracking and analyzing the data from both clinical and financial systems. A late-binding enterprise data warehouse provides the flexible architecture that makes it possible to liberate both kinds of data to link it together to provide a full picture of trends and opportunities.
Rising Healthcare Costs: Why We Have to ChangeHealth Catalyst
With rising healthcare costs, we hear so often about rate pressures on hospitals and the risk these pressures pose for their future. With healthcare reform, the burden of rising healthcare costs is shifting from payers to providers. Hospitals need to move toward value-based reimbursement models or they will face a -15.8 operating margin by 2021.Over the last 15 years premiums and employee contributions for an average family with health insurance sponsored by an employer have risen 167%. Along with these facts, government payers are reimbursing at lower levels becoming a negative margin for hospitals. These changes are not necessarily easy and can seem overwhelming. The question is whether your hospital will be a pioneer on the trail or will delay until it’s too late. The best way to get started is to understand exactly where you are today—your current cost structure and how each area of your organization is performing in terms of quality and cost, using an EDW.
How to Assess the ROI of Your Population Health InitiativeHealth Catalyst
In the brave new world of value-based healthcare, investing in population health management (PHM) is a requirement for success. Defining PHM isn’t easy, but there is one common term that appears among all the diverse interpretations—outcomes. Assessing the potential ROI for investments in PHM using a clear, understandable framework, can help organizations methodically identify and prioritize their PHM investments. While not every PHM intervention makes sense for every situation, it is important to determine which programs provide the most benefit, as well as determining when the investment will begin paying dividends, to achieve success in the era of PHM.
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.
How Physicians Can Prepare for the Financial Impact of MACRAHealth Catalyst
If all goes according to plan, the first performance period for the new Medicare Access and Chip Reauthorization Act (MACRA) is just around the calendar corner. It’s a complicated reimbursement structure with multiple tracks that are guaranteed to reward with bonuses or inflict pain through penalties in CMS’s new zero sum game. To the physicians and practices that adopt this new program early and position themselves for the best fiscal outcomes, go the spoils. But for many smaller practices and those that consistently underperform, the outlook may be glum regardless. Here are some highlights of the new program and the financial impact it will have on clinicians and practices.
Is That Data Valid? Getting Accurate Financial Data in HealthcareHealth Catalyst
A consolidated EDW is not a replacement or threat to the individual financial systems and reporting tools employed for general ledger, billing, payroll, or supply management. On the contrary, each of those systems is designed with sophisticated functionality that drives organizational efficiency. But alone, these systems realize only a portion of their true return on investment for the enterprise. As a consolidated data resource, these systems provide untold potential to address the underlying challenges to efficient, cost-effective health care.
How to Sustain Healthcare Quality Improvement in 3 Critical StepsHealth Catalyst
Many healthcare organizations don’t hold quality and cost gains because they don’t make improvement the backbone of their organization. Rather, they approach improvement as a series of initiatives. Ronald D. Snee, a fellow with the American Society for Quality states, “Many organizations focus on sustaining the gains only after improvement has been achieved. Intuitively, that may seem the correct sequence, but it is in fact backwards. The time to focus on sustaining improvement gains is well before the initiative is launched.”
Here are 3 critical organizational steps that can help sustain those gains.
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.
Hospital Readmissions Reduction Program: Keys to SuccessHealth Catalyst
Avoidable readmissions are a major financial major problem for the healthcare industry, especially for government payers. To tackle this problem, CMS launched the Hospital Readmissions Reduction Program (HRRP). While some hospitals may be able to absorb the financial penalties under HRRP, they still need to track increasingly complex reporting metrics. Most tracking solutions are inadequate for today’s complicated reporting needs. A healthcare enterprise data warehouse and analytics applications, however, are designed to solve the numerous reporting burdens. When used together, they also deliver a robust solution that enables hospitals to track and drive real cost and quality improvement initiatives, all without the need for users to be technical experts.
How to Drive ROI In Your Healthcare Quality Improvement Projects Health Catalyst
At a time when average hospital’s margins are stagnating, executives should be asking tough questions about the ROI of "indispensable" technologies. Will new technologies prove their worth or drive them further into the red? How do you measure and track ROI?
We need to educate clinicians on financial metrics and finance people need to learn more about the clinical processes and outcomes. One of the historical problems with calculating ROI has been the fundamental culture divide between clinicians and finance. Gone should be the days that clinicians deliver care without knowing the financial cost of that care.
This slide set give practical advice on how to set goals, measure ROI and gives excel templates that are based on years of experience by the authors
Organizing for Analytics Success - HAS Session 7Health Catalyst
Many organizations underestimate the need for organizational shifts and changes required for successful data-driven decision making. In this session, we will explain the three types of ongoing systems that are needed for sustainable analytics improvement and implementation. We will share best practices in how organizations can structure executive teams, clinical integration and guidance teams, and workgroup teams, as well as share examples of successes and setbacks when these principles are implemented or missed. We will also describe key roles and responsibilities and charters, show sample meeting agendas and recommended frequencies, and give you a set of tools that you can leverage for your initiatives.
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.
While Healthcare 1.0 was broadly defined by a focus on defensive medicine, billing, and fee-for-service, culminating in the mass adoption of EMRs, Healthcare 2.0 is a new wave focused on improving clinical efficiency, quality of care, affordability, and fee-for-value; culminating in a new age of healthcare analytics. This new age of analytics will require a new set of organizational skills and a foundational set of analytic information systems that many executives have not anticipated.
Join Dale Sanders, a 20-year healthcare CIO veteran and the industry's leading analytics expert, as he discusses his lessons learned, best practices in analytics, and what the C-level suite needs to know about this topic, now. Listen to Dale discuss 1) A step-by-step curriculum for analytic adoption and maturity in healthcare organizations, 2) the basic approach to a late-binding data warehouse, 3) pros and cons of early versus late binding, 4) the volatility in vocabulary and business rules in healthcare, 5) how to engineer your data to accommodate volatility in the future
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.
The Top Seven Quick Wins You Get with a Healthcare Data WarehouseHealth Catalyst
In an industry known for its complex challenges that can take years to overcome, health systems can leverage healthcare data warehouses to generate seven quick wins—reporting and analytics efficiencies that empower healthcare organizations to thrive in a value-based world:
Provides significantly faster access to data.
Improves data-driven decision making.
Enables a data-driven culture.
Provides world class report automation.
Significantly improves data quality and accuracy.
Provides significantly faster product implementation.
Improves data categorization and organization.
Health systems that leverage healthcare data warehouses position themselves to do more than just survive the transition to value-based care; they empower themselves to achieve and sustain long-term outcomes improvement by enabling data-driven decision making based on high quality data.
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.
Late-Binding Data Warehouse - An Update on the Fastest Growing Trend in Healt...Health Catalyst
Now that the industry has had some time to study, react, and apply the concepts, Dale Sanders is going to provide an update on the topic. As a CIO in the Air Force and healthcare, consistently specializing in decision support and analytics for the past 30 years, Dale will share the stories of the failures and successes that led him to the unconventional approach of late binding in the design of data warehouses— a design pattern that is now implemented in over a dozen leading healthcare organizations and serving over 35 million patients. Dale will talk about:
The basic approach to a late-binding data warehouse.
Pros and cons of early- versus late-binding.
The historical volatility in vocabulary and business rules.
How to predict the rate and specifics of volatility in the future.
New learnings and helpful advice based on numerous discussions, forums, and Interactions with many of you.
A robust, interactive question and answer period with attendees.
Data Science for Healthcare: What Today’s Leaders Must KnowHealth Catalyst
Healthcare leaders who understand data science can embrace the significant improvement potential of the industry’s vast data stores, including an estimated $300 billion in annual costs savings. Executives must know the value of data science to understand the urgency in investing and supporting the technology and data scientists to fully leverage data’s capabilities. Today’s data science-savvy executives will lead the healthcare transformation by enabling faster, more accurate diagnoses and more effective, lower-risk treatments.
Big data, RWE and AI in Clinical Trials made simpleHadas Jacoby
Technology is slowly but surely penetrating the healthcare industry in general and the clinical trials sector in particular. New and advanced solutions offer a variety of possibilities aimed to both improving existing processes and creating new and more efficient ones. And on top of all stands the desire to make clinical trials more patient centric.
In all of this, even though some of the technologies have yet to mature enough to meet the high quality standards necessary, it is important to know them and begin imagining the promise they hold for clinical trials.
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.
Harness Your Clinical and Financial Data with an Enterprise Health Informat...Perficient, Inc.
The importance of Enterprise Health Information Exchange (EHIE) as a key way to empower your physicians and patients and demonstrate meaningful use of electronic health records:
- Present the business case for EHIE as an important architecture that matters to progressive health systems
- Take a look at some of the market-leading EHIE architectures and products
- Provide real exam...ples of organizations that are using EHIE to improve their operations
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
Each year the CPT and the HCPCS code sets undergo significant changes, and your 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 one in a three-part series, with a CDM focus.
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 non-surgical sections of the CPT book.
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
Prepare for mandatory ICD-10 PCS diagnosis code updates, which take effect on October 1, 2023. By attending this 60-minute educational session, medical coders and healthcare professionals will gain a comprehensive understanding of the changes to the 2024 ICD-10 procedure codes and their guidelines, enabling accurate and compliant coding for optimal billing and reimbursement.
Vitalware Insight Into the 2024 ICD10 CM Updates.pdfHealth Catalyst
Prepare for mandatory ICD-10 CM diagnosis code updates, which take effect on October 1, 2023. By attending this 60-minute educational session, medical coders and healthcare professionals will gain a comprehensive understanding of the changes to the 2024 ICD-10 diagnosis codes and their guidelines, along with major complication or comorbidity (MCC), complication or comorbidity (CC), and Medicare Severity Diagnosis Related Groups (MS-DRGs) classification changes. With this information, professionals can ensure accurate and compliant diagnosis coding for optimal billing and reimbursement.
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
During this webinar you'll learn the following:
The importance of optimizing performance, reducing labor costs and sourcing talent given current market challenges.
Highlighting the need for a balanced approach to cost reduction.
How to reap the benefits of outsourcing (cost cutting, expertise, etc) while protecting yourself from the collateral damage that often comes with them.
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.
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
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Anti ulcer drugs and their Advance pharmacology ||
Anti-ulcer drugs are medications used to prevent and treat ulcers in the stomach and upper part of the small intestine (duodenal ulcers). These ulcers are often caused by an imbalance between stomach acid and the mucosal lining, which protects the stomach lining.
||Scope: Overview of various classes of anti-ulcer drugs, their mechanisms of action, indications, side effects, and clinical considerations.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
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2 Case Reports of Gastric Ultrasound
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
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Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
3. Healthcare Analytics Goal
Why have an EDW?
●It is a means to a greater end
●It exists to improve:
1. The effectiveness of care delivery (and safety)
2. The efficiency of care delivery (e.g. workflow)
3. Reduce Mean Time To Improvement (MTTI)
3
To help move to a more standardized system with more consistent and predictable outcomes, we have identified three areas of care delivery that need a systematic approach—an analytic system, a deployment system, and a content system.
Thank you Steve
Based on feedback from people like yourselves, we have designed a short analogy that compares Data Warehousing to Shopping. We use this analogy to highlight, in a non-technical way, the differences between the three models Steve just described.
Let’s start by looking at the Enterprise Shopping Model displayed on the screen. Notice that it is well organized and highly structured. Next I am going to put up on the screen a list of items you need to get at the store. I want you to map those items to the Enterprise Shopping Model. While doing that imagine you get a call from your significant other asking you to also get the following items.
Okay, now that you have attempted the exercise let’s take a quick poll.
Describe your experience with that exercise
Worked great
Frustrating
Stopped trying
I can see that many of you were frustrated even though the model was designed to have everything you needed and to make creating a shopping list easier. It lacked flexibility and it could not be easily adjusted to address the new requirement of making non-food purchases.
This shopping approach is much like the Enterprise Data Model which works very well in industries like retail and banking where what you need to capture is much more standardized and stable over time. Why this model breaks down in healthcare is because Medical knowledge is always expanding and changing so it is impossible to anticipate what the new data will look like and how it could fit into a model. Additionally, concepts like Length of Stay or Readmission Rates may have different definitions
Now let’s look at the Dimensional Shopping Model. In this model, instead of a shopping list, we have specific recipes that we need to create which are Chocolate Chip Cookies, a Cake and Apple Pie. Prior to the webinar you were sent a link to a short video that depicted this approach.
Let’s take a quick poll,
Did you watch the video prior to the start of the webinar?
For those of you that were not able to watch the video, I will high spot what was depicted. So we start with our shopper getting a call from the school board to bring cookies to the board meeting. She goes to the grocery store to get exactly what she needs to make the cookies, 2 cups of shortening, 4 cups of flour, 4 eggs and so on. Now imagine our shopper returning home to bake the cookies and getting a second call requesting she bring a cake too. So back to the store she goes to purchase many of the same ingredients. She returns home to get yet another call and the video ends as the exasperated shopper heads to the store for the third time.
So this Dimensional Shopping Trip started out fine for our shopper, getting just exactly what she needed but as soon as she added another recipe and another recipe she was making redundant trips to the store.
This shopping approach is like the Dimensional Data Model which starts out really great with a couple impressive point solutions for a few departments. But as the demand for analytics grows the model starts to become a mass of redundant data feeds from the source systems to multiple applications.
So for organization’s that are looking for an enterprise data strategy verses a few point solutions, they would quickly become encumbered by the Dimensional Data Model. Spending all their time designing trips to the store.
Now let’s explore our final model, the Adaptive Shopping model. In this exercise you are given a shopping card with a simple structure that allows you to indicate the store you are shopping and the items you need. Now using the same shopping list we saw earlier, imagine how you would fill out your Adaptive Shopping Model. Likely you selected a store you were familiar with and then you started organizing the items in a way the reflected the layout of the store. Now when we add new items to the shopping list, if they don’t fit within your first shopping list you can just take another card and so one.
Now let’s think about bringing all those items back to your house. Could you still make the recipes? You can and you can do it without all the redundant trips to the store.
This shopping approach is like Catalyst’s Adaptive Data model. The cards represented source systems that are brought into the Data Warehouse and these are going to match the transactional system exactly so it doesn’t take a lot of effort to bring that data into the warehouse. There is no manipulating or forcing the data into a model. Additionally, you have all the data you need for the analytic applications you know you know and for the requests that will come in the future. You can build all of these without having to go back out to the source for more data.
This approach works really well in healthcare because the data requirements are typically not well understood at the outset. With the Adaptive approach you can build incrementally adding source systems as you need them.
Time for another poll question. Which shopping approach would you prefer?
Enterprise
Dimensional
Late-Binding
Now no analogy works perfectly, but hopefully it will help you remember the characteristics and differences between each model.
A final poll question, Did this analogy help you better understand the differences between the three models?
Yes
No
I already understood the differences