The average academic research organization (ARO) and hospital has many systems that house patient-related information, such as patient records and genomic data. Combining data from a variety of sources in an ongoing manner can enable complex and meaningful querying, reporting and analysis for the purposes of improving patient safety and care, boosting operational efficiency, and supporting personalized medicine initiatives.
In this webinar, Perficient’s Mike Grossman, a director of clinical data warehousing and analytics, and Martin Sizemore, a healthcare strategist, discussed:
-How AROs and hospitals can benefit from a systematic approach to combining data from diverse systems and utilizing a suite of data extraction, reporting, and analytical tools, in order to support a wide variety of needs and requests
-Examples of proposed solutions to real-life challenges AROs and hospitals often encounter
How Northwestern Medicine is Leveraging Epic to Enable Value-Based CarePerficient, Inc.
Value-based care and payment reform are prompting hospitals and healthcare providers to more closely manage population health. Hospitals and health systems rely on technology and data to outline the characteristics of their population and identify high-risk patients in order to manage chronic diseases and deliver enhanced preventative care.
Our webinar covered how Cadence Health, now part of Northwestern Medicine, is leveraging the native capabilities of Epic to manage their population health initiatives and value-based care relationships across the continuum of care.
Our speakers:
-Analyzed how Epic’s Healthy Planet and Cogito platforms can be used to manage value-based care initiatives.
-Examined the three steps for effective population health management: Collect data, analyze data and engage with patients.
-Covered how access to analytics allows physicians at Northwestern Medicine to deliver enhanced preventive care and better manage chronic diseases.
-Discussed Northwestern Medicine’s strategy to integrate data from Epic and other data sources.
How to Load Data More Quickly and Accurately into Oracle's Life Sciences Data...Perficient, Inc.
Sponsors and CROs know the value of having a consolidated and regulatory-compliant data warehouse, such as Oracle’s Life Sciences Data Hub (LSH), as well as the importance of consistently loading data into that warehouse quickly and accurately.
However, as data structures from the source files change over time, it can be very time consuming to modify the data structure in the warehouse itself. Additionally, for the large groups of SAS datasets that are typical for a clinical trial, the out-of-the-box load times can be quite long, as the data is loaded one set at a time.
Perficient has the answer. In this webinar, we discussed and demonstrated an autoloader tool that greatly simplifies the data loading process for LSH. We showed how the autoloader can automatically load files, detect metadata changes, upgrade target structures, and load data, all with no human intervention. In addition, we demonstrated how Perficient’s autoloader tool can load multiple datasets in parallel to minimize load times.
Using JReview to Analyze Clinical and Pharmacovigilance Data in Disparate Sys...Perficient, Inc.
Sponsors and CROs naturally rely on various clinical and safety systems from a multitude of software vendors. However, continuously accessing disparate sources for the reporting, analysis, and monitoring of data can be a treacherous undertaking, if you don't have a solution that connects to them right out of the box.
That's where JReview comes in. For almost two decades, life sciences companies, research organizations, in addition to the government, have relied on JReview for the comprehensive analysis and monitoring of clinical and pharmacovigilance data.
The analytics solution works with many Oracle Health Sciences applications, including Argus Safety, Oracle AERS, Oracle Clinical (OC), Remote Data Capture (RDC), Thesaurus Management System (TMS), InForm, Life Sciences Data Hub (LSH), and Clinical Development Center (CDC). JReview also works with non-Oracle solutions, such as ARISg, Medidata Rave, and SAS Drug Development.
In this slideshare, you will learn:
The features and benefits of JReview, including the new functionality in v10.0 (e.g., risk-based monitoring analytics reporting on the clinical data itself, etc.)
Benefits of using JReview for:
Reporting and query of your clinical data
Supplying internal and/or external users/sponsors information
Providing a secure way for your internal users and/or sponsor users to access the clinical data
Examples of how customers use JReview with OC/RDC
The implementation process and options
Harness Your Clinical and Financial Data with an Enterprise Health Informat...Perficient, Inc.
The importance of Enterprise Health Information Exchange (EHIE) as a key way to empower your physicians and patients and demonstrate meaningful use of electronic health records:
- Present the business case for EHIE as an important architecture that matters to progressive health systems
- Take a look at some of the market-leading EHIE architectures and products
- Provide real exam...ples of organizations that are using EHIE to improve their operations
ACO = HIE + Analytics - a Healthcare IT PresentationPerficient, Inc.
With the release of the Accountable Care Organization (ACO) regulations, healthcare providers must be able to identify, access, and seamlessly share patient information to drive efficiencies and enjoy a potential share in ACO program incentives. Additionally, more than half of the 93 draft National Committee for Quality Assurance (NCQA) ACO measures are also Meaningful Use measures, which further elevates the need to achieve meaningful use stage 2 or higher.
Given these goals, success will ultimately depend on an organization’s ability to share patient data at the point of care and its ability to gain meaning from historical and longitudinal data for use in managing population health. Healthcare organizations will need to give focused attention to the IT strategies, appropriate architectures, and roadmaps they will use to move from desired state to reality.
We discuss the practical architectural approach for creating an ACO. As Health Information Exchanges (HIEs) evolve into their second generation, they are able to the support the functional ACO tasks of delivering and managing care for a defined population, accept payment, distribute savings to participants, and perform disease management with predictive modeling to improve outcomes. We will also discuss the need to achieve meaningful use stage 2 or higher and the data/analytics requirements for ACO participants.
Presenter Martin Sizemore is the Director of Healthcare Strategy for Perficient. Martin has been a consultant and trusted advisor to CEOs, COOs, CIOs and senior managers for global multi-national companies and healthcare organizations, and is a certified Enterprise Architect with specialized skills in Enterprise Application Integration (EAI) and Service Oriented Architecture (SOA).
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
How Northwestern Medicine is Leveraging Epic to Enable Value-Based CarePerficient, Inc.
Value-based care and payment reform are prompting hospitals and healthcare providers to more closely manage population health. Hospitals and health systems rely on technology and data to outline the characteristics of their population and identify high-risk patients in order to manage chronic diseases and deliver enhanced preventative care.
Our webinar covered how Cadence Health, now part of Northwestern Medicine, is leveraging the native capabilities of Epic to manage their population health initiatives and value-based care relationships across the continuum of care.
Our speakers:
-Analyzed how Epic’s Healthy Planet and Cogito platforms can be used to manage value-based care initiatives.
-Examined the three steps for effective population health management: Collect data, analyze data and engage with patients.
-Covered how access to analytics allows physicians at Northwestern Medicine to deliver enhanced preventive care and better manage chronic diseases.
-Discussed Northwestern Medicine’s strategy to integrate data from Epic and other data sources.
How to Load Data More Quickly and Accurately into Oracle's Life Sciences Data...Perficient, Inc.
Sponsors and CROs know the value of having a consolidated and regulatory-compliant data warehouse, such as Oracle’s Life Sciences Data Hub (LSH), as well as the importance of consistently loading data into that warehouse quickly and accurately.
However, as data structures from the source files change over time, it can be very time consuming to modify the data structure in the warehouse itself. Additionally, for the large groups of SAS datasets that are typical for a clinical trial, the out-of-the-box load times can be quite long, as the data is loaded one set at a time.
Perficient has the answer. In this webinar, we discussed and demonstrated an autoloader tool that greatly simplifies the data loading process for LSH. We showed how the autoloader can automatically load files, detect metadata changes, upgrade target structures, and load data, all with no human intervention. In addition, we demonstrated how Perficient’s autoloader tool can load multiple datasets in parallel to minimize load times.
Using JReview to Analyze Clinical and Pharmacovigilance Data in Disparate Sys...Perficient, Inc.
Sponsors and CROs naturally rely on various clinical and safety systems from a multitude of software vendors. However, continuously accessing disparate sources for the reporting, analysis, and monitoring of data can be a treacherous undertaking, if you don't have a solution that connects to them right out of the box.
That's where JReview comes in. For almost two decades, life sciences companies, research organizations, in addition to the government, have relied on JReview for the comprehensive analysis and monitoring of clinical and pharmacovigilance data.
The analytics solution works with many Oracle Health Sciences applications, including Argus Safety, Oracle AERS, Oracle Clinical (OC), Remote Data Capture (RDC), Thesaurus Management System (TMS), InForm, Life Sciences Data Hub (LSH), and Clinical Development Center (CDC). JReview also works with non-Oracle solutions, such as ARISg, Medidata Rave, and SAS Drug Development.
In this slideshare, you will learn:
The features and benefits of JReview, including the new functionality in v10.0 (e.g., risk-based monitoring analytics reporting on the clinical data itself, etc.)
Benefits of using JReview for:
Reporting and query of your clinical data
Supplying internal and/or external users/sponsors information
Providing a secure way for your internal users and/or sponsor users to access the clinical data
Examples of how customers use JReview with OC/RDC
The implementation process and options
Harness Your Clinical and Financial Data with an Enterprise Health Informat...Perficient, Inc.
The importance of Enterprise Health Information Exchange (EHIE) as a key way to empower your physicians and patients and demonstrate meaningful use of electronic health records:
- Present the business case for EHIE as an important architecture that matters to progressive health systems
- Take a look at some of the market-leading EHIE architectures and products
- Provide real exam...ples of organizations that are using EHIE to improve their operations
ACO = HIE + Analytics - a Healthcare IT PresentationPerficient, Inc.
With the release of the Accountable Care Organization (ACO) regulations, healthcare providers must be able to identify, access, and seamlessly share patient information to drive efficiencies and enjoy a potential share in ACO program incentives. Additionally, more than half of the 93 draft National Committee for Quality Assurance (NCQA) ACO measures are also Meaningful Use measures, which further elevates the need to achieve meaningful use stage 2 or higher.
Given these goals, success will ultimately depend on an organization’s ability to share patient data at the point of care and its ability to gain meaning from historical and longitudinal data for use in managing population health. Healthcare organizations will need to give focused attention to the IT strategies, appropriate architectures, and roadmaps they will use to move from desired state to reality.
We discuss the practical architectural approach for creating an ACO. As Health Information Exchanges (HIEs) evolve into their second generation, they are able to the support the functional ACO tasks of delivering and managing care for a defined population, accept payment, distribute savings to participants, and perform disease management with predictive modeling to improve outcomes. We will also discuss the need to achieve meaningful use stage 2 or higher and the data/analytics requirements for ACO participants.
Presenter Martin Sizemore is the Director of Healthcare Strategy for Perficient. Martin has been a consultant and trusted advisor to CEOs, COOs, CIOs and senior managers for global multi-national companies and healthcare organizations, and is a certified Enterprise Architect with specialized skills in Enterprise Application Integration (EAI) and Service Oriented Architecture (SOA).
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
Extending Your EMR with Business Intelligence SolutionsPerficient, Inc.
The best business intelligence applications start with one part EMR, one part financial applications, and one part operational applications stirred into real insights. These slides show examples from speakers that have successfully extended EMRs into managing costs, transmitting information to disease registries and improving patient care.
Developing a Strategic Analytics Framework that Drives Healthcare TransformationTrevor Strome
About the presentation.
Based on Chapter 3 of my book "Healthcare Analytics for Quality and Performance Improvement", this presentation describes the key components of a strategic analytics framework that can enable your healthcare organization to leverage data from source-systems to achieve its quality, safety, and performance improvement goals.
What is an analytics strategy?
Analytics is currently a very “trendy” topic. The internet is scattered with many buzzwords, marketing angles, white papers, and opinions on the topic of healthcare analytics. With all this “noise”, it is easy to get distracted from what is actually required, from an analytics perspective, by your organization. An analytics strategy helps cut through the noise and keep focus on what is important for the organization. Regardless of what the latest “buzz” is, your analytics strategy will enable your organization to Invest now for what is required now, and invest later for what is required in the future.
An analytics strategy helps ensure that analytics development and capabilities are in alignment with enterprise quality and performance goals and helps avoids the “all dashboard, no improvement” syndrome. Furthermore, a well formed strategy document helps to achieve optimal use of analytics within a healthcare organization and can mean the difference between a “collection of reports” versus a high-value information resource.
An analytics strategy can rarely stand on its own. In general, the analytics strategy should use as input an organization’s Quality Improvement (QI) strategy and should be used to inform an organization’s Business Intelligence (BI) or Information Technology (IT) strategy. The analytics strategy is an important input to technical strategies because analytics, after all, can involve a sophisticated use of data and technology. Requirements for analytics may trigger a cascade of enhancements throughout other components of IT and BI (i.e., reporting, data storage, ETL, etc)
The document is intended to accompany Chapter 3, “Developing an Analytics Strategy to Drive Change”, so please refer to the chapter for further information about developing an analytics strategy.
Healthcare Business Intelligence for Power UsersPerficient, Inc.
The Healthcare industry is accustomed to volumes of clinical and administrative data. Business intelligence helps convert these large amounts of data into actionable insights to reduce costs, streamline processes, and improve healthcare delivery. Our first webinar, “An Introduction to Business Intelligence for Healthcare,” introduces business intelligence in healthcare and common concepts.
In the second of this series of two webinars, Health BI Practice Manager, Mike Jenkins addresses:
- The BI Maturity Level
- Examples of Levels 3 and 4
- Attaining Level 5
Transforming Pharmacovigilance Workflows with AI & Automation Perficient, Inc.
Medical information call centers have an opportunity to transform the way they capture, code, and analyze adverse events (AEs) and product quality complaints (PQCs) with artificial intelligence (AI) and automation.
The use of such innovative technology improves data quality and consistency, compliance, and operational efficiency. It helps reduce the frequency of your pharmacovigilance (PV) operations resources going home, saying, “I have more to do at the end of the day than I did when I started."
Our one-hour, on-demand webinar shows you how you can use AI and automation to turbo-charge your end-to-end PV system. Use cases and demonstrations will include:
Analyzing safety data
Auto-coding verbatim terms to official medical dictionary terms
Auto-creating an AE case in your database
Converting speech to text
Impact 2014: Optimizing a Retail Distribution chain from Cargo Shipment to St...Perficient, Inc.
Presentation from Impact 2014 showing the how product recalls fit into the product shipment lifecycle as well as what needs to happen once a recall is implemented.
Levi Thatcher, Health Catalyst Director of Data Science and his team provide a live demonstration using healthcare.ai to implement a healthcare-specific machine learning model from data source to patient impact. Levi goes through a hands-on coding example while sharing his insights on the value of predictive analytics, the best path towards implementation, and avoiding common pitfalls. Frequently asked questions are answered during the session.
During the webinar, we will:
Describe and install healthcare.ai
Build and evaluate a machine learning model
Deploy interpretable predictions to SQL Server
Discuss the process of deploying into a live analytics environment.
If you’d like to follow along, you should download and install R and RStudio prior to the event. We look forward to you joining us!
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
PAREXEL's Matt Neal joins experts from Microsoft and Allergan to discuss how innovations in technology can help patients by reducing the time and expense of bringing life-saving treatments to market.
Challenges in Clinical Research: Aridhia's Disruptive Technology Approach to ...Aridhia Informatics Ltd
This webinar with our partner Pivotal aired in July 2016.
The increasing sophistication of modern medicine, a seemingly endless supply of data, and the ability to perform large-scale computation is transforming clinical research. However, utilising data to generate new treatments and therapies has continued to prove complicated. The silo-based information systems built over the last 30 years are simply unable to scale to support today’s use cases.
Aridhia, creators of AnalytiXagility, the ground-breaking research and healthcare data analysis platform, is now enabling its customers to rapidly analyse massive amounts of data in meaningful ways to change how diseases are understood, managed and treated. Powered by Pivotal Greenplum, AnalytiXagility is at the forefront of Advanced Clinical Research Information Systems (ACRIS), one of Gartner’s 10 “Transformational Digital Disruptors in Healthcare by 2025”.
Learn how big data and data science are being applied to clinical research and:
• Why research-oriented healthcare delivery organizations and academic medical centers need an ACRIS
• How improving collaboration and productivity accelerates the discovery of insights and increases competiveness
• Why robust data security is critical to modernizing engagement between academia, industry and healthcare
• How to reduce research costs while improving commercialization opportunities
• Why enabling transparent analysis and reproducibility of research are key to scientific progress
• Best practices to get started on your digital transformation and Big Data journey
Enterprise Integration Engine for Large Scale InteroperabilityOrion Health
In this webinar, we examine how Catholic Health Initiatives manages an enterprise integration engine for the nation’s third-largest nonprofit health system.
Business Intelligence & Analytics solutions enable healthcare service providers to build sustainable competitive advantage with the help of insights derived from their existing operations and patient data.
These are the slides from the workshop I delivered at the Healthcare Analytics Symposium in July 2014. This 3-hour workshop walked the attendees step-by-step through the requirements to start a healthcare predictive analytics program and some of the areas already showing progress.
This presentation is designed to illustrate how you can better improve personnel productivity through training, work flow management, and reporting, which will allow management to proactively address performance flaws and employee needs.
HMIS is an integrated Hospital management system, which addresses All requirements of hospitals. It is a powerful, flexible and easy to use application designed and developed to convey real conceivable benefits to hospitals and clinics which reduce the paper overload.
Extending Your EMR with Business Intelligence SolutionsPerficient, Inc.
The best business intelligence applications start with one part EMR, one part financial applications, and one part operational applications stirred into real insights. These slides show examples from speakers that have successfully extended EMRs into managing costs, transmitting information to disease registries and improving patient care.
Developing a Strategic Analytics Framework that Drives Healthcare TransformationTrevor Strome
About the presentation.
Based on Chapter 3 of my book "Healthcare Analytics for Quality and Performance Improvement", this presentation describes the key components of a strategic analytics framework that can enable your healthcare organization to leverage data from source-systems to achieve its quality, safety, and performance improvement goals.
What is an analytics strategy?
Analytics is currently a very “trendy” topic. The internet is scattered with many buzzwords, marketing angles, white papers, and opinions on the topic of healthcare analytics. With all this “noise”, it is easy to get distracted from what is actually required, from an analytics perspective, by your organization. An analytics strategy helps cut through the noise and keep focus on what is important for the organization. Regardless of what the latest “buzz” is, your analytics strategy will enable your organization to Invest now for what is required now, and invest later for what is required in the future.
An analytics strategy helps ensure that analytics development and capabilities are in alignment with enterprise quality and performance goals and helps avoids the “all dashboard, no improvement” syndrome. Furthermore, a well formed strategy document helps to achieve optimal use of analytics within a healthcare organization and can mean the difference between a “collection of reports” versus a high-value information resource.
An analytics strategy can rarely stand on its own. In general, the analytics strategy should use as input an organization’s Quality Improvement (QI) strategy and should be used to inform an organization’s Business Intelligence (BI) or Information Technology (IT) strategy. The analytics strategy is an important input to technical strategies because analytics, after all, can involve a sophisticated use of data and technology. Requirements for analytics may trigger a cascade of enhancements throughout other components of IT and BI (i.e., reporting, data storage, ETL, etc)
The document is intended to accompany Chapter 3, “Developing an Analytics Strategy to Drive Change”, so please refer to the chapter for further information about developing an analytics strategy.
Healthcare Business Intelligence for Power UsersPerficient, Inc.
The Healthcare industry is accustomed to volumes of clinical and administrative data. Business intelligence helps convert these large amounts of data into actionable insights to reduce costs, streamline processes, and improve healthcare delivery. Our first webinar, “An Introduction to Business Intelligence for Healthcare,” introduces business intelligence in healthcare and common concepts.
In the second of this series of two webinars, Health BI Practice Manager, Mike Jenkins addresses:
- The BI Maturity Level
- Examples of Levels 3 and 4
- Attaining Level 5
Transforming Pharmacovigilance Workflows with AI & Automation Perficient, Inc.
Medical information call centers have an opportunity to transform the way they capture, code, and analyze adverse events (AEs) and product quality complaints (PQCs) with artificial intelligence (AI) and automation.
The use of such innovative technology improves data quality and consistency, compliance, and operational efficiency. It helps reduce the frequency of your pharmacovigilance (PV) operations resources going home, saying, “I have more to do at the end of the day than I did when I started."
Our one-hour, on-demand webinar shows you how you can use AI and automation to turbo-charge your end-to-end PV system. Use cases and demonstrations will include:
Analyzing safety data
Auto-coding verbatim terms to official medical dictionary terms
Auto-creating an AE case in your database
Converting speech to text
Impact 2014: Optimizing a Retail Distribution chain from Cargo Shipment to St...Perficient, Inc.
Presentation from Impact 2014 showing the how product recalls fit into the product shipment lifecycle as well as what needs to happen once a recall is implemented.
Levi Thatcher, Health Catalyst Director of Data Science and his team provide a live demonstration using healthcare.ai to implement a healthcare-specific machine learning model from data source to patient impact. Levi goes through a hands-on coding example while sharing his insights on the value of predictive analytics, the best path towards implementation, and avoiding common pitfalls. Frequently asked questions are answered during the session.
During the webinar, we will:
Describe and install healthcare.ai
Build and evaluate a machine learning model
Deploy interpretable predictions to SQL Server
Discuss the process of deploying into a live analytics environment.
If you’d like to follow along, you should download and install R and RStudio prior to the event. We look forward to you joining us!
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
PAREXEL's Matt Neal joins experts from Microsoft and Allergan to discuss how innovations in technology can help patients by reducing the time and expense of bringing life-saving treatments to market.
Challenges in Clinical Research: Aridhia's Disruptive Technology Approach to ...Aridhia Informatics Ltd
This webinar with our partner Pivotal aired in July 2016.
The increasing sophistication of modern medicine, a seemingly endless supply of data, and the ability to perform large-scale computation is transforming clinical research. However, utilising data to generate new treatments and therapies has continued to prove complicated. The silo-based information systems built over the last 30 years are simply unable to scale to support today’s use cases.
Aridhia, creators of AnalytiXagility, the ground-breaking research and healthcare data analysis platform, is now enabling its customers to rapidly analyse massive amounts of data in meaningful ways to change how diseases are understood, managed and treated. Powered by Pivotal Greenplum, AnalytiXagility is at the forefront of Advanced Clinical Research Information Systems (ACRIS), one of Gartner’s 10 “Transformational Digital Disruptors in Healthcare by 2025”.
Learn how big data and data science are being applied to clinical research and:
• Why research-oriented healthcare delivery organizations and academic medical centers need an ACRIS
• How improving collaboration and productivity accelerates the discovery of insights and increases competiveness
• Why robust data security is critical to modernizing engagement between academia, industry and healthcare
• How to reduce research costs while improving commercialization opportunities
• Why enabling transparent analysis and reproducibility of research are key to scientific progress
• Best practices to get started on your digital transformation and Big Data journey
Enterprise Integration Engine for Large Scale InteroperabilityOrion Health
In this webinar, we examine how Catholic Health Initiatives manages an enterprise integration engine for the nation’s third-largest nonprofit health system.
Business Intelligence & Analytics solutions enable healthcare service providers to build sustainable competitive advantage with the help of insights derived from their existing operations and patient data.
These are the slides from the workshop I delivered at the Healthcare Analytics Symposium in July 2014. This 3-hour workshop walked the attendees step-by-step through the requirements to start a healthcare predictive analytics program and some of the areas already showing progress.
This presentation is designed to illustrate how you can better improve personnel productivity through training, work flow management, and reporting, which will allow management to proactively address performance flaws and employee needs.
HMIS is an integrated Hospital management system, which addresses All requirements of hospitals. It is a powerful, flexible and easy to use application designed and developed to convey real conceivable benefits to hospitals and clinics which reduce the paper overload.
The presentation is all about patient registration in hospital in which the receptionist register the details of patient and data is directly access by doctor.
What is the best Healthcare Data Warehouse Model for Your Organization?Health Catalyst
Join Steve Barlow as he addresses the strengths and weaknesses of each of the following three primary Data Model approaches for data warehousing in healthcare:
1. Enterprise Data Model
2. Independent Data Marts
3. Late-binding Solutions
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
Big data, RWE and AI in Clinical Trials made simpleHadas Jacoby
Technology is slowly but surely penetrating the healthcare industry in general and the clinical trials sector in particular. New and advanced solutions offer a variety of possibilities aimed to both improving existing processes and creating new and more efficient ones. And on top of all stands the desire to make clinical trials more patient centric.
In all of this, even though some of the technologies have yet to mature enough to meet the high quality standards necessary, it is important to know them and begin imagining the promise they hold for clinical trials.
Computer validation of e-source and EHR in clinical trials-KuchinkeWolfgang Kuchinke
Clinical Trials in the Learning Health System (LHS): Computer System Validation of eSource and EHR Data.
The question that was addressed: How to make a clinical trial data management system that uses EHR data, Patient Reported Outcome (PRO) and eSource data as part of the Learning Health System compliant with regulations and with Good Clinical Practice (GCP)?
The Learning Health System (LHS) connects health care with translational and clinical research. It generates new medical knowledge as a by-product of the care process and its aim is to improve health and safety of patients. The LHS generates and applies knowledge. For this purpose, clinical research, which is research involving humans, must be part of the LHS. Two general types of research exists: observational studies and clinical trials.
Clinical data drive the LHS, because results from randomized controlled trials are seen as “gold standard” for medical evidence. For this reason the concept of using data gathered directly from the patient care environment has enormous potential for accelerating the rate at which useful knowledge is generated.
All computer systems involved in clinical trials must undergo Computer System Validation (CSV). For this process, a legal framework for the TRANSFoRm project was developed. It was used for data privacy analysis of the data flow in two research use cases: an epidemiological cohort study on Diabetes and a randomised clinical trial about different GORD treatment regimes.
Computerized system validation is the documented process to produce evidence that a computerized system does exactly what it is designed to do in a consistent and reproducible manner. The validation of electronic source data in clinical trials presents many challenges because of the blurring of the border between care and research. Here we present our approach for the validation of eSource data capture and the developed documentation for the CSV of the complete data flow in the LHS developed by the TRANSFoRm project. An important part hereby played the GORD Valuation Study.
Computer System Validation - privacy zones, eSource and EHR data in clinical ...Wolfgang Kuchinke
Clinical Trials in the Learning Health System (LHS): Computer System Validation of eSource and EHR Data.
The question that was addressed: How to make a clinical trial data management system that uses EHR data, Patient Reported Outcome (PRO) and eSource data as part of the Learning Health System compliant with regulations and with Good Clinical Practice (GCP)?
The Learning Health System (LHS) connects health care with translational and clinical research. It generates new medical knowledge as a by-product of the care process and its aim is to improve health and safety of patients. The LHS generates and applies knowledge. For this purpose, clinical research, which is research involving humans, must be part of the LHS. Two general types of research exists: observational studies and clinical trials.
Clinical data drive the LHS, because results from randomized controlled trials are seen as “gold standard” for medical evidence. For this reason the concept of using data gathered directly from the patient care environment has enormous potential for accelerating the rate at which useful knowledge is generated.
All computer systems involved in clinical trials must undergo Computer System Validation (CSV). For this process, a legal framework for the TRANSFoRm project was developed. It was used for data privacy analysis of the data flow in two research use cases: an epidemiological cohort study on Diabetes and a randomised clinical trial about different GORD treatment regimes.
Computerized system validation is the documented process to produce evidence that a computerized system does exactly what it is designed to do in a consistent and reproducible manner. The validation of electronic source data in clinical trials presents many challenges because of the blurring of the border between care and research. Here we present our approach for the validation of eSource data capture and the developed documentation for the CSV of the complete data flow in the LHS developed by the TRANSFoRm project. An important part hereby played the GORD Valuation Study.
Computer System Validation with privacy zones, e-source and clinical trials b...Wolfgang Kuchinke
Clinical Trials in the Learning Health System: Computer System Validation of eSource and EHR Data. Basic question is how to make a clinical trial data management system that uses EHR data, Patient Reported Outcome (PRO) and eSource data as part of the Learning Health System compliant with regulations and with Good Clinical Practice (GCP)? Computer System Validation (CSV) is a requirement for all computer systems involved in clinical trials for drug submission. It consists of documented processes to produce evidence that a computerized system does exactly what it is designed to do in a consistent and reproducible manner. Validation begins with the system requirements definition and continues until system retirement. For example, the components of a clinical trials
framework used in our case are: Patient eligibility checks and enrolment, pre-population of eCRFs with data from EHRs, PROM data collection by patients, storing of a copy of study data in the EHR, and validation of the Study System that coordinates all study and data collection events.
eSource direct data entry in clinical trials and GCP requirements. It is the duty of physicians who are involved in medical research to protect the privacy and confidentiality of personal information of research subjects. Any eSource system should be fully compliant with the provisions of applicable data protection legislation. This creates the need to develop and implement processes that ensure the continuous control of the investigators over these data. This has to be the focus of CSV. Clinical Data drive the LHS. The results from randomized controlled trials are seen as the “gold standard” for medical evidence, but such trials are often performed outside the usual system of care and recruit highly selected populations. For this reason, the concept of using data gathered directly from the patient care environment has enormous potential for accelerating the rate at which useful knowledge is generated.
This leads to the requirement for validating electronic source data in clinical trials. This includes validation for clinical data that is either captured from the subject directly or from the subject’s medical records. The problem is the correct and appropriate system validation of electronic source data. The main componenets of CSV are the Validation Master Plan), User Requirements Specification, Hardware Requirements Specification, Design qualification, Installation qualification, Operational qualification, Performance qualification.
Any instrument used to capture source data should ensure that the data are captured as specified within the protocol. Source data should be accurate, legible, contemporaneous, original, attributable, complete and consistent. An audit trail should be maintained as part of the source documents for the original creation and subsequent modification of all source data.
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.
ASSESSMENT OF BIOMEDICAL LITERATURE
Components of internal and external validity of controlled clinical trials
Internal validity — extent to which systematic error (bias) is minimized in clinical trials
Selection bias: biased allocation to comparison groups
Performance bias: unequal provision of care apart from treatment under evaluation
Detection bias: biased assessment of outcome
Attrition bias: biased occurrence and handling of deviations from protocol and loss to follow up
Requirements, needs
Planning, direction
Information collection
Information Assessment
- Evaluation for accuracy, correctness, relevance, usefulness
- Source reliability assessment (competency and past behavior based)
- Bias assessment (motivators, interests, funding, objectives)
- Conflicts of interest
- Sources of funding, important business relationships
- Grading of individual items (study, report, analysis, article)
Collation of information
- Exclusion of irrelevant, incorrect, and useless information
-Arrangement of information in a form which enables real-time analysis
- System for rapid retrieval of information
External validity — extent to which results of trials provide a correct basis for generalization to other circumstances
Patients: age, sex, severity of disease and risk factors, comorbidity
Treatment regimens: dosage, timing and route of administration, type of treatment within a class of treatments, concomitant treatments
Settings: level of care (primary to tertiary) and experience and specialization of care provider
Modalities of outcomes: type or definition of outcomes and duration of follow up
The Learning Health System: Thinking and Acting Across ScalesPhilip Payne
A Learning Health System (LHS) can be defined as an environment in which knowledge generation processes are embedded into daily clinical practice in order to continually improve the quality, safety, and outcomes of healthcare delivery. While still largely an aspirational goal, the promise of the LHS is a future in which every patient encounter is an opportunity to learn and improve that patient’s care, as well as the care their family and broader community receives. The foundation for building such an LHS can and should be the Electronic Health Record (EHR), which provides the basis for the comprehensive instrumentation and measurement of clinical phenotypes, as well as a means of delivering new evidence at the patient- and population levels. In this presentation, we will explore the ways in which such EHR-derived phenotypes can be combined with complementary data across a spectrum from biomolecules to population level trends, to both generate insights and deliver such knowledge in the right time, place, and format, ultimately improving clinical outcomes and value.
Provenance abstraction for implementing security: Learning Health System and ...Vasa Curcin
Discussion of provenance usage in the Learning Health System paradigm, as implemented in the TRANSFoRm project, with focus on security requirements and how they can be addressed using provenance graph abstraction.
Health research, clinical registries, electronic health records – how do they...Koray Atalag
This is a talk I gave at my own organisation - National Institute for Health Innovation (NIHI) of the University of Auckland on 6 Aug 2014. Abstract as follows:
In this talk I’ll first cover the topic of clinical registry – an invaluable tool for supporting clinical practice but also gaining momentum in research and quality improvement. NIHI has been very active in this space: we have delivered the prestigious and highly successful National Cardiac Registry (ANZACS-QI) together with VIEW research team and also very recently launched the Gestational Diabetes Registry with Counties Manukau DHB & Diabetes Projects Trust. A few others are in likely to come down the line. This is a huge opportunity for health data driven research and NIHI to position itself as ‘the health data steward’ in the country given our independent status and existing IT infrastructure and “good culture” of working with health data . NIHI’s ‘health informatics’ twist in delivering these projects is how we go about defining ‘information’ – using a scientifically credible and robust methodology: openEHR. This is an international (and now national too) standard to non-ambiguously define health information so that they are easy to understand and also are computable. We build software (even automatically in some cases!) using models created by this formalism. I’ll give basics of openEHR approach and then walk you through how to make sense out of all these. Hopefully you may have an idea about its ‘value proposition’ (as business people call) or Science merit as I like to call it ;)
Chapter 4 Knowledge Discovery, Data Mining, and Practice-Based Evi.docxchristinemaritza
Chapter 4 Knowledge Discovery, Data Mining, and Practice-Based Evidence
Mollie R. Cummins
Ginette A. Pepper
Susan D. Horn
The next step to comparative effectiveness research is to conduct more prospective large-scale observational cohort studies with the rigor described here for knowledge discovery and data mining (KDDM) and practice-based evidence (PBE) studies.
Objectives
At the completion of this chapter the reader will be prepared to:
1.Define the goals and processes employed in knowledge discovery and data mining (KDDM) and practice-based evidence (PBE) designs
2.Analyze the strengths and weaknesses of observational designs in general and of KDDM and PBE specifically
3.Identify the roles and activities of the informatics specialist in KDDM and PBE in healthcare environments
Key Terms
Comparative effectiveness research, 69
Confusion matrix, 62
Data mining, 61
Knowledge discovery and data mining (KDDM), 56
Machine learning, 56
Natural language processing (NLP), 58
Practice-based evidence (PBE), 56
Preprocessing, 56
Abstract
The advent of the electronic health record (EHR) and other large electronic datasets has revolutionized efficient access to comprehensive data across large numbers of patients and the concomitant capacity to detect subtle patterns in these data even with missing or less than optimal data quality. This chapter introduces two approaches to knowledge building from clinical data: (1) knowledge discovery and data mining (KDDM) and (2) practice-based evidence (PBE). The use of machine learning methods in retrospective analysis of routinely collected clinical data characterizes KDDM. KDDM enables us to efficiently and effectively analyze large amounts of data and develop clinical knowledge models for decision support. PBE integrates health information technology (health IT) products with cohort identification, prospective data collection, and extensive front-line clinician and patient input for comparative effectiveness research. PBE can uncover best practices and combinations of treatments for specific types of patients while achieving many of the presumed advantages of randomized controlled trials (RCTs).
Introduction
Leaders need to foster a shared learning culture for improving healthcare. This extends beyond the local department or institution to a value for creating generalizable knowledge to improve care worldwide. Sound, rigorous methods are needed by researchers and health professionals to create this knowledge and address practical questions about risks, benefits, and costs of interventions as they occur in actual clinical practice. Typical questions are as follows:
•Are treatments used in daily practice associated with intended outcomes?
•Can we predict adverse events in time to prevent or ameliorate them?
•What treatments work best for which patients?
•With limited financial resources, what are the best interventions to use for specific types of patients?
•What types of indi ...
As the author of “Big Data in Healthcare Hype and Hope,” Dr. Feldman has interviewed over 180 emerging tech and healthcare companies, always asking, “How can your new approach help patients?” Her research shows that data, as an enabling tool, has the power to give us critical new insights into not only what causes disease, but what comprises normal. Despite this promise, few patients have reaped the benefits of personalized medicine. A panel of leading big data innovators will discuss the evolving health data ecosystem and how big data is being leveraged for research, discovery, clinical trials, genomics, and cancer care. Case studies and real-life examples of what’s working, what’s not working, and how we can help speed up progress to get patients the right care at the right time will be explored and debated.
• Bonnie Feldman, DDS, MBA - Chief Growth Officer, @DrBonnie360
• Colin Hill - CEO, GNS Healthcare
• Jonathan Hirsch - Founder & President, Syapse
• Andrew Kasarskis, PhD - Co-Director, Icahn Institute for Genomics & Multiscale Biology; Associate Professor, Genetics & Genomic Studies, Icaahn School of Medicine at Mt. Sinai
• William King - CEO, Zephyr Health
New York eHealth Collaborative Digital Health Conference
November 18, 2014
The world is quite a different place than it was six months ago, and with the 2020 holiday season fast approaching, the pressure is on to meet revenue goals in what’s been an uncertain year.
In August, we surveyed 154 marketing executives to find out what they think is likely to happen this holiday season and how they are preparing for it. The results are fascinating, and we’ve distilled them into clear actions you can take right now to adapt and prepare for a very different 2020 holiday season.
In this webinar, Eric Enge (Principal, Digital Marketing at Perficient) and Jim Hertzfeld (Chief Strategist, Digital at Perficient) discussed:
How marketers have already adapted and where they see the most opportunity moving forward
What will be different this holiday season and how to adjust your strategy accordingly
Ways to identify and meet changing customer expectations, wants, and needs
How to determine if your priorities or investments should change
What actions you can take right now to be successful
The Secret to Acquiring and Retaining Customers in Financial ServicesPerficient, Inc.
Data, when leveraged effectively, can help you segment and target customers, analyze spending habits, and can create a personalized experience that builds value and customer loyalty.
Without a 360-degree view of your customers, you can’t properly target them with real-time personalized offers, advice, and other services. In addition, lack of customer intelligence creates lost opportunities for banks and insurers to cross-sell and upsell new products and services.
Our one-hour webinar covered how customer intelligence platforms can help you engage, acquire, and retain customers.
Oracle Strategic Modeling Live: Defined. Discussed. Demonstrated.Perficient, Inc.
The only thing certain about forecasting in a volatile economy is that the future is unpredictable. Historically, organizations have effectively utilized statistical techniques for short-term business planning, but leveraging actuals no longer allows us to predict the future. The ability to be prepared, responsive, and agile under these conditions is becoming a crucial success factor. Oracle Strategic Modeling can help you better navigate change to cope with uncertainty.
If your CFO’s questions regarding earnings, liquidity, and cash flow are unceasing and far-reaching, watch our on-demand webinar for a deep dive into strategic modeling. We modeled real-world scenarios to show how you can:
Quickly and easily develop a hierarchical model of your business
Leverage multiple pre-built functions to forecast key performance drivers
Provide transparency on forecasted financials via audit trail
Utilize goal seek to set financial targets and estimate the financials drivers necessary to achieve it
Perform sophisticated “what-if” analysis via simulations to improve the accuracy of your forecast
Use built-in dashboard functionality to deliver powerful reporting capabilities
While many stay-at-home orders have been lifted, consumers’ new digital buying behaviors and habits are here to stay. Watch our panel discussion on the accelerated need for commerce and learn how commerce and content can transform our digital economy.
Topics include:
-What is the “experience economy” and how do you leverage it? -If you move beyond product and price, what’s next?
-How business models have shifted and what you can do to break down silos and leverage new processes to capture the digital dollar.
-How organizations have built agile teams to address the ever-changing needs of customers, including responsive approaches that address the omnichannel consumer.
-Technologies that are best suited to enable your business and customers – and how headless commerce has changed the game.
-How the future of commerce is changing, and what you should do now to prepare.
Our panel features Jordan Jewell, IDC Research analyst known for his insight into the commerce industry. Joining him from Perficient is general manager Brian Beckham, who brings deep expertise in content management and empowering organizations in their digital transformations. Rounding out the panel is Episerver’s Joey Moore, who has spent the last decade helping organizations across the globe advance their digital maturity.
Centene's Financial Transformation Journey: A OneStream Success StoryPerficient, Inc.
Centene, a large multi-line managed care organization, was looking to modernize and streamline its corporate performance management (CPM) applications.
Centene had to move data between platforms multiple times during the close process so that close data could be fully consolidated and made available for reporting. This process had numerous challenges and inefficiencies that Centene wished to improve upon so that they could provide a more streamlined and more transparent process to the functional teams that leverage consolidated financials in their systems for reporting and analysis.
Centene chose OneStream XF for global and US consolidations, currency conversion, eliminations, and ownership percentage.
Michael Vannoni, director, financial systems solutions discussed the migration to OneStream XF including:
-Factors leading to the selection of OneStream XF
-Details of the solution design
-Benefits realized with global consolidation implementation
-Future planned enhancements
WHODrug Koda, developed by Uppsala Monitoring Centre (UMC), is an automated coding service, which uses artificial intelligence (AI) to automate the coding of drug names and ATC selections, improving consistency and operational efficiency. It can also be used to accelerate dictionary upgrades, including the transition from WHODrug B2 format to B3.
Through API (Application Programming Interface) web services, the coding engine can be integrated with custom or off-the-shelf drug safety, medical coding, or data management systems.
In this webinar, Perficient and UMC discussed WHODrug Koda and how you can integrate it into your medical coding activities.
Preparing for Your Oracle, Medidata, and Veeva CTMS Migration ProjectPerficient, Inc.
There are multiple reasons why companies migrate to a new clinical trial management system (CTMS). Still, the two most common are mergers and acquisitions (i.e., CTMS consolidation) and the desire to switch CTMS vendors. Regardless of the reason, many of the best practices, processes, and tools are the same.
In this webinar, we looked at the migration approaches taken across several case studies. You’ll come away with an understanding of:
Pros and cons of each CTMS migration method
Types of migration tools, including APIs, ETL tools, and adapters
Approximate timelines and costs associated with each migration method
The topics discussed in this webinar can be applied to any CTMS migration project, whether you’re moving to or from Oracle’s Siebel CTMS, Medidata’s Rave CTMS, and Veeva’s Vault CTMS.
Accelerating Partner Management: How Manufacturers Can Navigate Covid-19Perficient, Inc.
The pandemic has ushered in a new normal for manufacturers, and the impact of digital communication is more important than ever.
View our on-demand webinar with Tony Kratovil, Regional Vice President of Manufacturing at Salesforce, and Eric Dukart, National Sales Executive at Perficient. They covered why the right digital strategies are critical for manufacturers in the wake of COVID-19.
Our webinar covered:
Current challenges with forecasting, collaboration, and disruptions to distribution networks.
Insights for stabilizing operations, accelerating partner management, and developing a digital strategy that differentiates your business.
Candid Q&A with real-world examples.
New Work.com resources to help manufacturers restart safely and rebuild.
Tools and resources to move forward – fast.
The Critical Role of Audience Intelligence with Eric Enge and Rand FishkinPerficient, Inc.
Things move quickly in marketing. How do you identify what your customers need and how you can help? Now more than ever, audience intelligence is the key.
Audience intelligence is about understanding your target customers, their needs, what resonates with them, and how you can reach them. Eric Enge (Digital Marketing Principal, Perficient) and Rand Fishkin (Co-Founder & CEO, SparkToro) discussed this topic live on May 7, 2020. Watch to hear tactics for gaining a better understanding of your customers, how to use audience intelligence to optimize your marketing now, and more.
Cardtronics, the global leader in ATM deployment and management, decided to retire its on-premises Hyperion solution to gain the operational efficiencies, features, and functionality provided by a best-in-class cloud solution.
Cardtronics chose Oracle EPM Cloud including Financial Consolidation and Close, Planning, Management Reporting, Account Reconciliation, Enterprise Data Management, as well as Oracle Analytics Cloud.
In this video, project owner Richard Ng, director, financial systems, Cardtronics, discusses the migration to Oracle EPM Cloud including:
Multi-release 18-month deployment schedule across multiple countries
Benefits of a global Chart of Accounts for ERP and EPM
Seamless integration across ERP Cloud, HCM Cloud, and EPM Cloud
Preparing for Project Cortex and the Future of Knowledge ManagementPerficient, Inc.
Microsoft has turned traditional enterprise content management on its head with its recent announcement of Project Cortex.
Project Cortex uses advanced artificial intelligence to harness collective knowledge from across the enterprise and automatically organize it into shared topics like projects, products, processes, and customers. Using AI, Cortex creates a knowledge network based on relationships among topics, content, and people and delivers it in the apps you use every day – Office, Outlook, and Teams.
This webinar examined Project Cortex in more detail, including:
• What is Project Cortex?
• Why is Project Cortex different than other knowledge network projects previously introduced?
• How does incorporating AI and automation change the game?
• What is possible with Project Cortex?
• What can you do to prepare?
Utilizing Microsoft 365 Security for Remote Work Perficient, Inc.
With an increasingly mobile workforce, and the spread of shadow IT, the rapid rise of cybercrime - companies must find unique ways to effectively manage their sprawling SaaS portfolio.
Crisis Management & Remote Work w/ Microsoft 365Perficient, Inc.
During this webinar, Perficient's Microsoft experts discuss best practices for leveraging Microsoft Teams for remote work collaboration and crisis communication.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Designing Great Products: The Power of Design and Leadership by Chief Designe...
Combining Patient Records, Genomic Data and Environmental Data to Enable Translational Medicine
1. Combining Patient Records, Genomic Data and
Environmental Data to Enable Translational
Medicine
Martin Sizemore, Principal, Healthcare Strategist
Mike Grossman, Practice Director, Clinical Data Warehousing & Analytics, Life Sciences
facebook.com/perficient
twitter.com/Perficient_HC
linkedin.com/company/perficient twitter.com/Perficient_LS
2. About Perficient
Perficient is a leading information technology consulting firm serving clients throughout
North America and Europe.
We help clients implement business-driven technology solutions that integrate business
processes, improve worker productivity, increase customer loyalty and create a more agile
enterprise to better respond to new business opportunities.
3. Perficient Profile
• Founded in 1997
• Public, NASDAQ: PRFT
• 2013 revenue ~$373 million
• Major market locations throughout North America
• Atlanta, Boston, Charlotte, Chicago, Cincinnati, Columbus,
Dallas, Denver, Detroit, Fairfax, Houston, Indianapolis, Los
Angeles, Minneapolis, New York City, Northern California,
Oxford (UK), Philadelphia, Southern California, St. Louis,
Toronto and Washington, D.C.
• Global delivery centers in China, Europe and India
• >2,200 colleagues
• Dedicated solution practices
• ~85% repeat business rate
• Alliance partnerships with major technology vendors
• Multiple vendor/industry technology and growth awards
4. Oracle Partnership
• Oracle Platinum Partner
• Oracle Certified Education Training Partner
• 12+ year relationship of loyalty and trust
• Hundreds of successful implementations
• Over 200 delivery consultants on-shore and off-shore
• Five pillar practices
5. Healthcare Practice
Connected
Health
Experts in Consumer-Driven Healthcare Technology
CONSUMERS
HEALTH PLAN PROVIDER
Business Intelligence
and Analytics
Interoperability
and Integration
Information
Exchange
Regulatory
Compliance
Solutions & Services
Select Clients
Global Delivery Centers/Offshore Delivery
Domestic Delivery Center
6. Life Sciences Practice Practices / Solutions
Deep Clinical and Pharmacovigilance Applications Expertise
Implementation
Migration
Integration
Validation
Consulting
Upgrades
Managed Services
Application Development
Private Cloud Hosting
Application Support
Sub-licensing
Study Setup
Services
Clinical Trial
Management
Clinical Trial Planning and Budgeting
Oracle ClearTrial
CTMS
Oracle Siebel CTMS / ASCEND
Mobile CRA
Clinical Data Management
& Electronic Data Capture
CDMS
Oracle Clinical
Electronic Data Capture
Oracle Remote Data Capture
Oracle InForm
Medical Coding
Oracle Thesaurus Management System
Safety &
Pharmacovigilance
Adverse Event Reporting
Oracle Argus Safety Suite
Oracle AERS / EmpiricaTrace
Axway Synchrony Gateway
Signal Management
Oracle Empirica Signal/Topics
Medical Coding
Oracle Thesaurus Management System
Clinical Data
Warehousing & Analytics
Clinical Data Warehousing
Oracle Life Sciences Data Hub
Clinical Data Analytics
Oracle Clinical Development Analytics
JReview
Data Review and Cleansing
Oracle Data Management Workbench
Clients
8. Welcome & Introductions
Martin Sizemore, Principal Healthcare Strategist
Martin Sizemore is a healthcare strategist, senior consultant and trusted
C-level advisor for healthcare organizations including both payers and providers.
He specializes in clinical data warehousing, clinical data models and healthcare
business intelligence for improving operational efficiencies and clinical outcomes.
Mike Grossman, Practice Director, Clinical Data Warehousing and Analytics
Mike Grossman has over 27 years in the life sciences industry including 10 years
of experience designing and developing the Oracle Life Sciences hub for Oracle.
Since 2010, Mike has been the CDW/CDA practice lead, where he leads the team
that implements, supports, enhances and integrates Oracle’s LSH and other data
warehousing and analytics solutions. Mike has many years of experience
managing data for all phases and styles of clinical trials.
9. What is Translational Medicine?
• Targeted therapies that address the
unique biological mechanisms
involved in a patient’s illness
• Medicines will become truly
“personalized,” allowing for a fully
customized approach to health care
• Translating scientific advances into
targeted therapies has not proven to
be quick or easy
• Taking advantage of innovative
clinical trial designs could lead to
more efficient clinical trials that do a
better job of matching treatments to
specific patient populations and
speed the development of targeted
therapies
10. Why is a New Approach Needed?
• Our current clinical trial and drug
regulatory process – the formal
system by which novel medicines
are evaluated and approved by the
U.S. Food and Drug Administration
(FDA) – has lagged behind
advances in scientific research
• Many have suggested that novel
clinical trial designs could capitalize
on our growing knowledge of patient
subpopulations for which a therapy
may be more effective without
compromising FDA’s rigorous safety
standards
• One of the most promising areas for
investigation is oncology
11. Where Do We Start?
• Need for an integrated
approach from the electronic
medical record to population
subgroups (cohorts) and their
related genomics, proteomics
and biomarkers
• Ability to manage increasing
complexity, data volume and
computation power necessary
for success
Routine
tests
Carrier
testing
Simple
Mendelian
Pre‐natal
testing
Complex
disease
Cardiology
Immunology
Pathogenic
Pharmacoge
nomics
Adverse
reactions
Dosing
frequency
Dose size
Oncology
Tumor profiling
Residual
disease testing
Progression
analysis
Challenges
• Scalability
• System interoperability
• Speed of knowledge delivery
• Evolution of traditional care models
• Regulatory implications
13. Data Integration and Analytics Vision
Master Person Index
Patients Service
Providers
Source Systems
Epic
Data Staging
(HDI)
Cerner
GE
Centricity
Lawson
Research
Data
Other
Sources
(HDI)
(HDI)
(HDI)
Staging Tables
Integrated Data Storage Data Marts Reporting/
(HDI)
(HDI)
Integr(aHtDeId)
Storage Tables
Analytics
EHA
14. The integration of environmental data is a
great example!
• Far too many Americans -- about 25 million
people -- are intimately acquainted with the
symptoms of an asthma attack. When
asthma strikes, your airways become
constricted and swollen, filling with mucus.
In severe cases, asthma attacks can be
deadly. They kill more than 3,000 people
every year in the United States.
• Asthma is a chronic, sometimes debilitating
condition that has no cure. It keeps kids out
of school (for a total of more than 10 million
lost school days each year, according to the
Centers for Disease Control) and sidelines
them from physical activity. Employers lose
14 million work days every year when
asthma keeps adults out of the workplace.
The disease is also responsible for nearly 2
million emergency room visits a year.
• Roughly 30 percent of childhood asthma is
due to environmental exposures, costing the
nation $2 billion per year.
What About External Data?
15. Source
Systems
Healthcare Data Model (EHA)
Lawson
(UCH)
Research &
Other
EPIC
(CHCO)
GE Centricity
(UPI)
An Integration Solution
Analytic Models
End‐User
Analytic
Interface
Analytic Data
Enc
Costing Clinic Billing
Schlg
Svc
Rnd
Adv
Events
Med
Mgmt
Lab
Orders
Atmosph
eric Data
EPIC
(UCH)
Master Data
Pt Demo
Enc Type Fac
Dx Location
Event
Date Meds Svc
Master
Svc Pvdr
Chg
Master
Pt
Familial
Rel
Fee Sch
Insurers
Omics Data
Spec‐imens
Studies Seq‐ Variants
uences
Files
Gene
Compo‐nents
Genes
Species
Proteins
Path‐ways
Chromo‐somes
Nomen‐clature
Personalized Medicine
Anonymizer
Research
Analytic Data Marts
Cohorts Diag‐nosis
Diag test DX
Ethnicity Medicati
ons
History Pro‐cedures
Spec‐imen
Study
16. Structured Patient Data
Re-Used for Research
• Pre-defined models such as Oracle’s EHA already has the data
structured from the patient record and other systems
• Vocabulary (for example ICD-10) should be unified as part of the
loading process to allow for aggregated analysis across data sources
• Domain areas selected for other purposes like encounter and
complaint may be used for analysis along with genomics and
proteomics sample results
• Are there additional domains of clinical data that we need to add to
enable effective research analysis?
• Pre-existing analysis data marts downstream form the data storage
such Oracle’s Translational Research Center provide analytical
models and can be extended as needed
17. Role of Omics Samples
• In the long run, omics can play a big role in personalizing the
treatment of patients
• Research looking for patterns in genomic and other variants can
greatly improve the targeting of research results to specific patient
populations
• What is the current policy and approach on when and omics
samples are taken and stored?
• The goal is to take full advantage of existing approaches
before requiring any changes
• Pathology results where the data has already been curated are
necessary before looking at non-curated omics samples
18. Integration, PHI and Anonymization
• In the Translational Research Center, patient data can be linked to the
omics data
• How do we link the information?
• The use of both patient data and omics data can potentially reveal PHI that
is not explicitly needed for the research.
• Depending on how the analysis performed, some results could go down
to the patient level
• The data marts should detenify some simple information such as birth
date
• What processes, procedures and controls need to be put into place to
use the research data for research without compromising PHI? How
has this been handled in the past?
• What role does consent play in the delivery of research data and does it
need to be enforced electronically? If so, are the desired algorithms
defined?
19. • What are the sources for the omics and other sample data?
• What format will that data be available in?
• There are potentially > 100 different possible data formats
(http://en.wikipedia.org/wiki/List_of_sequence_alignment_softwa
re)
• This can be based on the highest priority set of sample sources.
For example, if the desired samples are being analyzed using
an illumina HiSeq 2500, you will get a different selection of
output formats than a machine from Roche.
• What will the transport mechanism be? Files (most likely) or direct
integration?
Consolidation of Cross Source Studies
20. Reference Data for Human Genome
• When analyzing omics data, most analysis is performed by
comparing your samples to a set of references and variants
• There are several reference variants available for example
• Mutation Annotation Format (MAF) (From NCI)
• miRbase (mirbase.org)
• dbSNP (ncbi.nlm.nih.gov/SNP/)
• RefGene (refgene.com)
21. Analysis Lifecycle, Methods and Tools
• The following life cycle is typical for analysis
• Prepare a question to create a cohort of patients based on
clinical criteria
• Refine that cohort based on some genomics characteristics
• Look at a series of hypotheses based on that refined cohort
looking across a broader set of clinical characteristics
• Draw conclusions and refine
• Formalize results
• What tools are required to access the data?
• What analytical methods are commonly used?
Preparation
Selection &
Exploration
Analytics &
Model
Building
Deployment
& Reuse
22. • Once an analysis has been completed, where are the results
stored?
• Are the cohorts and methods used recorded as part of the
analysis?
• Are these methods and cohorts available for future use by other
users and studies?
Analysis Results Management
23. • We need to set the initial priorities for preparing and integrating the
clinical and samples data in order to create an implementation
plan
• Are there some immediate drivers or studies planned that can help
with the prioritization?
• Are there some past studies where we can improve the overall
approach?
• Are there some key subject matter experts within your organization
to help guide this prioritization?
Prioritization Based on Past
and Planned Studies
24. Recommended Direction Forward
• Prioritize data sources for answering key translational research questions
• Identify the reference data model and tools to build a production level
translational research center system
• Integrate the samples data with the clinical domains that are identified for
other purposes (i.e. encounters, observations, procedures, concerns) and add
new domains as required
• Establish rules for ananomyzation/de-identification
• Use the analysis data marts as the basis for research analysis
• Establish methods for direct access to data marts using a verity of tools
• Predefined analytics dashboards can follow in a later phase
• Management and re-use of methods and analytic results can follow at a later
phase
• Perficient can assist in all stages and aspects of implementing a translational
research center
26. Mike Grossman, Director, Clinical Data Warehousing
Perficient Life Sciences
(617) 447‐2603
Mike.Grossman@perficient.com
Contact Information
Martin Sizemore, Principal, Healthcare Strategist
Perficient Healthcare
(336) 847‐1802
Martin.Sizemore@perficient.com