This presentation is about basics of Big data Analytics along with Characteristics,Challenges,Structures,Differences between Traditional and Big data,How Big data is getting benefited in Healthcare Industry,Big data in Real time
Big data in the real world opportunities and challenges facing healthcare -...Leo Barella
The Healthcare system will be target of major disruption more than any other industry in the next 10 years.
The Digital economics and increasing demand by consumers for more real time information in order to make better decisions on who they want to "hire" to perform services for them or in their behalf will be the driver of this disruption. Analytics, Big Data and Machine Learning will lay the foundation for the next generation of healthcare yet there are still many challenges to truly revolutionize the healthcare system end to end (Providers, Pharma, Payers)
Big Data Analytics for Smart Health CareEshan Bhuiyan
Healthcare big data refers to the vast quantities of data that is now available to healthcare providers.
As a response to the digitization of healthcare information and the rise of value-based care, the industry has taken advantage of big data and analytics to make strategic business decisions.
This white paper offers a detailed perspective on how big data is impacting the healthcare industry and its underlying implication on the industry as a whole. It outlines the role of big data in healthcare, its benefits, core components and challenges faced by the healthcare sector towards full-fledged adoption & implementation.
This presentation is about basics of Big data Analytics along with Characteristics,Challenges,Structures,Differences between Traditional and Big data,How Big data is getting benefited in Healthcare Industry,Big data in Real time
Big data in the real world opportunities and challenges facing healthcare -...Leo Barella
The Healthcare system will be target of major disruption more than any other industry in the next 10 years.
The Digital economics and increasing demand by consumers for more real time information in order to make better decisions on who they want to "hire" to perform services for them or in their behalf will be the driver of this disruption. Analytics, Big Data and Machine Learning will lay the foundation for the next generation of healthcare yet there are still many challenges to truly revolutionize the healthcare system end to end (Providers, Pharma, Payers)
Big Data Analytics for Smart Health CareEshan Bhuiyan
Healthcare big data refers to the vast quantities of data that is now available to healthcare providers.
As a response to the digitization of healthcare information and the rise of value-based care, the industry has taken advantage of big data and analytics to make strategic business decisions.
This white paper offers a detailed perspective on how big data is impacting the healthcare industry and its underlying implication on the industry as a whole. It outlines the role of big data in healthcare, its benefits, core components and challenges faced by the healthcare sector towards full-fledged adoption & implementation.
Big Data Analytics for Healthcare Decision Support- Operational and ClinicalAdrish Sannyasi
Splunk’s data analytics platform could be utilized to solve many high impact business problems in healthcare delivery systems to reduce cost, improve patient outcome and safety, and enhance care coordination experience. Analyze observed behavior from healthcare event data and metadata to discover patterns, monitor compliance, and optimize the workflow. Furthermore 80% of healthcare data is unstructured (clinical free text and documentation), or semi-structured and many new data sources are such as tele health, mobile health, sensors, and devices are getting integrated in many healthcare systems specifically in the area of chronic disease management. So, one need analytics software that can harvest, interpret, enrich, normalize, and model diverse structured and unstructured data and analytics approaches that embrace the “data turmoil” by relying less on standardized data items and more on the capability to process data in any format.
By leveraging Big Data, the healthcare industry has an incredible potential to improve lives. This session will give examples of how data volume, velocity and variety is transforming the “art” of a doctor to the science of care. It will describe how the use of machine learning and massive amount of data will drive the new Consumer Drive healthcare movement.
Healthcare is changing rapidly. It is clear that humans need mechanisms to automate some parts of data processing and help humans in decision making. This talk will concentrate on how to improve the machine understanding of unstructured data.
Healthcare and Life Sciences organizations are leveraging Big Data technology to capture data in order to get a better insight into patient centric and research centric information. Combining these two requires extreme computing power. We will discuss use cases where Big Data technology was instrumental ; Merging Genomic and Clinical Data in order to advance personalized Medicine
HIMSS Analytics® - Healthcare IT State of the Market 2016HIMSS Analytics
'Now that you have all this data, what do you do with it?'
By now, everyone's got an EMR. And most providers are also making use of ancillary technologies to help harness patient data toward more efficient care and better outcomes. But many components of health IT are still surprisingly underused in the U.S. hospital market. "While the EMR market itself is pretty saturated, and usage has really improved since the HITECH Act, the challenge for hospitals and health systems is, now that you have all this data, what do you do with it?" says Matt Schuchardt, Director of Market Intelligence Solutions Sales at HIMSS Analytics.
There's no shortage of technologies out there to help hospitals improve operations. But it may surprise you to realize how relatively untapped they often still are.
HIMSS Analytics LOGIC keeps tabs on all manner of IT products, and its list of the tools with biggest positive growth potential points to where the market will be heading in the coming years.
This deck will provide you a high level overview of the State of the Market. We encourage you take part in a webinar with the full presentation on the topic from Matt Schuchardt on May 2nd at 2pm EST.
Sign up for the webinar through the HIMSS Learning Center here: http://ow.ly/10szOD
CIKM2020 Keynote: Accelerating discovery science with an Internet of FAIR dat...Michel Dumontier
Biomedicine has always been a fertile and challenging domain for computational discovery science. Indeed, the existence of millions of scientific articles, thousands of databases, and hundreds of ontologies, offer exciting opportunities to reuse our collective knowledge, were we not stymied by incompatible formats, overlapping and incomplete vocabularies, unclear licensing, and heterogeneous access points. In this talk, I will discuss our work to create computational standards, platforms, and methods to wrangle knowledge into simple, but effective representations based on semantic web technologies that are maximally FAIR - Findable, Accessible, Interoperable, and Reuseable - and to further use these for biomedical knowledge discovery. But only with additional crucial developments will this emerging Internet of FAIR data and services enable automated scientific discovery on a global scale.
bio:
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research focuses on the development of computational methods for scalable and responsible discovery science. Dr. Dumontier obtained his BSc (Biochemistry) in 1998 from the University of Manitoba, and his PhD (Bioinformatics) in 2005 from the University of Toronto. Previously a faculty member at Carleton University in Ottawa and Stanford University in Palo Alto, Dr. Dumontier founded and directs the interfaculty Institute of Data Science at Maastricht University to develop sociotechnological systems for responsible data science by design. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon 2020, the European Open Science Cloud, the US National Institutes of Health and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
This presentation was given on October 21, 2020 at CIKM2020.
The application of big data in health care is a fast-growing field, with many discoveries and methodologies published in the last five years. Big data refers to datasets that are not only big but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Moreover, medical data is one of the most growing data, as it is obtained from Electronic Health Records (EHRs) or patients themselves. Due to the rapid growth of such medical data, we need to provide suitable tools and techniques in order to handle and extract value and knowledge from these datasets to improve the quality of patient care and reduces healthcare costs. Furthermore, such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper presents an overview of big data content, sources, technologies, tools, and challenges in health care. It also intends to identify the strategies to overcome the challenges.
Med Device Vendors Have Big Opportunities in Health IT Software, Services, an...Shahid Shah
If you’re in the medical device manufacturing or hardware sales business your revenue growth (CAGR) is under pressure like never before. You’re being asked to do more with less but you’re probably going to find that hard to accomplish because of one or more of the following challenges:
* Longer product development timelines caused by more FDA and other government regulations
* Increased demand by customers to have your devices deliver user experiences that are more like “consumer” devices such as cell phones and tablets
* Lower margins as a reaction to commodity competition (your sensor hardware business will be commoditized faster and faster over time)
* More complex and longer sales cycles because devices are now being approved for sale not by facilities and clinical executives alone but increasingly by CIOs and IT teams
* Increased cost of risk management and compliance caused by connectivity requirements
Any one of these challenges is difficult to meet but these days you’re probably being asked to meet more than one simultaneously. The solutions are not simple but the good news is that medical device manufacturers have many revenue generation opportunities today that can fund the new strategic imperatives you’ll need to put into place to meet the challenges listed above.
This briefing, presented by Netspective CEO Shahid Shah, describes some of the opportunities and how device vendors can take advantage of them.
Part of the "2016 Annual Conference: Big Data, Health Law, and Bioethics" held at Harvard Law School on May 6, 2016.
This conference aimed to: (1) identify the various ways in which law and ethics intersect with the use of big data in health care and health research, particularly in the United States; (2) understand the way U.S. law (and potentially other legal systems) currently promotes or stands as an obstacle to these potential uses; (3) determine what might be learned from the legal and ethical treatment of uses of big data in other sectors and countries; and (4) examine potential solutions (industry best practices, common law, legislative, executive, domestic and international) for better use of big data in health care and health research in the U.S.
The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School 2016 annual conference was organized in collaboration with the Berkman Center for Internet & Society at Harvard University and the Health Ethics and Policy Lab, University of Zurich.
Learn more at http://petrieflom.law.harvard.edu/events/details/2016-annual-conference.
Are we FAIR yet? And will it be worth it?
The FAIR Principles propose essential characteristics that all digital resources (e.g. datasets, repositories, web services) should possess to be Findable, Accessible, Interoperable, and Reusable by both humans and machines. The Principles act as a guide that researchers and data stewards should expect from contemporary digital resources, and in turn, the requirements on them when publishing their own scholarly products. As interest in, and support for the Principles has spread, the diversity of interpretations has also broadened, with some resources claiming to already “be FAIR”.
This talk will elaborate on what FAIR is, what it entails, and how we should evaluate FAIRness. I will describe new social and technological infrastructure to support the creation and evaluation of FAIR resources, and how FAIR fits into institutional, national and international efforts. Finally, I will discuss the merits of the FAIR principles (and what we ask of people) in the context of strengthening data-driven scientific inquiry.Are we FAIR yet? And will it be worth it?
The FAIR Principles propose essential characteristics that all digital resources (e.g. datasets, repositories, web services) should possess to be Findable, Accessible, Interoperable, and Reusable by both humans and machines. The Principles act as a guide that researchers and data stewards should expect from contemporary digital resources, and in turn, the requirements on them when publishing their own scholarly products. As interest in, and support for the Principles has spread, the diversity of interpretations has also broadened, with some resources claiming to already “be FAIR”.
This talk will elaborate on what FAIR is, what it entails, and how we should evaluate FAIRness. I will describe new social and technological infrastructure to support the creation and evaluation of FAIR resources, and how FAIR fits into institutional, national and international efforts. Finally, I will discuss the merits of the FAIR principles (and what we ask of people) in the context of strengthening data-driven scientific inquiry.
Keynote given at NETTAB2018 - http://www.igst.it/nettab/2018/
BIG Data & Hadoop Applications in HealthcareSkillspeed
Explore the applications of BIG Data & Hadoop in Healthcare via Skillspeed.
BIG Data & Hadoop in Healthcare is a key differentiator, especially in terms of providing superior patient care. They are used for optimizing clinical trials, disease detection & boosting healthcare profitability.
To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com
PYA Highlights Next Steps of Meaningful UsePYA, P.C.
At the 2013 AICPA Healthcare Industry Conference, PYA Principal David McMillan and Senior Manager Chris Wilson recently explored the “new normal” of meaningful use as compliance and strategic standards in new care/reimbursement-model development.
Big Data Analytics for Healthcare Decision Support- Operational and ClinicalAdrish Sannyasi
Splunk’s data analytics platform could be utilized to solve many high impact business problems in healthcare delivery systems to reduce cost, improve patient outcome and safety, and enhance care coordination experience. Analyze observed behavior from healthcare event data and metadata to discover patterns, monitor compliance, and optimize the workflow. Furthermore 80% of healthcare data is unstructured (clinical free text and documentation), or semi-structured and many new data sources are such as tele health, mobile health, sensors, and devices are getting integrated in many healthcare systems specifically in the area of chronic disease management. So, one need analytics software that can harvest, interpret, enrich, normalize, and model diverse structured and unstructured data and analytics approaches that embrace the “data turmoil” by relying less on standardized data items and more on the capability to process data in any format.
By leveraging Big Data, the healthcare industry has an incredible potential to improve lives. This session will give examples of how data volume, velocity and variety is transforming the “art” of a doctor to the science of care. It will describe how the use of machine learning and massive amount of data will drive the new Consumer Drive healthcare movement.
Healthcare is changing rapidly. It is clear that humans need mechanisms to automate some parts of data processing and help humans in decision making. This talk will concentrate on how to improve the machine understanding of unstructured data.
Healthcare and Life Sciences organizations are leveraging Big Data technology to capture data in order to get a better insight into patient centric and research centric information. Combining these two requires extreme computing power. We will discuss use cases where Big Data technology was instrumental ; Merging Genomic and Clinical Data in order to advance personalized Medicine
HIMSS Analytics® - Healthcare IT State of the Market 2016HIMSS Analytics
'Now that you have all this data, what do you do with it?'
By now, everyone's got an EMR. And most providers are also making use of ancillary technologies to help harness patient data toward more efficient care and better outcomes. But many components of health IT are still surprisingly underused in the U.S. hospital market. "While the EMR market itself is pretty saturated, and usage has really improved since the HITECH Act, the challenge for hospitals and health systems is, now that you have all this data, what do you do with it?" says Matt Schuchardt, Director of Market Intelligence Solutions Sales at HIMSS Analytics.
There's no shortage of technologies out there to help hospitals improve operations. But it may surprise you to realize how relatively untapped they often still are.
HIMSS Analytics LOGIC keeps tabs on all manner of IT products, and its list of the tools with biggest positive growth potential points to where the market will be heading in the coming years.
This deck will provide you a high level overview of the State of the Market. We encourage you take part in a webinar with the full presentation on the topic from Matt Schuchardt on May 2nd at 2pm EST.
Sign up for the webinar through the HIMSS Learning Center here: http://ow.ly/10szOD
CIKM2020 Keynote: Accelerating discovery science with an Internet of FAIR dat...Michel Dumontier
Biomedicine has always been a fertile and challenging domain for computational discovery science. Indeed, the existence of millions of scientific articles, thousands of databases, and hundreds of ontologies, offer exciting opportunities to reuse our collective knowledge, were we not stymied by incompatible formats, overlapping and incomplete vocabularies, unclear licensing, and heterogeneous access points. In this talk, I will discuss our work to create computational standards, platforms, and methods to wrangle knowledge into simple, but effective representations based on semantic web technologies that are maximally FAIR - Findable, Accessible, Interoperable, and Reuseable - and to further use these for biomedical knowledge discovery. But only with additional crucial developments will this emerging Internet of FAIR data and services enable automated scientific discovery on a global scale.
bio:
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research focuses on the development of computational methods for scalable and responsible discovery science. Dr. Dumontier obtained his BSc (Biochemistry) in 1998 from the University of Manitoba, and his PhD (Bioinformatics) in 2005 from the University of Toronto. Previously a faculty member at Carleton University in Ottawa and Stanford University in Palo Alto, Dr. Dumontier founded and directs the interfaculty Institute of Data Science at Maastricht University to develop sociotechnological systems for responsible data science by design. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon 2020, the European Open Science Cloud, the US National Institutes of Health and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
This presentation was given on October 21, 2020 at CIKM2020.
The application of big data in health care is a fast-growing field, with many discoveries and methodologies published in the last five years. Big data refers to datasets that are not only big but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Moreover, medical data is one of the most growing data, as it is obtained from Electronic Health Records (EHRs) or patients themselves. Due to the rapid growth of such medical data, we need to provide suitable tools and techniques in order to handle and extract value and knowledge from these datasets to improve the quality of patient care and reduces healthcare costs. Furthermore, such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper presents an overview of big data content, sources, technologies, tools, and challenges in health care. It also intends to identify the strategies to overcome the challenges.
Med Device Vendors Have Big Opportunities in Health IT Software, Services, an...Shahid Shah
If you’re in the medical device manufacturing or hardware sales business your revenue growth (CAGR) is under pressure like never before. You’re being asked to do more with less but you’re probably going to find that hard to accomplish because of one or more of the following challenges:
* Longer product development timelines caused by more FDA and other government regulations
* Increased demand by customers to have your devices deliver user experiences that are more like “consumer” devices such as cell phones and tablets
* Lower margins as a reaction to commodity competition (your sensor hardware business will be commoditized faster and faster over time)
* More complex and longer sales cycles because devices are now being approved for sale not by facilities and clinical executives alone but increasingly by CIOs and IT teams
* Increased cost of risk management and compliance caused by connectivity requirements
Any one of these challenges is difficult to meet but these days you’re probably being asked to meet more than one simultaneously. The solutions are not simple but the good news is that medical device manufacturers have many revenue generation opportunities today that can fund the new strategic imperatives you’ll need to put into place to meet the challenges listed above.
This briefing, presented by Netspective CEO Shahid Shah, describes some of the opportunities and how device vendors can take advantage of them.
Part of the "2016 Annual Conference: Big Data, Health Law, and Bioethics" held at Harvard Law School on May 6, 2016.
This conference aimed to: (1) identify the various ways in which law and ethics intersect with the use of big data in health care and health research, particularly in the United States; (2) understand the way U.S. law (and potentially other legal systems) currently promotes or stands as an obstacle to these potential uses; (3) determine what might be learned from the legal and ethical treatment of uses of big data in other sectors and countries; and (4) examine potential solutions (industry best practices, common law, legislative, executive, domestic and international) for better use of big data in health care and health research in the U.S.
The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School 2016 annual conference was organized in collaboration with the Berkman Center for Internet & Society at Harvard University and the Health Ethics and Policy Lab, University of Zurich.
Learn more at http://petrieflom.law.harvard.edu/events/details/2016-annual-conference.
Are we FAIR yet? And will it be worth it?
The FAIR Principles propose essential characteristics that all digital resources (e.g. datasets, repositories, web services) should possess to be Findable, Accessible, Interoperable, and Reusable by both humans and machines. The Principles act as a guide that researchers and data stewards should expect from contemporary digital resources, and in turn, the requirements on them when publishing their own scholarly products. As interest in, and support for the Principles has spread, the diversity of interpretations has also broadened, with some resources claiming to already “be FAIR”.
This talk will elaborate on what FAIR is, what it entails, and how we should evaluate FAIRness. I will describe new social and technological infrastructure to support the creation and evaluation of FAIR resources, and how FAIR fits into institutional, national and international efforts. Finally, I will discuss the merits of the FAIR principles (and what we ask of people) in the context of strengthening data-driven scientific inquiry.Are we FAIR yet? And will it be worth it?
The FAIR Principles propose essential characteristics that all digital resources (e.g. datasets, repositories, web services) should possess to be Findable, Accessible, Interoperable, and Reusable by both humans and machines. The Principles act as a guide that researchers and data stewards should expect from contemporary digital resources, and in turn, the requirements on them when publishing their own scholarly products. As interest in, and support for the Principles has spread, the diversity of interpretations has also broadened, with some resources claiming to already “be FAIR”.
This talk will elaborate on what FAIR is, what it entails, and how we should evaluate FAIRness. I will describe new social and technological infrastructure to support the creation and evaluation of FAIR resources, and how FAIR fits into institutional, national and international efforts. Finally, I will discuss the merits of the FAIR principles (and what we ask of people) in the context of strengthening data-driven scientific inquiry.
Keynote given at NETTAB2018 - http://www.igst.it/nettab/2018/
BIG Data & Hadoop Applications in HealthcareSkillspeed
Explore the applications of BIG Data & Hadoop in Healthcare via Skillspeed.
BIG Data & Hadoop in Healthcare is a key differentiator, especially in terms of providing superior patient care. They are used for optimizing clinical trials, disease detection & boosting healthcare profitability.
To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com
PYA Highlights Next Steps of Meaningful UsePYA, P.C.
At the 2013 AICPA Healthcare Industry Conference, PYA Principal David McMillan and Senior Manager Chris Wilson recently explored the “new normal” of meaningful use as compliance and strategic standards in new care/reimbursement-model development.
Unit VI Case StudyAnimal use in toxicity testing has long been .docxdickonsondorris
Unit VI: Case Study
Animal use in toxicity testing has long been a controversial issue; however, there can be benefits. Read “The Use of Animals in Research,” which is an article that can be retrieved from http://www.toxicology.org/pubs/docs/air/AIR_Final.pdf.
Evaluate the current policies outlined in the Position Statement on page 5 of the article. Use the SOT Guiding Principles in the Use of Animals in Toxicology to guide you in your analysis. Feel free to use additional information and avenues of information, including the textbook, to critically analyze this policy.
In addition, answer the following questions:
How do toxicologists determine which exposures may cause adverse health effects?
How does the information apply to what you are learning in the course?
What were the objectives of this toxicity testing?
What were the endpoints of this toxicity testing?
Finally, include whether or not you agree with the Society of Toxicology's position on animal testing.
Your Case Study assignment should be three to four pages in length. Use APA style guidelines in writing this assignment, following APA rules for formatting, quoting, paraphrasing, citing, and referencing.
Adventure Works Marketing Plan
Centralizing Medical Information To Improve Patient Care
(
Centralizing Medical Information To Improve patient Care
)
Contents
Centralizing Medical Information To Improve patient Care0
Contents1
History2
Executive Summary2
High-Level Functional Requirements:4
Project Charter4
Business Problem Statement5
Project Scope5
Budget and Schedule6
Strategy6
SWOT ANALYSIS6
Technology Constraints7
Project Documentation and Communication9
Project Organization and Staffing Approach9
Project Value Statement9
History
The Affordable Care Act law was passed to improve healthcare for its citizens in the United States by increasing the people that have health insurance and by decreasing healthcare cost. A benefactor to this law is the Medicare/Medicaid program which provides medical coverage to the poor, elderly and disabled individuals which is funded by the federal government. The Federal government covers funding for Medicare programs while it provides reimbursement funds for Medicaid programs provided by the states. (The National Federation Of Independent Business V Sebellius, Secretary Of Health And Human Services, 2012). The primary benefits of the Affordable Care Act Law are covering more consumers with improved quality of services while reducing healthcare cost, access to healthcare, and consumer protection. (ASPA, 2014) Centers For Medicare and Medicaid Services (CMS) manages both of these programs and by modernizing and strengthening the current system they will be lowering cost and providing quality care. Executive Summary
The Center for Medicare and Medicaid (CMS) is the federal office to organized the integration of Medicaid and Medicare services across multiple agencies nationwide. Its purpose is to improve access to services, ...
Big Data: Implications of Data Mining for Employed Physician Compliance Manag...PYA, P.C.
PYA Principal Denise Hall, along with King & Spalding’s Michael Paulhus, co-presented “Big Data: Implications of Data Mining for Employed Physician Compliance Management” at the Health Care Compliance Association’s (HCCA) 19th Annual Compliance Institute.
SROA Presentation - Clinical Results of a Medical Error Reduction/Compliance ...edbkline
Clinical results from application of paper-based medical error reduction/compliance program vs software-based MERP program implenented at 30 free-standing radiation oncology centers.
The Alphabet Soup of Clinical Quality Measures ReportingBill Presley
CMS is transitioning to what the they call "a new and more responsive regulatory framework" for quality reporting and reimbursement. CMS goals are "…electronic health records helping physicians, clinicians, and hospitals to deliver better care, smarter spending, and healthier people". Over the next couple years, we will see a transformation of fee for service into value-based care models driven by the VBP, Quality Payment Program, MACRA, Merit-based Incentive Payment System (MIPS) and Alternative Payment Models (APM). Healthcare organizations will no longer be motivated by implementing and meeting Meaningful Use, but instead will be driven by value-based care and risk-based payment models that focus on quality outcomes for reimbursements.
In this Education Session we will review:
• How CMS is aligning clinical quality measures (CQMs) to reduce the reporting burden for healthcare organizations and providers. We will cover the vision and goals for achieving quality alignment for CMS.
• We will dive into the following CMS reporting programs and how they interact with each other: Value-Based Purchasing (VBP), Medicare Access and CHIP Reauthorization Act (MACRA), Merit-based Incentive Payments (MIPS), Hospital Inpatient Quality Reporting (IQR), The Joint Commission (ORYX), Outpatient Quality Reporting (OQR), and Alternative Payment Models (APM).
Aami hitech mu impact on the future on HC ITAmy Stowers
Relate the components of The HITECH Act and Meaningful Use to health management technology
Identify whether existing systems meet requirements
Communicate technology needs and request feedback from end users for a smooth transition
Implement best practices to move people and systems forward under these new requirements
What are the ways to improve your Revenue Cycle Management strategy (1).pdfRaj Joshi
Unlock the secrets to enhancing your Revenue Cycle Management (RCM) strategy with our comprehensive PDF guide. Explore proven methods and expert insights to optimize efficiency, reduce errors, and maximize revenue generation. Discover the key tactics and best practices that will propel your RCM strategy to new heights. Download now and revolutionize your healthcare revenue cycle operations.
2016 IBM Interconnect - medical devices transformationElizabeth Koumpan
Emerging technologies such as Internet of Things, 3D Printing are driving the creation of new business models and forcing the Industry for transformation. The product centric model where the Industry main objective was to develop the device, is moving to software and services model, with the focus on Big Data & Analytics, Integration and Cloud.
The maturation of technologies such as social, mobile, analytics, cloud, 3D printing, bio- and nanotechnology are rapidly shifting the competitive landscape. These emerging technologies create an environment that is connected and open, simple and intelligent, fast and scalable. Organizations must embrace disruptive technologies to drive innovation
10/12/22, 1:51 PM Print
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Part IV Best Practices in Healthcare Analytics Across the Ecosystem
10/12/22, 1:51 PM Print
https://content.uagc.edu/print/McNeill.2947.17.1?sections=part04,ch17,ch18,ch19,ch20,ch21,ch22,ch23,ch24,ch25,app01&content=all&clientToken=… 2/45
17 Overview of Healthcare Analytics Best Practices Across the Ecosystem
Dwight McNeill
Analytics in healthcare is old and new. Science has been a strong underpinning of healthcare in the research devoted to the discovery of causes and
treatments of disease. However, delivering this knowledge from the bench to the bedside to optimize the care of every patient has been an ongoing
challenge. Although treating sickness, that is, the interaction between a patient and her caregivers, is the raison d’être of healthcare, the industry is
more complex than that. The American way of healthcare requires large doses of payment, finance, regulation, research and development, and
administrative and business supports. Healthcare is a huge part of the U.S. economy, accounting for 18% of GDP at a spending rate of $8,500 for every
man, woman, and child.1 (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/ch17#ch17end01) And it is big business. Annual hospital expenses are
approaching $1 trillion, and physician services are more than $0.5 trillion. Both of these categories of providers amount to more than 50% of spending.
The next highest spending area is for prescription drugs, which amounts to 10% of spending.
Healthcare is both an informational and a personal business. It is personal because it deals with people, and communications and relationship skills
are fundamental to making change happen. It is informational in that it is about discovery, measurement, improvement, and running a business.
Analytics is the high octane fuel to feed the thirsty information engines. It holds the promise to improve people’s lives, increase revenues and reduce
costs, and to change the very nature of what healthcare is and what it can be.
Part IV (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/part04#part04) is on best practices and includes eight case studies of leading
organizations in healthcare analytics. These are bellwether organizations and represent the best of the art and science of analytics as of 2012. The case
studies are inclusive of the settings where analytics is practiced including providers, payers, and a life sciences company. It includes both the public
and private sectors.
The chapters in part IV (http://content.thuzelearning.com/books/McNeill.2947.17.1/sections/part04#part04) address the “whats” and “hows” of analytics
to support organizational strategies and goals. The whats include the domains of the content of analytics, including clinical, business, and marketing
purposes. The hows include the function.
Presenting an update on building BlueButton on FHIR at CMS to enable beneficiaries to point their data at applications, services or research programs they trust using a REST API and HL7 FHIR structured data formats.
Presented to CinderBlocks3 in Grantsville, MD on May 20th, 2016.
CSR Automation: Streamlining Clinical Study ReportingClinosolIndia
Clinical Study Reports (CSRs) play a pivotal role in communicating the results and findings of clinical trials. The traditional process of creating CSRs is resource-intensive and time-consuming. The integration of automation technologies offers a transformative solution to streamline CSR generation, enhancing efficiency, accuracy, and overall study reporting. This article explores the key aspects, benefits, and considerations associated with CSR automation.
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Department of Statistics
Faculty of Commerce and Accountancy
Chulalongkorn University
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
2. 2
Agenda
• Big Data in Government
• Challenge for Government healthcare
• The roles of government (include healthcare)
• Case Study:
• The Electronic Health Records Incentive Program
• Meaningful Use of Data
• Core Measure
• The future: connecting health to care
2
3. 3
Government Big Data
3
h"p://cacm.acm.org/magazines/2014/3/172509-‐big-‐data-‐applica?ons-‐in-‐the-‐government-‐sector/abstract
4. 4
Government Big Data
4
h"p://cacm.acm.org/magazines/2014/3/172509-‐big-‐data-‐applica?ons-‐in-‐the-‐government-‐sector/abstract
5. 5
Big Data in Government
5
h"p://cacm.acm.org/magazines/2014/3/172509-‐big-‐data-‐applica?ons-‐in-‐the-‐government-‐sector/abstract
6. 6
EHR Incentive Program
6
h"p://medicaleconomics.modernmedicine.com/medical-‐economics/content/tags/ehr-‐incen?ve-‐
program/financial-‐penal?es-‐nearing-‐physician-‐incen?ve?page=full
The
Medicare
and
Medicaid
Electronic
Health
Records
(EHR)
Incen;ve
Programs
will
provide
incen;ve
payments
to
eligible
professionals
and
eligible
hospitals
as
they
demonstrate
adop;on,
implementa;on,
upgrading,
or
meaningful
use
of
cer;fied
EHR
technology.
These
incen;ve
programs
are
designed
to
support
providers
in
this
period
of
Health
IT
transi;on
and
ins;ll
the
use
of
EHRs
in
meaningful
ways
to
help
our
na;on
to
improve
the
quality,
safety,
and
efficiency
of
pa;ent
health
care.
9. 9
9
Meaningful Use of Data
Meaningful
Use
is
a
Medicare
and
Medicaid
program
that
awards
incen;ves
for
using
cer;fied
electronic
health
records
(EHRs)
to
improve
pa;ent
care.
To
achieve
Meaningful
Use
and
avoid
penal;es,
providers
must
follow
a
set
of
criteria
that
serve
as
a
roadmap
for
effec;vely
using
an
EHR.
hPp://www.sfms.org/NewsPublica;on/SFMSBlog/TabId/467/PostId/1179/mu-‐audit-‐ca.aspx
10. 10
10
Core Measure for MU
Core
Measures
The
goal
of
"core
measures,"
is
not
to
measure.
Rather,
it's
to
drive
improved
performance
across
health
care
organiza;ons
and
depending
on
whom
you
ask,
it's
to
police
hospitals
to
ensure
certain
pa;ents
are
treated
in
line
with
what
has
been
determined
to
be
standard
and
necessary
based
on
their
diagnosis.
• Acute
Myocardial
Infarc;on
(AMI)
–
Heart
APack
• Heart
Failure
• Pneumonia
• Surgical
Care
Improvement
Project
(SCIP)
• Computerized
Provider
Order
Entry
(CPOE)
h"p://www.huffingtonpost.com/janet-‐dillione/easing-‐core-‐measure-‐pain-‐_b_1573695.html
13. 13
The
future:
connec4ng
health
to
care
13
h"p://healthit.gov/sites/default/files/na?onwide-‐c-‐roadmap-‐draM-‐version-‐1.0.pdf
Na4onwide
Interoperability
Roadmap
14. 14
The
future:
connec4ng
health
to
care
14
h"p://healthit.gov/sites/default/files/na?onwide-‐interoperability-‐roadmap-‐draM-‐
version-‐1.0.pdf
Na4onwide
Interoperability
Roadmap