Healthcare analytics has the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases, and improve quality of life. It can improve processes, enhance patient care, and save lives by using analytics to better predict patient needs and staff accordingly. Electronic health records store a patient's comprehensive medical history digitally, allowing doctors to track changes over time with no risk of lost data or duplication. Analyzing demographic health data allows for strategic planning to identify factors that discourage treatment uptake. Analytics also helps prevent security threats, fraud, and inaccurate insurance claims while streamlining the claims process. The patient experience, overall population health, and operational costs can all be improved through healthcare analytics.
Understand what healthcare analytics is.
Identify the 5-stage Analytics Program Lifecycle (APL).
Understand how data analytics can be used in healthcare.
Check it on Experfy: https://www.experfy.com/training/courses/introduction-to-healthcare-analytics.
Lisa Annaly, Head of Provider Analytics at the Care Quality Commission, discusses lessons learned from the CQC as they have worked to monitor care quality over time.
Monitoring quality of care: making the most of dataNuffield Trust
Chris Sherlaw-Johnson, Senior Research Analyst at the Nuffield Trust, introduced the Monitoring quality of care conference and gives an overview of some of the approaches that we've been using at the Trust to identify where care quality has been improving, especially for frail and older people.
This document presents a proof of concept for using Twitter data to conduct syndromic surveillance for public health monitoring. It analyzed tweets containing the keyword "measles" between 2014-2015 and found 1,408 relevant tweets. The number of tweets mentioning measles was compared to confirmed measles cases from a national surveillance system, showing potential for Twitter data as an early warning system. However, limitations include using a single keyword and the free Twitter API. Future work proposed improving data collection, applying machine learning techniques, and validating tweets with other health data sources.
Problems such as inaccurate diagnoses and poor drug-adherence pose challenges to individual health and safety. These challenges are now being alleviated with big data analytics using personalized drug regimes, follow-up alerts and real-time diagnosis monitoring.
In this paper, learn how predictive analytics is helping healthcare industry with technologies such as Clinical Decision Support, Medical Text Analysis and Electronic Health Record (EHR).
Automated Abstracting - NCRA San Antonio 2015Victor Brunka
Artificial intelligence can help automate the process of completing cancer registry abstracts. Recent successes in automating casefinding from pathology and imaging reports and extracting standardized data show promise. Continued progress in natural language processing, along with consolidation of diverse health records into a common data architecture, may allow auto-population of most abstract fields with high accuracy and completeness. This would enhance quality and timeliness of cancer reporting while reducing costs. The registry's role then focuses on complex tasks, maintaining standards and oversight.
Healthcare analytics has the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases, and improve quality of life. It can improve processes, enhance patient care, and save lives by using analytics to better predict patient needs and staff accordingly. Electronic health records store a patient's comprehensive medical history digitally, allowing doctors to track changes over time with no risk of lost data or duplication. Analyzing demographic health data allows for strategic planning to identify factors that discourage treatment uptake. Analytics also helps prevent security threats, fraud, and inaccurate insurance claims while streamlining the claims process. The patient experience, overall population health, and operational costs can all be improved through healthcare analytics.
Understand what healthcare analytics is.
Identify the 5-stage Analytics Program Lifecycle (APL).
Understand how data analytics can be used in healthcare.
Check it on Experfy: https://www.experfy.com/training/courses/introduction-to-healthcare-analytics.
Lisa Annaly, Head of Provider Analytics at the Care Quality Commission, discusses lessons learned from the CQC as they have worked to monitor care quality over time.
Monitoring quality of care: making the most of dataNuffield Trust
Chris Sherlaw-Johnson, Senior Research Analyst at the Nuffield Trust, introduced the Monitoring quality of care conference and gives an overview of some of the approaches that we've been using at the Trust to identify where care quality has been improving, especially for frail and older people.
This document presents a proof of concept for using Twitter data to conduct syndromic surveillance for public health monitoring. It analyzed tweets containing the keyword "measles" between 2014-2015 and found 1,408 relevant tweets. The number of tweets mentioning measles was compared to confirmed measles cases from a national surveillance system, showing potential for Twitter data as an early warning system. However, limitations include using a single keyword and the free Twitter API. Future work proposed improving data collection, applying machine learning techniques, and validating tweets with other health data sources.
Problems such as inaccurate diagnoses and poor drug-adherence pose challenges to individual health and safety. These challenges are now being alleviated with big data analytics using personalized drug regimes, follow-up alerts and real-time diagnosis monitoring.
In this paper, learn how predictive analytics is helping healthcare industry with technologies such as Clinical Decision Support, Medical Text Analysis and Electronic Health Record (EHR).
Automated Abstracting - NCRA San Antonio 2015Victor Brunka
Artificial intelligence can help automate the process of completing cancer registry abstracts. Recent successes in automating casefinding from pathology and imaging reports and extracting standardized data show promise. Continued progress in natural language processing, along with consolidation of diverse health records into a common data architecture, may allow auto-population of most abstract fields with high accuracy and completeness. This would enhance quality and timeliness of cancer reporting while reducing costs. The registry's role then focuses on complex tasks, maintaining standards and oversight.
This document summarizes presentations from a health informatics seminar covering three main themes: data collection and analytics, patient-technology interaction, and clinical and translational science. It describes several presentations within each theme, including topics on sensor-based data collection, data analytics in healthcare, usability of patient portals, telerehabilitation, and using health informatics to provide healthcare solutions for those in transitional housing. The document concludes that health informatics is a growing field aimed at improving healthcare quality and reducing costs through the use of health information technology.
This document summarizes a presentation on using data and informatics to improve allied health services. It discusses the history of allied health and challenges with data collection. Examples are provided of projects in New Zealand that used data to enhance patient and clinician experiences, reduce hospital-acquired infections, and inform staffing needs. The presentation emphasizes standardizing data to facilitate benchmarking and applying knowledge gained from data analysis to drive improvements in allied health.
This document discusses the challenges and opportunities of healthcare analytics from UPMC's perspective. It notes that healthcare analytics is expensive and difficult due to the complexity of healthcare data and systems. However, UPMC has heavily invested in technology and leverages that investment across its organization. Healthcare analytics aims to gain new insights not previously possible from paper records alone and generate hypotheses without preconceived notions. This can help understand patient sharing patterns, regional population health issues, and how patients move between chronic disease clusters. The document warns that while big data creates new opportunities, it also risks consuming the information it produces if not approached carefully.
Evaluating new models of care: Improvement Analytics UnitNuffield Trust
Martin Caunt, Improvement Analytics Unit Project Director and NHS England and Adam Steventon, Director of Data Analytics at The Health Foundation share insights into how they have approached evaluating new models of care.
Ramani Moonesinghe, Associate National Clinical Director for Elective Care at NHS England, discusses the use of data for monitoring care quality at various levels within the system.
This document summarizes a kick-off meeting for the SAFTINet project. The meeting welcomed collaborators and outlined goals of establishing a distributed research network to conduct comparative effectiveness research using electronic health data from multiple healthcare organizations. The agenda included introductions of participating organizations, presentations on comparative effectiveness research and the technical capabilities needed, and discussions around engaging partners and getting started with the work.
This document summarizes a presentation on the state of data science and healthcare analytics. It discusses:
1) The increasing sophistication of analytics in healthcare, from basic reporting to predictive modeling.
2) New opportunities for applying data science and analytics across healthcare stakeholders like payers, providers, life sciences companies, consumers, and employers.
3) The future of data science and analytics, including new data sources, artificial intelligence applications, and "algorithmic medicine" to personalize treatment through aggregating diverse data on individual patients.
Heather Dawe: Applications of risk estimationNuffield Trust
The document discusses applications of risk estimation in healthcare. It summarizes that clinical indicators use risk adjustment models to estimate expected patient outcomes based on casemix and other factors. These expected outcomes are compared to actual observed outcomes to monitor quality and performance across providers and over time. It notes challenges in ensuring risk estimation models appropriately account for variables like innovative treatments and in identifying new indicators to monitor.
Predictive analytics for personalized healthcareJohn Cai
This document discusses how predictive analytics can help enable personalized health care through three main points:
1) Integrating diverse data sources like genomics, healthcare records, and insurance claims can provide insights for personalized care, drug development, and comparative effectiveness research.
2) Predictive models built using data from clinical trials can identify subgroups of patients most likely to respond or not respond to treatments early in the treatment course, improving outcomes.
3) Personalized comparative effectiveness research aims to determine which treatments work best for which patient subgroups and disease stages by integrating real-world data and predictive analytics into drug development and clinical decision-making.
This document discusses using statistical process control (CUSUM) charts to monitor mortality rates at the level of individual general practitioners and health authorities. It describes how CUSUM charts could potentially have detected Harold Shipman, a GP who murdered over 200 patients, by spotting outliers in the routine mortality data. The document also discusses challenges in risk adjusting outcomes to account for differences in patient characteristics and casemix between providers. Accurately adjusting for factors like age, comorbidities, and emergency status is important for fair comparisons but difficult using only administrative data.
H2O World - Machine Learning to Save Lives - Taposh Dutta RoySri Ambati
The document discusses how Kaiser Permanente is using machine learning to develop an early warning system (EWS) to predict unplanned transfers from medical/surgical wards to the intensive care unit (ICU). The EWS, called Advanced Alert Monitoring (AAM), analyzes patient data like vitals, labs, demographics and comorbidities to identify patients at risk of deterioration in the next 12 hours. When AAM exceeds a threshold, clinicians receive a pop-up alert to intervene early and potentially prevent ICU transfers. Kaiser is continuously improving AAM by refining the model and validating predictions to help save lives through integrated, technology-enabled care delivery.
Effectiveness of the current dominant approach to integrated care in the NHS:...Sarah Wilson
Jonathan Stokes of the Greater Manchester Primary Care Patient Safety Translational Research Centre presents a systematic review of case management in integrated care.
Clinicians Satisfaction Before and After Transition from a Basic to a Compreh...Allison McCoy
Healthcare organizations are transitioning from basic to comprehensive electronic health records (EHRs) to meet Meaningful Use requirements and improve patient safety. Yet, full adoption of EHRs is lagging and may be linked to clinician dissatisfaction. In depth assessment of satisfaction before, during, and after EHR transition is rarely done. Using an adapted published tool to assess adoption and satisfaction with EHRs, we surveyed clinicians at a large, non-profit academic medical center before (baseline) and 6-12 months (short-term follow-up) and 12-24 months (long-term follow-up) after transition from a basic, locally-developed to a comprehensive, commercial EHR. Satisfaction with the EHR (overall and by component) was captured at each interval. Overall satisfaction was highest at baseline (85%), lowest at short-term follow-up (66%), and increasing at long-term follow-up (79%). This trend was similar for satisfaction with EHR components designed to improve patient safety including clinical decision support, patient communication, health information exchange, and system reliability. Conversely, at baseline, short-term and long-term follow-up, perceptions of productivity, ability to provide better care with the EHR, and satisfaction with available resources, were lower at both short- and long-term follow-up compared to baseline. Persistent dissatisfaction with productivity and resources was identified. Addressing determinants of dissatisfaction may increase full adoption of EHRs. Further investigation in larger populations is warranted.
Big Data Analytics for Treatment Pathways John CaiJohn Cai
This document discusses using real-world big data analytics to understand treatment pathways. It begins by explaining the need for real-world evidence from real-world data to assess effectiveness and outcomes beyond randomized clinical trials. It then describes the volume, variety, and velocity characteristics of real-world big data from sources like claims, EMRs, surveys, and devices. Technical challenges of reconstructing complex patient journeys are discussed. Hadoop and MapReduce are presented as a potential solution by breaking the work into mappers that extract patient data and reducers that organize it into timelines. Examples are given of how this could enable cost, pathway, and outcomes analyses to better inform decision making.
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.
The document summarizes a study that evaluated the acceptability of a personally controlled health record (PCHR) system called Indivo in a community-based setting. Over 300 participants were involved in formative research activities to understand awareness, beliefs and reactions. The study found moderate awareness of privacy issues and high support for patient autonomy. Results informed guidelines on design improvements, literacy tools, and safety protocols for PCHR systems. Limitations included a lack of detail on methodology and sample selection.
Providing actionable healthcare analytics at scale: Understanding improvement...Nuffield Trust
This document discusses measurement for quality improvement. It explains that measurement in improvement aims to provide a basis for action to improve processes and outcomes, rather than just estimating parameters. Improvement measures should be simple, specific, and available in real-time. Statistical process control methods are important to separate normal variation from changes resulting from interventions. Examples are provided of run charts measuring improvements in recording BMI for mental health patients and compliance with care bundles. The document advocates making the theories behind improvement efforts more explicit.
1. The document discusses four implemented clinical decision support apps that deliver AI to clinicians in real time within their workflow in the electronic health record.
2. The apps varied in the complexity of the AI/computation used, amount of patient-specific data required, and how the decision support was delivered to clinicians.
3. Apps that maintained patient data and computation externally were able to support more complex AI, while still delivering results seamlessly within the EHR using standards like SMART on FHIR.
This document discusses how machine learning and artificial intelligence are increasingly being applied to disease management and healthcare. It outlines several key trends driving this, such as widespread EHR adoption and the availability of large healthcare datasets. The document then provides examples of how supervised, unsupervised, and reinforcement learning are being used in applications like cancer diagnosis, echocardiography analysis, and sepsis treatment optimization. It also discusses regulatory considerations around FDA approval of AI clinical decision support systems. In summary, machine learning is becoming an important tool in healthcare, but ensuring its safe, effective, and appropriate use remains an ongoing challenge.
The document summarizes presentations from a health IT seminar in North Carolina. It discusses the NC strategy for health IT which aims to improve healthcare quality and outcomes through better use of technology. It also discusses using telehealth for rehabilitation and the CCNC informatics center which uses data to help manage patient populations. Finally, it discusses NCB Prepared which focuses on using analytics for early detection of biological hazards. Key themes included using data and technology to improve patient care, population health, and public health surveillance.
Connected Health & Me - Matic Meglic - Nov 24th 2014ipposi
This document discusses how data sharing is changing healthcare by empowering patients. It outlines a shift from a traditional care model, where patients are passive recipients of care, to one where patients are engaged and empowered through access to their own health data and contextual knowledge. Key drivers of this change include affordable technology, the quantified self-movement, big data, and empowered patients. The document discusses how patient registries and personalized medicine can utilize data to better understand treatment efficacy for similar patients and provide personalized care plans. It also notes challenges around data privacy and the need for guidelines. Overall, the document advocates for empowering patients through access to their own health data while using data and technology to coordinate and improve healthcare.
This document summarizes presentations from a health informatics seminar covering three main themes: data collection and analytics, patient-technology interaction, and clinical and translational science. It describes several presentations within each theme, including topics on sensor-based data collection, data analytics in healthcare, usability of patient portals, telerehabilitation, and using health informatics to provide healthcare solutions for those in transitional housing. The document concludes that health informatics is a growing field aimed at improving healthcare quality and reducing costs through the use of health information technology.
This document summarizes a presentation on using data and informatics to improve allied health services. It discusses the history of allied health and challenges with data collection. Examples are provided of projects in New Zealand that used data to enhance patient and clinician experiences, reduce hospital-acquired infections, and inform staffing needs. The presentation emphasizes standardizing data to facilitate benchmarking and applying knowledge gained from data analysis to drive improvements in allied health.
This document discusses the challenges and opportunities of healthcare analytics from UPMC's perspective. It notes that healthcare analytics is expensive and difficult due to the complexity of healthcare data and systems. However, UPMC has heavily invested in technology and leverages that investment across its organization. Healthcare analytics aims to gain new insights not previously possible from paper records alone and generate hypotheses without preconceived notions. This can help understand patient sharing patterns, regional population health issues, and how patients move between chronic disease clusters. The document warns that while big data creates new opportunities, it also risks consuming the information it produces if not approached carefully.
Evaluating new models of care: Improvement Analytics UnitNuffield Trust
Martin Caunt, Improvement Analytics Unit Project Director and NHS England and Adam Steventon, Director of Data Analytics at The Health Foundation share insights into how they have approached evaluating new models of care.
Ramani Moonesinghe, Associate National Clinical Director for Elective Care at NHS England, discusses the use of data for monitoring care quality at various levels within the system.
This document summarizes a kick-off meeting for the SAFTINet project. The meeting welcomed collaborators and outlined goals of establishing a distributed research network to conduct comparative effectiveness research using electronic health data from multiple healthcare organizations. The agenda included introductions of participating organizations, presentations on comparative effectiveness research and the technical capabilities needed, and discussions around engaging partners and getting started with the work.
This document summarizes a presentation on the state of data science and healthcare analytics. It discusses:
1) The increasing sophistication of analytics in healthcare, from basic reporting to predictive modeling.
2) New opportunities for applying data science and analytics across healthcare stakeholders like payers, providers, life sciences companies, consumers, and employers.
3) The future of data science and analytics, including new data sources, artificial intelligence applications, and "algorithmic medicine" to personalize treatment through aggregating diverse data on individual patients.
Heather Dawe: Applications of risk estimationNuffield Trust
The document discusses applications of risk estimation in healthcare. It summarizes that clinical indicators use risk adjustment models to estimate expected patient outcomes based on casemix and other factors. These expected outcomes are compared to actual observed outcomes to monitor quality and performance across providers and over time. It notes challenges in ensuring risk estimation models appropriately account for variables like innovative treatments and in identifying new indicators to monitor.
Predictive analytics for personalized healthcareJohn Cai
This document discusses how predictive analytics can help enable personalized health care through three main points:
1) Integrating diverse data sources like genomics, healthcare records, and insurance claims can provide insights for personalized care, drug development, and comparative effectiveness research.
2) Predictive models built using data from clinical trials can identify subgroups of patients most likely to respond or not respond to treatments early in the treatment course, improving outcomes.
3) Personalized comparative effectiveness research aims to determine which treatments work best for which patient subgroups and disease stages by integrating real-world data and predictive analytics into drug development and clinical decision-making.
This document discusses using statistical process control (CUSUM) charts to monitor mortality rates at the level of individual general practitioners and health authorities. It describes how CUSUM charts could potentially have detected Harold Shipman, a GP who murdered over 200 patients, by spotting outliers in the routine mortality data. The document also discusses challenges in risk adjusting outcomes to account for differences in patient characteristics and casemix between providers. Accurately adjusting for factors like age, comorbidities, and emergency status is important for fair comparisons but difficult using only administrative data.
H2O World - Machine Learning to Save Lives - Taposh Dutta RoySri Ambati
The document discusses how Kaiser Permanente is using machine learning to develop an early warning system (EWS) to predict unplanned transfers from medical/surgical wards to the intensive care unit (ICU). The EWS, called Advanced Alert Monitoring (AAM), analyzes patient data like vitals, labs, demographics and comorbidities to identify patients at risk of deterioration in the next 12 hours. When AAM exceeds a threshold, clinicians receive a pop-up alert to intervene early and potentially prevent ICU transfers. Kaiser is continuously improving AAM by refining the model and validating predictions to help save lives through integrated, technology-enabled care delivery.
Effectiveness of the current dominant approach to integrated care in the NHS:...Sarah Wilson
Jonathan Stokes of the Greater Manchester Primary Care Patient Safety Translational Research Centre presents a systematic review of case management in integrated care.
Clinicians Satisfaction Before and After Transition from a Basic to a Compreh...Allison McCoy
Healthcare organizations are transitioning from basic to comprehensive electronic health records (EHRs) to meet Meaningful Use requirements and improve patient safety. Yet, full adoption of EHRs is lagging and may be linked to clinician dissatisfaction. In depth assessment of satisfaction before, during, and after EHR transition is rarely done. Using an adapted published tool to assess adoption and satisfaction with EHRs, we surveyed clinicians at a large, non-profit academic medical center before (baseline) and 6-12 months (short-term follow-up) and 12-24 months (long-term follow-up) after transition from a basic, locally-developed to a comprehensive, commercial EHR. Satisfaction with the EHR (overall and by component) was captured at each interval. Overall satisfaction was highest at baseline (85%), lowest at short-term follow-up (66%), and increasing at long-term follow-up (79%). This trend was similar for satisfaction with EHR components designed to improve patient safety including clinical decision support, patient communication, health information exchange, and system reliability. Conversely, at baseline, short-term and long-term follow-up, perceptions of productivity, ability to provide better care with the EHR, and satisfaction with available resources, were lower at both short- and long-term follow-up compared to baseline. Persistent dissatisfaction with productivity and resources was identified. Addressing determinants of dissatisfaction may increase full adoption of EHRs. Further investigation in larger populations is warranted.
Big Data Analytics for Treatment Pathways John CaiJohn Cai
This document discusses using real-world big data analytics to understand treatment pathways. It begins by explaining the need for real-world evidence from real-world data to assess effectiveness and outcomes beyond randomized clinical trials. It then describes the volume, variety, and velocity characteristics of real-world big data from sources like claims, EMRs, surveys, and devices. Technical challenges of reconstructing complex patient journeys are discussed. Hadoop and MapReduce are presented as a potential solution by breaking the work into mappers that extract patient data and reducers that organize it into timelines. Examples are given of how this could enable cost, pathway, and outcomes analyses to better inform decision making.
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.
The document summarizes a study that evaluated the acceptability of a personally controlled health record (PCHR) system called Indivo in a community-based setting. Over 300 participants were involved in formative research activities to understand awareness, beliefs and reactions. The study found moderate awareness of privacy issues and high support for patient autonomy. Results informed guidelines on design improvements, literacy tools, and safety protocols for PCHR systems. Limitations included a lack of detail on methodology and sample selection.
Providing actionable healthcare analytics at scale: Understanding improvement...Nuffield Trust
This document discusses measurement for quality improvement. It explains that measurement in improvement aims to provide a basis for action to improve processes and outcomes, rather than just estimating parameters. Improvement measures should be simple, specific, and available in real-time. Statistical process control methods are important to separate normal variation from changes resulting from interventions. Examples are provided of run charts measuring improvements in recording BMI for mental health patients and compliance with care bundles. The document advocates making the theories behind improvement efforts more explicit.
1. The document discusses four implemented clinical decision support apps that deliver AI to clinicians in real time within their workflow in the electronic health record.
2. The apps varied in the complexity of the AI/computation used, amount of patient-specific data required, and how the decision support was delivered to clinicians.
3. Apps that maintained patient data and computation externally were able to support more complex AI, while still delivering results seamlessly within the EHR using standards like SMART on FHIR.
This document discusses how machine learning and artificial intelligence are increasingly being applied to disease management and healthcare. It outlines several key trends driving this, such as widespread EHR adoption and the availability of large healthcare datasets. The document then provides examples of how supervised, unsupervised, and reinforcement learning are being used in applications like cancer diagnosis, echocardiography analysis, and sepsis treatment optimization. It also discusses regulatory considerations around FDA approval of AI clinical decision support systems. In summary, machine learning is becoming an important tool in healthcare, but ensuring its safe, effective, and appropriate use remains an ongoing challenge.
The document summarizes presentations from a health IT seminar in North Carolina. It discusses the NC strategy for health IT which aims to improve healthcare quality and outcomes through better use of technology. It also discusses using telehealth for rehabilitation and the CCNC informatics center which uses data to help manage patient populations. Finally, it discusses NCB Prepared which focuses on using analytics for early detection of biological hazards. Key themes included using data and technology to improve patient care, population health, and public health surveillance.
Connected Health & Me - Matic Meglic - Nov 24th 2014ipposi
This document discusses how data sharing is changing healthcare by empowering patients. It outlines a shift from a traditional care model, where patients are passive recipients of care, to one where patients are engaged and empowered through access to their own health data and contextual knowledge. Key drivers of this change include affordable technology, the quantified self-movement, big data, and empowered patients. The document discusses how patient registries and personalized medicine can utilize data to better understand treatment efficacy for similar patients and provide personalized care plans. It also notes challenges around data privacy and the need for guidelines. Overall, the document advocates for empowering patients through access to their own health data while using data and technology to coordinate and improve healthcare.
This document discusses implementing clinical decision support (CDS) in electronic health records (EHRs). It defines CDS and describes common CDS tools like alerts, order checks, and reminders. It discusses the value of CDS in improving healthcare quality and addressing medical errors. The document then covers topics like the history and definitions of CDS, approaches to modern CDS, issues around alerts, and grand challenges in the field. Hands-on exercises are provided to demonstrate CDS tools in a simulated EHR environment.
1) The role of health care data analysts is evolving as the volume of available data grows exponentially. With zettabytes of data being generated, analysts must make sense of both structured and unstructured information.
2) Data analytics can provide insights to improve patient outcomes, lower costs, and enhance the health care experience. Examples show how visualizing data helps health systems better understand utilization and identify at-risk patients.
3) As incentives shift from fee-for-service to value-based models, health systems must transform to focus on population health. Advanced analytics and predictive modeling will be crucial to achieving the goals of better care, lower costs, and improved health.
The document provides an overview of the University of California Health's data analytics platform which combines healthcare data from the six University of California medical centers. It includes details on the health data warehouse such as the total number of patients, types of data collected, and tools used. The platform aims to enable researchers across UC to conduct studies using the large collection of standardized clinical data.
Title: Closing Keynote: Winning the Battle Against Brain Attacks: Fighting Back with Telehealth
Description: The final keynote will showcase how the University of Virginia Health System is leveraging its Stroke Telemedicine and Tele-education program (STAT) to efficiently manage care both pre- and post-stroke for patients, providing improved and timely access. This session will highlight what's been successful, and how advances in mobile health are advancing the ability of this program to succeed for patients and providers.
Speaker: Andrew Southerland, MD, MSc
Objectives: Discuss how telehealth technology can be leveraged to optimize stroke management. Describe how telehealth can be used to achieve cost and quality goals
Outline how telehealth can be used to improve both patient and provider satisfaction.
Independent forces on the biomedical ecosystem is causing a convergence of care, quality measurement, and clinical research at the point of care. The presentation outlines some of the informatics implications of this convergence.
IV Congresso Internacional CBA2017
Emerging Technologies and the Quality of Care
David W. Bates, MD, MSc, Chief, Division of General Internal Medicine, Brigham and Women’s Hospital, Past President, ISQua
The document discusses the physician voice in adopting new technologies like electronic medical records (EMRs). It notes that the physician voice has both an external role advocating for patients and an inner role considering personal impacts. Successful adoption requires addressing physician concerns about privacy, workload, and local needs through collaboration between physicians and other stakeholders. It outlines models used in Vancouver Coastal Health to engage physicians through user groups and champions to provide feedback and guide implementation.
The document discusses patient engagement requirements under Meaningful Use Stage 2, Accountable Care Organizations, and the Patient-Centered Medical Home model. It outlines 7 proposed core measures for Stage 2 that focus on clinical summaries, education resources, secure messaging, and reminders. It also lists 7 patient experience measures required by ACOs and notes the 66 factors assessed by NCQA for medical homes. The document emphasizes that meaningful patient engagement requires real change by both providers and patients through improved experiences and patient involvement.
Evaluation of a Clinical Information Systemnrodrock
The document discusses electronic health records (EHRs) and clinical information systems. It defines an EHR as a digital version of a patient's paper medical record that contains the patient's medical history and treatment. EHRs allow clinicians to securely access patient data and improve care coordination. The document also examines eight components of EHRs including health information, order entry, decision support, and administrative processes. It notes that effective EHR implementation depends on involving end-users such as nurses and physicians. Proper training and education is also essential for a successful transition to EHR.
HEALTH INFORMATICS;PRINCIPLES OF HEALTH INFORMATICSKrishna Gandhi
The document discusses various topics related to health informatics including definitions of key terms like health informatics, nursing informatics, and public health informatics. It describes the need for and applications of nursing informatics in areas like nursing practice, administration, and limitations. Examples are provided of how data, information and knowledge are used in healthcare for education, hospital management, research, and data management. Emerging technologies like nanotechnology, artificial intelligence, real-time data, robotics, and virtual reality are discussed as applied examples of knowledge and information in healthcare.
A clinical decision support system (CDSS) is an interactive computer program that uses patient data to generate advice to help clinicians make decisions. A CDSS uses a dynamic knowledge base and rules derived from experts to make suggestions, which clinicians can then use along with their own expertise to determine diagnoses and treatments. CDSS systems are used at the point of care to assist clinicians before, during, and after making diagnoses. They work by taking in patient data, applying medical knowledge, and providing recommendations to aid clinical decision making.
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.
A joint presentation on Real People, Real Data at the 2016 International Forum on Quality and Safety in Healthcare in Gothenburg, Sweden. Presented by Leanne Wells of the Consumers Health Forum of Australia; Sam Vaillancourt of St. Michael’s Hospital, Toronto, Canada, and; Dr Paresh Dawda of the Australian National University.
Dave Tyas- Beyond 2010: SMART Living Paneleventwithme
The Whole System Demonstrator trial aimed to test whether new telehealth technologies could help people stay healthy at home. It involved 6000 patients across three sites including Cornwall. The trial provided patients with devices to monitor health readings like blood pressure and weight at home, which were transmitted to nurses. Initial concerns from doctors about increased workload were alleviated as the technology allowed remote monitoring and helped prevent unnecessary visits. Patients found the systems easy to use and that it increased their independence and empowerment.
This document discusses developing an effective clinical information system. It recommends understanding information needs, conceptualizing problems at the patient, service and research levels. An example system in Wales integrates data from multiple sources using common standards like SNOMED-CT. The document outlines a vision of seamless integration between systems focused on the patient rather than organizations. It emphasizes using examples to understand core informatics requirements and taking an iterative approach to development. Examples provided show how the system supports clinical decision making, research, and justifying service needs with aggregated data.
The document provides an overview of clinical analytics (CA), which involves analyzing clinical data to improve healthcare quality, safety, and efficiency. It defines CA and describes common uses like tracking quality measures. Challenges to CA include the heterogeneity of medical data and lack of data integration. The document also outlines the types of practitioners involved in CA, common tools used like data warehouses, and examples of how hospitals have leveraged CA to reduce infections, improve coding to increase revenues, and plan for public health issues. The future of CA is presented as moving from academic centers to broader healthcare and enabling personalized medicine through integrated genomic and other data.
How to successfully provide the pre-hospital medical oversight that EMS professionals want so they can improve patient outcomes while enhancing EMS agency operations with limited resources.
How to 'hack' the data world without having a computer expert on standby. Why the professionalization of paramedicine is important? When will we be professionals? How will professionalization affect the future of EMS?
The document is a study from the Paramedic Foundation analyzing the optimal configuration of advanced life support (ALS) agencies in King County, Washington. It finds that consolidating agencies could reduce costs and increase efficiency. Specifically, operating more medic units per agency reduces costs per response, transport, and capita. The optimal configuration from a financial perspective is a single countywide ALS agency, though political and operational challenges exist. Formal changes to agency boundaries should follow a transparent process involving stakeholders.
Finding The Answers That Are Right Under Your FeetNick Nudell
As an EMS executive, keeping up with the burden of requirements for contractual reasons or accountability while preparing your operation for the future has your time stretched thin. With so much of your organization geared towards collecting and reporting information to others, finding the time and responsive tools for your own internal benchmarking and performance improvement can be a challenge. Furthermore, with new national performance standards coming, combined with the pressures on your existing operation, performance improvement driven by your internal data may seem daunting. This talk looks at the potential that executives have today to harness the data in their organization and transform it into information that highlights the areas of friction in your organization. Nick Nudell will share his insights on some of the various analytical tools and methods that EMS executives can use today track their clinical, operational, and safety performance in real time today for actionable positive change in their organization.
EMS Compass Overview Call For Measures May 2015Nick Nudell
The EMS Compass Initiative opened a call for measures to be submitted during May 2015. This provides an overview of the project and how these performance measures will be designed by EMS and used by EMS providers. The measures will demonstrate the value of EMS care for a community and for patients.
This document discusses challenges with data during disasters and provides recommendations. It summarizes key data needs during disasters for the public and responders. It describes data issues during past disasters like Hurricane Katrina where 911 call centers were crippled. Lessons learned include the inability to rapidly share data across different systems and barriers. The document recommends pre-disaster solutions like real-time communication tools and situational awareness technologies. It also provides examples of post-disaster data needs like patient tracking and medical records. Recommendations are provided to plan for interoperability, test disaster scenarios, and establish data sharing agreements.
Paramedic Information Privacy Security and Assurance Alliance iCERT 2015Nick Nudell
Paramedic data systems supporting clinical and business operations are now very sophisticated. Managing these systems requires special training and credentialing for safe and secure paramedic operations. The Paramedic Information Privacy Security & Assurance Alliance (PIPSAA) introduced this subject to the Industry Council for Emergency Response Technologies (iCERT) 2015 forum on Cybersecurity.
Electronic Patient Tracking Intro For Healthcare 2005Nick Nudell
Tracking of patients is important. Here's a presentation describing the first application of electronic technologies for patient tracking - that I authored as an employee of the City and County of San Francisco in 2004.
Through the EMS Compass initiative, the EMS community will develop tools that can be used to measure EMS system performance and the quality of patient care. This will lead to unprecedented capability for local EMS agencies, systems, regions and states to assess conditions and embark on widespread improvement.
The heart is electrically controlled and pumps blood in a coordinated sequence of contraction and relaxation. During contraction, the inside of the heart contracts inward and longitudinally, then the outside twists. Relaxation occurs in the mostly opposite sequence, with the outside of the heart untwisting first before the inside relaxes from the basal to apical regions. Electrically, depolarization occurs from the inside out while repolarization follows from the outside in, coordinating the mechanical sequences of the heart.
This document discusses pandemic preparedness for rural emergency medical services (EMS) in Connecticut. It outlines the history of past influenza pandemics and their impacts. A key concern is the potential for an H5N1 avian influenza virus to cause a severe human pandemic. The document reviews pandemic planning requirements for health systems, including adequate staffing, protective equipment, pharmaceutical supplies, and dedicated pandemic response facilities. It summarizes Connecticut's influenza pandemic plan and estimates the potential scale of illness and deaths from a moderate or severe pandemic within the state. The document emphasizes the need for EMS and other rural providers to be actively involved in pandemic planning.
Enhancing Hip and Knee Arthroplasty Precision with Preoperative CT and MRI Im...Pristyn Care Reviews
Precision becomes a byword, most especially in such procedures as hip and knee arthroplasty. The success of these surgeries is not just dependent on the skill and experience of the surgeons but is extremely dependent on preoperative planning. Recognizing this important need, Pristyn Care commits itself to the integration of advanced imaging technologies like CT (Computed Tomography) and MRI (Magnetic Resonance Imaging) into the surgical planning process.
Digital Health in India_Health Informatics Trained Manpower _DrDevTaneja_15.0...DrDevTaneja1
Digital India will need a big trained army of Health Informatics educated & trained manpower in India.
Presently, generalist IT manpower does most of the work in the healthcare industry in India. Academic Health Informatics education is not readily available at school & health university level or IT education institutions in India.
We look into the evolution of health informatics and its applications in the healthcare industry.
HIMMS TIGER resources are available to assist Health Informatics education.
Indian Health universities, IT Education institutions, and the healthcare industry must proactively collaborate to start health informatics courses on a big scale. An advocacy push from various stakeholders is also needed for this goal.
Health informatics has huge employment potential and provides a big business opportunity for the healthcare industry. A big pool of trained health informatics manpower can lead to product & service innovations on a global scale in India.
India Medical Devices Market: Size, Share, and In-Depth Competitive Analysis ...Kumar Satyam
According to TechSci Research report, “India Medical Devices Market Industry Size, Share, Trends, Competition, Opportunity and Forecast, 2019-2029,” the India Medical Devices Market was valued at USD 15.35 billion in 2023 and is anticipated to witness impressive growth in the forecast period, with a Compound Annual Growth Rate (CAGR) of 5.35% through 2029. This growth is driven by various factors, including strategic collaborations and partnerships among leading companies, a growing population, and the increasing demand for advanced healthcare solutions.
Recent Trends
Strategic Collaborations and Partnerships
One of the most significant trends driving the India Medical Devices Market is the increasing number of collaborations and partnerships among leading companies. These alliances aim to merge the expertise of individual companies to strengthen their market position and enhance their product offerings. For instance, partnerships between local manufacturers and international companies bring advanced technologies and manufacturing techniques to the Indian market, fostering innovation and improving product quality.
Browse over XX market data Figures and spread through XX Pages and an in-depth TOC on " India Medical Devices Market.” - https://www.techsciresearch.com/report/india-medical-devices-market/8161.html
Health Tech Market Intelligence Prelim Questions -Gokul Rangarajan
The Ultimate Guide to Setting up Market Research in Health Tech part -1
How to effectively start market research in the health tech industry by defining objectives, crafting problem statements, selecting methods, identifying data collection sources, and setting clear timelines. This guide covers all the preliminary steps needed to lay a strong foundation for your research.
This lays foundation of scoping research project what are the
Before embarking on a research project, especially one aimed at scoping and defining parameters like the one described for health tech IT, several crucial considerations should be addressed. Here’s a comprehensive guide covering key aspects to ensure a well-structured and successful research initiative:
1. Define Research Objectives and Scope
Clear Objectives: Define specific goals such as understanding market needs, identifying new opportunities, assessing risks, or refining pricing strategies.
Scope Definition: Clearly outline the boundaries of the research in terms of geographical focus, target demographics (e.g., age, socio-economic status), and industry sectors (e.g., healthcare IT).
3. Review Existing Literature and Resources
Literature Review: Conduct a thorough review of existing research, market reports, and relevant literature to build foundational knowledge.
Gap Analysis: Identify gaps in existing knowledge or areas where further exploration is needed.
4. Select Research Methodology and Tools
Methodological Approach: Choose appropriate research methods such as surveys, interviews, focus groups, or data analytics.
Tools and Resources: Select tools like Google Forms for surveys, analytics platforms (e.g., SimilarWeb, Statista), and expert consultations.
5. Ethical Considerations and Compliance
Ethical Approval: Ensure compliance with ethical guidelines for research involving human subjects.
Data Privacy: Implement measures to protect participant confidentiality and adhere to data protection regulations (e.g., GDPR, HIPAA).
6. Budget and Resource Allocation
Resource Planning: Allocate resources including time, budget, and personnel required for each phase of the research.
Contingency Planning: Anticipate and plan for unforeseen challenges or adjustments to the research plan.
7. Develop Research Instruments
Survey Design: Create well-structured surveys using tools like Google Forms to gather quantitative data.
Interview and Focus Group Guides: Prepare detailed scripts and discussion points for qualitative data collection.
8. Sampling Strategy
Sampling Design: Define the sampling frame, size, and method (e.g., random sampling, stratified sampling) to ensure representation of target demographics.
Participant Recruitment: Plan recruitment strategies to reach and engage the intended participant groups effectively.
9. Data Collection and Analysis Plan
Data Collection: Implement methods for data gathering, ensuring consistency and validity.
Analysis Techniques: Decide on analytical approaches (e.g., statistical
Fit to Fly PCR Covid Testing at our Clinic Near YouNX Healthcare
A Fit-to-Fly PCR Test is a crucial service for travelers needing to meet the entry requirements of various countries or airlines. This test involves a polymerase chain reaction (PCR) test for COVID-19, which is considered the gold standard for detecting active infections. At our travel clinic in Leeds, we offer fast and reliable Fit to Fly PCR testing, providing you with an official certificate verifying your negative COVID-19 status. Our process is designed for convenience and accuracy, with quick turnaround times to ensure you receive your results and certificate in time for your departure. Trust our professional and experienced medical team to help you travel safely and compliantly, giving you peace of mind for your journey.www.nxhealthcare.co.uk
At Malayali Kerala Spa Ajman, Full Service includes individualized care for every client. We specifically design each massage session for the individual needs of the client. Our therapists are always willing to adjust the treatments based on the client's instruction and feedback. This guarantees that every client receives the treatment they expect.
By offering a variety of massage services, our Ajman Spa Massage Center can tackle physical, mental, and emotional illnesses. In addition, efficient identification of specific health conditions and designing treatment plans accordingly can significantly enhance the quality of massaging.
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The story of Dr. Ranjit Jagtap's daughters is more than a tale of inherited responsibility; it's a narrative of passion, innovation, and unwavering commitment to a cause greater than oneself. In Poulami and Aditi Jagtap, we see the beautiful continuum of a father's dream and the limitless potential of compassion-driven healthcare.
Ensure the highest quality care for your patients with Cardiac Registry Support's cancer registry services. We support accreditation efforts and quality improvement initiatives, allowing you to benchmark performance and demonstrate adherence to best practices. Confidence starts with data. Partner with Cardiac Registry Support. For more details visit https://cardiacregistrysupport.com/cancer-registry-services/
Mental Health and well-being Presentation. Exploring innovative approaches and strategies for enhancing mental well-being. Discover cutting-edge research, effective strategies, and practical methods for fostering mental well-being.
Hypertension and it's role of physiotherapy in it.Vishal kr Thakur
This particular slides consist of- what is hypertension,what are it's causes and it's effect on body, risk factors, symptoms,complications, diagnosis and role of physiotherapy in it.
This slide is very helpful for physiotherapy students and also for other medical and healthcare students.
Here is summary of hypertension -
Hypertension, also known as high blood pressure, is a serious medical condition that occurs when blood pressure in the body's arteries is consistently too high. Blood pressure is the force of blood pushing against the walls of blood vessels as the heart pumps it. Hypertension can increase the risk of heart disease, brain disease, kidney disease, and premature death.
India Home Healthcare Market: Driving Forces and Disruptive Trends [2029]Kumar Satyam
According to the TechSci Research report titled "India Home Healthcare Market - By Region, Competition, Forecast and Opportunities, 2029," the India home healthcare market is anticipated to grow at an impressive rate during the forecast period. This growth can be attributed to several factors, including the rising demand for managing health issues such as chronic diseases, post-operative care, elderly care, palliative care, and mental health. The growing preference for personalized healthcare among people is also a significant driver. Additionally, rapid advancements in science and technology, increasing healthcare costs, changes in food laws affecting label and product claims, a burgeoning aging population, and a rising interest in attaining wellness through diet are expected to escalate the growth of the India home healthcare market in the coming years.
Browse over XX market data Figures spread through 70 Pages and an in-depth TOC on "India Home Healthcare Market”
https://www.techsciresearch.com/report/india-home-healthcare-market/15508.html
CHAPTER 1 SEMESTER V COMMUNICATION TECHNIQUES FOR CHILDREN.pdfSachin Sharma
Here are some key objectives of communication with children:
Build Trust and Security:
Establish a safe and supportive environment where children feel comfortable expressing themselves.
Encourage Expression:
Enable children to articulate their thoughts, feelings, and experiences.
Promote Emotional Understanding:
Help children identify and understand their own emotions and the emotions of others.
Enhance Listening Skills:
Develop children’s ability to listen attentively and respond appropriately.
Foster Positive Relationships:
Strengthen the bond between children and caregivers, peers, and other adults.
Support Learning and Development:
Aid cognitive and language development through engaging and meaningful conversations.
Teach Social Skills:
Encourage polite, respectful, and empathetic interactions with others.
Resolve Conflicts:
Provide tools and guidance for children to handle disagreements constructively.
Encourage Independence:
Support children in making decisions and solving problems on their own.
Provide Reassurance and Comfort:
Offer comfort and understanding during times of distress or uncertainty.
Reinforce Positive Behavior:
Acknowledge and encourage positive actions and behaviors.
Guide and Educate:
Offer clear instructions and explanations to help children understand expectations and learn new concepts.
By focusing on these objectives, communication with children can be both effective and nurturing, supporting their overall growth and well-being.
The Ultimate Guide in Setting Up Market Research System in Health-TechGokul Rangarajan
How to effectively start market research in the health tech industry by defining objectives, crafting problem statements, selecting methods, identifying data collection sources, and setting clear timelines. This guide covers all the preliminary steps needed to lay a strong foundation for your research.
"Market Research it too text-booky, I am in the market for a decade, I am living research book" this is what the founder I met on the event claimed, few of my colleagues rolled their eyes. Its true that one cannot over look the real life experience, but one cannot out beat structured gold mine of market research.
Many 0 to 1 startup founders often overlook market research, but this critical step can make or break a venture, especially in health tech.
But Why do they skip it?
Limited resources—time, money, and manpower—are common culprits.
"In fact, a survey by CB Insights found that 42% of startups fail due to no market need, which is like building a spaceship to Mars only to realise you forgot the fuel."
Sudharsan Srinivasan
Operational Partner Pitchworks VC Studio
Overconfidence in their product’s success leads founders to assume it will naturally find its market, especially in health tech where patient needs, entire system issues and regulatory requirements are as complex as trying to perform brain surgery with a butter knife. Additionally, the pressure to launch quickly and the belief in their own intuition further contribute to this oversight. Yet, thorough market research in health tech could be the key to transforming a startup's vision into a life-saving reality, instead of a medical mishap waiting to happen.
Example of Market Research working
Innovaccer, founded by Abhinav Shashank in 2014, focuses on improving healthcare delivery through data-driven insights and interoperability solutions. Before launching their platform, Innovaccer conducted extensive market research to understand the challenges faced by healthcare organizations and the potential for innovation in healthcare IT.
Identifying Pain Points: Innovaccer surveyed healthcare providers to understand their difficulties with data integration, care coordination, and patient engagement. They found widespread frustration with siloed systems and inefficient workflows.
Competitive Analysis: Analyzed competitors offering similar solutions in healthcare analytics and interoperability. Identified gaps in comprehensive data aggregation, real-time analytics, and actionable insights.
Regulatory Compliance: Ensured their platform complied with HIPAA and other healthcare data privacy regulations. This compliance was crucial to gaining trust from healthcare providers wary of data security issues.
Customer Validation: Conducted pilot programs with several healthcare organizations to validate the platform's effectiveness in improving care outcomes and operational efficiency. Gathered feedback to refine features and user interface.
nursing management of patient with Empyema pptblessyjannu21
prepared by Prof. BLESSY THOMAS, SPN
Empyema is a disease of respiratory system It is defines as the accumulation of thick, purulent fluid within the pleural space, often with fibrin development.
Empyema is also called pyothorax or purulent pleuritis.
It’s a condition in which pus gathers in the area between the lungs and the inner surface of the chest wall. This area is known as the pleural space.
Pus is a fluid that’s filled with immune cells, dead cells, and bacteria.
Pus in the pleural space can’t be coughed out. Instead, it needs to be drained by a needle or surgery.
Empyema usually develops after pneumonia, which is an infection of the lung tissue. it is mainly caused due in infectious micro-organisms. It can be treated with medications and other measures.
4. • Required “protocols” to protect physicians providing
“delegated practice” to subordinates:
– Designed for lowest common denominator
– Some states rely heavily and some do not
• NJ dual paramedic required with MD contact ASAP
• CA base contact in many situations including MICN based orders
• TX nears independent practice, similar to many other countries such as
• AUS peer based professional accountability
Paramedicine of The
Past
5. • Call Taker (211, 911, etc) reviews patient history for appropriate
dispatch
• Responding providers briefed on recent past history PTA
• Paramedic looks up lab values and radiology reports in real-time at
patient side
• Prescription info automatically incorporated to ePCR
• Remote monitoring devices send data that allows “system” to alert
for unusual trends or major deviations
• Physician consult online eliminates need for transport with Rx
transmitted for delivery – contemporary medical control
• Case management team notification for automated scheduling of
patient visit
Paramedicine of The
Future
6. …and how HIE will change EMS (my
philosophical predictions)
• 1973-2014: Deductive-Nomothetic Reasoning
• 2015+: Inductive-Idiographic Reasoning
Paramedic Logic Models
7. • Deductive:
– Are observed on a relatively large sample and have a more general
outlook
– Protocol based approach a.k.a. stereotyping or generalizing
– Allopathic medicine is based on a deductive-nomothetic method
– Deductive reasoning: if something is true of a class of things in
general, it is also true for all members of that class
– Top-down approach
– "Nomothetic Fallacy” is the belief that naming a problem effectively
solves it
• Example: Normal resting heart rate = 60-100
• The way paramedicine was originally designed
Nomothetic Reasoning
Source: Wikipedia
8. • Inductive (case based):
– Study or discovery of particular scientific facts and processes, as
distinct from general laws
– Its all about trying to understand the individual case/condition
– Patient condition based approach a.k.a. diagnostic formulation
– Homeopathic medicine is based on an inductive-idiographic method
– Bottom-up approach
• Example: Normal resting heart rate for Nick the ultrarunner is
52
• The way Health Exchange will change paramedicine for the
better
Idiographic Reasoning
Source: Wikipedia
10. • Patients will be able to access and update their health record
anywhere anytime
• All care providers will have appropriate access
• Ultimate continuity of care also requires accountability by all
providers
– Opens up care silos with cross visibility (ex.Texas Presby Ebola issue)
• Allows for patient specific research & outcome studies for
optimal advice
– Better than Google Searching for advice with hundreds of millions of
potentially specific cause>effect (case based) examples
For The Patient
11. • Can provide the specific care needed in real-time (holistically)
• Cross Care Coordination (CCC)
• Improves patient experience for complete experience
opportunity at each encounter
• Optimal care pathways leading to best outcomes reducing
readmissions (its all about the patient)
– Readmission avoidance maximized
• Cost reduction
– Better outcomes in general – maximal efficiency of the process
– Fewer test repeats - repeat testing is a major risk factor for incidental
detection and overdiagnosis
For The System
16. Previvors
• Prophylactic mastectomy
= 90-95% reduction in breast cancer
• Prophylactic salpingo-oophorectomy
= 90% reduction in ovarian and 50%
in breast cancer
18. Every 60 Seconds:
• 204+ million email messages
• 2+ million Google search queries
• 48 hours of new YouTube videos
• 684,000 bits of content shared on Facebook
• 100,000+ tweets
• $272,000 spent on e-commerce
19. World Digital Data
If it was on paper, the stack of paper would be:
• 2007: 280 exabytes = 2.8B miles
• 2011: 1.8 zettabytes = 17.7B miles
• 2012: 2.8 zettabytes = 27.5B miles
• 2020: 40 zettabytes = 393B miles
Zettabyte =1 thousand Exabytes and that is 1 Million gigabytes.
1 gigabyte = 158,000 pages of text.
(http://www.worldcadaccess.com/blog/2004/12/1gb_1_truckload.html)
Moon is 238k miles, Pluto is 3B miles. Space shuttle could get there in 15.1 years.
http://www.webopedia.com/quick_ref/just-how-much-data-is-out-there.html
22. Health Care Data?
• 2011: 150 exabytes (150 billion gigabytes)
=1.5M miles
– Stack of paper to Uranus
– Space shuttle takes 7.3 years to get there!
• 2014: 420 million wearable, wireless health
monitors
34. P.I.M.P.
Every EMS agency needs a PIMP!
Paramedic Information - Management Practitioner
• Paramedic Data Analyst (PDA)
• Paramedic Data Forensics (PDF)
• Paramedic Data Governance (PDG)
• Paramedic Data Integration (PDI)
• Paramedic Data Manager (PDM)
• Paramedic Data Protection (PDP)
• Paramedic Decision Support (PDS)
40. • Calls for each of us to be in charge of our health
• To get the care we need (not less and not more) in timely,
effective, and personal ways consistent with our values
• Shared decision making by consumers
• Training health care professionals in supporting active
patients
• Anticipating health and long-term care needs for individuals
• Adopting the Institute of Medicine's (IOM) simple rules for
health care
• Making the patient perspective a priority in policy and
planning.
Patient Centered Care
41. Clinical Decision
Support
• Increases quality of care
• Enhanced health outcomes
• Avoidance of errors and adverse events
• Improved efficiency, cost-benefit, and provider
and patient satisfaction
Quadruple Aim?
45. To Do This
Future Proof Your Organization and Career by
Preparing for Paramedic Data Overload
• Develop data oriented “clinical intelligence analysts” that understand the data and
how all the pieces can be used together to see the larger picture.
• Paramedics of the future on scene or remotely with advanced communication
technologies.
• Redesign paramedic education to integrate data throughout. Data from all devices
& systems, clinical research, genomic mapping, and risk calculators.
• Education focused on idiographic (case based) reasoning for making better
decisions rather than memorizing flowcharts.