HIMSS Analytics, with a goal of helping healthcare organizations understand and advance healthcare analytics, has developed the Adoption Model for Analytics Maturity (AMAM) published here on www.SlideShare.net for healthcare industry reference.
This 8 stage international prescriptive analytics oriented maturity model offers an easy assessment and a detailed industry specific road map to help healthcare providers interested in analytics advance their capabilities.
For further information please see www.HIMSSAnalytics.org
Digitalisation Of Healthcare - Towards A Better Future - Free Download E bookkevin brown
Digital health has been around for quite some
time. Advancements in technology, rising
demand for better care, and governments' focus
on improved health economy have contributed
to the digital transformation in the healthcare
sector. Healthcare providers and professionals
are continuously challenged to come up with
innovative and cost-effective ways of providing
effective care and better patient outcomes.
In the past few years, digital technologies
have changed the healthcare landscape into
becoming more patient-centric, with care givers
focusing on engaging patients and improving
their experiences.
According a Deloitte report, global healthcare
spending is estimated to cross US$10 trillion by
2022. As the global healthcare market embraces
digitalisation, innovation has a major role to
play. Healthcare companies have been investing
heavily in digital technologies to drive innovation
and value-based care, while making care giving
more accessible and efficient. Digitalisation results
in better usage of patient data by care givers
enabling them to offer personalised healthcare
to the patients.
Digital Healthcare Trends: Transformation Towards Better Care RelationshipKumaraguru Veerasamy
Digital health encompasses digital care programs, technologies with health, healthcare, living, and society to enhance the efficiency of healthcare delivery and to make medicine more personalized and precise. With the increasing adoption of telemedicine, wearable devices, mobile health apps (especially during the recent COVID-19 pandemic) and VR/AR; digital health is poised to take healthcare forward.
Bangladesh Directorate General of Family Planning implements the DHIS2 in collaboration with USAID eMIS partners (MEASURE Evaluation, MNCSP, icddrb) and UNFPA.
International Classification of Health Interventions - development phase 2014...Bedirhan Ustun
This slide describes how an international code set could be created using an ontology approach: creating a CONTENT MODEL and populating the model with examples from ICHI-alpha and AMA's CPT. We have proposed a collaborating arrangement to make this technical view applied taking note of the legal and other organizational concerns.
Chronic illness: 75% of health system costs in North America
* Reimbursement models & care pathways focused
on disease management will continue to escalate
HIMSS Analytics, with a goal of helping healthcare organizations understand and advance healthcare analytics, has developed the Adoption Model for Analytics Maturity (AMAM) published here on www.SlideShare.net for healthcare industry reference.
This 8 stage international prescriptive analytics oriented maturity model offers an easy assessment and a detailed industry specific road map to help healthcare providers interested in analytics advance their capabilities.
For further information please see www.HIMSSAnalytics.org
Digitalisation Of Healthcare - Towards A Better Future - Free Download E bookkevin brown
Digital health has been around for quite some
time. Advancements in technology, rising
demand for better care, and governments' focus
on improved health economy have contributed
to the digital transformation in the healthcare
sector. Healthcare providers and professionals
are continuously challenged to come up with
innovative and cost-effective ways of providing
effective care and better patient outcomes.
In the past few years, digital technologies
have changed the healthcare landscape into
becoming more patient-centric, with care givers
focusing on engaging patients and improving
their experiences.
According a Deloitte report, global healthcare
spending is estimated to cross US$10 trillion by
2022. As the global healthcare market embraces
digitalisation, innovation has a major role to
play. Healthcare companies have been investing
heavily in digital technologies to drive innovation
and value-based care, while making care giving
more accessible and efficient. Digitalisation results
in better usage of patient data by care givers
enabling them to offer personalised healthcare
to the patients.
Digital Healthcare Trends: Transformation Towards Better Care RelationshipKumaraguru Veerasamy
Digital health encompasses digital care programs, technologies with health, healthcare, living, and society to enhance the efficiency of healthcare delivery and to make medicine more personalized and precise. With the increasing adoption of telemedicine, wearable devices, mobile health apps (especially during the recent COVID-19 pandemic) and VR/AR; digital health is poised to take healthcare forward.
Bangladesh Directorate General of Family Planning implements the DHIS2 in collaboration with USAID eMIS partners (MEASURE Evaluation, MNCSP, icddrb) and UNFPA.
International Classification of Health Interventions - development phase 2014...Bedirhan Ustun
This slide describes how an international code set could be created using an ontology approach: creating a CONTENT MODEL and populating the model with examples from ICHI-alpha and AMA's CPT. We have proposed a collaborating arrangement to make this technical view applied taking note of the legal and other organizational concerns.
Chronic illness: 75% of health system costs in North America
* Reimbursement models & care pathways focused
on disease management will continue to escalate
5 healthcare technology transformation trends to watch out for in 2017Rahul Gupta
Healthcare is all set to undergo a massive technology/ Digital transformation in 2017. The slides talk about the current challenges faced by the US Healthcare sector, the key technology transformation to watch out for and how they stack up on the hype cycle
Overcoming Barriers to Scale in Digital TherapeuticsChris Hogg
Presentation at Clinically Validated DTx Conference in Boston (November 2019). What paths have DTx products taken toward commercialization, what are the barriers, what is changing?
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.
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.
Large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). Those data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. More details are available here http://dmkd.cs.wayne.edu/TUTORIAL/Healthcare/
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.
Short overview over possibilities and challenges of using artificial intelligence in health care. Presentation from the MultiHelix ThinkTank, May 14 2020.
The Future of Health insurance in a digital World - The Digital Insurer The Digital Insurer
Hugh Terry delivered a presentation attempting to look at the future of the industry in 2035 . The presentation has an Asian perspective and was delivered on 15th June at the Swiss Re 2017 ARMS conference in Fukuoka Japan
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.
Predictive Analytics: It's The Intervention That MattersHealth Catalyst
In this two-part webinar, get the detailed knowledge you need to make informed decisions about adopting predictive analytics in healthcare so you can separate today's hype from reality. In part 1, you'll learn key learnings from Dale Sanders including 1) our fixation on predictive analytics in readmissions, 2) the common trap of predictions without interventions, 3) the common misconceptions of correlations verses causation, 4) examples of predictions without algorithms, and 5) the importance of putting the basics first.
In part 2, you'll hear from industry expert David Crockett, PhD in a "graduate level" crash course cover key concepts such as machine learning, algorithms, feature selection, classification, tools and more.
5 healthcare technology transformation trends to watch out for in 2017Rahul Gupta
Healthcare is all set to undergo a massive technology/ Digital transformation in 2017. The slides talk about the current challenges faced by the US Healthcare sector, the key technology transformation to watch out for and how they stack up on the hype cycle
Overcoming Barriers to Scale in Digital TherapeuticsChris Hogg
Presentation at Clinically Validated DTx Conference in Boston (November 2019). What paths have DTx products taken toward commercialization, what are the barriers, what is changing?
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.
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.
Large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). Those data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. More details are available here http://dmkd.cs.wayne.edu/TUTORIAL/Healthcare/
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.
Short overview over possibilities and challenges of using artificial intelligence in health care. Presentation from the MultiHelix ThinkTank, May 14 2020.
The Future of Health insurance in a digital World - The Digital Insurer The Digital Insurer
Hugh Terry delivered a presentation attempting to look at the future of the industry in 2035 . The presentation has an Asian perspective and was delivered on 15th June at the Swiss Re 2017 ARMS conference in Fukuoka Japan
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.
Predictive Analytics: It's The Intervention That MattersHealth Catalyst
In this two-part webinar, get the detailed knowledge you need to make informed decisions about adopting predictive analytics in healthcare so you can separate today's hype from reality. In part 1, you'll learn key learnings from Dale Sanders including 1) our fixation on predictive analytics in readmissions, 2) the common trap of predictions without interventions, 3) the common misconceptions of correlations verses causation, 4) examples of predictions without algorithms, and 5) the importance of putting the basics first.
In part 2, you'll hear from industry expert David Crockett, PhD in a "graduate level" crash course cover key concepts such as machine learning, algorithms, feature selection, classification, tools and more.
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
Suggested ResourcesThe resources provided here are optional. You.docxdeanmtaylor1545
Suggested Resources
The resources provided here are optional. You may use other resources of your choice to prepare for this assessment; however, you will need to ensure that they are appropriate, credible, and valid. The MHA-FP5064 Health Care Information Systems Analysis and Design for Administrators Library Guide can help direct your research, and the Supplemental Resources and Research Resources, both linked from the left navigation menu in your courseroom, provide additional resources to help support you.
The Role of Informatics in Health Care
The following articles address the increasingly important role of informatics, which may provide useful insight when examining the data needs of an organization.
· Centers for Medicare & Medicaid Services. (2017). Data and program reports. Retrieved from https://www.cms.gov/regulations-and-guidance/legislation/ehrincentiveprograms/dataandreports.html
. The Web page provides access to Medicare and Medicaid Electronic Health Records Incentive Program payment and registration data contained in various reports.
· Chen, M., Lukyanenko, R., & Tremblay, M. C. (2017). Information quality challenges in shared healthcare decision making. Journal of Data and Information Quality (JDIQ), 9(1), 1–3.
. Discusses the challenges for patients in making sense of the enormous volume of health information made available through current information and communications technologies and how the quality of that information affects shared decision-making between patients and providers.
· Crawford, M. (2014). Making data smart. Journal of AHIMA, 85(2), 24–27, 28.
. Discusses applied informatics and how it can be used to derive useful information from big data, as health care becomes a data-driven industry.
· Dinov, I. D. (2016). Methodological challenges and analytic opportunities for modeling and interpreting big healthcare data. GigaScience, 5(1), 1–15.
. Discusses the challenges of big data analysis and addresses the need for technology and education in creating valuable knowledge assets from big data.
· Hegwer, L. R. (2014). Digging deeper into data. Healthcare Financial Management, 68(2), 80–84.
. Discusses the role of data analysts in improving the financial and clinical performance of health care organizations.
2
Running Head: Organizational Data needs
2
Organizational Data needs
Organization Data Needs Capella UniversityAssignment 2
Internal data sources can include data systems, for example, a radiology data system, medical library data, or the patient finance and billing system. Internal data sources also include EHR data systems such as the demographics, medical history of patients and disease records, medication and allergies records, laboratory test results, personal patient statistics such as gender age, weight and billing information (Porter et al, 2018).
External data sources include data from Centres for Medicare and Medicaid Services (CMS), benchmarking data from other hospitals are ex.
Reviewing the Healthcare Analytics Adoption Model: A Roadmap and Recipe for A...Health Catalyst
Dale Sanders provides an update on the Healthcare Analytics Adoption Model. Dale published the first version of this model in 2002, calling it the Analytics Capability Maturity Model. The three intentions at that time are the same as they are today: 1) Provide healthcare leaders with a clear roadmap for the progression of analytic maturity in their organization. 2) Provide vendors with a roadmap to meet the analytic needs of clients. 3) Create a common framework to benchmark the progressive adoption of analytics at the industry level.
In 2012, Dale co-published a new version of the Model with Dr. Denis Protti, rebranding it the Healthcare Analytics Adoption Model and purposely borrowing from the widespread adoption of the EMR Adoption Model (EMRAM) published and supported by HIMSS. In 2015, Dale transferred the model under a creative commons copyright to HIMSS to create a vendor-independent industry standard that is now widely applied to support the original three intentions. He continues to collaborate with HIMSS to progress the Model.
During this webinar, Dale:
-Reviews the current state of the Health Catalyst Model, including recent changes that advocate a ninth level—direct-to-patient analytics and AI.
-Shares his observations of maturity in the market.
-Provides an update on the current state of the HIMSS Adoption Model for Analytic Maturity.
> Definition of RWD
> RWD - Big Data Characteristics
> Sources of RWD
> Important Stakeholders
> Benefits of RWD
> Why Data Sharing is Important?
> Benefits of Data Sharing
> Who Benefits?
> Ultimate Goals
> Case Studies
> Challenges
> Data Privacy Scenario
> Data Security in India
> Regulatory Perspectives Around RWD
> How to Encourage Data Sharing?
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
Data science and the use of big data in healthcare delivery could revolutionize the field by decreasing costs and vastly improving efficiency and outcomes. There is an abundance of healthcare data in Canada, but it is mostly siloed and difficult to access due to privacy and security challenges. This session will offer insights into best practices for healthcare analytics programs, as well as use cases that demonstrate the potential benefits that can be realized through this work.
Going Beyond the EMR for Data-driven Insights in HealthcarePerficient, Inc.
Join Dr. Marcie Stoshak-Chavez, MD, FACEP, Director of Healthcare Strategic Advisory Services at Perficient and Mr. J.D. Whitlock, Director of Clinical & Business Intelligence at Catholic Health Partners to learn how analytics is being used to measure and monitor performance and provide service-line directors and financial administrators with reporting and analysis that enhances clinical care processes and business operations.
Learn how clinicians and administrators armed with the data-driven insights from the EMR and beyond can:
Derive meaningful insights for care delivery by analyzing clinical, financial and operational data
Collaborate more effectively and improve quality of care by securely sharing insights among providers
Meaningfully measure and understand performance across key Federally mandated measures and take prescribed action
Stay on top of shifts in regulatory policy that impact reimbursements and quality requirements
The Philosophy, Psychology, and Technology of Data in HealthcareDale Sanders
Over-application of data and analytics in healthcare is alienating clinicians and, for the most part, not bending the cost-quality curves. This lecture spends 60% of the time on the softer issues, 40% on the technology.
Healthcare Analytics Summit Keynote Fall 2017Dale Sanders
The Data Operating System. Changing the Digital Trajectory of Healthcare. Why do we need to change the current digital trajectory? What’s the business case for a Data Operating System? What is a Data Operating System and how did we get here? What difference will DOS make? What should we do with it and what should we expect?
Why should we care about integrating data? What should we be trying to achieve? Population Health. The Softer, Human Side of Being “Data Driven” not “Driven By Data." The New Era of Decision Support in Healthcare. Top 10 Challenges To Integrating External Data.
The Data Operating System: Changing the Digital Trajectory of HealthcareDale Sanders
This is the next evolution in health information exchanges and data warehouses, specifically designed to support analytics, transaction processing, and third party application development, in one platform, the Data Operating System.
Healthcare Best Practices in Data Warehousing & AnalyticsDale Sanders
This is from a class lecture that I gave in 2005. Rather dated, but 95% of content is still very relevant today, which is a bit unfortunate. That's an indication of how little we've progressed in the healthcare domain.
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?
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.
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.
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.
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.
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.
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.
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.
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
Follow us on: Pinterest
Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
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.
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.
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.
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
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.
HOT NEW PRODUCT! BIG SALES FAST SHIPPING NOW FROM CHINA!! EU KU DB BK substit...GL Anaacs
Contact us if you are interested:
Email / Skype : kefaya1771@gmail.com
Threema: PXHY5PDH
New BATCH Ku !!! MUCH IN DEMAND FAST SALE EVERY BATCH HAPPY GOOD EFFECT BIG BATCH !
Contact me on Threema or skype to start big business!!
Hot-sale products:
NEW HOT EUTYLONE WHITE CRYSTAL!!
5cl-adba precursor (semi finished )
5cl-adba raw materials
ADBB precursor (semi finished )
ADBB raw materials
APVP powder
5fadb/4f-adb
Jwh018 / Jwh210
Eutylone crystal
Protonitazene (hydrochloride) CAS: 119276-01-6
Flubrotizolam CAS: 57801-95-3
Metonitazene CAS: 14680-51-4
Payment terms: Western Union,MoneyGram,Bitcoin or USDT.
Deliver Time: Usually 7-15days
Shipping method: FedEx, TNT, DHL,UPS etc.Our deliveries are 100% safe, fast, reliable and discreet.
Samples will be sent for your evaluation!If you are interested in, please contact me, let's talk details.
We specializes in exporting high quality Research chemical, medical intermediate, Pharmaceutical chemicals and so on. Products are exported to USA, Canada, France, Korea, Japan,Russia, Southeast Asia and other countries.
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.
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
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
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!
2. Healthcare Analytics Adoption Model
Borrowing lessons from the HIMSS Analytics EMR Adoption Model
●
Provide a roadmap for organizations to measure
their own progress towards analytic adoption
●
Provide a framework for evaluating the industry’s
adoption of analytics
●
Provide a framework for evaluating vendor
products
3. Reviewers & Editors
Many thanks to the following colleagues
Jim Adams
The Advisory Board Company
Meg Aranow
The Advisory Board Company
Dr. David Burton
Health Catalyst
Tom Burton
Health Catalyst
Mike Davis
Mountain Summit Partners
Dr. Dick Gibson
Providence Health & Services
Denis Protti
University of Victoria BC
Dale Sanders
Health Catalyst
Herb Smaltz
Health Care DataWorks
4. Healthcare Analytics Adoption Model
Level 8
Personalized Medicine
& Prescriptive Analytics
Tailoring patient care based on population outcomes and
genetic data. Fee-for-quality rewards health maintenance.
Level 7
Clinical Risk Intervention
& Predictive Analytics
Organizational processes for intervention are supported
with predictive risk models. Fee-for-quality includes fixed
per capita payment.
Level 6
Population Health Management
& Suggestive Analytics
Tailoring patient care based upon population metrics. Feefor-quality includes bundled per case payment.
Level 5
Waste & Care Variability Reduction
Reducing variability in care processes. Focusing on
internal optimization and waste reduction.
Level 4
Automated External Reporting
Efficient, consistent production of reports & adaptability to
changing requirements.
Level 3
Automated Internal Reporting
Efficient, consistent production of reports & widespread
availability in the organization.
Level 2
Standardized Vocabulary
& Patient Registries
Relating and organizing the core data content.
Level 1
Enterprise Data Warehouse
Collecting and integrating the core data content.
Level 0
Fragmented Point Solutions
Inefficient, inconsistent versions of the truth. Cumbersome
internal and external reporting.
5. Progression in the Model
The patterns at each level
•
Data content expands
•
•
Data timeliness increases
•
•
To support faster decision cycles and lower “Mean Time To
Improvement”
Data governance expands
•
•
Adding new sources of data to expand our understanding of care
delivery and the patient
Advocating greater data access, utilization, and quality
The complexity of data binding and algorithms increases
•
From descriptive to prescriptive analytics
•
From “What happened?” to “What should we do?”
6. The Expanding Ecosystem of Data Content
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Real time 7x24 biometric monitoring
data for all patients in the ACO
Genomic data
Long term care facility data
Patient reported outcomes data*
Home monitoring data
Familial data
External pharmacy data
Bedside monitoring data
Detailed cost accounting data*
HIE data
Claims data
Outpatient EMR data
Inpatient EMR data
Imaging data
Lab data
Billing data
* - Not currently being addressed by vendor products
2-4 years
1-2 years
3-12 months
7. Six Phases of Data Governance
You need to move through
these phases in no more
than two years
•
Phase 6: Acquisition of Data
•
Phase 5: Utilization of Data
•
Phase 4: Quality of Data
•
Phase 3: Stewardship of Data
•
Phase 2: Access to Data
•
2-4 years
Phase 1: Cultural Tone of “Data Driven”
1-2 years
3-12 months
7
8. Healthcare Analytics Adoption Model: One Page Self-Inspection Guide
Level 8
Personalized Medicine & Prescriptive Analytics: Analytic motive expands to wellness management, physical and behavioral functional health, and
mass customization of care. Analytics expands to include NLP of text, prescriptive analytics, and interventional decision support. Prescriptive analytics
are available at the point of care to improve patient specific outcomes based upon population outcomes. Data content expands to include 7x24
biometrics data, genomic data and familial data. The EDW is updated within a few minutes of changes in the source systems.
Level 7
Clinical Risk Intervention & Predictive Analytics: Analytic motive expands to address diagnosis-based, fixed-fee per capita reimbursement models.
Focus expands from management of cases to collaboration with clinician and payer partners to manage episodes of care, using predictive modeling,
forecasting, and risk stratification to support outreach, triage, escalation and referrals. Physicians, hospitals, employers, payers and members/patients
collaborate to share risk and reward (e.g., financial reward to patients for healthy behavior). Patients are flagged in registries who are unable or
unwilling to participate in care protocols. Data content expands to include home monitoring data, long term care facility data, and protocol-specific
patient reported outcomes. On average, the EDW is updated within one hour or less of source system changes.
Level 6
Population Health Management & Suggestive Analytics: The “accountable care organization” shares in the financial risk and reward that is tied to
clinical outcomes. At least 50% of acute care cases are managed under bundled payments. Analytics are available at the point of care to support the
Triple Aim of maximizing the quality of individual patient care, population management, and the economics of care. Data content expands to include
bedside devices, home monitoring data, external pharmacy data, and detailed activity based costing. Data governance plays a major role in the
accuracy of metrics supporting quality-based compensation plans for clinicians and executives. On average, the EDW is updated within one day of
source system changes. The EDW reports organizationally to a C-level executive who is accountable for balancing cost of care and quality of care.
Level 5
Waste & Care Variability Reduction: Analytic motive is focused on measuring adherence to clinical best practices, minimizing waste, and reducing
variability. Data governance expands to support care management teams that are focused on improving the health of patient populations. Populationbased analytics are used to suggest improvements to individual patient care. Permanent multidisciplinary teams are in-place that continuously monitor
opportunities to improve quality, and reduce risk and cost, across acute care processes, chronic diseases, patient safety scenarios, and internal
workflows. Precision of registries is improved by including data from lab, pharmacy, and clinical observations in the definition of the patient cohorts.
EDW content is organized into evidence-based, standardized data marts that combine clinical and cost data associated with patient registries. Data
content expands to include insurance claims (if not already included) and HIE data feeds. On average, the EDW is updated within one week of source
system changes.
Level 4
Automated External Reporting: Analytic motive is focused on consistent, efficient production of reports required for regulatory and accreditation
requirements (e.g. CMS, Joint Commission, tumor registry, communicable diseases); payer incentives (e.g. MU, PQRS, VBP, readmission reduction);
and specialty society databases (e.g. STS, NRMI, Vermont-Oxford). Adherence to industry-standard vocabularies is required. Clinical text data content
is available for simple key word searches. Centralized data governance exists for review and approval of externally released data.
Level 3
Automated Internal Reporting: Analytic motive is focused on consistent, efficient production of reports supporting basic management and operation
of the healthcare organization. Key performance indicators are easily accessible from the executive level to the front-line manager. Corporate and
business unit data analysts meet regularly to collaborate and steer the EDW. Data governance expands to raise the data literacy of the organization
and develop a data acquisition strategy for Levels 4 and above.
Level 2
Standardized Vocabulary & Patient Registries: Master vocabulary and reference data identified and standardized across disparate source system
content in the data warehouse. Naming, definition, and data types are consistent with local standards. Patient registries are defined solely on ICD
billing data. Data governance forms around the definition and evolution of patient registries and master data management.
Level 1
Enterprise Data Warehouse: At a minimum, the following data are co-located in a single data warehouse, locally or hosted: HIMSS EMR Stage 3
data, Revenue Cycle, Financial, Costing, Supply Chain, and Patient Experience. Searchable metadata repository is available across the enterprise.
Data content includes insurance claims, if possible. Data warehouse is updated within one month of source system changes. Data governance is
forming around the data quality of source systems. The EDW reports organizationally to the CIO.
Level 0
Fragmented Point Solutions: Vendor-based and internally developed applications are used to address specific analytic needs as they arise. The
fragmented point solutions are neither co-located in a data warehouse nor otherwise architecturally integrated with one another. Overlapping data
content leads to multiple versions of analytic truth. Basic internal & external reports are labor intensive and inconsistent. Data governance is nonexistent.
9. Healthcare Analytics Adoption Model
Level 8
Level 7
Fragmented Point Solutions
●
Vendor-based and internally developed applications are
used to address specific analytic needs as they arise.
●
The fragmented point solutions are neither co-located
in a data warehouse nor otherwise architecturally
integrated with one another.
●
Overlapping data content leads to multiple versions of
analytic truth.
●
Basic internal & external reports are labor intensive and
inconsistent.
●
Data governance is non-existent.
Level 6
Level 5
Level 4
Level 3
Level 2
Level 1
Level 0
10. Healthcare Analytics Adoption Model
Level 8
Level 7
Integrated, Enterprise Data Warehouse
●
At a minimum, the following data are co-located in a
single data warehouse, locally or hosted: HIMSS EMR
Stage 3 data, Revenue Cycle, Financial, Costing,
Supply Chain, and Patient Experience.
●
Searchable metadata repository is available across the
enterprise.
Level 3
●
Data content includes insurance claims, if possible.
Level 2
●
Data warehouse is updated within one month of source
system changes.
●
Data governance is forming around the data quality of
source systems.
●
The EDW reports organizationally to the CIO.
Level 6
Level 5
Level 4
Level 1
Level 0
11. Healthcare Analytics Adoption Model
Level 8
Standardized Vocabulary & Patient Registries
Level 7
●
Master vocabulary and reference data identified and
standardized across disparate source system content in
the data warehouse.
●
Naming, definition, and data types are consistent with
local standards.
●
Patient registries are defined solely on ICD billing data.
●
Data governance forms around the definition and
evolution of patient registries and master data
management.
Level 6
Level 5
Level 4
Level 3
Level 2
Level 1
Level 0
12. Healthcare Analytics Adoption Model
Level 8
Automated Internal Reporting
Level 7
●
Analytic motive is focused on consistent, efficient
production of reports supporting basic management
and operation of the healthcare organization.
●
Key performance indicators are easily accessible from
the executive level to the front-line manager.
●
Corporate and business unit data analysts meet
regularly to collaborate and steer the EDW.
●
Data governance expands to raise the data literacy of
the organization and develop a data acquisition
strategy for Levels 4 and above.
Level 6
Level 5
Level 4
Level 3
Level 2
Level 1
Level 0
13. Healthcare Analytics Adoption Model
Level 8
Level 7
Automated External Reporting
●
Analytic motive is focused on consistent, efficient
production of reports required for regulatory and
accreditation requirements (e.g. CMS, Joint
Commission, tumor registry, communicable diseases);
payer incentives (e.g. MU, PQRS, VBP, readmission
reduction); and specialty society databases (e.g. STS,
NRMI, Vermont-Oxford).
●
Adherence to industry-standard vocabularies is
required.
●
Clinical text data content is available for simple key
word searches.
●
Centralized data governance exists for review and
approval of externally released data.
Level 6
Level 5
Level 4
Level 3
Level 2
Level 1
Level 0
14. Healthcare Analytics Adoption Model
Level 8
Waste & Care Variability Reduction (1)
●
Analytic motive is focused on measuring adherence to
clinical best practices, minimizing waste, and reducing
variability.
●
Data governance expands to support care
management teams that are focused on improving the
health of patient populations.
●
Population-based analytics are used to suggest
improvements to individual patient care.
●
Level 7
Permanent multidisciplinary teams are in-place that
continuously monitor opportunities to improve quality,
and reduce risk and cost, across acute care processes,
chronic diseases, patient safety scenarios, and internal
workflows.
Level 6
Level 5
Level 4
Level 3
Level 2
Level 1
Level 0
continued
15. Healthcare Analytics Adoption Model
Level 8
Waste & Care Variability Reduction (2)
●
Precision of registries is improved by including data
from lab, pharmacy, and clinical observations in the
definition of the patient cohorts.
●
EDW content is organized into evidence-based,
standardized data marts that combine clinical and cost
data associated with patient registries.
●
Data content expands to include insurance claims (if
not already included) and HIE data feeds.
●
Level 7
On average, the EDW is updated within one week of
source system changes.
Level 6
Level 5
Level 4
Level 3
Level 2
Level 1
Level 0
16. Healthcare Analytics Adoption Model
Level 8
Population Health Management & Suggestive Analytics (1)
Level 7
●
The “accountable care organization” shares in the
financial risk and reward that is tied to clinical
outcomes.
●
At least 50% of acute care cases are managed under
bundled payments. Analytics are available at the point
of care to support the Triple Aim of maximizing the
quality of individual patient care, population
management, and the economics of care.
●
Data content expands to include bedside devices,
home monitoring data, external pharmacy data, and
detailed activity based costing.
Level 6
Level 5
Level 4
Level 3
Level 2
Level 1
Level 0
continued
17. Healthcare Analytics Adoption Model
Level 8
Level 7
Population Health Management & Suggestive Analytics (2)
●
Data governance plays a major role in the accuracy of
metrics supporting quality-based compensation plans
for clinicians and executives.
●
On average, the EDW is updated within one day of
source system changes.
●
The EDW reports organizationally to a C-level
executive who is accountable for balancing cost of care
and quality of care.
Level 6
Level 5
Level 4
Level 3
Level 2
Level 1
Level 0
18. Healthcare Analytics Adoption Model
Level 8
Level 7
Clinical Risk Intervention & Predictive Analytics (1)
●
Analytic motive expands to address diagnosis-based,
fixed-fee per capita reimbursement models.
●
Focus expands from management of cases to
collaboration with clinician and payer partners to
manage episodes of care, using predictive modeling,
forecasting, and risk stratification to support outreach,
triage, escalation and referrals.
●
Physicians, hospitals, employers, payers and
members/patients collaborate to share risk and reward
(e.g., financial reward to patients for healthy behavior).
Level 6
Level 5
Level 4
Level 3
Level 2
Level 1
Level 0
continued
19. Healthcare Analytics Adoption Model
Level 8
Level 7
Clinical Risk Intervention & Predictive Analytics (2)
●
Patients are flagged in registries who are unable or
unwilling to participate in care protocols.
●
Data content expands to include home monitoring data,
long term care facility data, and protocol-specific patient
reported outcomes.
●
On average, the EDW is updated within one hour or
less of source system changes.
Level 6
Level 5
Level 4
Level 3
Level 2
Level 1
Level 0
20. Healthcare Analytics Adoption Model
Level 8
Level 7
Personalized Medicine & Prescriptive Analytics
●
Analytic motive expands to wellness management,
physical and behavioral functional health, and mass
customization of care.
●
Analytics expands to include NLP of text, prescriptive
analytics, and interventional decision support.
●
Prescriptive analytics are available at the point of care
to improve patient specific outcomes based upon
population outcomes.
●
Data content expands to include 7x24 biometrics data,
genomic data and familial data.
●
The EDW is updated within a few minutes of changes
in the source systems.
Level 6
Level 5
Level 4
Level 3
Level 2
Level 1
Level 0