1) Electronic medical records have the potential to transform medicine by serving as a platform for clinical decision support, personalized medicine, and precision medicine approaches through integration of diverse data sources.
2) Registries built from EMR data can be used to study conditions, compare treatment effectiveness, and recruit for clinical trials, with the goal of reducing the lag time between research and practice.
3) Advances in predictive modeling, diagnostic and treatment algorithms, and artificial intelligence may help optimize clinical decision making if effectively integrated into clinical workflow and EMRs.
How Clinical Decision Support Systems (CDSS) is the right tool for physicians?Eurostars Programme EUREKA
We believe that CDSS delivered using information systems, ideally with the electronic medical record as the platform, will finally provide decision makers with tools making it possible to achieve large gains in performance, narrow gaps between knowledge and practice, and improve safety.
How Clinical Decision Support Systems (CDSS) is the right tool for physicians?Eurostars Programme EUREKA
We believe that CDSS delivered using information systems, ideally with the electronic medical record as the platform, will finally provide decision makers with tools making it possible to achieve large gains in performance, narrow gaps between knowledge and practice, and improve safety.
Advanced Laboratory Analytics — A Disruptive Solution for Health SystemsViewics
As US healthcare systems grapple with the recent upheavals in care payment and delivery, they are turning to advanced analytics as their “central nervous systems” for driving care and financial performance.
Laboratory information — spanning chemistry, pathology, microbiology and molecular testing, for example — is among the best sources of data for these advanced analytics, including clinician decision support, predictive analytics, population health management, and personalized medicine. When strategically harnessed and integrated to create a patient-centric lab data lake, laboratory information can form an affordable yet competitively powerful advanced analytics solution well suited for many health systems — i.e., a disruptive option.
L. Eleanor J. Herriman, MD, MBA, Chief Medical Informatics Officer of Viewics, explains why laboratory data should be a core strategic component for achieving success in value-based healthcare.
Best Practices for a Data-driven Approach to Test UtilizationViewics
Would you like to learn how data-driven interventions can improve laboratory test utilization in your organization? Would you like to hear about the impact that leading hospitals/health systems and managed care organizations have made through these interventions?
If so, you might be interested in this presentation by utilization management expert Dr. Michael Astion, Medical Director at the Department of Laboratories at Seattle Children’s Hospital and Clinical Professor of Laboratory Medicine at the University of Washington.
In this presentation, Dr. Astion discusses the current state of the misuse of laboratory testing in the United States and some of the interventions that are being implemented to improve it. He covers a number of common areas of unnecessary testing — from pure abuse to tests that could be useful but are ordered inappropriately.
You'll learn about:
• Two areas of laboratory testing where misordering of tests occur frequently
• Three interventions to improve the value of testing for patients
• The role of genetic counselors and other laboratory professionals in improving lab test ordering
• The national endeavor known as PLUGS, the Pediatric Laboratory Utilization Guidance Service
Patient Blood Management: Impact of Quality Data on Patient OutcomesViewics
Patient blood management (PBM) has been proven to improve patient outcomes and save hospitals millions of dollars. Ensuring the quality of your data is central to decision making and critical to having a strong PBM program.
Would you like to learn how your organization can improve patient outcomes by implementing a PBM program based on accurate data?
If so, view this presentation by blood management expert Lance Trewhella. Lance presents how to develop a successful, evidence-based, multidisciplinary PBM program aimed at optimizing the care of patients who might need transfusion.
You’ll learn:
• Current recommendations for blood transfusion utilization
• The impact of quality data on PBM programs
• Best data practices in PBM
Revolutionizing Renal Care With Predictive Analytics for CKDViewics
Chronic Kidney Disease (CKD) is a common and growing condition, affecting about half of the Medicare population and of diabetics. In the United States, the lifetime risk of CKD for 30-year-olds is now greater than half, and the prevalence of CKD is projected to rise significantly over the next 15 years.
Current methods of predicting which CKD patients will progress to renal failure and require dialysis or transplant have low accuracy rates, causing great anxiety and suboptimal care. Without accurate risk prediction, many patients are over-treated, effectively wasting limited resources and negatively impacting outcomes. Conversely, other patients may receive inadequate treatment, restricting options to only the most costly and least desirable interventions.
Watch this on-demand webinar with Dr. Navdeep Tangri, developer of the Kidney Failure Risk Equation, which revolutionizes the way CKD patients are managed by leveraging laboratory data to accurately predict the risk of kidney failure in patients with CKD.
You’ll learn:
• How CKD is burdening our healthcare system, and the need for better care management tools
• How the Kidney Failure Risk Equation was researched, developed, and validated
• How Viewics is implementing CKD predictive analytics to automatically deliver risk information to clinicians and issue customized, educational reports to patients and clinicians
Tackling the U.S. Healthcare System’s Infectious Disease Management ProblemViewics
The United States healthcare system has a serious infectious disease management problem. The antibiotic resistance crisis is widespread, serious, costly, and deadly. Delays in pathogen identification lead to poor clinical outcomes, including increased mortality risk. And, optimally managing outbreaks is critical to health systems whose reimbursement is tied to the health of a population, such as ACOs.
Eleanor Herriman, MD, MBA, Chief Medical Informatics Officer at Viewics led an informative panel discussion with industry leaders on the issues surrounding the infectious disease management crisis. Margret Oethinger, MD, Ph.D., Medical Director of Providence Health & Services, and Susan E. Sharp, Ph.D., DABMM, FAAM, Regional Director of Microbiology and the Molecular Infectious Disease Laboratories, Department of Pathology, Kaiser Permanente and President-Elect, American Society for Microbiology cover the current state of infectious disease management in the U.S., and what can be done to improve it.
You’ll learn about:
• The magnitude of the U.S. health system’s infectious disease management problem
• The most serious concerns and trends for healthcare institutions and communities across the nation
• The most promising solutions to health systems’ most urgent infectious disease management challenges
Using real-world evidence to investigate clinical research questionsKarin Verspoor
Adoption of electronic health records to document extensive clinical information brings with it the opportunity to utilise that information to support clinical research, and ultimately to support clinical decision making. In this talk, I discuss both these opportunities and the challenges that we face when working with real-world clinical data, and introduce some of the strategies that we are adopting to make this data more usable, and to extract more value from it. I specifically discuss the use of natural language processing to transform clinical documentation into structured data for this purpose.
Advanced Lab Analytics for Patient Blood Management ProgramsViewics
Reports indicate that 30 – 70% of blood transfusions are inappropriate. Inappropriate blood transfusions put patients at increased risk of post-surgical infections, multi-system organ failure, longer hospital stays, and higher mortality rates. The transfusion guidelines most clinicians learned in their training are now outdated. As such, blood transfusion practices vary widely, and overutilization remains a major quality and cost problem.
Patient Blood Management (PBM) programs are designed to optimize the use of transfusions through a team-based approach, evidence-based guidelines, and algorithms that together guide decisions regarding specifically which patients and clinical procedures warrant blood products, and how much to transfuse. PBM programs have been quite successful in improving patient morbidity and mortality outcomes and generating millions of dollars in savings for hospitals.
Laboratory analytics can be an effective means of instituting restrictive transfusion programs, and advanced lab analytics can be critical in implementing PBM programs, as lab testing and tracking blood usage is central to decision making, changing behavior, and improving performance.
Watch a presentation by Dr. Eleanor Herriman, Chief Medical Informatics Officer at Viewics. She unveils a new suite of advanced analytics tools that support PBS and other restrictive blood management programs, enabling health systems to better leverage their valuable lab medicine assets and fully integrate this key service line into these programs.
You’ll learn:
• How inappropriate blood transfusions are burdening our healthcare system, and the need for better utilization management tools
• New guidelines restricting red blood cell transfusions
• The role of advanced lab analytics in PBM programs
• How Viewics is leveraging advanced lab analytics to help health systems more easily and cost-effectively implement PBM programs
Precision Medicine: Opportunities and Challenges for Clinical TrialsMedpace
The momentum and muscle behind "finding the right drug for the right patient at the right dose" has further escalated with President Barack Obama’s announcement of a $215 million dollar Precision Medicine Initiative earlier this year. In this webinar, Dr. Frank Smith will explore advances in precision medicine and how it is affecting clinical research. As a pediatric hematologist/oncologist, he will use his extensive clinical and research background as a backdrop for the discussion.
Topics will include:
The evolution of "personalized medicine" to "precision medicine"
How state-of-the-art molecular biology is creating new diagnostic and prognostic strategies
How these new strategies are helping inform the design of clinical trials
Case study: How precision medicine is improving clinical trials in hematology and oncology
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 ;)
IMPACT OF HEALTH INFORMATICS TECHNOLOGY ON THE IMPLEMENTATION OF A MODIFIED E...hiij
The Modified Early Warning System (MEWS) is based on a patient score that helps the medical team monitor patients to identify a patient that may be experiencing a sudden decline in care. This study consists of a detailed review of clinical data and patient outcomes to assess impact of technology and patient care.There are a total of thirteen hospitals included in this review. These facilities have implemented vitals capture and the MEWS scoring system.
I gave this prezo to Auckland Regional Clinical IS Leadership Group on Feb 21, 2014. It shows how difficult it can be to deal with certain kinds of health information when developing systems by an impressive example (originally from Dr. Sam Heard). Therefore we need rigorous and scientific methods to tackle this - in this case using openEHR's multi-level modelling approach to create a single content model from which all health information exchange payload definitions will be derived. New Zealand's Interoperability Reference Architecture (HISO 10040) is underpinned by openEHR Archetypes to create this content model. The bottom line of the prezo is that almost every national programme starts health information standardisation from the wrong place; most of them are complex technical speficifications, like CDA, which are almost impossible for clinicians to comprehend and provide feedback. The process is flawed! Instead it should start from simple to understand representations, such as simple diagrams, mindmaps etc.and then handed over to techies once clinical validity and utility is agreed upon.That's the beauty of Archetype approach - great tooling and the Clinical Knowledge Manager (CKM) enable clinicians and other domain experts to collaborate and develop clinical models easily.
The last few years have seen a sea of changes in business intelligence (BI). The proliferation of data and advances in technologies are pushing the pace of innovation. Here are 10 trends to watch for in 2012.
Advanced Laboratory Analytics — A Disruptive Solution for Health SystemsViewics
As US healthcare systems grapple with the recent upheavals in care payment and delivery, they are turning to advanced analytics as their “central nervous systems” for driving care and financial performance.
Laboratory information — spanning chemistry, pathology, microbiology and molecular testing, for example — is among the best sources of data for these advanced analytics, including clinician decision support, predictive analytics, population health management, and personalized medicine. When strategically harnessed and integrated to create a patient-centric lab data lake, laboratory information can form an affordable yet competitively powerful advanced analytics solution well suited for many health systems — i.e., a disruptive option.
L. Eleanor J. Herriman, MD, MBA, Chief Medical Informatics Officer of Viewics, explains why laboratory data should be a core strategic component for achieving success in value-based healthcare.
Best Practices for a Data-driven Approach to Test UtilizationViewics
Would you like to learn how data-driven interventions can improve laboratory test utilization in your organization? Would you like to hear about the impact that leading hospitals/health systems and managed care organizations have made through these interventions?
If so, you might be interested in this presentation by utilization management expert Dr. Michael Astion, Medical Director at the Department of Laboratories at Seattle Children’s Hospital and Clinical Professor of Laboratory Medicine at the University of Washington.
In this presentation, Dr. Astion discusses the current state of the misuse of laboratory testing in the United States and some of the interventions that are being implemented to improve it. He covers a number of common areas of unnecessary testing — from pure abuse to tests that could be useful but are ordered inappropriately.
You'll learn about:
• Two areas of laboratory testing where misordering of tests occur frequently
• Three interventions to improve the value of testing for patients
• The role of genetic counselors and other laboratory professionals in improving lab test ordering
• The national endeavor known as PLUGS, the Pediatric Laboratory Utilization Guidance Service
Patient Blood Management: Impact of Quality Data on Patient OutcomesViewics
Patient blood management (PBM) has been proven to improve patient outcomes and save hospitals millions of dollars. Ensuring the quality of your data is central to decision making and critical to having a strong PBM program.
Would you like to learn how your organization can improve patient outcomes by implementing a PBM program based on accurate data?
If so, view this presentation by blood management expert Lance Trewhella. Lance presents how to develop a successful, evidence-based, multidisciplinary PBM program aimed at optimizing the care of patients who might need transfusion.
You’ll learn:
• Current recommendations for blood transfusion utilization
• The impact of quality data on PBM programs
• Best data practices in PBM
Revolutionizing Renal Care With Predictive Analytics for CKDViewics
Chronic Kidney Disease (CKD) is a common and growing condition, affecting about half of the Medicare population and of diabetics. In the United States, the lifetime risk of CKD for 30-year-olds is now greater than half, and the prevalence of CKD is projected to rise significantly over the next 15 years.
Current methods of predicting which CKD patients will progress to renal failure and require dialysis or transplant have low accuracy rates, causing great anxiety and suboptimal care. Without accurate risk prediction, many patients are over-treated, effectively wasting limited resources and negatively impacting outcomes. Conversely, other patients may receive inadequate treatment, restricting options to only the most costly and least desirable interventions.
Watch this on-demand webinar with Dr. Navdeep Tangri, developer of the Kidney Failure Risk Equation, which revolutionizes the way CKD patients are managed by leveraging laboratory data to accurately predict the risk of kidney failure in patients with CKD.
You’ll learn:
• How CKD is burdening our healthcare system, and the need for better care management tools
• How the Kidney Failure Risk Equation was researched, developed, and validated
• How Viewics is implementing CKD predictive analytics to automatically deliver risk information to clinicians and issue customized, educational reports to patients and clinicians
Tackling the U.S. Healthcare System’s Infectious Disease Management ProblemViewics
The United States healthcare system has a serious infectious disease management problem. The antibiotic resistance crisis is widespread, serious, costly, and deadly. Delays in pathogen identification lead to poor clinical outcomes, including increased mortality risk. And, optimally managing outbreaks is critical to health systems whose reimbursement is tied to the health of a population, such as ACOs.
Eleanor Herriman, MD, MBA, Chief Medical Informatics Officer at Viewics led an informative panel discussion with industry leaders on the issues surrounding the infectious disease management crisis. Margret Oethinger, MD, Ph.D., Medical Director of Providence Health & Services, and Susan E. Sharp, Ph.D., DABMM, FAAM, Regional Director of Microbiology and the Molecular Infectious Disease Laboratories, Department of Pathology, Kaiser Permanente and President-Elect, American Society for Microbiology cover the current state of infectious disease management in the U.S., and what can be done to improve it.
You’ll learn about:
• The magnitude of the U.S. health system’s infectious disease management problem
• The most serious concerns and trends for healthcare institutions and communities across the nation
• The most promising solutions to health systems’ most urgent infectious disease management challenges
Using real-world evidence to investigate clinical research questionsKarin Verspoor
Adoption of electronic health records to document extensive clinical information brings with it the opportunity to utilise that information to support clinical research, and ultimately to support clinical decision making. In this talk, I discuss both these opportunities and the challenges that we face when working with real-world clinical data, and introduce some of the strategies that we are adopting to make this data more usable, and to extract more value from it. I specifically discuss the use of natural language processing to transform clinical documentation into structured data for this purpose.
Advanced Lab Analytics for Patient Blood Management ProgramsViewics
Reports indicate that 30 – 70% of blood transfusions are inappropriate. Inappropriate blood transfusions put patients at increased risk of post-surgical infections, multi-system organ failure, longer hospital stays, and higher mortality rates. The transfusion guidelines most clinicians learned in their training are now outdated. As such, blood transfusion practices vary widely, and overutilization remains a major quality and cost problem.
Patient Blood Management (PBM) programs are designed to optimize the use of transfusions through a team-based approach, evidence-based guidelines, and algorithms that together guide decisions regarding specifically which patients and clinical procedures warrant blood products, and how much to transfuse. PBM programs have been quite successful in improving patient morbidity and mortality outcomes and generating millions of dollars in savings for hospitals.
Laboratory analytics can be an effective means of instituting restrictive transfusion programs, and advanced lab analytics can be critical in implementing PBM programs, as lab testing and tracking blood usage is central to decision making, changing behavior, and improving performance.
Watch a presentation by Dr. Eleanor Herriman, Chief Medical Informatics Officer at Viewics. She unveils a new suite of advanced analytics tools that support PBS and other restrictive blood management programs, enabling health systems to better leverage their valuable lab medicine assets and fully integrate this key service line into these programs.
You’ll learn:
• How inappropriate blood transfusions are burdening our healthcare system, and the need for better utilization management tools
• New guidelines restricting red blood cell transfusions
• The role of advanced lab analytics in PBM programs
• How Viewics is leveraging advanced lab analytics to help health systems more easily and cost-effectively implement PBM programs
Precision Medicine: Opportunities and Challenges for Clinical TrialsMedpace
The momentum and muscle behind "finding the right drug for the right patient at the right dose" has further escalated with President Barack Obama’s announcement of a $215 million dollar Precision Medicine Initiative earlier this year. In this webinar, Dr. Frank Smith will explore advances in precision medicine and how it is affecting clinical research. As a pediatric hematologist/oncologist, he will use his extensive clinical and research background as a backdrop for the discussion.
Topics will include:
The evolution of "personalized medicine" to "precision medicine"
How state-of-the-art molecular biology is creating new diagnostic and prognostic strategies
How these new strategies are helping inform the design of clinical trials
Case study: How precision medicine is improving clinical trials in hematology and oncology
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 ;)
IMPACT OF HEALTH INFORMATICS TECHNOLOGY ON THE IMPLEMENTATION OF A MODIFIED E...hiij
The Modified Early Warning System (MEWS) is based on a patient score that helps the medical team monitor patients to identify a patient that may be experiencing a sudden decline in care. This study consists of a detailed review of clinical data and patient outcomes to assess impact of technology and patient care.There are a total of thirteen hospitals included in this review. These facilities have implemented vitals capture and the MEWS scoring system.
I gave this prezo to Auckland Regional Clinical IS Leadership Group on Feb 21, 2014. It shows how difficult it can be to deal with certain kinds of health information when developing systems by an impressive example (originally from Dr. Sam Heard). Therefore we need rigorous and scientific methods to tackle this - in this case using openEHR's multi-level modelling approach to create a single content model from which all health information exchange payload definitions will be derived. New Zealand's Interoperability Reference Architecture (HISO 10040) is underpinned by openEHR Archetypes to create this content model. The bottom line of the prezo is that almost every national programme starts health information standardisation from the wrong place; most of them are complex technical speficifications, like CDA, which are almost impossible for clinicians to comprehend and provide feedback. The process is flawed! Instead it should start from simple to understand representations, such as simple diagrams, mindmaps etc.and then handed over to techies once clinical validity and utility is agreed upon.That's the beauty of Archetype approach - great tooling and the Clinical Knowledge Manager (CKM) enable clinicians and other domain experts to collaborate and develop clinical models easily.
The last few years have seen a sea of changes in business intelligence (BI). The proliferation of data and advances in technologies are pushing the pace of innovation. Here are 10 trends to watch for in 2012.
The $1000 genome is here, and the fundamental problems have shifted... it is no longer about shrinking the cost of sequencing but the explosive growth of big data: the downstream analytics with rapidly evolving parameters, data sources and formats; the storage, movement and management of massive datasets and workloads, and the challenge of articulating the results and translating the latest findings directly into improving patient outcomes. This presentation talks to the work Intel Corp. is doing with it's partners to make research and clinical genomics mainstream - "Taking Precision Medicine Mainstream."
Usability-focused Clinical Decision Support with the Help of Semantic Technologies. Braga S. eHealth week 2010 (Barcelona: CCIB Convention Centre; 2010)
ФАРМТАКСИ - система “умных” электронных рецептов и контроля распространения л...blejyants
АО "Соцмедика" - IT компания, резидент инновационного центра «Сколково», специализирующаяся на создании экспертных систем в области медицины.
Цель проекта – разработка экспертной системы поддержки принятия решений врача «Гиппократ», которая будет применяться на этапе диагностики, профилактики и лечения различных заболеваний.
Внедрение в клиническую практику системы «Гиппократ» позволит персонифицировать подход к каждому пациенту, уменьшить риск возникновения врачебных ошибок и клинических осложнений.
Особенностью базовых технологий лежащих в основе проекта и определяющих его новизну, является сочетание технологий глубокого машинного обучения при обработке больших объемов данных c Объединенной Базой Медицинских Знаний (UMKB), основанной на системах классификаторов, медицинских онтологий, уникальной модели представления знаний и алгоритмов по аналогии мышления врача.
На этапах реализации проекта и по мере наполнения UMKB разработаны отдельные инновационные продукты:
1. Электронный клинический фармаколог (ЭКФ) – система поддержки принятия решений врача по фармакотерапии. При использовании в клиниках ЭКФ снижаются затраты медицинской организации на медикаменты, за счёт более рациональной фармакотерапии, уменьшается риск осложнений и побочных эффектов от применения лекарств, оптимизируется работа врача (уменьшается время приёма, повышается качество оказания медицинской помощи). Подробнее о продукте можно посмотреть по ссылке: ecp.umkb.com
2. Система “умных” электронных рецептов и контроля распространения лекарственных средства (ФАРМТАКСИ) - единая сеть, объединяющая разных участников фармацевтичес
Taking Splunk to the Next Level - ArchitectureSplunk
Are you outgrowing your initial Splunk deployment? Is Splunk becoming mission critical and you need to make sure it's Enterprise ready? Attend this session led by Splunk experts to learn about taking your Splunk deployment to the next level. Learn about Splunk high availability architectures with Splunk Search Head Clustering and Index Replication. Additionally, learn how to manage your deployment with Splunk’s operational and management controls to manage Splunk capacity and end user experience.
Decision Support System for clinical practice created on the basis of the Un...blejyants
The company Socmedica developing an expert system of decision support for medical information systems. The product is aimed at solving the problem of medical errors.
Thoughts on a research platform architecture: Simplify your application portf...Pistoia Alliance
The lessons of SAP are relevant to research, where operational systems supporting business processes likely consume more than two thirds of an informatics portfolio. This poster from the Pistoia Alliance Conference in April 2011 postulates that open source solutions can provide SAP-like agility and cost effectiveness to life science organizations seeking to simplify their research informatics architecture.
Data Science Deep Roots in Healthcare IndustryDinesh V
Data Science transforms the healthcare industry with impeccable solutions that can improve patient care through EHRs, medical imaging, drug discovery, predictive medicines and genetics and genomics.
Personalized medicine tools for clinical trials - kuchinkeWolfgang Kuchinke
Tools for personalised medicine in clinical trials. ---------
The implementation of clinical trials in personalized medicine is a different way of doing clinical research compared to the standard way of large clinical trials aiming for statistical significance. Personalized medicine uses a medical model that separates people into different groups with medical decisions, practices, drugs, interventions being tailored to the individual patient based on their predicted response. Basis for this approach is the progress of the study of the human genome and its variation over the last two decades. Especially advancement in automated DNA sequencing and PCR and the use of expressed sequence tags (ESTs), cDNAs, antisense molecules, small nterfering RNAs (siRNAs), full-length genes and their expression products and haplotypes.
But adoption of personalized medicine requires an active and flexible and highly integrated infrastructure, which allows joining of many different competences and technologies. We asked the question: can the tools developed for personalized medicine in the p-pedicine project be employed effectively in a clinical trials network to support personalised clinical trials. We conducted an analysis of tool integration and the evaluation tool usage requirements. Based on the survey results, the tendency for clinical trial network ECRIN is to use software as a service in the form as SaaS or ASP. ECRIN data centres will (probably) not install and employ p-medicine tools in one of their data centres. A robust business model for the provision of services and the implementation and employment of tools does not yet exist.
How can the personalized medicine infrastructure p-medicine and the clinical trials network ECRIN gain from each other to allow the conduct of personalized clinical trials?
We suggest a business model, in which personal medicine infrastructures and clinical trials networks exchange their services to gain jointly from each other. Therefore: an integration by reciprocal exchange of services may be the solution. Not only software as a service will be exchanged, but also knowledge, personnel and joint staff trainings.
Personalized medicine tools for clinical trials - KuchinkeWolfgang Kuchinke
Tools for personalised medicine in clinical trials.
The implementation of clinical trials in personalized medicine is a different way of doing clinical research, compared to the standard way of large clinical trials aiming for statistical significance. Personalized medicine uses a medical model that separates people into different groups with medical decisions, practices, drugs, interventions being tailored to the individual patient based on their predicted response. Basis for this approach is the progress of the study of the human genome and its variation over the last two decades. Especially advancements in automated DNA sequencing, PCR technologies and the use of expressed sequence tags (ESTs), cDNAs, antisense molecules, small interfering RNAs (siRNAs).
But the adoption of personalized medicine requires an active and flexible and highly integrated infrastructure, which must allow the joining of many different competences and technologies. We asked the question: can the tools developed for personalized medicine in the p-pedicine project be employed effectively in a clinical trials network to support personalised clinical trials? We conducted an analysis of tool integration and the evaluation of tool usage requirements. Based on the survey results, the tendency for the clinical trial network ECRIN is to use software as a service in the form of SaaS or ASP. ECRIN data centres will (probably) not install and employ p-medicine tools in one of their data centres. A robust business model for the provision of services and the implementation and employment of tools does not yet exist.
How can the personalized medicine infrastructure p-medicine and the clinical trials network ECRIN gain from each other to allow the conduct of personalized clinical trials? We suggest a business model, in which personalized medicine infrastructures and clinical trials networks exchange their services to gain jointly from each other. An integration of networks by reciprocal exchange of services may be the solution. Not only software as a service will be exchanged, but also knowledge, personnel and staff trainings.
Methods for Observational Comparative Effectiveness Research on Healthcare De...Marion Sills
Research Objective: The SAFTINet project was funded by the AHRQ to build a distributed network of existing clinical and claims data that would support comparative effectiveness research (CER), with a focus on underserved populations and healthcare delivery system (HDS) characteristics. Observational research methods are appropriate, but require detailed protocols with a priori hypotheses and analytic plans. SAFTINet research specifically concerns the effects of a discrete set of HDS features (those often included in Patient-Centered Medical Home (PCMH) models) on health outcomes for primary care patients with asthma, hypertension, and hypercholesterolemia. Our objective is to present a description of this study’s measurement challenges, and to specify a priori hypotheses, analytic strategies, and plans for addressing bias and confounding for our asthma cohorts.
Study Design: An observational, longitudinal cohort study of primary care patients with asthma, with both secondary use of existing clinical and claims data and primary data collection for HDS features and patient- reported outcomes.
Population Studied: Our sample consists of 59 primary care practices in 5 healthcare organizations in Colorado, Utah and Tennessee; all practices serve underserved populations. These practices care for about 275,000 patients per year, of whom an estimated 22,000 have a diagnosis of asthma.
Principal Findings: We will present the processes used to define and measure the HDS features, covariates and asthma outcomes, along with planned analysis. Challenges include valid measurement of a multi-faceted HDS “exposure” variable, the inability to identify exposure onset, and the non-dichotomous nature of HDS characteristics. To measure HDS characteristics, we created a practice-level survey assessing 9 PCMH domains, including care coordination, specialty care and mental health integration, and patient-centeredness, as well as asthma-specific HDS characteristics (e.g., the use of asthma registries). Asthma outcomes included (1) those available as a result of routine electronic documentation of clinical care and claims administration (utilization indicative of an exacerbation), and (2) patient reported outcomes tools (Asthma Control Test). We used directed acyclic graphs to identify potential confounders of the relationship between HDS characteristics and asthma control, as well as other potential biases. The analytic plan is based on linear mixed effects models. Perspectives of the CER team, the technology team and the community engagement group were considered in the operationalization of all variables.
Conclusions: The design of rigorous observational CER observational CER should recognize the need for an intense planning phase. In accordance with good practice guidance for observational studies, an important component of the planning phase is to disseminate and obtain feedback on the research design in advance of its conduct.
Regulatory requirements for drug approval unit3Aman chourasia
New Drug Application (NDA) is an application submitted to the individual regulatory authority for authorization to market a new drug i.e. innovative product. To gain this permission a sponsor submits preclinical and clinical test data for analyzing the drug information, description of manufacturing trials.
To learn more visit:
https://insidescientific.com/webinar/cutting-edge-conversations-fighting-neurodegenerative-diseases/
Evelyn Pyper, MPH discusses how a patient-centered approach to real-world data collection and evidence generation can transform research in neurodegeneration. Neurodegenerative diseases often affect both motor and cognitive function, produce emotional and social changes, and require significant caregiver support, all while stretching across a fragmented healthcare ecosystem. Participatory research that directly obtains patient consent, empowers patients, and simplifies the task of linking multiple data sources, can lead to a more comprehensive capture of medical histories. This presentation briefly explores ways in which patient-centered research can improve understanding of disease diagnoses, symptomatology, and progression.
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.
> Definition of RWD
> RWD - Big Data Characteristics
> Sources of RWD
> Important Stakeholders
> Benefits of RWD
> Why Data Sharing is Important?
> Benefits of Data Sharing
> Who Benefits?
> Ultimate Goals
> Case Studies
> Challenges
> Data Privacy Scenario
> Data Security in India
> Regulatory Perspectives Around RWD
> How to Encourage Data Sharing?
Similar to From Clinical Decision Support to Precision Medicine (20)
To make remote monitoring devices interoperable, we must examine a variety of use cases and the current evidence of their effectiveness. The presentation is from the January 2020 IHE Connectathon in Cleveland, Oho.
Building Consumer-Facing Health Devices and Apps and Doing it RightKent State University
For the HIMSS Delaware Valley Chapter. solve a problem; prototype, pilot, adopt and scale. FDA regulations, evidence, health behavior change, data integration
How Interconnectivity Is Enabling The Future Of Patient-Driven HealthKent State University
Connected health and remote patient monitoring, combined with interactive patient engagement apps, have a tremendous potential to transform chronic disease management for patients to manage at home allowing them to live their lives with independence. But deploying such programs, and managing the IoT assets they often depend on, can pose a host of challenges at both the provider and the patient level.
Building Consumer-Facing Health Devices and Apps and Doing it RightKent State University
Presentation to the Medical Capital Innovation Competition in Cleveland 4/23/18 including the regulatory pathway, importance of evidence and data integration.
With @Atreja at the NODE Health Conference - Digital Medicine http://digitalmedicineconference.com/ on the events and studies which moved the field forward
PCHAlliance conducted a systematic review of published literature to gather the available data on health outcome measures, reviewing over 1,450 citations. Fifty-three randomized controlled studies and trials were selected for analysis, on topics related to mobile technologies, remote patient monitoring, web-based counseling and other personal connected health technologies. This publication aims to set an initial baseline for the current body of evidence in personal connected health in key areas, namely behavior change and self-care, remote patient monitoring, remote counseling and mental health, as well as more broadly through key condition-specific studies.
Download the paper here: http://www.pchalliance.org/personal-connected-health-state-evidence-and-call-action
Patient Engagement is more that an patient portal
Connected Health tools are available to enhance engagement
Personalization is needed to engage
How patient engagement technologies fit with population health
Helping those lacking health and digital literacy and access
The future is bright for Personal Connected Health
The Internet of Things (IoT) is the latest buzzword out of the interface between information technology and business. As technologies like Bluetooth and sensors enable connections between devices and networks, innovation has brought connections between devices and a human interface. In healthcare, this has been termed the Internet of Medical Things or Healthy things. Medical devices and consumer health devices generate data which can be analyzed, synthesized and displayed for the consumer and healthcare provider to get a broader picture of one’s health. Everything from fitness devices to glucose monitors can give us information about our current health status as never before. How this will integrate into a clinician’s workflow is a new journey of discovery as medical practice catches up with these innovations.
Registries are a powerful informatics tool for research and public health. As both commercial payers and the Centers for Medicare and Medicaid Services work to shift incentives shift toward value based-purchasing, demand for reliable, accessible data on populations is growing. The purpose of this poster is to define accountable care organizations (ACOs), explain the importance of registries in managing data for ACOs, and discuss specific informatics requirements unique to accountable care registries.
The HIMSS Connected Health Conference took place on November 8-11, 2015 at the National Harbor in Washington, DC. It included Mobile Health, Cybersecurity and Population Health topics.
My presentation to the Personalized Medicine meet of the American Cancer Society Cancer Action Network on August 11, 2015 at the HIMSS Innovation Center in Cleveland, Ohio.
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
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micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
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TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
9. Themes
1. EMR as the platform for clinical
decision support
2. Impact on quality of care
3. Role of disease registries
4. Personalized and Precision
Medicine
5. Reducing the Lethal Lag Time
10. Lethal Lag Time
• It takes an average of 17 years to
implement clinical research results
into daily practice
• Unacceptable to patients
• Can Electronic Medical Records and
Clinical Decision Support Systems
change this?
11. Electronic Medical Records
• Comprehensive
medical information
• Images
• Communication with
other physicians,
medical professionals
• Communication with
patients
• 3 million active
patients, 10 years
12. EMR Inputs and Outputs
Inputs EMR Tools Outputs
• Clinical • Alerts Secondary Use
• Labs • Best practices • Data sets
• Devices • Smart sets • Registries
• Remote monitoring • Workflow • Quality reports
• Pt outcomes • Communication to
• Omics other providers,
• Social media? patients
13. Clinical Decision Support
• Process for enhancing health-related
decisions and actions with pertinent,
organized clinical knowledge and
patient information
• to improve health and healthcare
delivery.
• Information recipients can include
patients, clinicians and others involved
in patient care delivery
http://www.himss.org/ASP/topics_clinic
alDecision.asp
14. Like a GPS, CDS supplies
information tailored to the current
situation, and organized for
maximum value.
17. EMR Alert Types
Clinical Decision Support
Target Area of Care Example
Preventive care Immunization, screening, disease management
guidelines for secondary prevention
Diagnosis Suggestions for possible diagnoses that match a
patient’s signs and symptoms
Planning or implementing Treatment guidelines for specific diagnoses, drug
treatment dosage recommendations, alerts for drug-drug
interactions
Followup management Corollary orders, reminders for drug adverse event
monitoring
Hospital, provider efficiency Care plans to minimize length of stay, order sets
Cost reductions and improved Duplicate testing alerts, drug formulary guidelines
patient convenience
18. The CDS Toolbox
(more examples)
• Drug-Drug Interactions • Rules to meet strategic
• Drug-Allergy interactions objectives (core measures,
• Dose Range Checking antibiotic usage, blood
management)
• Standardized evidence
based ordersets • Diagnostic decision
support tools
• Links to knowledge
references
• Links to local policies
19. Clinical Decision Support
Examples
• New diagnosis of Rheumatoid
Arthritis
• Prompted to refer to preventive
cardiology
20. Clinical Decision Support
Examples
• Age > 50 and a fragile fracture
diagnosis
• order set for bone density scan and
appropriate medication regimen
22. Virtuous Cycle of
Clinical Decision Support
Registry Measure
Practice Guideline
CDS
http://www2.eerp.usp.br/Nepien/DisponibilizarArquivos/tomada_de_decis%C3%A3o.pdf
24. EMR and Quality of Care
• Diabetes care was 35.1 percentage points
higher at EHR sites than at paper-based
sites
• Standards for outcomes was 15.2
percentage points higher
• Better Health Greater Cleveland Project
25. The Role of Registries
• EMR data available to create a
registry for any condition
• Study the condition
– progression, treatments
• Comparative effectiveness of
treatments
• Recruit for clinical trials
• Develop clinical decision support
26. Chronic Kidney
Disease Registry
• Chronic Kidney Disease Registry
• Established 2009
• 60,000 patients from the health
system
• Cohort – Adults with two eGFRs less
than 60 within 3 months, outpatient
results only, or diagnosis of CKD
• http://www.chrp.org/pdf/HSR_120220
11_Slides.pdf
27. Validation Results
• Our dataset’s agreement with EHR-
extracted data for documentation of the
presence and absence of comorbid
conditions, ranged from substantial to
near perfect agreement.
• Hypertension and coronary artery
disease were exceptions
• EMR data accurate for research use
28. Pediatric Surgical Site
Infection Registry
• Data from the EMR and the operative
record
• When did antibiotics start?
• Was pre-op skin prep done?
• Was the time-out and checklist
observed in the OR
• Post-op care quality
29. Patient Reported Outcomes
• Understanding the outcomes of
treatment incomplete without
• Patient Reported Outcomes
Measurement Information System
http://www.nihpromis.org/
• Patient-Centered Outcomes
Research Institute
http://www.pcori.org/
30. Patient Reported Outcomes
• Quality of life
• Activities of daily living
• Recording weight, diet, exercise
using apps
• Quantified Self
31. Mining of electronic health records (EHRs)
has the potential for establishing new
patient stratification principles and
for revealing unknown disease correlations.
- Nature Reviews | Genetics, June 2012
32. Evidence Generating
Medicine
• The next step beyond
evidence-based medicine
• The systematic incorporation of
research and quality improvement
considerations into the organization
and practice of healthcare
• to advance biomedical science and
thereby improve the health of
individuals and populations.
33. Predictive Models
• Predicting 6-Year Mortality Risk in Patients
With Type 2 Diabetes
• Cohort of 33,067 patients with type 2
diabetes identified in the Cleveland EMR
• Prediction tool created in this study was
accurate in predicting 6-year mortality risk
among patients with type 2 diabetes
• Diabetes Care December 2008, vol. 31 no. 12: 2301-2306
37. Information Overload
• New information in • Information about
the medical an individual
literature patient
- PubMed adding - Medical history
over 670,000 new - Lab results
entries per year - Vitals
- Imaging
- Genomics
39. New Paradigm for CDS
Family History | Whole Genome | Clinical Data | Patient Reported |Monitoring
Algorithms
Clinical Decision Support
Personalized Medicine
40. Personalized Medicine
• The boundaries are fading between
basic research and the clinical
applications of systems biology and
proteomics
• New therapeutic models
• Journal of Proteome Research Vol. 3, No. 2, 2004, 179-196.
41. Personalized Medicine
Parkinson’s Disease
• New Cleveland Clinic partnership
with 23andMe to collect DNA from
Parkinson’s patients
• Looking for Genome Wide
Associations (GWAS)
• 23andme.com/pd/
42. Precision Medicine
• ”state-of-the-art molecular profiling to
create diagnostic, prognostic, and
therapeutic strategies precisely tailored
to each patient's requirements.”
• ”The success of precision medicine
will depend on establishing frameworks
for …interpreting the influx of
information that can keep pace with
rapid scientific developments.”
• N Engl J Med 2012; 366:489-491, 2/ 9/2012
43. Artificial Intelligence in
Medicine
• Developing a search engine that
will scan thousands of medical
records to turn up documents
related to patient queries.
• Learn based on how it is used
• “We are not contemplating ―
unless this were an
unbelievably fantastic success
― letting a machine practice
medicine.”
• http://www.health2news.com/20
12/02/10/the-national-library-of-
medicine-explores-a-i/
44. IBM Watson
• Medical records, texts, journals and
research documents are all written in
natural language – a language that
computers traditionally struggle to
understand. A system that instantly
delivers a single, precise answer
from these documents could
transform the healthcare industry.
• “This is no longer a game”
• http://tinyurl.com/3b8y8os
45. Digital Humans
Convergence of:
• Genomics
• Social media
• mHealth
• Rebooting Clinical
Trials
46. Conclusion - 1
• EMR as the platform for the future of
medicine
• Data incoming
- Clinical
- Patient Reported
- Genomic
- Proteomic
- Home monitoring
47. Conclusion - 2
• Exploit all uses of the EMR
- Improve practice efficiency
- Ensure patient safety
- Learn about your patients
(registries)
- Compare treatments
- Engage with patients
48. Conclusion - 3
• Understand Personalized
and Precision medicine
• How will we integrate
genomic data in clinical
practice in the future?
49. Conclusion - 4
• Predictive models inform care
• Diagnostic & treatment
algorithms
• How do we integrate
these into practice
in the EMR?
50. Conclusion - 5
• How can we reduce
the lethal lag time?
• Getting medical findings into practice
more rapidly
• How can we engage patients?
• New technology for Big Data in
health care