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.
Learning Objectives:
Share common definitions of community
Summarize the importance of applying models to public health intervention design
Summarize the application of the Social Ecological Model
Describe the components of community that may have a role or influence on health behaviors
Health Care Analytics
Table of Content:
What is Healthcare Analytics
Objectives of Healthcare Analytics
Types of Analytics
Source of Data
What do Healthcare companies achieve with healthcare analytics
Booming technologies in the Healthcare Industries with some of their uses
Existing Healthcare analytics tool in the market
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Objectives of Healthcare Analytics
The fundamental objective of healthcare analytics is to help people make and execute rational decisions.
Data - Driven
Analytics in healthcare can help ensure that all decisions are made based on the best possible evidence derived from accurate and verified sources of information.
Transparent
Healthcare analytics can break down silos based on program, department or even facility by promoting the sharing of accurate, timely and accessible information
Verifiable
The selected option can be tested and verified, based on the available data and decision-making model, to be as good as or better than other alternatives.
Robust
Healthcare is a dynamic environment; decisions making models must be robust enough to perform in non-optimal conditions such as missing data, calculation error, failure to consider all available options and other issues.
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Types of Analytics
Descriptive Analytics
Uses business Intelligence and data mining to ask: “What has Happened”
Diagnostics Analytics
Examines data to answer, “Why did it happen ?”
Predictive Analytics
Uses optimization and simulation to ask: “What should we do”
Prescriptive Analytics
Uses optimization and simulation to ask: “What should we do”
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Sources of Data
Human Generated data
Web and social media data
Machine to Machine data
Transaction data
Biometric data
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What do Healthcare companies achieve with healthcare analytics
Hospitals
Reducing Cost
Reducing cost of analytics by building an easy-to-use analytics platform
Identifying and preventing anomalies such as fraud
Automating external and internal reporting
Improving patient outcomes
Clinical decision support
Pharmacy
Randomized clinical trials are expensive to conduct and are not effective at identifying rare events, heterogeneous treatment effects, long-term outcomes. Pharma companies rely on healthcare analytics to identify such relationships. However, inferring causal relations can be difficult as data can be easily misinterpreted to view unrelated factors as inter-dependent.
COVID-19 heightened chronic challenges within the global healthcare industry. It became a catalyst amid fierce competition and tight regulations for health providers and payers to focus on digital health, cybersecurity, patient data transparency, and a variety of customer-centric and operational enhancements. As a result, we found the 2022 trendline pointing to improvements in access and quality of care.
Healthcare challenges such as optimizing the cost of care while simultaneously enabling personalized interventions and consumer-friendly shoppable services are long-standing − but, historically, the industry has been slow to react.
Read our Top Trends 2022 report to examine the lingering ramifications of the pandemic, responses from medical and insurance organizations, and the worldwide impact of ever-changing regulatory standards and mandates.
Presentation by Adewale Troutman, MD, MPH, MA at the 2009 Virginia Health Equity Conference - Provides an overview of the health equity and social justice framework that is gaining support nationally as a paradigm to understand and address the root causes of health inequity. Highlights specific strategies being led by the National Association of County and City Health Officials (NACCHO) and the Louisville Metro Health Department to promote health equity.
Learning Objectives:
Share common definitions of community
Summarize the importance of applying models to public health intervention design
Summarize the application of the Social Ecological Model
Describe the components of community that may have a role or influence on health behaviors
Health Care Analytics
Table of Content:
What is Healthcare Analytics
Objectives of Healthcare Analytics
Types of Analytics
Source of Data
What do Healthcare companies achieve with healthcare analytics
Booming technologies in the Healthcare Industries with some of their uses
Existing Healthcare analytics tool in the market
-----------------------------------------------------------------------
Objectives of Healthcare Analytics
The fundamental objective of healthcare analytics is to help people make and execute rational decisions.
Data - Driven
Analytics in healthcare can help ensure that all decisions are made based on the best possible evidence derived from accurate and verified sources of information.
Transparent
Healthcare analytics can break down silos based on program, department or even facility by promoting the sharing of accurate, timely and accessible information
Verifiable
The selected option can be tested and verified, based on the available data and decision-making model, to be as good as or better than other alternatives.
Robust
Healthcare is a dynamic environment; decisions making models must be robust enough to perform in non-optimal conditions such as missing data, calculation error, failure to consider all available options and other issues.
-------------------------------------------------------------------------------
Types of Analytics
Descriptive Analytics
Uses business Intelligence and data mining to ask: “What has Happened”
Diagnostics Analytics
Examines data to answer, “Why did it happen ?”
Predictive Analytics
Uses optimization and simulation to ask: “What should we do”
Prescriptive Analytics
Uses optimization and simulation to ask: “What should we do”
----------------------------------------------------------------------------------
Sources of Data
Human Generated data
Web and social media data
Machine to Machine data
Transaction data
Biometric data
---------------------------------------------------------------------------------
What do Healthcare companies achieve with healthcare analytics
Hospitals
Reducing Cost
Reducing cost of analytics by building an easy-to-use analytics platform
Identifying and preventing anomalies such as fraud
Automating external and internal reporting
Improving patient outcomes
Clinical decision support
Pharmacy
Randomized clinical trials are expensive to conduct and are not effective at identifying rare events, heterogeneous treatment effects, long-term outcomes. Pharma companies rely on healthcare analytics to identify such relationships. However, inferring causal relations can be difficult as data can be easily misinterpreted to view unrelated factors as inter-dependent.
COVID-19 heightened chronic challenges within the global healthcare industry. It became a catalyst amid fierce competition and tight regulations for health providers and payers to focus on digital health, cybersecurity, patient data transparency, and a variety of customer-centric and operational enhancements. As a result, we found the 2022 trendline pointing to improvements in access and quality of care.
Healthcare challenges such as optimizing the cost of care while simultaneously enabling personalized interventions and consumer-friendly shoppable services are long-standing − but, historically, the industry has been slow to react.
Read our Top Trends 2022 report to examine the lingering ramifications of the pandemic, responses from medical and insurance organizations, and the worldwide impact of ever-changing regulatory standards and mandates.
Presentation by Adewale Troutman, MD, MPH, MA at the 2009 Virginia Health Equity Conference - Provides an overview of the health equity and social justice framework that is gaining support nationally as a paradigm to understand and address the root causes of health inequity. Highlights specific strategies being led by the National Association of County and City Health Officials (NACCHO) and the Louisville Metro Health Department to promote health equity.
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.
Digital Health Market has exploded in the last few years. Will that continue? What are the main areas of growth in digital days and what the future will bring us.
Health Equity: Why it Matters and How to Achieve itHealth Catalyst
According to the Robert Wood Johnson Foundation, health equity is achieved when everyone can attain their full health potential and no one is disadvantaged from achieving this potential because of social position of any other socially defined circumstance.
Without health equity, there are endless social, health, and economic consequences that negatively impact patients, communities, and organizations. The U.S. ranks last on measures of health equity compared to other industrialized countries. Healthcare contributes to this problem in many ways, including ignoring clinician biases toward certain populations and overlooking the importance of social determinants of health.
Fortunately, there are effective, tested steps organizations can take to tackle their health inequities and disparities (e.g., incorporating nonmedical vital signs into their health assessment processes and partnering with community organizations to connect underserved populations with the services they need to be healthy). Some health systems, such as Allina Health, have achieved impressive results by making health equity a systemwide strategic priority.
The Biggest Barriers to Healthcare InteroperabilityHealth Catalyst
Improving healthcare interoperability is a top priority for health systems today. Fundamental problems around improving interoperability include standardization of terminology and normalization of data to those standards. And, the volume of data healthcare IT systems produce exacerbates these problems.
While interoperability regulations focus on trying to make it easy to find and exchange patient data across multiple organizations and HIEs, the legislation’s lack of fine print and aggressive implementation timelines nearly ensures the proliferation of existing interoperability problems. This article discusses the biggest barriers to interoperability, possible solutions to interoperability problems, and why it matters.
At the end of this presentation you will be able to:
Define evidence-based practice
Describe process & outline steps of EBP
Understand PICO elements & search strategy
Identify resources to support EBP
The focus of this presentation is nursing practice, although it is still of value to physicians and other health care professionals.
Understand why hospitals must take the lead in eliminating disparities in care
Learn about the various dimensions of health care disparities. This presentation provides a background on the factors contributing to health care disparities, the ways in which race, ethnicity and language (REaL) data may be applied to improve health equity, as well as strategies through which to enhance the collection of REaL data.
Authors: Bohr D, Bostick N
Comprehensive geriatric assessment (CGA) is a multidimensional, interdisciplinary diagnostic process to determine the medical, psychological and functional capabilities of a frail elderly person in order to develop a co-ordinated and integrated plan for treatment and long-term follow up
Microsoft: A Waking Giant in Healthcare Analytics and Big DataDale Sanders
Ten years ago, critics didn’t believe that Microsoft could scale in the second generation of relational data warehouses, but they did. More recently, many of these same pundits have criticized Microsoft for missing the technology wave du jour in cloud offerings, mobile technology, and big data. But, once again, Microsoft has been quietly reengineering its culture and products, and as a result, they now offer the best value and most visionary platform for cloud services, big data, and analytics in healthcare.
Break All The Rules: What the Leading Health Systems Do Differently with Anal...Dale Sanders
This was my attempt to capture the intangible differences between leaders and followers in data driven healthcare. It should be noted that the organizations listed are not necessarily Health Catalyst clients. This slide deck is not intended to market or advertise Health Catalyst, but rather highlight leadership in analytics, wherever it exists.
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.
Digital Health Market has exploded in the last few years. Will that continue? What are the main areas of growth in digital days and what the future will bring us.
Health Equity: Why it Matters and How to Achieve itHealth Catalyst
According to the Robert Wood Johnson Foundation, health equity is achieved when everyone can attain their full health potential and no one is disadvantaged from achieving this potential because of social position of any other socially defined circumstance.
Without health equity, there are endless social, health, and economic consequences that negatively impact patients, communities, and organizations. The U.S. ranks last on measures of health equity compared to other industrialized countries. Healthcare contributes to this problem in many ways, including ignoring clinician biases toward certain populations and overlooking the importance of social determinants of health.
Fortunately, there are effective, tested steps organizations can take to tackle their health inequities and disparities (e.g., incorporating nonmedical vital signs into their health assessment processes and partnering with community organizations to connect underserved populations with the services they need to be healthy). Some health systems, such as Allina Health, have achieved impressive results by making health equity a systemwide strategic priority.
The Biggest Barriers to Healthcare InteroperabilityHealth Catalyst
Improving healthcare interoperability is a top priority for health systems today. Fundamental problems around improving interoperability include standardization of terminology and normalization of data to those standards. And, the volume of data healthcare IT systems produce exacerbates these problems.
While interoperability regulations focus on trying to make it easy to find and exchange patient data across multiple organizations and HIEs, the legislation’s lack of fine print and aggressive implementation timelines nearly ensures the proliferation of existing interoperability problems. This article discusses the biggest barriers to interoperability, possible solutions to interoperability problems, and why it matters.
At the end of this presentation you will be able to:
Define evidence-based practice
Describe process & outline steps of EBP
Understand PICO elements & search strategy
Identify resources to support EBP
The focus of this presentation is nursing practice, although it is still of value to physicians and other health care professionals.
Understand why hospitals must take the lead in eliminating disparities in care
Learn about the various dimensions of health care disparities. This presentation provides a background on the factors contributing to health care disparities, the ways in which race, ethnicity and language (REaL) data may be applied to improve health equity, as well as strategies through which to enhance the collection of REaL data.
Authors: Bohr D, Bostick N
Comprehensive geriatric assessment (CGA) is a multidimensional, interdisciplinary diagnostic process to determine the medical, psychological and functional capabilities of a frail elderly person in order to develop a co-ordinated and integrated plan for treatment and long-term follow up
Microsoft: A Waking Giant in Healthcare Analytics and Big DataDale Sanders
Ten years ago, critics didn’t believe that Microsoft could scale in the second generation of relational data warehouses, but they did. More recently, many of these same pundits have criticized Microsoft for missing the technology wave du jour in cloud offerings, mobile technology, and big data. But, once again, Microsoft has been quietly reengineering its culture and products, and as a result, they now offer the best value and most visionary platform for cloud services, big data, and analytics in healthcare.
Break All The Rules: What the Leading Health Systems Do Differently with Anal...Dale Sanders
This was my attempt to capture the intangible differences between leaders and followers in data driven healthcare. It should be noted that the organizations listed are not necessarily Health Catalyst clients. This slide deck is not intended to market or advertise Health Catalyst, but rather highlight leadership in analytics, wherever it exists.
Managing National Health: An Overview of Metrics & OptionsDale Sanders
This is a presentation that I gave at the annual international healthcare conference hosted by the Cayman Islands government. It summarizes the international standards and frameworks for planning and managing the health of a nation. One of the most fun parts of a very fun career was the time that I spent working and living in the Cayman Islands and serving as the CIO of the national health system. The Cayman Islands national health system sat at the intersection of three very influential healthcare ecosystems-- the United States, United Kingdom, and the Pan-American Healthcare Organization. As a result, I was fortunate enough to learn from these international settings and contrast that to the US healthcare system. Other healthcare systems tend to benchmark themselves internationally more so than the United States, where we tend to benchmark ourselves internally. Unfortunately, those internal US benchmarks are the lowest in the developed world by almost every measure of national health.
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?
Predicting the Future of Predictive Analytics in HealthcareDale Sanders
This is the latest version of a slide deck that discusses some of the less technical, but very important issues, related to the effective use of predictive analytics in healthcare.
Healthcare Best Practices in Data Warehousing & AnalyticsDale Sanders
This is from a class lecture that I gave in 2005. Rather dated, but 95% of content is still very relevant today, which is a bit unfortunate. That's an indication of how little we've progressed in the healthcare domain.
Introduction to Population Health Analytics, Predictive Analytics, Big Data a...Frank Wang
UNH HCAD 6635 Healthcare Analytics Session 12, the last session of Health Information Analytics. Details of the topics of this session will be covered in HCAD 6637 "Advanced Analytics and Health Data Mining"
This presentation shows reco4j features and vision. In particular we add the new concept of context aware recommendation and how we integrate it into reco4j. See the project site for more details here: http://www.reco4j.org
These slides were presentet at Munich Meetup of April 18th. They present the reco4j project, its high view and it vision.
See the project site for more details here: http://www.reco4j.org
This presentation shows reco4j features and vision. In particular we add the new concept of context aware recommendation and how we integrate it into reco4j. In this new presentation there is also some piece of code that show how simple is integrate our software. See the project site for more details here: http://www.reco4j.org
The social graph of Facebook is the most popular application for a graph database. In addition, there are far more exciting applications, such as spatial data, financial trail, indexing, and others. If you combine different graphs, you are able to evaluate those together with the algorithms known from the graph theory. As a graph, a domain can often be easier and more natural designed. This talk introduces the topic of graph databases and shows how to implement mediated models with large, complex and highly connected data with Neo4j. Subsequently, topics like querying, indexing, import / export are considered as well.
Precise Patient Registries for Clinical Research and Population ManagementDale Sanders
Patient registries have evolved from external, mandatory reporting databases to playing a critical role in internal clinical research, clinical quality, cost reduction, and population health management. This slide deck describes how to design those precise registries.
Strategic Options for Analytics in HealthcareDale Sanders
There are essentially four analytic strategies available in the healthcare IT market at present. This slide summarizes those options, the pros and cons, and vendors in the space.
Healthcare Billing and Reimbursement: Starting from ScratchDale Sanders
The healthcare billing environment in the US is a disaster. It creates huge waste in care and cost. As presented at the Cayman Islands International Healthcare Conference in October 2010, this slide deck suggests what the billing system might look like, if we could start over.
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.
Why Precise, Tailored Patient Registries Lead to Cost-Effective Care Manageme...Health Catalyst
Early this year, CMS began a per member per month reimbursement for Medicare beneficiaries with two or more chronic conditions. It immediately validated the need for care management programs. Three models are used to measure the savings of an effective care management program:
Historical or intent-to-treat design
Matching comparison design
Randomized control design
All three place a heavy reliance on data and precise, tailored patient registries. Reliable patient registries are one of the most valuable tools in the care management toolbox. And the means to that reliability is an enterprise data warehouse, which essentially gives program managers an all-access pass to stratifying patient risk and leads to a more successful population health initiative.
In Pursuit of the Patient Stratification Gold Standard: Getting There with He...Health Catalyst
Even the healthiest among us would benefit from some level of care management, but resources are limited and patients must be stratified to facilitate prioritized enrollment into care management programs. Therefore, health systems need to identify not only high-cost, high-risk, and rising-risk patients, but also patients who are truly impactable.
This article explains how systems can use healthcare analytics, at varying levels of maturity, to improve patient stratification and, ultimately, achieve the gold standard:
Level 1 (where to start): use healthcare analytics to identify high-cost, high-risk patients in a population.
Level 2: use healthcare analytics to identify patients with rising-risk profiles.
Level 3 (highest level of maturity): use healthcare analytics to identify patients who are truly impactable (the patient stratification gold standard).
Analytics is key to achieving the patient stratification gold standard, but should enhance (not replace) clinical judgement. Stratification lists need to go through workflows in which clinicians remove patients that aren’t appropriate for enrollment.
Quality Data is Essential for Doctors Concerned with Patient EngagementHealth Catalyst
It might be a bit of a leap to associate quality data with improving the patient experience. But the pathway is apparent when you consider that physicians need data to track patient diagnoses, treatments, progress, and outcomes. The data must be high quality (easily accessible, standardized, comprehensive) so it simplifies, rather than complicates, the physician’s job. This becomes even more important in the pursuit of population health, as care teams need to easily identify at-risk patients in need of preventive or follow-up care. Patients engaged in their own care via portals and personal peripherals contribute to the volume and quality of data and feel empowered in the process. This physician and patient engagement leads to improved care and outcomes, and, ultimately, an improved patient experience.
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.
Five Data-driven Patient Empowerment StrategiesHealth Catalyst
Data plays a big role toward empowering patients to become more involved in their care. With data, digital tools, and education, patient empowerment can act like a blockbuster drug to produce exceptional outcomes.
Data empowers patients five ways:
Promotes patient engagement.
Produces patient-centered outcomes.
Helps patients practice self-care.
Improves communication with clinicians.
Leads to faster healing and independence.
Clinicians using creative, innovative care strategies, and patients with access to the right tools and technology, can produce remarkable results in terms of cost, health outcomes, and experience.
DATA-DRIVEN CARE: THE KEY TO ACCOUNTABLE CARE DELIVERY FROM A PHYSICIAN GROUP...Health Catalyst
Hospitals, payers and physician groups alike are facing changes in healthcare that require their attention. These changes are a result of financial forces that are changing the ways healthcare services are paid, cost of care pressures, ever-changing patient population behaviors, improvements in the science of health care and federal regulations tied to incentives that are soon turning to penalties. Anyone in health care is grappling to understand these changes and chart their strategies to be prepared for the future.
The presenters have proven expertise developing their strategies to care for patients in an accountable care model using data to drive their strategies. The presenting organizations will talk through their strategy including their future expectations and early results using data to identify improvement opportunities and to shift the clinical approach to health care. In addition to strategy, they will share solutions and analytic applications critical to the current and future expected results of their strategy.
Effective Patient Stratification: Four Solutions to Common HurdlesHealth Catalyst
Accurate patient stratification, the first step of any effective population health strategy, identifies patients who will benefit most from a population health intervention. Successful patient stratification is critical when laying the foundation for any population health initiative, yet many health systems struggle with this step.
Care teams can apply four solutions to overcome common patient stratification hurdles, target the most impactable patients, and carry out population health initiatives:
Consider both the physical and the mental.
Prove and measure return on investment.
Complete data sets.
Transparent, customizable technology.
Precise Patient Registries: The Foundation for Clinical Research & Population...Health Catalyst
Join Dale Sanders as he shares his experience in developing disease registries, the history of patient registries, and the current design patterns in data engineering to create highly precise registries to support clinical research and population health management.
Topics:
*How the definition of the term “patient registry" has evolved from being associated with a federal- or state-mandated reporting requirement to a hospital or health system’s own population of patients, including device registries, drug registries, and procedure registries.
*Why engaging certain populations via group registries allows them to better understand their conditions and reach out for support from others who share their condition.
*Several untapped benefits of registries for disease and quality management.
*When to utilize patient registries to guide decision-making and drive change, especially at the point of care.
*Which of the critical steps to building a disease registry is most important.
*The keys to winning organizational support in order to implement a successful registry initiative.
*Precise patient registries play a significant role in the management of a broad variety of healthcare processes, including chronic diseases and conditions, as well as clinical research.
Understanding how registries are currently built vs. how they should be built is critical to the future of healthcare outcomes improvement, cost reduction, and translational research.
2015 and Beyond: 6 Predictions for Healthcare and Population HealthHealth Catalyst
Healthcare will undergo a number of changes in 2015, particularly as organizations look to manage population health. Dr. David A. Burton outlines what he believes will happen in terms of at-risk contracting, risk evaluation, network optimization, quality and safety, cost reduction, and infrastructure, and how 2015 can develop into opportunity for all.
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.
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?
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.
Medical Practices’ Survival Depends on Four Analytics StrategiesHealth Catalyst
With limited resources compared to large healthcare organizations and fewer personnel to shoulder burdens like COVID-19, medical practices must find ways to deliver better care with less. Delivering quality care, especially in a pandemic, is challenging, but analytics insight can guide effective care delivery methods, especially for smaller practices.
Comprehensive data combined with team members who can turn numbers into real-world information are essential for medical practices to ensure a strong financial, clinical, and operational future. Independent medical practices can rely on four analytics strategies to survive the uncertain healthcare market and plan for a sustainable future:
Prioritize access to up-to-date, comprehensive data sources.
Form a multidisciplinary approach to data governance.
Translate data into analytics insight.
Invest in analytics infrastructure to support rapid response.
The Philosophy, Psychology, and Technology of Data in HealthcareDale Sanders
Over-application of data and analytics in healthcare is alienating clinicians and, for the most part, not bending the cost-quality curves. This lecture spends 60% of the time on the softer issues, 40% on the technology.
Healthcare Analytics Summit Keynote Fall 2017Dale Sanders
The Data Operating System. Changing the Digital Trajectory of Healthcare. Why do we need to change the current digital trajectory? What’s the business case for a Data Operating System? What is a Data Operating System and how did we get here? What difference will DOS make? What should we do with it and what should we expect?
Why should we care about integrating data? What should we be trying to achieve? Population Health. The Softer, Human Side of Being “Data Driven” not “Driven By Data." The New Era of Decision Support in Healthcare. Top 10 Challenges To Integrating External Data.
The Data Operating System: Changing the Digital Trajectory of HealthcareDale Sanders
This is the next evolution in health information exchanges and data warehouses, specifically designed to support analytics, transaction processing, and third party application development, in one platform, the Data Operating System.
Data Driven Clinical Quality and Decision SupportDale Sanders
From a lecture about the use of data warehousing, analytics, and point of care clinical decision support to improve the quality and reduce the cost of healthcare.
Adv. biopharm. APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMSAkankshaAshtankar
MIP 201T & MPH 202T
ADVANCED BIOPHARMACEUTICS & PHARMACOKINETICS : UNIT 5
APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMS By - AKANKSHA ASHTANKAR
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
The Gram stain is a fundamental technique in microbiology used to classify bacteria based on their cell wall structure. It provides a quick and simple method to distinguish between Gram-positive and Gram-negative bacteria, which have different susceptibilities to antibiotics
Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
Basavarajeeyam is a Sreshta Sangraha grantha (Compiled book ), written by Neelkanta kotturu Basavaraja Virachita. It contains 25 Prakaranas, First 24 Chapters related to Rogas& 25th to Rasadravyas.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
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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.
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.