To address family history collection, interpretation, and application in busy primary care practices, NCHPEG has collaborated collaborating with the March of Dimes, Genetic Alliance, Harvard Partners, and the Health Resources and Services Administration to develop and evaluate a novel family history tool that focuses on prenatal and neonatal health. The tool helps to improve health outcomes for the female patient, fetus, and family by providing clinical decision support and educational resources for risk assessment based on family history. A set of screenshots and an overview of the module can be reviewed via this downloadable ppt.
This document discusses the challenges of using generic electronic health records (EHRs) for specialists like breast surgeons. It notes that EHR implementation initially reduced physician productivity by 25-33% on average, though internists recovered productivity more than pediatricians or family practitioners. The EHR interface does not account for specialists' unique data needs. Alternative interfaces have been developed for anesthesia, pathology, and breast imaging to work around this issue. The document proposes a breast surgery module that could integrate seamlessly with EHRs. It would standardize data entry, apply clinical decision support, and generate documentation to reduce workload and improve quality of care.
The Pre-Anesthesia Evaluation Module is designed to manage the data and workflow of pre-anesthesia evaluation, either at the pre-admission testing visit or at the surgeon’s office. Medical history is collected from patients via a self-administered Tablet questionnaire, and available data regarding that patient is also downloaded from the EHR. This data is used to determine what testing is needed prior to anesthesia. This system can be used in the surgeon’s office, to help avoid anesthesia complications and help prevent canceled or delayed cases. A set of screenshots and an overview of the module can be reviewed via this downloadable PowerPoint presentation.
Investigating Integration Of Computerized Decision Support Into Workflow Hsr&...Brad Doebbeling
“So preventive care gets pushed aside for acute care issues?”
Physician: “Absolutely. That’s the nature of primary care.”
Physician: “Preventive care is the first thing to go when you’re busy.”
Physician: “When you’re in the middle of seeing patients, the last thing you want to do is address a preventive care issue.”
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.
Adrianne Miller is a Clinical Research Assistant at Ohio State University Wexner Medical Center with experience in data entry, obtaining vitals, blood draws, EKGs, and processing/shipping specimens for multiple clinical trials. She has a Bachelor's degree in Exercise Science and Health Promotion from Otterbein University and additional exercise physiology coursework from the University of Pittsburgh. Her skills include complying with regulations, maintaining databases, excellent organization, and interacting with clinical teams. Previously she was an Inpatient Cardiac/Pulmonary Rehab Intern where she educated patients and ambulated those recovering from cardiac/pulmonary events.
Christie Carter has over 10 years of experience in oncology clinical research, hospital nursing, home health, quality assurance, risk adjustment, and medical coding. She has a Bachelor's degree in Nursing and is licensed in Alabama. Her most recent role is as a Clinical Research Coordinator at Southern Cancer Center, where she facilitates clinical trials, recruits and enrolls participants, provides education, and ensures protocol compliance. Prior experience includes chart reviewing, risk assessment, quality assurance, and home health case management.
To address family history collection, interpretation, and application in busy primary care practices, NCHPEG has collaborated collaborating with the March of Dimes, Genetic Alliance, Harvard Partners, and the Health Resources and Services Administration to develop and evaluate a novel family history tool that focuses on prenatal and neonatal health. The tool helps to improve health outcomes for the female patient, fetus, and family by providing clinical decision support and educational resources for risk assessment based on family history. A set of screenshots and an overview of the module can be reviewed via this downloadable ppt.
This document discusses the challenges of using generic electronic health records (EHRs) for specialists like breast surgeons. It notes that EHR implementation initially reduced physician productivity by 25-33% on average, though internists recovered productivity more than pediatricians or family practitioners. The EHR interface does not account for specialists' unique data needs. Alternative interfaces have been developed for anesthesia, pathology, and breast imaging to work around this issue. The document proposes a breast surgery module that could integrate seamlessly with EHRs. It would standardize data entry, apply clinical decision support, and generate documentation to reduce workload and improve quality of care.
The Pre-Anesthesia Evaluation Module is designed to manage the data and workflow of pre-anesthesia evaluation, either at the pre-admission testing visit or at the surgeon’s office. Medical history is collected from patients via a self-administered Tablet questionnaire, and available data regarding that patient is also downloaded from the EHR. This data is used to determine what testing is needed prior to anesthesia. This system can be used in the surgeon’s office, to help avoid anesthesia complications and help prevent canceled or delayed cases. A set of screenshots and an overview of the module can be reviewed via this downloadable PowerPoint presentation.
Investigating Integration Of Computerized Decision Support Into Workflow Hsr&...Brad Doebbeling
“So preventive care gets pushed aside for acute care issues?”
Physician: “Absolutely. That’s the nature of primary care.”
Physician: “Preventive care is the first thing to go when you’re busy.”
Physician: “When you’re in the middle of seeing patients, the last thing you want to do is address a preventive care issue.”
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.
Adrianne Miller is a Clinical Research Assistant at Ohio State University Wexner Medical Center with experience in data entry, obtaining vitals, blood draws, EKGs, and processing/shipping specimens for multiple clinical trials. She has a Bachelor's degree in Exercise Science and Health Promotion from Otterbein University and additional exercise physiology coursework from the University of Pittsburgh. Her skills include complying with regulations, maintaining databases, excellent organization, and interacting with clinical teams. Previously she was an Inpatient Cardiac/Pulmonary Rehab Intern where she educated patients and ambulated those recovering from cardiac/pulmonary events.
Christie Carter has over 10 years of experience in oncology clinical research, hospital nursing, home health, quality assurance, risk adjustment, and medical coding. She has a Bachelor's degree in Nursing and is licensed in Alabama. Her most recent role is as a Clinical Research Coordinator at Southern Cancer Center, where she facilitates clinical trials, recruits and enrolls participants, provides education, and ensures protocol compliance. Prior experience includes chart reviewing, risk assessment, quality assurance, and home health case management.
This document discusses the potential for telemedicine to address healthcare access issues. It notes that there will be a shortage of 150,000 physicians in the next decade. Specialty care is becoming more complex and patients often have to travel long distances to receive it. However, technology now allows remote exams and treatments to be conducted with the same standard of care. The document argues that telemedicine can bring specialized medical expertise and experience to more patients, improving access and lowering costs compared to building more brick-and-mortar clinics or training additional providers. Several examples involving neurology and multiple sclerosis care are provided.
This document discusses using an electronic medical record (EMR) to support clinical research. It outlines how EMR data can be used throughout the research process, including determining study feasibility, including data in grant applications, patient recruitment, study interventions, data collection, and assessing study outcomes. While EMRs provide rich clinical data and can streamline aspects of research, the data was primarily collected for clinical care so requires validation for research purposes. Fully integrating research workflows into EMRs remains a challenge.
Clinical decision support systems use information technology to reduce errors and improve clinical decision making by providing clinicians with patient data, knowledge resources, and clinical guidance at the point of care through tools such as diagnostic decision trees, drug databases, alerts, and reminders. However, implementing clinical decision support systems poses challenges around usability, workflow integration, and clarifying the relationship between machine recommendations and human clinical judgment.
Clinical quality measures are an important tool for assessing and improving healthcare quality, but implementing and validating them based on electronic health records can be complex, time-consuming, and poorly defined. The authors analyzed issue tracking data from building an analytics platform with nine heart failure clinical quality measures. They identified major challenges encountered and make recommendations to help others implement electronic health record-based quality measures more efficiently.
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 ;)
Melissa Hall has over 2 years of experience as a cardiac sonographer at various medical facilities, where she has performed echocardiograms, stress tests, and maintained high quality diagnostic imaging and patient care. She obtained her Bachelor of Science degree in Cardiovascular Sonography from West Coast Ultrasound Institute in 2015 and is a licensed and certified diagnostic medical sonographer. Her resume demonstrates strong skills in cardiac ultrasound imaging, patient care, and maintaining compliance with medical standards and regulations.
Edward O'Melia is an experienced cardiac sonographer seeking a new position where he can utilize his scanning techniques. He has worked as a cardiac sonographer for MedStaffInc. at Drexel Medicine and Penn Medicine since 2014, where he performs various scans, interprets results, and maintains medical records. O'Melia completed his cardiac sonography internship at Albert Einstein Medical Center and AtlantiCare Regional Medical Center in 2013-2014, gaining over 1,000 clinical hours. He obtained his Associate's Degree in Allied Health with a specialty in cardiovascular sonography from Sanford Brown Institute in 2014.
Early prediction of post-acute care discharge disposition - An opportunity to...Avishek Choudhury
Objective: Hospital deferments for patients squared to post-acute care (PAC) are lengthier and expensive than routine discharges. Patient’s medical insurance coverage plays a desperate role in determining their PAC discharge disposition. At Unity Point Health, retrieving a patient’s insurance coverage information accedes the PAC discharge disposition process by four days. In this study, we implement predictive analytics for early prediction of PAC discharge disposition to foster the needed care, in the suitable location, just in time. Methodology: To study the existing PAC discharge disposition method at Unity Point Health we conducted a group discussion involving twenty-five patient care facilitators (PCF) and two registered nurses (RN). We manually retrieved sixteen hundred patient’s data (July 2018 through August 2018) from discharge notes and initial nursing assessment to conduct a retrospective analysis of PAC discharge disposition. The analysis is limited to the patients discharged to AR, or SNF. We employed predictive analytics to develop a clinical decision support system (CDSS) that can efficiently identify patients eligible for AR and SNF by the first day of their inpatient stay. All evaluations were conducted using the SPSS Modeler, RStudio, Microsoft Visio and Excel. Results: Chi-Squared Automatic Interaction Detector (CHAID) algorithm was selected to be the best fit model with an (a) overall accuracy of 84.16%, and (b) the area under the receiver operating characteristic (ROC) curve of 0.81. Conclusion: CHAID algorithm is recommended to develop CDSS that can steer early prediction of PAC discharge disposition and thus minimize inpatient length of stay. Early prediction of PAC discharge disposition enabled UnityPoint-Health in reducing inpatient length of stay by forty-four percent and recuperated patient flow.
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
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.
Patricia Vann is seeking a respiratory therapist position with over a decade of experience. She has worked in various healthcare settings including hospitals, home health, and medical facilities. Her experience includes ventilator management, arterial blood gas analysis, tracheostomy care, and providing respiratory care to patients of all ages. She is currently pursuing a Bachelor's degree in Respiratory Care and is committed to delivering quality patient care.
Patricia Vann is seeking a respiratory therapist position with over a decade of experience. She has worked in various healthcare settings including hospitals, home health, and medical facilities. Her experience includes ventilator management, arterial blood gas analysis, tracheostomy care, and providing respiratory care to patients of all ages. She is currently pursuing a Bachelor's degree in Respiratory Care and is committed to delivering quality patient care.
The document discusses an electronic clinical trial recruitment system that aims to address the problems of slow clinical research translation, labor-intensive and costly trial recruitment methods. It presents a solution of leveraging existing electronic medical record data across different healthcare systems and practices to more efficiently identify and enroll qualified patients for clinical trials in a networked system. The system could benefit research sponsors, investigators, medical practices, and patients by speeding recruitment times and lowering costs while improving clinical care engagement and outcomes. It analyzes the potential market opportunity and differentiation of this federated network approach to electronic clinical trial recruitment.
This document provides an outline for a presentation on electronic medical records (EMRs). It begins with defining the components of an EMR, including labs, admissions/discharge/transfer data, orders, radiology, notes, and billing. It then discusses the history and adoption of EMRs from the 1960s to present. The document reviews studies showing the effectiveness of EMRs in improving quality of care and achieving treatment standards. It also outlines how EMR data is structured in databases and data warehouses and describes common health data standards like ICD, CPT, LOINC, SNOMED, and HL7. The presentation covers meaningful use incentives and provides examples of using EMR data for research studies.
Imaging Techniques for the Diagnosis and Staging of Hepatocellular CarcinomaImran Javed
This document summarizes a comparative effectiveness review conducted by the Pacific Northwest Evidence-based Practice Center on imaging techniques for the diagnosis and staging of hepatocellular carcinoma. The review was prepared for the Agency for Healthcare Research and Quality and involved a team of investigators who analyzed available evidence on imaging modalities used for diagnosing and determining the extent of hepatocellular carcinoma. The document provides background on the review and discloses conflicts of interest.
Lynne E. Becker seeks a position in corporate project research based on her extensive experience managing clinical research projects and studies. She has over 20 years of experience developing research protocols, recruiting study sites and participants, ensuring regulatory compliance, and using information technology to efficiently achieve research goals. Becker has managed both small and large studies of up to $250,000 and $5 million respectively. She is skilled in all aspects of clinical research including protocol development, site selection and training, patient recruitment, database design, and regulatory reporting.
This document summarizes factors that contribute to hospital readmissions and strategies to reduce readmission rates. It identifies that patients are at higher risk of readmission if they have multiple chronic conditions, lack social support, or have issues with following discharge plans. High nursing workload is also linked to higher readmission rates for certain conditions. Successful interventions discussed include implementing transition plans, increasing education for patients using "teach back" methods, designating nurse discharge advocates, enhancing post-discharge follow-up including remote monitoring, and improving communication between inpatient and outpatient providers.
The document discusses analyzing and improving the inpatient discharge process at a hospital. It outlines the current discharge process flow, collects data on discharge times, and identifies inefficiencies and their causes. Suggestions are made to standardize the process, improve communication and reduce non-value adding steps to decrease discharge times. A proposed discharge checklist model is presented to help streamline the process.
The patient handoff is a contemporaneous, interactive process of passing patient-specific information from one caregiver to another to ensure the continuity and safety of patient care. It is well recognized that the handoff is a point of vulnerability where valuable patient information can be distorted and omitted [1, 2]. A plethora of studies in the nursing literature have identified a variety of problems, including incomplete or inaccurate information [3-6], uneven quality [7], repeated interruptions and lack of anticipatory guidance [8]. Many reports have focused on characterizing the weaknesses with non-operative patient handovers, the use of handoff checklists and aviation safety models for specific groups of patients [1,5,9], and the pre- and post-implementation comparisons. [10-12] However, few studies have focused on prospective cohort studies validating and testing patient information management systems such as smart-templates in the setting of handover quality. [10]
Electronic templates containing patient information help to standardize the type of information conveyed during interactions, discourages ambiguous findings,[13] improves provider satisfaction and improves continuity of care.[14] Within the department, we developed the transfer template (T2) to address the issues in provider workflow and efficiency. With the press of a button, the T2 template automatically extracts live information from the anesthetic record, pertinent fields from the PAC note and laboratory values from IView, and provides a concise output of these relevant details.
APOORVA MEADOWS 2BHK & 3BHK APARTMENTS FOR SALEBangalore Prj
This short document promotes creating presentations using Haiku Deck, a tool for making slideshows. It encourages the reader to get started making their own Haiku Deck presentation and sharing it on SlideShare. In just one sentence, it pitches the idea of using Haiku Deck to easily create engaging slideshows.
With Twitter surpassing a hundred million users, it’s no longer a force you can deny. Join Claire to learn how can nonprofits articulate impact at 140 characters a pop and harness the power of this growing social network.
This document discusses the potential for telemedicine to address healthcare access issues. It notes that there will be a shortage of 150,000 physicians in the next decade. Specialty care is becoming more complex and patients often have to travel long distances to receive it. However, technology now allows remote exams and treatments to be conducted with the same standard of care. The document argues that telemedicine can bring specialized medical expertise and experience to more patients, improving access and lowering costs compared to building more brick-and-mortar clinics or training additional providers. Several examples involving neurology and multiple sclerosis care are provided.
This document discusses using an electronic medical record (EMR) to support clinical research. It outlines how EMR data can be used throughout the research process, including determining study feasibility, including data in grant applications, patient recruitment, study interventions, data collection, and assessing study outcomes. While EMRs provide rich clinical data and can streamline aspects of research, the data was primarily collected for clinical care so requires validation for research purposes. Fully integrating research workflows into EMRs remains a challenge.
Clinical decision support systems use information technology to reduce errors and improve clinical decision making by providing clinicians with patient data, knowledge resources, and clinical guidance at the point of care through tools such as diagnostic decision trees, drug databases, alerts, and reminders. However, implementing clinical decision support systems poses challenges around usability, workflow integration, and clarifying the relationship between machine recommendations and human clinical judgment.
Clinical quality measures are an important tool for assessing and improving healthcare quality, but implementing and validating them based on electronic health records can be complex, time-consuming, and poorly defined. The authors analyzed issue tracking data from building an analytics platform with nine heart failure clinical quality measures. They identified major challenges encountered and make recommendations to help others implement electronic health record-based quality measures more efficiently.
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 ;)
Melissa Hall has over 2 years of experience as a cardiac sonographer at various medical facilities, where she has performed echocardiograms, stress tests, and maintained high quality diagnostic imaging and patient care. She obtained her Bachelor of Science degree in Cardiovascular Sonography from West Coast Ultrasound Institute in 2015 and is a licensed and certified diagnostic medical sonographer. Her resume demonstrates strong skills in cardiac ultrasound imaging, patient care, and maintaining compliance with medical standards and regulations.
Edward O'Melia is an experienced cardiac sonographer seeking a new position where he can utilize his scanning techniques. He has worked as a cardiac sonographer for MedStaffInc. at Drexel Medicine and Penn Medicine since 2014, where he performs various scans, interprets results, and maintains medical records. O'Melia completed his cardiac sonography internship at Albert Einstein Medical Center and AtlantiCare Regional Medical Center in 2013-2014, gaining over 1,000 clinical hours. He obtained his Associate's Degree in Allied Health with a specialty in cardiovascular sonography from Sanford Brown Institute in 2014.
Early prediction of post-acute care discharge disposition - An opportunity to...Avishek Choudhury
Objective: Hospital deferments for patients squared to post-acute care (PAC) are lengthier and expensive than routine discharges. Patient’s medical insurance coverage plays a desperate role in determining their PAC discharge disposition. At Unity Point Health, retrieving a patient’s insurance coverage information accedes the PAC discharge disposition process by four days. In this study, we implement predictive analytics for early prediction of PAC discharge disposition to foster the needed care, in the suitable location, just in time. Methodology: To study the existing PAC discharge disposition method at Unity Point Health we conducted a group discussion involving twenty-five patient care facilitators (PCF) and two registered nurses (RN). We manually retrieved sixteen hundred patient’s data (July 2018 through August 2018) from discharge notes and initial nursing assessment to conduct a retrospective analysis of PAC discharge disposition. The analysis is limited to the patients discharged to AR, or SNF. We employed predictive analytics to develop a clinical decision support system (CDSS) that can efficiently identify patients eligible for AR and SNF by the first day of their inpatient stay. All evaluations were conducted using the SPSS Modeler, RStudio, Microsoft Visio and Excel. Results: Chi-Squared Automatic Interaction Detector (CHAID) algorithm was selected to be the best fit model with an (a) overall accuracy of 84.16%, and (b) the area under the receiver operating characteristic (ROC) curve of 0.81. Conclusion: CHAID algorithm is recommended to develop CDSS that can steer early prediction of PAC discharge disposition and thus minimize inpatient length of stay. Early prediction of PAC discharge disposition enabled UnityPoint-Health in reducing inpatient length of stay by forty-four percent and recuperated patient flow.
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
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.
Patricia Vann is seeking a respiratory therapist position with over a decade of experience. She has worked in various healthcare settings including hospitals, home health, and medical facilities. Her experience includes ventilator management, arterial blood gas analysis, tracheostomy care, and providing respiratory care to patients of all ages. She is currently pursuing a Bachelor's degree in Respiratory Care and is committed to delivering quality patient care.
Patricia Vann is seeking a respiratory therapist position with over a decade of experience. She has worked in various healthcare settings including hospitals, home health, and medical facilities. Her experience includes ventilator management, arterial blood gas analysis, tracheostomy care, and providing respiratory care to patients of all ages. She is currently pursuing a Bachelor's degree in Respiratory Care and is committed to delivering quality patient care.
The document discusses an electronic clinical trial recruitment system that aims to address the problems of slow clinical research translation, labor-intensive and costly trial recruitment methods. It presents a solution of leveraging existing electronic medical record data across different healthcare systems and practices to more efficiently identify and enroll qualified patients for clinical trials in a networked system. The system could benefit research sponsors, investigators, medical practices, and patients by speeding recruitment times and lowering costs while improving clinical care engagement and outcomes. It analyzes the potential market opportunity and differentiation of this federated network approach to electronic clinical trial recruitment.
This document provides an outline for a presentation on electronic medical records (EMRs). It begins with defining the components of an EMR, including labs, admissions/discharge/transfer data, orders, radiology, notes, and billing. It then discusses the history and adoption of EMRs from the 1960s to present. The document reviews studies showing the effectiveness of EMRs in improving quality of care and achieving treatment standards. It also outlines how EMR data is structured in databases and data warehouses and describes common health data standards like ICD, CPT, LOINC, SNOMED, and HL7. The presentation covers meaningful use incentives and provides examples of using EMR data for research studies.
Imaging Techniques for the Diagnosis and Staging of Hepatocellular CarcinomaImran Javed
This document summarizes a comparative effectiveness review conducted by the Pacific Northwest Evidence-based Practice Center on imaging techniques for the diagnosis and staging of hepatocellular carcinoma. The review was prepared for the Agency for Healthcare Research and Quality and involved a team of investigators who analyzed available evidence on imaging modalities used for diagnosing and determining the extent of hepatocellular carcinoma. The document provides background on the review and discloses conflicts of interest.
Lynne E. Becker seeks a position in corporate project research based on her extensive experience managing clinical research projects and studies. She has over 20 years of experience developing research protocols, recruiting study sites and participants, ensuring regulatory compliance, and using information technology to efficiently achieve research goals. Becker has managed both small and large studies of up to $250,000 and $5 million respectively. She is skilled in all aspects of clinical research including protocol development, site selection and training, patient recruitment, database design, and regulatory reporting.
This document summarizes factors that contribute to hospital readmissions and strategies to reduce readmission rates. It identifies that patients are at higher risk of readmission if they have multiple chronic conditions, lack social support, or have issues with following discharge plans. High nursing workload is also linked to higher readmission rates for certain conditions. Successful interventions discussed include implementing transition plans, increasing education for patients using "teach back" methods, designating nurse discharge advocates, enhancing post-discharge follow-up including remote monitoring, and improving communication between inpatient and outpatient providers.
The document discusses analyzing and improving the inpatient discharge process at a hospital. It outlines the current discharge process flow, collects data on discharge times, and identifies inefficiencies and their causes. Suggestions are made to standardize the process, improve communication and reduce non-value adding steps to decrease discharge times. A proposed discharge checklist model is presented to help streamline the process.
The patient handoff is a contemporaneous, interactive process of passing patient-specific information from one caregiver to another to ensure the continuity and safety of patient care. It is well recognized that the handoff is a point of vulnerability where valuable patient information can be distorted and omitted [1, 2]. A plethora of studies in the nursing literature have identified a variety of problems, including incomplete or inaccurate information [3-6], uneven quality [7], repeated interruptions and lack of anticipatory guidance [8]. Many reports have focused on characterizing the weaknesses with non-operative patient handovers, the use of handoff checklists and aviation safety models for specific groups of patients [1,5,9], and the pre- and post-implementation comparisons. [10-12] However, few studies have focused on prospective cohort studies validating and testing patient information management systems such as smart-templates in the setting of handover quality. [10]
Electronic templates containing patient information help to standardize the type of information conveyed during interactions, discourages ambiguous findings,[13] improves provider satisfaction and improves continuity of care.[14] Within the department, we developed the transfer template (T2) to address the issues in provider workflow and efficiency. With the press of a button, the T2 template automatically extracts live information from the anesthetic record, pertinent fields from the PAC note and laboratory values from IView, and provides a concise output of these relevant details.
APOORVA MEADOWS 2BHK & 3BHK APARTMENTS FOR SALEBangalore Prj
This short document promotes creating presentations using Haiku Deck, a tool for making slideshows. It encourages the reader to get started making their own Haiku Deck presentation and sharing it on SlideShare. In just one sentence, it pitches the idea of using Haiku Deck to easily create engaging slideshows.
With Twitter surpassing a hundred million users, it’s no longer a force you can deny. Join Claire to learn how can nonprofits articulate impact at 140 characters a pop and harness the power of this growing social network.
Residential Plots for sale in IVC Road, Devanahalli, Bangalore at Gateway Homes
2BHK Apartments in Bangalore
Site at Bangalore
Villa Houses in Bangalore
Apartments for sale at Electronic city
For More: http://bangalore5projects.blogspot.in/2015/12/gateway-homes-in-residential-plots-for.html
The St. Lawrence Neighbourhood experiences disproportionate commercial break-ins and thefts from automobiles due to the many storefronts and mix of open parking lots. Between June 12th and July 9th, there were 6-10 commercial break-ins compared to 12-13 residential break-ins, with bars, restaurants, convenience stores and shops as main targets occurring between 10pm-5am. Investigators suggest business owners install security and activate surveillance equipment to help identify suspects.
BDA SEEKS A WEEK FOR REPORT ON LINGANAHALLI LAKEBangalore Prj
This short document promotes creating presentations using Haiku Deck on SlideShare. It encourages the reader to get started making their own Haiku Deck presentation by providing a button to click to begin the process. The document is advertising the creation of presentations on Haiku Deck and SlideShare.
This document contains the lyrics to the song "Someone Like You" by Adele. The lyrics tell the story of the singer running into an ex-lover who is now married and has realized their dreams. The singer admits to coming uninvited because they couldn't stay away or fight their feelings, hoping to remind the ex that it isn't over for them. The chorus expresses wishing the ex nothing but the best but begging them not to forget the singer and remembering what they said about love sometimes lasting and sometimes hurting instead.
This document provides instructions for calculating averages in Excel. It outlines selecting cells of data, going to the Formulas tab, and clicking the Average button under the AutoSum arrow to automatically calculate the average of the selected cells. It also notes that the average function can be manually entered as =AVERAGE(cell range) in the formula bar.
Altmetrics trends - A survey of the #altmetrics landscapeWilliam Gunn
The document discusses the growing interest in altmetrics for measuring the impact of scholarly research. It provides an overview of various altmetric data aggregators and tools, such as Altmetric.com, Plum Analytics, Impact Story, and Crossref's DOI Event Tracker. The document also examines issues with altmetrics, such as concerns about the reliability, quality, and consistency of altmetric data.
Presentation by Dr. Orhan Agirdag (University of Leuven) at the Rutu Roundtable on Multilingual Education for Migrant Children in Europe.
The Roundtable was hosted by Utrecht University in Utrecht, the Netherlands and was held on 6 November 2015.
More info: http://www.rutufoundation.org/rutu-roundtable-utrecht/
San Petersburgo es la segunda ciudad más grande de Rusia y se encuentra en el noroeste del país, a orillas del río Neva. Fue fundada por Pedro el Grande en 1703 y se construyó sobre pantanos y marismas. Actualmente es un importante centro cultural, político y económico de Rusia.
O empreendedorismo e oportunidades disfarçadas - Prof. Alessandro SaadeAlessandro Saade
Palestra dos Empreendedores Compulsivos na BSP - Business School São Paulo, apresentando os desafios que os empreendedores enfrentam e as oportunidades disfarçadas que o mercado nos oferece.
FEV.2013
There is overwhelming evidence that bilingual children perform better, gain more self-confidence and learn the school language faster when their mother tongues are included in the classroom. The UN has encouraged mother tongue based instruction as best practice since the 1950s. Yet, implementation is rare. The result is lost opportunities, wasted talent, marginalisation, ignorance, as well as massive and growing inequality.
Generations of people grow up failed by their education systems from day one. A systematic human rights failure which is likely to continue unabated unless we act now.
The Rutu Roadmap: we believe that it is time for mother tongue based multilingual education becoming the norm, rather than the exception. This roadmap contains our plan on how to achieve this mission.
Oliver Hurst-Hiller, CTO & EVP, Product, DonorsChoose
Anna Doherty, Marketing Manager, Engagement and Social Media, DonorsChoose
Twitter Handles: Oliverhh & @anna_doherty
How do you maximize your social media presence when you have a small staff and limited resources? DonorsChoose.org uses data and a systematic approach to focus social media efforts to drive the action they want—join two of their senior executives and learn how.
Presentation by Dr. Emmanuelle LePichon (Utrecht University) at the Rutu Roundtable on Multilingual Education for Migrant Children in Europe.
The Roundtable was hosted by Utrecht University in Utrecht, the Netherlands and held on 6 November 2015.
More info: http://www.rutufoundation.org/rutu-roundtable-utrecht/
This document discusses how social media has changed qualitative research. It describes how qualitative research can now be done on a mass scale using tools like netnography, research communities, crowdsourcing, and co-creation. These new approaches actively involve users and take research in a more bottom-up direction. Social media sites provide rich insights into people's behaviors, opinions, and interactions online.
The document provides crop budgets for several regions in North Dakota, including estimates of revenues, costs, and returns to labor and management for spring wheat, durum, malting barley, and corn grain. It notes that the budgets are intended as guides and should be adjusted to individual situations. Users can modify the budgets to estimate cash flow or compare crop enterprises based on direct costs. Primary assumptions about yields, prices, fertilizer and other input costs, machinery and land charges, and insurance are provided.
Great article on how to integrate machine learning and optimization technique.
One group of researchers was able to reduce heart failure readmissions by 35% by combining machine learning and decision science technique, see "Data-driven decisions for reducing readmissions for heart failure: general methodology and case study" (Bayati, et. al., 2014).
According to the U.S. National Center for Health, a chronic disease is defined as a disease lasting 3 months or more. The document discusses the development of a chronic disease diagnosis (CDD) system using large datasets to accurately predict disease risk and recommend medical advice. Current CDD methods use multiple classification algorithms and collaborative filtering, but more testing is needed improve accuracy, especially using data from diabetes cases. Further research on CDD could help provide 24/7 remote patient monitoring and reduce the burden on healthcare providers.
This document provides information about various research projects and areas of expertise at the UVM Medical Center. It describes projects related to osteoporosis, inter-hospital transfers, chronic kidney disease, asthma, vaccinations, liver disease, critical care, and more. Contact information is provided for principal investigators studying topics like statin use in chronic kidney disease, acute kidney injury following cardiopulmonary bypass, and physiological phenotyping of asthma.
Primary care-based, teleretinal-screening protocol (Los Angeles Safety Net) UCLA CTSI
UCLA CTSI-Los Angeles County Department of Health Services (DHS) Projects
Principal Investigators: Lauren Daskivich (DHS), Carol Mangione (UCLA)
Diabetic retinopathy (DR) is the leading cause of blindness among working-age Americans, and among Los Angeles Latinos—the ethnic majority of patients in the Los Angeles County (LAC) safety net—the prevalence of DR is ~50%. Despite evidence that early detection and treatment can prevent blindness from DR, a significant number of persons with diabetes in our system fail to receive annual screening examinations and/or sight-saving treatments due to lack of access to specialty care. To date, the effect of a system level intervention on improving access to eye care and definitive treatment for diabetic retinopathy in an urban medically underserved, or safety net, population has not been evaluated. The objective of this project is to evaluate the impact of teleretinal screening on access to specialty ophthalmic care for diabetic patients in LAC who need monitoring or treatment for diabetic retinopathy. We propose a pre-post analysis of the LAC teleretinal screening implementation, and we aim to evaluate the number of patients screened for diabetic retinopathy, the number presenting for timely ophthalmic follow-up care and treatment, and the cost of the program.
The document describes how decision trees can be used to predict hospital readmission risk for patients with acute myocardial infarction (AMI), heart failure (HF), or pneumonia (PN). Decision trees were trained on 2010 California hospital data and tested on 2011 data. The decision trees achieved AUC scores of 0.612 for AMI, 0.583 for HF, and 0.650 for PN, indicating moderate predictive ability. However, decision trees provide the advantage of transparent, clinically relevant rules that can help hospitals target high-risk patient groups and design interventions to reduce readmissions.
This document discusses the debate between randomized clinical trials (RCTs) and observational studies using big data. While RCTs are better for minimizing bias, observational studies can include more patients and answer questions RCTs cannot. The document outlines several large cancer databases that can help learn from every patient, including SEER and NCDB registries. It describes how these databases are being enriched with additional data sources like EHRs, genomic data, and mobile devices. This evolving use of big data from numerous sources can improve outcomes by better understanding toxicity, costs, and quality of cancer care.
This document summarizes a presentation given by Peter Embi on clinical and translational research and informatics literature from 2012-2013. It begins with Embi's background and approach to identifying relevant papers. It then describes the topics covered in the presentation, which are grouped into categories like clinical data reuse, data management/discovery, researcher support/resources, and recruitment. For each category, 1-2 key papers are summarized in 1-3 sentences. The summaries highlight the papers' goals, methods, and conclusions. The document concludes by mentioning other notable papers and events from the past year.
Brad Doebbeling Slides for AHRQ Kick-Off EventShawnHoke
This document provides an overview of a project aimed at improving the integration of clinical decision support (CDS) into outpatient clinical workflow for colorectal cancer screening. The project will identify best practices for CDS integration across four health systems, develop and test redesigned CDS alternatives through simulation, and implement and evaluate the redesigned CDS in primary care clinics. The goal is to create CDS designs that improve efficiency, usability and reduce workload for providers.
This document discusses the development of a chronic disease diagnosis (CDD) system using large datasets to predict disease risk and provide medical advice recommendations. Currently, a hybrid CDD method uses multiple classification algorithms and collaborative filtering to build an accurate predictive model. Testing of the CDD system is being done using Middle Eastern data and future work involves further testing using diabetes case studies to improve accuracy and feasibility of recommendations.
Electronic Medical Records: From Clinical Decision Support to Precision MedicineKent State University
This document discusses the transition from traditional clinical decision support using electronic medical records to precision medicine. It provides examples of how Cleveland Clinic has used electronic medical records to create registries for conditions like chronic kidney disease, develop predictive models, and power algorithms for precision treatment recommendations. The document envisions precision medicine relying on vast amounts of molecular, genomic, and patient-reported data integrated into clinical decision support.
Are you interested in learning how to prevent hospital readmissions for your diabetic population? It is a popular belief that measuring blood glucose for your diabetic population is the most predictive variable in determining a hospital readmission for a diabetic. However, many providers of care simply do not perform the test on known diabetic patients. This study takes a look at an advanced analytic method that works within the current healthcare providers workflow to looks to identify the likelihood of a future 30-day unplanned readmission before hospital discharge.
Cognitive Computing: Company presentation by Avner Halperin, Co-Founder & CEO of EarlySense at the NOAH Conference 2019 in Tel Aviv, Hangar 11, 10-11 April 2019.
Heart Attack Prediction System Using Fuzzy C Means ClassifierIOSR Journals
This document presents a heart attack prediction system using a fuzzy C-means classifier. The system utilizes 13 patient attributes as inputs to the fuzzy C-means classifier to determine the risk of a heart attack. The classifier was tested on medical records from 270 patients and achieved a classification accuracy of 92%. Fuzzy C-means clustering allows data points to belong to multiple clusters, providing a more efficient and cost-effective way to predict the likelihood of patients experiencing a heart attack compared to other algorithms.
Comparisonof Clinical Diagnoses versus Computerized Test Diagnoses Using the ...Nelson Hendler
The Diagnostic Paradigm from www.MarylandClinicalDiagnostics.com was able to help the former Dean of Los Angeles Chiropractic College detect medical diagnoses which he had overlooked, and he later confirmed.
Johns Hopkins Hospital doctors report that 40%-80% of chronic pain patient are misdiagnosed, and that MRIs and CTs miss pathology 56%-78% of the time, Therefore, during extensive chart reviews of current medical data will produce a classic case of GIGO-garbage in giving garbage out. The need for accurate diagnoses and testing is critical for AI to work.
This document discusses using data from the Veterans Affairs (VA) healthcare system to conduct precision oncology research. It describes extracting data from the VA Corporate Data Warehouse, including clinical records from cancer registries and records of patients who received tumor sequencing and immunotherapy. The author builds a cohort of 330 non-small cell lung cancer patients who received immunotherapy before 2018 and had their cancer verified in the registry to study outcomes like the impact of PD-L1 expression on response to treatment. Challenges include lag times in cancer registry reporting and building a large enough cohort to draw powerful conclusions from retrospective analyses.
Data in precision oncology SAMSI Precision Medicine Meeting mar 2019Warren Kibbe
Talk at the March 14-15 2019 SAMSI Advances in Precision and Personalized Medicine held as part of the Program on Statistical, Mathematical, and Computational Methods for Precision Medicine (PMED) at NCSU, Raleigh, NC
This document discusses how machine learning can benefit the healthcare industry. It begins by outlining several ways machine learning can help, such as improving diagnosis accuracy, optimizing hospital processes like resource allocation, identifying patient subgroups for personalized medicine, and automating detection of medical findings. It then provides examples of machine learning research projects focused on chronic disease risk prediction, predicting patient resource needs, detecting cancer in pathology images, and discovering new medical knowledge by analyzing patterns in patient data. The document concludes by inviting questions and providing contact information.
Our backs are like superheroes, holding us up and helping us move around. But sometimes, even superheroes can get hurt. That’s where slip discs come in.
Histololgy of Female Reproductive System.pptxAyeshaZaid1
Dive into an in-depth exploration of the histological structure of female reproductive system with this comprehensive lecture. Presented by Dr. Ayesha Irfan, Assistant Professor of Anatomy, this presentation covers the Gross anatomy and functional histology of the female reproductive organs. Ideal for students, educators, and anyone interested in medical science, this lecture provides clear explanations, detailed diagrams, and valuable insights into female reproductive system. Enhance your knowledge and understanding of this essential aspect of human biology.
Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...Oleg Kshivets
Overall life span (LS) was 1671.7±1721.6 days and cumulative 5YS reached 62.4%, 10 years – 50.4%, 20 years – 44.6%. 94 LCP lived more than 5 years without cancer (LS=2958.6±1723.6 days), 22 – more than 10 years (LS=5571±1841.8 days). 67 LCP died because of LC (LS=471.9±344 days). AT significantly improved 5YS (68% vs. 53.7%) (P=0.028 by log-rank test). Cox modeling displayed that 5YS of LCP significantly depended on: N0-N12, T3-4, blood cell circuit, cell ratio factors (ratio between cancer cells-CC and blood cells subpopulations), LC cell dynamics, recalcification time, heparin tolerance, prothrombin index, protein, AT, procedure type (P=0.000-0.031). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and N0-12 (rank=1), thrombocytes/CC (rank=2), segmented neutrophils/CC (3), eosinophils/CC (4), erythrocytes/CC (5), healthy cells/CC (6), lymphocytes/CC (7), stick neutrophils/CC (8), leucocytes/CC (9), monocytes/CC (10). Correct prediction of 5YS was 100% by neural networks computing (error=0.000; area under ROC curve=1.0).
- Video recording of this lecture in English language: https://youtu.be/Pt1nA32sdHQ
- Video recording of this lecture in Arabic language: https://youtu.be/uFdc9F0rlP0
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Travel vaccination in Manchester offers comprehensive immunization services for individuals planning international trips. Expert healthcare providers administer vaccines tailored to your destination, ensuring you stay protected against various diseases. Conveniently located clinics and flexible appointment options make it easy to get the necessary shots before your journey. Stay healthy and travel with confidence by getting vaccinated in Manchester. Visit us: www.nxhealthcare.co.uk
These lecture slides, by Dr Sidra Arshad, offer a quick overview of the physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar lead (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
6. Describe the flow of current around the heart during the cardiac cycle
7. Discuss the placement and polarity of the leads of electrocardiograph
8. Describe the normal electrocardiograms recorded from the limb leads and explain the physiological basis of the different records that are obtained
9. Define mean electrical vector (axis) of the heart and give the normal range
10. Define the mean QRS vector
11. Describe the axes of leads (hexagonal reference system)
12. Comprehend the vectorial analysis of the normal ECG
13. Determine the mean electrical axis of the ventricular QRS and appreciate the mean axis deviation
14. Explain the concepts of current of injury, J point, and their significance
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. Chapter 3, Cardiology Explained, https://www.ncbi.nlm.nih.gov/books/NBK2214/
7. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Muktapishti is a traditional Ayurvedic preparation made from Shoditha Mukta (Purified Pearl), is believed to help regulate thyroid function and reduce symptoms of hyperthyroidism due to its cooling and balancing properties. Clinical evidence on its efficacy remains limited, necessitating further research to validate its therapeutic benefits.
First of all, I would like to thank the organizers of this didactic session, Brendan and Rahul for giving me an opportunity to talk here. I am PG, asst professor of emergency medicine at UCSF, a clinical researcher with a interest in the field of prehospital care and neurovascular emergencies. I will be discussing a few examples of funded research in Comparative Effectiveness Research in clinical care in emergency medicine, systems based research in prehospital care and public health. By presenting these examples, my goal is to let the group know about successful grants in emergency medicine and CER and also encourage young researchers to learn from these successful grants to prepare future grant applications.
This is a grant awarded to an EM investigator and was funded by the Agency of Healthcare Research and Quality to study HIV screening in the ED. Following the CDC mandate to perform non-targeted opt out screening for HIV in healthcare settings, the investigators designed a study to compare conventional methods (high risk screening) versus a HIV screening using a clinical prediction rule to identify high risk patients in the ED. This is an example of a effectiveness study comparing clinical outcomes of HIV identification for diagnostic test in the ED.
R01 – CER funding (UCSF PI). The second example is a R01 grant funded by AHRQ to study the effectiveness of three strategies in the ED for diagnosis of urolithiasis. This is a RCT and falls under CER since this compares diagnostic accuracy and effectiveness of modalities as well as cost of the strategies.
Another example of a funded research is similar to the HIV diagnostic test we talked about earlier. This is funded by the NIH and compares standard of care screening tool with another research tool for early diagnostic testing in undifferentiated CP and SOB in the ED. This falls under CER since we are comparing the diagnostic accuracy of two tests and its impact on outcomes in the second stage of the study
The next example I will be talking about is a study comparing the diagnostic accuracy of two triage protocols for stroke which has been funded by the American Heart/Stroke Association, Western States Affiliate Clinical research program. Since I am the PI of the study, I can elaborate on this a bit more than the others. The aims of this study are to compare the diagnostic accuracy of the old stroke protocol – card 28 with the new stroke protocol which is a combination of Card 28 and Cincinnati Stroke Scale. This is a prospective study and although not funded through the CER mechanism, this falls under CER since two diagnostic tests are being compared for accuracy of stroke recognition.
I would like to now shift gears and move onto examples from systems based research ( research with partnerships with community based healthcare settings)
Funded by K08 AHRQ to study the comparative effectiveness of regionalized and non-regionalized stroke care. This was funded by the CER program and aims to compare the rates of IV t-PA before and after regionalization of stroke in two counties in the state of CA. We will also compare the diagnostic accuracy of prehospital triage before and after regionalization and study the impact on treatment rates. In other words, we will compare the diagnostic accuracies after a system-wide intervention and effectiveness of the intervention on clinical outcomes.
R01 –AHRQ ( UCSF –PI). The second example is a R01 funded application to reduce inappropriate use of antibiotics for acute respiratory tract infections. They compared community education versus community and physician directed education on outcomes of care. This was also funded by AHRQ as 2 R01 grants. The first step involved comparing strategies in the office based setting and the second stage involved comparing effects of a multi-faceted intervention in the EDs and its impact on outcomes
The last example is also a R01 funded by AHRQ. This is different from the other studies I have presented in that this aims to create a statewide database to promote research in the future. The ARRA definition of CER includes “Encourage the development and use of clinical registries, clinical data networks, and other forms of electronic health data that can be used to generate or obtain outcomes data” and this study is an example of creating a linked database to obtain outcome data for assessment of prehospital care.