Presented at Predictive Analytics Innovation Summit, Chicago 2017 #PAChicago
https://theinnovationenterprise.com/summits/predictive-analytics-innovation-summit-chicago-2017/speakers
This presentation centers on currently published findings focused on the use of predictive analytics in healthcare venues of sports medicine and orthopedic rehabilitative settings. Aspects of data access via national patient registries as well as nascent applications of machine learning will also be covered. An example of one approach of incorporating a model of assessment, evidence-based practice, treatment augmentation, and resultant outcome evaluation will be provided as well.
Please be in touch
http://DrChrisStout.com
The Danger of Big Data by Kerry Bodine - Forrester research
Service design teams can glean big data insights from social media, financial systems, emails, surveys, call centers, and digital and analog sensors. But companies that fixate on amassing new data sources put themselves at risk of neglecting small data insights gathered through qualitative research methods. How can firms achieve balance?
Using Spark in Healthcare Predictive Analytics in the OR - Data Science Pop-u...Domino Data Lab
The prevailing issue when working with Operating Room (OR) scheduling within a hospital setting is that it is difficult to schedule and predict available OR block times. This leads to empty and unused operating rooms leading to longer waiting times for patients for their procedures. Using multi-variate linear regression, we will show how they can predict available OR block times using Spark MLlib resulting in better OR utilization and shorter wait times for patients. Presented by Denny Lee, Data Scientist and Evangelist at Databricks.
Elance Consultant provides freelance academic and business research support through Econometrics, Statistics and Data Sciences. You can book our freelancers from the best online workplace now.
Analysis of the article "A Predictive Analytics Primer" by Thomas H. DavenportVaibhav Srivastav
This presentation gives analysis of the article "A Predictive Analytics Primer" by Thomas H. Davenport
Slide 1: A Predictive Analytics Primer by Thomas H. Davenport
Slide 2: Thomas H. Davenport
Slide 3: Powers of Predictive analytics
Slide 4: Predictive analytics refers to predicting future from the data of the past.
Slide 5: The quantitative analysis isn’t magic—but it is normally done with a lot of past data, a little statistical wizardry, and some important assumptions.
Slide 6: The Data: Lack of good data is the most common barrier to organizations seeking to employ predictive analytics.
Slide 7: The Statistics: Regression analysis in its various forms is the primary tool that organizations use for predictive analytics.
Slide 8: An analyst hypothesizes that a set of independent variables (say, gender, income, visits to a website) are statistically correlated with the purchase of a product for a sample of customers. The analyst performs a regression analysis to see just how correlated each variable is; this usually requires some iteration to find the right combination of variables and the best model.
Slide 9: The Assumptions: That brings us to the other key factor in any predictive model—the assumptions that underlie it. Every model has them, and it’s important to know what they are and monitor whether they are still true. The big assumption in predictive analytics is that the future will continue to be like the past.
Slide 10: What can make assumptions invalid?
Slide 11: The most common reason is time. If your model was created several years ago, it may no longer accurately predict current behavior. The greater the elapsed time, the more likely customer behavior has changed.
Slide 12: Another reason a predictive model’s assumptions may no longer be valid is if the analyst didn’t include a key variable in the model, and that variable has changed substantially over time.
Slide 13: Managers should always ask analysts what the key assumptions are, and what would have to happen for them to no longer be valid. And both managers and analysts should continually monitor the world to see if key factors involved in assumptions might have changed over time.
Slide 14: With these fundamentals in mind, here are a few good questions to ask your analysts:
Can you tell me something about the source of data you used in your analysis?
Are you sure the sample data are representative of the population?
Are there any outliers in your data distribution? How did they affect the results?
What assumptions are behind your analysis?
Are there any conditions that would make your assumptions invalid?
Slide 15: Thank You!
Healthcare Predictive Analytics with the OR-(Denny Lee and Ayad Shammout, Dat...Spark Summit
This document discusses using predictive analytics within operating rooms (OR) at Beth Israel Deaconess Medical Center. It describes developing a predictive model to identify available OR time two weeks in advance to better schedule waitlisted cases and staff. Building the model using historical OR data and linear regression with stochastic gradient descent could help forecast case loads three weeks out. This would allow for improved OR utilization, reduced staff overtime and idle time, shorter patient wait times and fewer cancellations.
This document provides guidance on evaluating information sources using the C.A.R.P. test - evaluating sources for Currency, Accuracy, Reliability, and Purpose. It examines how to apply this test to different resource types like websites, government reports, journal articles, and provides tips for evaluating specific resources. Key factors include checking publication dates, author qualifications, peer-review status, and domain names for websites. Government and peer-reviewed journal articles generally require less careful evaluation than other sources like commercial websites. The document aims to teach readers how to critically assess sources to determine their usefulness and trustworthiness.
Driving Healthcare Operations with Data ScienceSandy Ryza
The document discusses using data science to drive healthcare operations. It describes using models to close gaps in patient care by predicting which diabetic patients will develop complications in the next 6 months based on demographic data, medical history, medications and lab tests. The challenges are class imbalance, with few patients historically developing complications, and missing lab data. Gradient boosting decision trees are able to handle these issues better than logistic regression. Testing shows the model can identify high-risk patients to call with a 24% precision and 66% recall. A trial using the model to select patients for home visits found more complications than random selection, showing the approach can improve outcomes.
Presented at Predictive Analytics Innovation Summit, Chicago 2017 #PAChicago
https://theinnovationenterprise.com/summits/predictive-analytics-innovation-summit-chicago-2017/speakers
This presentation centers on currently published findings focused on the use of predictive analytics in healthcare venues of sports medicine and orthopedic rehabilitative settings. Aspects of data access via national patient registries as well as nascent applications of machine learning will also be covered. An example of one approach of incorporating a model of assessment, evidence-based practice, treatment augmentation, and resultant outcome evaluation will be provided as well.
Please be in touch
http://DrChrisStout.com
The Danger of Big Data by Kerry Bodine - Forrester research
Service design teams can glean big data insights from social media, financial systems, emails, surveys, call centers, and digital and analog sensors. But companies that fixate on amassing new data sources put themselves at risk of neglecting small data insights gathered through qualitative research methods. How can firms achieve balance?
Using Spark in Healthcare Predictive Analytics in the OR - Data Science Pop-u...Domino Data Lab
The prevailing issue when working with Operating Room (OR) scheduling within a hospital setting is that it is difficult to schedule and predict available OR block times. This leads to empty and unused operating rooms leading to longer waiting times for patients for their procedures. Using multi-variate linear regression, we will show how they can predict available OR block times using Spark MLlib resulting in better OR utilization and shorter wait times for patients. Presented by Denny Lee, Data Scientist and Evangelist at Databricks.
Elance Consultant provides freelance academic and business research support through Econometrics, Statistics and Data Sciences. You can book our freelancers from the best online workplace now.
Analysis of the article "A Predictive Analytics Primer" by Thomas H. DavenportVaibhav Srivastav
This presentation gives analysis of the article "A Predictive Analytics Primer" by Thomas H. Davenport
Slide 1: A Predictive Analytics Primer by Thomas H. Davenport
Slide 2: Thomas H. Davenport
Slide 3: Powers of Predictive analytics
Slide 4: Predictive analytics refers to predicting future from the data of the past.
Slide 5: The quantitative analysis isn’t magic—but it is normally done with a lot of past data, a little statistical wizardry, and some important assumptions.
Slide 6: The Data: Lack of good data is the most common barrier to organizations seeking to employ predictive analytics.
Slide 7: The Statistics: Regression analysis in its various forms is the primary tool that organizations use for predictive analytics.
Slide 8: An analyst hypothesizes that a set of independent variables (say, gender, income, visits to a website) are statistically correlated with the purchase of a product for a sample of customers. The analyst performs a regression analysis to see just how correlated each variable is; this usually requires some iteration to find the right combination of variables and the best model.
Slide 9: The Assumptions: That brings us to the other key factor in any predictive model—the assumptions that underlie it. Every model has them, and it’s important to know what they are and monitor whether they are still true. The big assumption in predictive analytics is that the future will continue to be like the past.
Slide 10: What can make assumptions invalid?
Slide 11: The most common reason is time. If your model was created several years ago, it may no longer accurately predict current behavior. The greater the elapsed time, the more likely customer behavior has changed.
Slide 12: Another reason a predictive model’s assumptions may no longer be valid is if the analyst didn’t include a key variable in the model, and that variable has changed substantially over time.
Slide 13: Managers should always ask analysts what the key assumptions are, and what would have to happen for them to no longer be valid. And both managers and analysts should continually monitor the world to see if key factors involved in assumptions might have changed over time.
Slide 14: With these fundamentals in mind, here are a few good questions to ask your analysts:
Can you tell me something about the source of data you used in your analysis?
Are you sure the sample data are representative of the population?
Are there any outliers in your data distribution? How did they affect the results?
What assumptions are behind your analysis?
Are there any conditions that would make your assumptions invalid?
Slide 15: Thank You!
Healthcare Predictive Analytics with the OR-(Denny Lee and Ayad Shammout, Dat...Spark Summit
This document discusses using predictive analytics within operating rooms (OR) at Beth Israel Deaconess Medical Center. It describes developing a predictive model to identify available OR time two weeks in advance to better schedule waitlisted cases and staff. Building the model using historical OR data and linear regression with stochastic gradient descent could help forecast case loads three weeks out. This would allow for improved OR utilization, reduced staff overtime and idle time, shorter patient wait times and fewer cancellations.
This document provides guidance on evaluating information sources using the C.A.R.P. test - evaluating sources for Currency, Accuracy, Reliability, and Purpose. It examines how to apply this test to different resource types like websites, government reports, journal articles, and provides tips for evaluating specific resources. Key factors include checking publication dates, author qualifications, peer-review status, and domain names for websites. Government and peer-reviewed journal articles generally require less careful evaluation than other sources like commercial websites. The document aims to teach readers how to critically assess sources to determine their usefulness and trustworthiness.
Driving Healthcare Operations with Data ScienceSandy Ryza
The document discusses using data science to drive healthcare operations. It describes using models to close gaps in patient care by predicting which diabetic patients will develop complications in the next 6 months based on demographic data, medical history, medications and lab tests. The challenges are class imbalance, with few patients historically developing complications, and missing lab data. Gradient boosting decision trees are able to handle these issues better than logistic regression. Testing shows the model can identify high-risk patients to call with a 24% precision and 66% recall. A trial using the model to select patients for home visits found more complications than random selection, showing the approach can improve outcomes.
Health Apps by Design: A reference architectureKarim Keshavjee
This document outlines a reference architecture for creating the next generation of mHealth apps. It discusses challenges such as information asymmetry and low credibility of patient data that prevents adoption of mHealth apps. A solution is proposed that embeds the patient-physician relationship, uses behavior change models, and ensures bi-directional integration of apps with electronic medical records to collect and use patient experience and outcome data to improve care. The authors applied their criteria to review 201 diabetes apps but found none met all criteria, highlighting a need for higher quality, clinically-integrated apps.
08 13 209b ההוצאה הלאומית לחינוך בשנת 2010Anochi.com.
• בישראל, ההוצאה הלאומית לחינוך במוסדות חינוך - 7.4% מהתמ"ג – לעומת 6.5% במדינות ה-OECD.
• ההוצאה לחינוך לתלמיד בכל דרגי החינוך, נמוכה ב-20% עד 40% ממדינות ה-OECD, בדרגי החינוך השונים.
O documento apresenta informações sobre o que é um resumo e seus tipos. Explica que um resumo é uma redução do texto original que captura suas ideias essenciais de forma concisa. Apresenta os principais tipos de resumo sendo eles: indicativo, informativo e crítico. Fornece dicas para a elaboração de um resumo indicativo, como identificar as partes e ideias principais do texto a partir da introdução, desenvolvimento e conclusão.
דידי (ידידיה) מנוסי, נפטר הלילה בשנתו והוא בן 86. ההלוויה תתקיים ביום א' בשעה 15:00, בקיבוץ הולדתו גבע שבעמק יזרעאל. קודם לכן, בשעה 11:00, יועמד הארון בבית העתונאים ע"ש סוקולוב בתל אביב. לזכרו אני מעלה את אחד משיריו שנותר אקטואלי מאז פורסם בשנת 1996 בידיעות אחרונות.
"ושרים שבממשלת,
במכונית פאר-שררה,
מוכנים את מס-הדלק
להכפיל פי עשרה,
מה אכפת לכל הללו
בצאתם אל הדרכים?
גם את חשבונם על דלק
משלמים האזרחים!"
The document discusses the benefits of exercise for both physical and mental health. Regular exercise can improve cardiovascular health, reduce symptoms of depression and anxiety, enhance mood, and reduce stress levels. Staying physically active for at least 30 minutes each day can provide significant health advantages and improve overall well-being.
Things You Should Have Learned In SchoolAmye Scavarda
The document discusses things that should have been learned in school such as computer science applications in different career settings like startups and established companies. It also discusses the author's career path and recommends skills and mindsets for the workplace like using tools effectively, maintaining structure through self-directed productivity and quarterly planning, continuous learning through research and conferences, integrity by keeping promises and allowing failure, maintaining work-life balance through play, and having self-direction with no predefined syllabus or situations one is stuck in. The document uses images to illustrate points about tools, structure, research, integrity, play, and self-direction.
Sma case study - arab media influence report 2011Anochi.com.
1. Increased role of religion in politics. Groups like the Muslim Brotherhood and Salafi movements gained influence and supporters during the Arab Spring uprisings and will likely continue expanding their role in governments and shaping public policy.
2. Rise of populist, socialist economic policies. There is strong public support for larger government intervention in the economy through policies like subsidizing basic goods, increasing wages and jobs.
3. Growing pan-Arab nationalism. The uprisings sparked dreams of greater unity among Arab countries and people. While a full union may not be imminent, sentiments of Arab solidarity and opposition to foreign influence will remain powerful
how to secure web applications with owasp - isaca sep 2009 - for distributionSantosh Satam
This document discusses how to secure web applications using OWASP (Open Web Application Security Project). It recommends taking a systemic approach and implementing application security practices throughout the entire software development lifecycle (SDLC), from requirements to deployment. OWASP provides free tools, guides, and projects to help with tasks like threat modeling, code reviews, and vulnerability testing at each stage of the SDLC. Following OWASP best practices can help prevent security issues and ensure applications are secure before they go into production.
West Nile virus is spread by mosquitoes that become infected after biting infected birds. Most infections cause no symptoms or mild symptoms, but some can lead to West Nile fever or severe disease affecting the brain or spinal cord. To prevent infection, insect repellent should be applied outdoors, especially at dusk and dawn when mosquitoes that may carry West Nile virus are most active. Integrated mosquito control programs that include surveillance of West Nile virus in animals and humans can help prevent outbreaks.
The document discusses using Twitter for social recruiting by assessing potential candidates and engaging with them to build a community. It recommends using social monitoring tools to listen for potential candidates, conducting market analysis to target the right candidates, and using social engagement tools to interact with, recruit, interview, and hire candidates found on Twitter. Metrics should be analyzed before and after hiring to evaluate return on investment. Tips, tools, and dashboards are provided to optimize Twitter usage for recruiting purposes.
#ECE11 Pecha Kucha presentation: builds on ideas that researchers – postgraduates and advisors alike – can practice as writing researchers in order to gain comfort and flourish in as creative knowledge producers and collaborative meaning makers.
This document analyzes Canada's lagging adoption of electronic medical records (EMRs) compared to other countries. It finds that Canadian EMR policies have not created an enabling environment, economic drivers do not favor physicians implementing EMRs, and EMR programs in Canada have not incorporated global best practices for implementation factors. The document concludes that Canada needs to update its EMR policies and focus on strengthening the frameworks for policy, economics, and implementation in order to successfully increase EMR uptake.
1) The document discusses how healthcare culture must change to effectively adopt electronic health records (EHRs) and personal health records (PHRs).
2) Migrating from paper-based to electronic records is a "wicked problem" due to differing views among stakeholders and changing constraints.
3) An iterative approach considering people, processes, and platforms together is needed to solve complex problems in healthcare and drive innovation through technology.
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.
Health Apps by Design: A reference architectureKarim Keshavjee
This document outlines a reference architecture for creating the next generation of mHealth apps. It discusses challenges such as information asymmetry and low credibility of patient data that prevents adoption of mHealth apps. A solution is proposed that embeds the patient-physician relationship, uses behavior change models, and ensures bi-directional integration of apps with electronic medical records to collect and use patient experience and outcome data to improve care. The authors applied their criteria to review 201 diabetes apps but found none met all criteria, highlighting a need for higher quality, clinically-integrated apps.
08 13 209b ההוצאה הלאומית לחינוך בשנת 2010Anochi.com.
• בישראל, ההוצאה הלאומית לחינוך במוסדות חינוך - 7.4% מהתמ"ג – לעומת 6.5% במדינות ה-OECD.
• ההוצאה לחינוך לתלמיד בכל דרגי החינוך, נמוכה ב-20% עד 40% ממדינות ה-OECD, בדרגי החינוך השונים.
O documento apresenta informações sobre o que é um resumo e seus tipos. Explica que um resumo é uma redução do texto original que captura suas ideias essenciais de forma concisa. Apresenta os principais tipos de resumo sendo eles: indicativo, informativo e crítico. Fornece dicas para a elaboração de um resumo indicativo, como identificar as partes e ideias principais do texto a partir da introdução, desenvolvimento e conclusão.
דידי (ידידיה) מנוסי, נפטר הלילה בשנתו והוא בן 86. ההלוויה תתקיים ביום א' בשעה 15:00, בקיבוץ הולדתו גבע שבעמק יזרעאל. קודם לכן, בשעה 11:00, יועמד הארון בבית העתונאים ע"ש סוקולוב בתל אביב. לזכרו אני מעלה את אחד משיריו שנותר אקטואלי מאז פורסם בשנת 1996 בידיעות אחרונות.
"ושרים שבממשלת,
במכונית פאר-שררה,
מוכנים את מס-הדלק
להכפיל פי עשרה,
מה אכפת לכל הללו
בצאתם אל הדרכים?
גם את חשבונם על דלק
משלמים האזרחים!"
The document discusses the benefits of exercise for both physical and mental health. Regular exercise can improve cardiovascular health, reduce symptoms of depression and anxiety, enhance mood, and reduce stress levels. Staying physically active for at least 30 minutes each day can provide significant health advantages and improve overall well-being.
Things You Should Have Learned In SchoolAmye Scavarda
The document discusses things that should have been learned in school such as computer science applications in different career settings like startups and established companies. It also discusses the author's career path and recommends skills and mindsets for the workplace like using tools effectively, maintaining structure through self-directed productivity and quarterly planning, continuous learning through research and conferences, integrity by keeping promises and allowing failure, maintaining work-life balance through play, and having self-direction with no predefined syllabus or situations one is stuck in. The document uses images to illustrate points about tools, structure, research, integrity, play, and self-direction.
Sma case study - arab media influence report 2011Anochi.com.
1. Increased role of religion in politics. Groups like the Muslim Brotherhood and Salafi movements gained influence and supporters during the Arab Spring uprisings and will likely continue expanding their role in governments and shaping public policy.
2. Rise of populist, socialist economic policies. There is strong public support for larger government intervention in the economy through policies like subsidizing basic goods, increasing wages and jobs.
3. Growing pan-Arab nationalism. The uprisings sparked dreams of greater unity among Arab countries and people. While a full union may not be imminent, sentiments of Arab solidarity and opposition to foreign influence will remain powerful
how to secure web applications with owasp - isaca sep 2009 - for distributionSantosh Satam
This document discusses how to secure web applications using OWASP (Open Web Application Security Project). It recommends taking a systemic approach and implementing application security practices throughout the entire software development lifecycle (SDLC), from requirements to deployment. OWASP provides free tools, guides, and projects to help with tasks like threat modeling, code reviews, and vulnerability testing at each stage of the SDLC. Following OWASP best practices can help prevent security issues and ensure applications are secure before they go into production.
West Nile virus is spread by mosquitoes that become infected after biting infected birds. Most infections cause no symptoms or mild symptoms, but some can lead to West Nile fever or severe disease affecting the brain or spinal cord. To prevent infection, insect repellent should be applied outdoors, especially at dusk and dawn when mosquitoes that may carry West Nile virus are most active. Integrated mosquito control programs that include surveillance of West Nile virus in animals and humans can help prevent outbreaks.
The document discusses using Twitter for social recruiting by assessing potential candidates and engaging with them to build a community. It recommends using social monitoring tools to listen for potential candidates, conducting market analysis to target the right candidates, and using social engagement tools to interact with, recruit, interview, and hire candidates found on Twitter. Metrics should be analyzed before and after hiring to evaluate return on investment. Tips, tools, and dashboards are provided to optimize Twitter usage for recruiting purposes.
#ECE11 Pecha Kucha presentation: builds on ideas that researchers – postgraduates and advisors alike – can practice as writing researchers in order to gain comfort and flourish in as creative knowledge producers and collaborative meaning makers.
This document analyzes Canada's lagging adoption of electronic medical records (EMRs) compared to other countries. It finds that Canadian EMR policies have not created an enabling environment, economic drivers do not favor physicians implementing EMRs, and EMR programs in Canada have not incorporated global best practices for implementation factors. The document concludes that Canada needs to update its EMR policies and focus on strengthening the frameworks for policy, economics, and implementation in order to successfully increase EMR uptake.
1) The document discusses how healthcare culture must change to effectively adopt electronic health records (EHRs) and personal health records (PHRs).
2) Migrating from paper-based to electronic records is a "wicked problem" due to differing views among stakeholders and changing constraints.
3) An iterative approach considering people, processes, and platforms together is needed to solve complex problems in healthcare and drive innovation through technology.
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.
Healthcare Interoperability: New Tactics and TechnologyHealth Catalyst
Every provider agrees on the need for healthcare interoperability to achieve clinical data insights at the point of care. The question is how to get there from the myriad technologies and the volumes of data that comprise electronic medical records. It’s been difficult to organize among participants that have had little incentive to cooperate. And standards for sending and receiving data have been slow to develop. This is changing, but the key components that are still vital to realizing insights are closed-loop analytics and its accompanying tools, an enterprise data warehouse and analytics applications. This article defines the problems and explores the solutions to optimizing clinical decision making where it’s needed most.
The document discusses disruptive technology, convergence, and the purpose of hospitals. It notes that disruptive technology introduces new technologies that are simpler and cheaper than existing ones but eventually disrupt the market. Technology convergence involves different technologies performing similar tasks. The document questions what disruptive technologies may benefit healthcare and examines how convergence could improve areas like nurse call systems and tracking. It explores whether the purpose of hospitals is to serve communities or provide services efficiently, and how reducing waste and barriers could help address issues like overcrowding.
Introduction to Health Informatics and Health Information Technology (Part 2)...Nawanan Theera-Ampornpunt
This document provides an overview of health information technology (HIT) and electronic health records (EHRs). It discusses how healthcare is complex, information-rich, and prone to errors. HIT can help by providing timely access to patient information, assisting with clinical decision-making, and improving quality, safety and efficiency. However, HIT alone will not fix all healthcare issues and its benefits may vary by context. The document outlines how HIT can help achieve the six dimensions of quality healthcare as defined by the Institute of Medicine. While HIT has documented benefits, it is not a panacea and its implementation requires focus on ultimate goals like patient health rather than just technology adoption.
When Healthcare Data Analysts Fulfill the Data Detective RoleHealth Catalyst
There’s a new way to think about healthcare data analysts. Give them the responsibilities of a data detective. If ever there were a Sherlock Holmes of healthcare analytics, it’s the analyst who thinks like a detective. Part scientist, part bloodhound, part magician, the healthcare data detective thrives on discovery, extracting pearls of insight where others have previously returned emptyhanded. This valuable role comprises critical thinkers, story engineers, and sleuths who look at healthcare data in a different way. Three attributes define the data detective:
They are inquisitive and relentless with their questions.
They let the data inform.
They drive to the heart of what matters.
Innovative analytics leaders understand the importance of supporting the data analyst through the data detective career track, and the need to start developing this role right away in the pursuit of outcomes improvement in all healthcare domains.
This document provides an overview of health information technology (HIT) and electronic health records (EHRs). It discusses how healthcare is complex, information-rich, and prone to errors. HIT can help improve quality, safety and efficiency by providing timely access to patient information, assisting with clinical decision-making, and reducing errors. However, HIT alone does not fix all problems and benefits may vary by context. The goals of HIT should be improving individual health, population health and organizational health.
This document provides information about an industrial designer named Maynard Payumo. It includes sections about his education, work experience, skills, and interests. Some key details:
- He received a BFA in Industrial Design from the Cleveland Institute of Art and has worked as a designer at various companies, including as a co-founder of his own design firm.
- His skills include hand sketching, 3D modeling, rendering, and working with various design software programs.
- In the about me section, he discusses his passion for design since high school and his interest in being inspired by and contributing to his work.
- The resume provides concise information about his objective, qualifications, and work
This document discusses digital health transformation and the role of health information technology. It begins by exploring concepts like artificial intelligence, blockchain, cloud computing and big data. It then examines the potential for "smart" machines in healthcare while acknowledging the complexities of digitizing such a system. The document emphasizes that clinical judgment is still necessary given variations in patients. It outlines components of healthcare systems and forms of health IT both within and beyond hospitals. Finally, it discusses using health IT to support clinical decision making and reduce errors.
Using analytics to mine large datasets for insights, commonly known as Big Data, is already transforming industries ranging from consumer goods to transportation. Certainly, the healthcare sector has the raw information to join this group. For example, Kaiser Permanente, a California-based health network, has an estimated 27 to 44 million gigabytes of potentially useful patient information. Expectations are that the U.S. healthcare sector will soon have a zettabyte of these data.
To learn more about the research programme, visit http://hospitalresilience.eiu.com/.
The document discusses how AI and machine learning can help address challenges in healthcare by analyzing complex medical data. It provides examples of how AI can help with tasks like analyzing medical images to assist radiologists, predicting drug response from scans, and using electronic health records to better understand diseases and patient heterogeneity. The document also acknowledges challenges like the need for large labeled datasets and ensuring interpretability and avoidance of bias.
A presentation given at the Duke Margollis Health Policy meeting in 2015 and providing insights into the current challenges related to EHR data quality. Proposes a new approach - OneSource.
Mental Health Informatics - What we can learn from the past and where we can beHimanshu Tyagi
This document discusses how mental health informatics has evolved over the past 20 years and the barriers to further advancement. It notes that while technology has accelerated, healthcare has lagged in adopting new technologies. Barriers include unrealistic expectations of technology's capabilities, challenges around customizing solutions at scale, and data explosion outpacing ability to analyze information. The author advocates learning from other industries that develop customizable solutions and integrate diverse data sources. Clinical leadership is needed to help overcome barriers and advance mental health informatics.
Standards in health informatics - problem, clinical models and terminologySilje Ljosland Bakke
- Clinical information must be structured using shared and standardized clinical models and terminologies to enable semantic interoperability, longitudinal record access, and clinical decision support. However, structuring health information is complex due to the diversity and dynamic nature of clinical data.
- openEHR provides a free and open specification for structured health records, separating the reference model from archetypes and templates to define clinical content in a reusable way. National governance is needed to develop, review, and publish archetypes.
- Information models and terminologies are complementary - models define data structure while terminologies provide controlled vocabularies, but neither is sufficient alone due to contextual needs and complex concepts. Pragmatic choices must be made based on use case
This document summarizes a paper presented at the Workshop on Interactive Systems in Healthcare that describes the PaperChain system, a collaborative healthcare system designed to support real-time information sharing. The system was developed based on over 700 hours of field studies observing clinical handovers in a pediatric ambulance transport service. Key findings from the studies informed the system design, including that healthcare work is contingent and non-linear, and that any system must not add burden but provide immediate benefit. PaperChain uses digital pens and paper with a shared display to allow lightweight capture and viewing of clinical information in real-time between transport teams and physicians.
Presented at the Healthcare CEO50 Certificate Program, School of Hospital Management, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on October 4, 2021
Towards online universal quality healthcare through AIXavier Amatriain
This document discusses the potential for using AI and automation to improve online universal healthcare. It notes that physicians currently do not have enough time to properly diagnose and treat patients given the large amount of information involved. The document proposes that AI, through techniques like knowledge extraction from medical literature and patient data, conversational systems, automated diagnosis and treatment recommendations, and processing of multimodal inputs, could help scale and improve healthcare access and quality by assisting physicians online. The goal would be to create an online healthcare system as good as top doctors that is universally accessible at low cost.
Poster on governance for health IT infrastructures. Sustainability, scalability, standardization, planned sun-setting. Presented at the European Federation for Medical Informatics in Manchester, UK. 2017.
1) The document analyzes the costs of two approaches to obtaining clean data from electronic medical records (EMRs) - data discipline and data cleansing - and applies this to diabetes management in Canada.
2) A budget impact analysis finds that data cleansing would be quicker to implement and estimated to cost less at $21.6 million compared to $65.5 million for data discipline.
3) The analysis recommends considering a combination of the two approaches to improve data quality for diabetes management, which could save hundreds of millions to the healthcare system and billions to patients through reduced costs and improved health.
What we can learn from Amazon for Clinical Decision SupportKarim Keshavjee
This document discusses how clinical decision support systems (CDSS) can learn from features of Amazon to improve the user experience for clinicians. It describes how CDSS could incorporate real-time user data and feedback to rapidly improve functionality, provide personalized treatment recommendations and medication reviews based on similar patient experiences, and standardize care through shared treatment protocols. However, barriers include the need for multi-institutional collaboration and data sharing between different health records systems. The document concludes by stating these barriers can be overcome.
This document summarizes a presentation about using information technology (IT) to reduce healthcare costs by improving care for high-needs, high-cost patients. It outlines an IT framework that maps eight attributes of successful programs for these patients, including targeting them, using data strategically, and improving team communication, to components of an idealized regional IT infrastructure. These include a regional governance model, clinical and financial performance evaluation, and tools for patient management and care collaboration across providers. Feedback was requested on whether this framework sufficiently addresses the IT needs for coordinating care of high-needs patients.
The document describes a prototype for a web-based clinical form that can be used across electronic medical record (EMR) systems to capture standardized data on patients with multiple chronic conditions. The form automatically populates patient data, expands/collapses based on patient conditions, provides clinical guidelines, and classifies medications. User testing found high ratings for usefulness, ease of use, and productivity benefits. While the prototype shows promise, further work is needed to better integrate it within EMRs and allow two-way sharing of data.
The document summarizes research evaluating 201 diabetes mobile health apps based on screening criteria. Key findings include:
- No app met all criteria, which focused on integration with devices, medical records, and provider guidance.
- Most apps were replacements for paper journals and lacked more advanced functions.
- Better integration with electronic medical records and prescription/monitoring by healthcare providers could improve app quality and usefulness.
- Standardization of app certification, interoperability with records and devices, was recommended to facilitate development and adoption of higher quality diabetes management apps.
This document analyzes data from electronic medical records to examine opioid and benzodiazepine prescribing patterns for patients with chronic non-cancer pain. The analysis found that patients aged 64-83 years old received the most prescriptions for opioids and benzodiazepines. Elderly patients with osteoarthritis were prescribed more benzodiazepines per capita than patients with other chronic pain conditions such as fibromyalgia or low back pain. The study aims to validate these findings through further statistical analysis and publication.
1) The document discusses the potential benefits of a virtual family health team (vFHT) compared to traditional brick-and-mortar family health teams.
2) A vFHT uses remote telehealth providers who can provide 24/7 care through online and phone access at a lower cost than physical clinics.
3) Some barriers to vFHTs like maintaining continuity of care and privacy can be addressed through assigning patients to specific providers while also having backup providers available at all times to handle overflow calls.
The document discusses the need for next generation electronic medical records (EMRs) as current EMRs are not delivering hoped-for value and cannot be improved solely by vendors. It proposes a new solution architecture integrating EMRs, telehealth, clinical decision support systems, analytics, and research capabilities to create a learning health system. This system would provide team-based and population care, integrate multiple stakeholders, and accelerate EMR research and development to better address evolving healthcare demands.
Pitfalls and realities of working with Big DataKarim Keshavjee
This document summarizes Karim Keshavjee's presentation on designing a solution to enable standardized data collection across electronic medical records (EMRs) and the transmission of that data to a central repository. The presentation outlines the problem, lessons learned from previous related projects, stakeholder engagement activities, the proposed solution design, key barriers addressed by the design, and advantages of the approach. The proposed design features clinical data collection forms that are evidence-based, can be incorporated into any EMR instantly, and retrieve data via a standard server to facilitate research using big data.
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A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Basavarajeeyam is a Sreshta Sangraha grantha (Compiled book ), written by Neelkanta kotturu Basavaraja Virachita. It contains 25 Prakaranas, First 24 Chapters related to Rogas& 25th to Rasadravyas.
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Histololgy of Female Reproductive System.pptxAyeshaZaid1
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share - Lions, tigers, AI and health misinformation, oh my!.pptxTina Purnat
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• Building trust with communities online and offline
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Does Over-Masturbation Contribute to Chronic Prostatitis.pptxwalterHu5
In some case, your chronic prostatitis may be related to over-masturbation. Generally, natural medicine Diuretic and Anti-inflammatory Pill can help mee get a cure.
2. ¡ EHRs
have
pretty
much
failed
all
over
the
world
(I’m
talking
about
centralized
medical
records,
not
American
physician
medical
records)
¡ Why?
¡ There
are
a
thousand
reasons,
but
the
#1
reason
Our
collective
hypotheses
about
EHRs
are
incorrect
3. Doctors
need
data
to
look
after
patients
(duh!)
If
we
give
data
to
them
faster,
patients
will
get
better
faster
(The
“Replace
Postal
Service”
Hypothesis
of
Better
Care)
Let’s
also
make
it
Machine
Readable
so
Computers
can
double-‐guess
the
doctors!
4. ¡ It
may
be
true
(unproven)
¡ But
solving
this
problem
has
NOT
created
enough
value
to
justify
the
costs
¡ The
experiences
of
the
Health
Information
Exchanges
(HIEs)
in
the
US
bears
this
out
§ HIEs
are
purer
versions
of
the
“Replace
Postal
Service”
with
a
lower
cost
structure
than
EHRs
§ They
have
also
failed
¡ HL7
and
Interoperability
are
also
massive
failures
–they
are
also
based
on
the
RPS
hypothesis
5. ¡ We
should
be
identifying
and
testing
multiple
hypotheses
as
we
move
forward
¡ But
how
will
we
know
which
one
is
the
right
one?
¡ Easy!
¡ Humans
and
systems
are
good
at
identifying
value
and
embrace
it
quickly
§ Christoph
Lehmann
pointed
this
out
nicely
in
his
Keynote:
When
IT
goes
‘Viral’,
you
know
you’re
onto
something
¡ What
we
need
to
do
is
try
out
a
bunch
of
alternative
ideas
and
see
what
works
§ But….where
should
we
look
for
better
ideas?
6. Research
Study
Research
Study
Synthesis
Researchers
Academics
Clinician-‐Scientists
Clinicians
Patients
Guidelines
The
Healthcare
Value
Chain
7. ¡ The
steps
along
the
way
by
which
we
add
value
to
raw
materials
¡ In
the
Automotive
Industry,
they
take
iron
from
holes
in
the
ground
and
make
nice
shiny
cars
which
you
can
buy
from
your
local
neighborhood
dealer
§ They
don’t
make
you
go
to
Northern
Ontario
to
dig
up
the
iron
and
smelt
it
yourself
¡ In
healthcare,
we
discover
new
facts
and
treatments
that
we
can
deliver
to
patients
8. ¡ There
are
many
stakeholders
in
the
healthcare
system
who
ADD
value
to
the
system
¡ When
they’re
left
out
of
the
IT
equation,
real
value
cannot
be
created
9. Stakeholders
Researchers
&
Academics
Providers
Patients
Ministries
of
Health
Systems
Implementers
Guideline
Implementers
Vendors
10. ¡ Lowering
the
costs
of
providing
healthcare
through:
¡ Predictive
analytics
–to
provide
care
that
matters
to
patients
and
to
allocate
resources
better
§ Everyone
agrees
there’s
lots
of
waste
in
the
system,
but
nobody
knows
where
it
is!
§ Will
need
researchers
and
data
scientists
to
identify
waste
§ More
than
anything,
we
need
better
measurements
of
“What
is”
§ That’s
what
impressed
me
most
about
Christoph
Lehmann’s
presentation
–not
his
great
inventions,
but
the
fact
that
he
had
such
great
granular
data
¡ Shared
data
for
coordinated
care
§ Virtualization
of
care
beyond
bricks
and
mortar
11. ¡ Increased
patient
empowerment,
access
and
control
(mobile
apps,
block
chain)
¡ Chronic
disease
logistics
(lots
of
patients
falling
through
the
cracks)
¡ Ability
to
rapidly
test
hypotheses
§ Big
data
is
meaningless
without
hypothesis
testing
¡ Ability
to
share
innovations
using
non-‐
commercial
dissemination
mechanisms