Application of business analytics in healthcare
•Download as PPTX, PDF•
0 likes•56 views
With Business Analytics doing well in other industries, it is important to explore into Healthcare as well
Report
Share
Report
Share
Recommended
Patient record system
The document discusses patient record systems, including their definition, principles, value, purpose, importance, and classifications. It covers paper-based and electronic documentation systems. Paper-based systems can use source-oriented, problem-oriented, or graphic methods. Electronic systems include electronic medical records, electronic health records, and computerized patient records. The objectives of patient record systems are to review patient care, provide an archival record, support research, and facilitate administration and auditing. Both paper-based and electronic systems aim to properly document a patient's condition and care over time.
Patient record system. shilpa Jose
This document discusses patient record systems, including their definition, principles, value, and importance for individuals, doctors, nurses, and authorities. It describes different types of record systems such as dedicated, paper-based, hybrid, and electronic. Electronic patient record systems are defined as digital collections of health information that can be shared across settings. The advantages include faster access and sharing of information to improve patient care, quality, and safety. Issues around access, data protection, security, and privacy are discussed.
Medical records & health information technician course
Medical records & health information technician courseDelhi Paramedical & Management Institute In Agra
Medical Records and Health Information Technicians are also referred to as Medical Records Clerks, Health Information Clerks, Medical Records Technicians, File Clerks, Medical Records Coordinators, and Medical Records Analysts in the healthcare industry.Health information system
A hospital information system is a computer system that manages all clinical, financial, and administrative information to allow healthcare providers to perform their jobs effectively. Nursing information systems are designed to help nurses provide better patient care by performing functions like accurate patient charting and improving clinical data integration. Clinical information systems collect, integrate, and distribute information to the appropriate areas and allow nurses to assess patients and prepare care plans. When healthcare providers have access to complete patient information, patients receive better medical care with improved treatment outcomes and a more efficient evaluation of care goals, while preventing errors like medication mistakes and enhancing communication.
Analytics in healthcare
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.
Structure of medical data
The document discusses several key issues regarding medical data:
1) Medicine lacks standardized vocabulary and terminology, creating problems for data retrieval and analysis across different observers and analysts.
2) Coding systems are used for health reporting but have limitations in clinical settings. Researchers developed the Unified Medical Language System (UMLS) to connect various vocabularies.
3) Physicians use a hypothetico-deductive approach in data collection and interpretation, generating hypotheses to guide sequential data collection and narrowing differential diagnoses.
Role of nursing informatics in hospital information system
This document discusses the role of nursing informatics in hospital information systems. It defines nursing informatics as combining computer science, information science, and nursing science to manage and communicate nursing data, information, and knowledge. The impact of nursing informatics on hospital information systems is that it facilitates integrating data, information, and knowledge to support patients, nurses, and other providers in clinical decision making. Progress in information technology has greatly increased the volume of data available to support high quality healthcare delivery. Nursing informatics plays an important role in developing systematic and well-managed health information systems.
Electronic health records
This document discusses electronic health records (EHRs) and their components and benefits. It describes how EHRs contain comprehensive patient health information that can be shared electronically. Key parts of an EHR include clinical decision support systems, computerized physician order entry systems, and health information exchange capabilities. The implementation of EHRs can improve patient safety, enhance clinical outcomes, and reduce healthcare costs through increased efficiency and avoidance of errors. However, barriers to adoption include financial costs, workflow changes, and privacy/security concerns.
Recommended
Patient record system
The document discusses patient record systems, including their definition, principles, value, purpose, importance, and classifications. It covers paper-based and electronic documentation systems. Paper-based systems can use source-oriented, problem-oriented, or graphic methods. Electronic systems include electronic medical records, electronic health records, and computerized patient records. The objectives of patient record systems are to review patient care, provide an archival record, support research, and facilitate administration and auditing. Both paper-based and electronic systems aim to properly document a patient's condition and care over time.
Patient record system. shilpa Jose
This document discusses patient record systems, including their definition, principles, value, and importance for individuals, doctors, nurses, and authorities. It describes different types of record systems such as dedicated, paper-based, hybrid, and electronic. Electronic patient record systems are defined as digital collections of health information that can be shared across settings. The advantages include faster access and sharing of information to improve patient care, quality, and safety. Issues around access, data protection, security, and privacy are discussed.
Medical records & health information technician course
Medical records & health information technician courseDelhi Paramedical & Management Institute In Agra
Medical Records and Health Information Technicians are also referred to as Medical Records Clerks, Health Information Clerks, Medical Records Technicians, File Clerks, Medical Records Coordinators, and Medical Records Analysts in the healthcare industry.Health information system
A hospital information system is a computer system that manages all clinical, financial, and administrative information to allow healthcare providers to perform their jobs effectively. Nursing information systems are designed to help nurses provide better patient care by performing functions like accurate patient charting and improving clinical data integration. Clinical information systems collect, integrate, and distribute information to the appropriate areas and allow nurses to assess patients and prepare care plans. When healthcare providers have access to complete patient information, patients receive better medical care with improved treatment outcomes and a more efficient evaluation of care goals, while preventing errors like medication mistakes and enhancing communication.
Analytics in healthcare
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.
Structure of medical data
The document discusses several key issues regarding medical data:
1) Medicine lacks standardized vocabulary and terminology, creating problems for data retrieval and analysis across different observers and analysts.
2) Coding systems are used for health reporting but have limitations in clinical settings. Researchers developed the Unified Medical Language System (UMLS) to connect various vocabularies.
3) Physicians use a hypothetico-deductive approach in data collection and interpretation, generating hypotheses to guide sequential data collection and narrowing differential diagnoses.
Role of nursing informatics in hospital information system
This document discusses the role of nursing informatics in hospital information systems. It defines nursing informatics as combining computer science, information science, and nursing science to manage and communicate nursing data, information, and knowledge. The impact of nursing informatics on hospital information systems is that it facilitates integrating data, information, and knowledge to support patients, nurses, and other providers in clinical decision making. Progress in information technology has greatly increased the volume of data available to support high quality healthcare delivery. Nursing informatics plays an important role in developing systematic and well-managed health information systems.
Electronic health records
This document discusses electronic health records (EHRs) and their components and benefits. It describes how EHRs contain comprehensive patient health information that can be shared electronically. Key parts of an EHR include clinical decision support systems, computerized physician order entry systems, and health information exchange capabilities. The implementation of EHRs can improve patient safety, enhance clinical outcomes, and reduce healthcare costs through increased efficiency and avoidance of errors. However, barriers to adoption include financial costs, workflow changes, and privacy/security concerns.
When Telemedicine Does Deliver!: IT-Strategist, County Council of Uppsala
When Telemedicine Does Deliver!: IT-Strategist, County Council of UppsalaPlan de Calidad para el SNS
When Telemedicine Does Deliver!: IT-Strategist, County Council of Uppsala. Eklund B. eHealth week 2010 (Barcelona: CCIB Convention Centre; 2010)Checking in on Healthcare Data Analytics
Data science and the use of big data in healthcare delivery could revolutionize the field by decreasing costs and vastly improving efficiency and outcomes. There is an abundance of healthcare data in Canada, but it is mostly siloed and difficult to access due to privacy and security challenges.
Healthcare analytics
This document outlines an agenda and case studies for a healthcare analytics bootcamp. The bootcamp will use healthcare data to develop machine learning solutions to predict heart disease and identify high-risk patients. Case Study 1 will involve exploratory data analysis of tuberculosis data to analyze global trends, hotspots, and mortality rates. Case Study 2 will use a heart disease screening dataset and logistic regression to build a model to predict heart disease risk and develop treatment plans for high-risk patients. The document discusses the types of structured and unstructured healthcare data, sources of data, and applications of machine learning in healthcare analytics.
Clinical data munging
Clinical data munging involves manipulating raw clinical data to achieve an acceptable final form while respecting principles of clinical research ethics. It is an important process as analysts spend up to 85% of their time on data cleaning and preparation. The document outlines various phases in clinical data munging including screening for issues like missing or erroneous data, diagnosing the true nature of problems, and treating issues through actions like correcting, deleting, or leaving data unchanged. It also discusses specific challenges like dealing with missing values, extreme scores, erroneous dates and other errors, and recommends tools like Stata, R, and SAS that are well suited for clinical data munging.
Central monitoring system in healthcare
The document discusses the features and advantages of a central patient monitoring system. It allows nurses to monitor multiple patients from a central location, monitor vital signs, receive alerts, integrate with other systems like EMRs. It provides clinical decision support tools, helps manage alarms to reduce alarm fatigue, and supports a continuous patient record.
Electronic health records
This document discusses the benefits and challenges of electronic health records (EHRs) and their role in public health informatics. It outlines how EHRs can improve patient care by providing more legible, shareable records compared to paper charts. EHRs also enable clinical decision support, alerts, and reminders to help practitioners. On a larger scale, EHR data in clinical data repositories and registries can help public health by tracking diseases, exposures, and procedures. However, EHR adoption faces challenges such as costs, technical issues, security concerns, and lack of standardized data exchange. Overall, the document argues that EHRs have the potential to dramatically change clinical practice and safeguard populations through improved teamwork and surveillance
Big Data Analytics for Treatment Pathways John 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.
Health Information System (HIS) at Landspitali University Hospital
Landspítali University Hospital is Iceland's largest hospital, located in Reykjavik. It has 4,600 employees and serves as the main referral center for all of Iceland. The hospital uses a variety of electronic health record (EHR) systems, with the main EHR called Saga. Landspítali's EHR strategy is to implement a new comprehensive EHR system to support all clinical activity, with the goal of the new system being in use within 7-10 years. The project is expected to cost 6-8 billion Icelandic krónur and take 4-7 years to complete.
Health information system
A health information system (HIS) refers to a system designed to manage healthcare data, including a patient's electronic medical record, a hospital's operations, and supporting healthcare policy decisions. [HIS] has five core components: hardware, software, telecommunications, databases, and human resources/procedures. Good information management is crucial at all levels of healthcare from local to national as it provides data to policymakers, managers, and healthcare providers. A HIS aims to adequately enable information processing for patient care, administration, research, and education while considering economic and legal factors. It should provide the right information, knowledge, and data to the right people at the right time and place in the right format to support decision making and
Patient Registries
What is a patient registry?
In it’s simplest form, a registry is a list of all patients in a physician’s practice who share some characteristic, such as gender, age group, or disease state. By tracking like characteristics, physicians can better organize patient’s care.
Computer science for health
This document discusses the field of medical informatics, which deals with optimizing the storage, retrieval, and management of biomedical information. It defines medical informatics and related fields like medical decision making, computing, and decision support. It outlines key areas that medical informatics impacts like education, clinical decision support, and imaging. It also examines the role of informatics in clinical settings and clinical trials.
Major health care information systems (emr, ehr, phr, lhr)
This document provides an overview of major health care information systems including electronic medical records (EMR), electronic health records (EHR), personal health records (PHR), and legal health records (LHR). It discusses the definitions, components, benefits, and challenges of each system. The document emphasizes that health care information systems can improve patient care, administrative functions, and overall health care operations if implemented successfully. However, significant financial investment, user training, and overcoming resistance to change are necessary for full adoption and utilization of these systems.
Introduction to 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.
EHR Presentation-Jacksonville University
This document discusses the benefits of electronic health records (EHRs) and how to build consensus around their implementation in healthcare organizations. It outlines that EHRs provide complete patient information across providers, improve care coordination and clinical decision-making. The document also notes that EHRs can increase safety through features like computerized physician order entry and electronic medication administration. Finally, it emphasizes that proper training and support are essential to a successful EHR transition.
Building a consensus for the electronic health record
This document discusses building consensus for electronic health records (EHRs). It begins by defining EHRs and distinguishing them from electronic medical records (EMRs). The document outlines the benefits of EHRs, such as reducing medical errors, improving patient outcomes, and empowering patients. It also discusses meaningful use standards and key aspects of EHR implementation like computerized physician order entry. Overall, the document emphasizes that successful EHR adoption requires thorough preparation, customized training, and comprehensive security planning.
Electronic Health Record (EHR)
This presentation talks about the context of developing the Electronic Health records for India. the guidelines as mentioned in the GOI site is described vividly with examples, for better understanding.
N.B: Please download the ppt first, for the animations to work better.
Disease Management and Disease Registries
This document discusses disease management and disease registries. It defines disease management as a systematic approach to identify at-risk patients and provide care programs while measuring outcomes. Disease registries track chronic disease patients and help disease management programs provide reminders and appointments. The document compares manual, automatic, and integrated registry formats and provides examples like Chronic Disease Electronic Management Systems and Population Health Navigator. Overall, the document outlines how disease management relies on technology like registries and describes challenges in integrating registries into electronic health records.
Cis group project
A Clinical Information System (CIS) is a computer-based system that collects, stores, and provides access to clinical information for healthcare delivery. A CIS includes features like electronic medical records, clinical data repositories, order entry, and decision support. Key stakeholders in selecting and implementing a CIS include clinical staff, administrative staff, and information technology staff. Implementing a CIS requires significant costs for equipment, IT support, training, and maintenance. Ongoing education is also needed to ensure staff can properly use and adapt to changes in the system.
Terry cannestra resume 2016.07.30
Terry Cannestra is a registered nurse in Wisconsin with over 35 years of clinical experience. She has held roles as a clinical research nurse, data specialist, case coordinator, and bedside nurse. Her skills include research coordination, project management, data entry, clinical skills such as phlebotomy and IV placement, and strong computer and communication abilities. She is certified as a clinical research coordinator and in basic life support.
Business Analytics in healthcare industry.pptx
Hey there!
Exciting news – we're diving into the fascinating world of Business Analytics in the Healthcare sector, and I've just uploaded a killer PowerPoint presentation on SlideShare that you won't want to miss!
🏥 Title: Unveiling the Power of Business Analytics in Healthcare
🚀 Description:
Hey, fellow data enthusiasts! 👋 Get ready to embark on a journey through the dynamic realm where business analytics meets healthcare. Our latest presentation explores the impactful synergy between data-driven insights and the healthcare sector's ever-evolving landscape.
👉 Key Highlights:
Uncovering the role of analytics in optimizing healthcare operations.
Real-world examples showcasing improved patient outcomes through data analysis.
Navigating the challenges and opportunities in healthcare analytics.
Future trends that promise to reshape the healthcare analytics landscape.
🌐 SlideShare Link: Business Analytics in Healthcare
👀 Why You Should Check it Out:
Whether you're a healthcare professional, data enthusiast, or just someone intrigued by the magic that happens when numbers meet healthcare, this presentation is tailor-made for you! Gain insights, spark discussions, and stay ahead of the curve in understanding how analytics is revolutionizing the healthcare game.
Ready to elevate your understanding of business analytics in healthcare? Click the link above and let the learning begin! 🚀
Feel free to share with your network and dive into the discussion. Let's amplify the conversation around data-driven healthcare together!
Cheers !!!
Health Informatics
Nursing informatics combines nursing science, computer science, and information science to manage and communicate data, helping to develop more efficient healthcare processes and provide high-quality patient care. It requires the use of information technology to collect evidence-based research, develop skills to use electronic medical records, create health policies through data collection, and facilitate interdisciplinary communication using technologies like telehealth. Effectively using data can improve patient care through more accurate diagnosis, engagement, and predictive analytics, as well as administrative functions like revenue management, appointments, and staffing.
83cSZGd1SggbKECL708.pptx
The document discusses health management information systems (HMIS) and nursing management information systems (NMIS). It defines HMIS as a system that collects, uses, analyzes, and transmits health information to support health services, training, and research. It outlines the objectives, characteristics, domains/fields, information processes, sources, and problems/constraints of HMIS in India. It also discusses subsystems of HMIS, challenges it faces, and its benefits. For NMIS, it discusses its applications in workload measurement, staffing, personnel management, and fiscal resource management as well as its advantages for nursing administration, practice, research, and education.
More Related Content
What's hot
When Telemedicine Does Deliver!: IT-Strategist, County Council of Uppsala
When Telemedicine Does Deliver!: IT-Strategist, County Council of UppsalaPlan de Calidad para el SNS
When Telemedicine Does Deliver!: IT-Strategist, County Council of Uppsala. Eklund B. eHealth week 2010 (Barcelona: CCIB Convention Centre; 2010)Checking in on Healthcare Data Analytics
Data science and the use of big data in healthcare delivery could revolutionize the field by decreasing costs and vastly improving efficiency and outcomes. There is an abundance of healthcare data in Canada, but it is mostly siloed and difficult to access due to privacy and security challenges.
Healthcare analytics
This document outlines an agenda and case studies for a healthcare analytics bootcamp. The bootcamp will use healthcare data to develop machine learning solutions to predict heart disease and identify high-risk patients. Case Study 1 will involve exploratory data analysis of tuberculosis data to analyze global trends, hotspots, and mortality rates. Case Study 2 will use a heart disease screening dataset and logistic regression to build a model to predict heart disease risk and develop treatment plans for high-risk patients. The document discusses the types of structured and unstructured healthcare data, sources of data, and applications of machine learning in healthcare analytics.
Clinical data munging
Clinical data munging involves manipulating raw clinical data to achieve an acceptable final form while respecting principles of clinical research ethics. It is an important process as analysts spend up to 85% of their time on data cleaning and preparation. The document outlines various phases in clinical data munging including screening for issues like missing or erroneous data, diagnosing the true nature of problems, and treating issues through actions like correcting, deleting, or leaving data unchanged. It also discusses specific challenges like dealing with missing values, extreme scores, erroneous dates and other errors, and recommends tools like Stata, R, and SAS that are well suited for clinical data munging.
Central monitoring system in healthcare
The document discusses the features and advantages of a central patient monitoring system. It allows nurses to monitor multiple patients from a central location, monitor vital signs, receive alerts, integrate with other systems like EMRs. It provides clinical decision support tools, helps manage alarms to reduce alarm fatigue, and supports a continuous patient record.
Electronic health records
This document discusses the benefits and challenges of electronic health records (EHRs) and their role in public health informatics. It outlines how EHRs can improve patient care by providing more legible, shareable records compared to paper charts. EHRs also enable clinical decision support, alerts, and reminders to help practitioners. On a larger scale, EHR data in clinical data repositories and registries can help public health by tracking diseases, exposures, and procedures. However, EHR adoption faces challenges such as costs, technical issues, security concerns, and lack of standardized data exchange. Overall, the document argues that EHRs have the potential to dramatically change clinical practice and safeguard populations through improved teamwork and surveillance
Big Data Analytics for Treatment Pathways John 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.
Health Information System (HIS) at Landspitali University Hospital
Landspítali University Hospital is Iceland's largest hospital, located in Reykjavik. It has 4,600 employees and serves as the main referral center for all of Iceland. The hospital uses a variety of electronic health record (EHR) systems, with the main EHR called Saga. Landspítali's EHR strategy is to implement a new comprehensive EHR system to support all clinical activity, with the goal of the new system being in use within 7-10 years. The project is expected to cost 6-8 billion Icelandic krónur and take 4-7 years to complete.
Health information system
A health information system (HIS) refers to a system designed to manage healthcare data, including a patient's electronic medical record, a hospital's operations, and supporting healthcare policy decisions. [HIS] has five core components: hardware, software, telecommunications, databases, and human resources/procedures. Good information management is crucial at all levels of healthcare from local to national as it provides data to policymakers, managers, and healthcare providers. A HIS aims to adequately enable information processing for patient care, administration, research, and education while considering economic and legal factors. It should provide the right information, knowledge, and data to the right people at the right time and place in the right format to support decision making and
Patient Registries
What is a patient registry?
In it’s simplest form, a registry is a list of all patients in a physician’s practice who share some characteristic, such as gender, age group, or disease state. By tracking like characteristics, physicians can better organize patient’s care.
Computer science for health
This document discusses the field of medical informatics, which deals with optimizing the storage, retrieval, and management of biomedical information. It defines medical informatics and related fields like medical decision making, computing, and decision support. It outlines key areas that medical informatics impacts like education, clinical decision support, and imaging. It also examines the role of informatics in clinical settings and clinical trials.
Major health care information systems (emr, ehr, phr, lhr)
This document provides an overview of major health care information systems including electronic medical records (EMR), electronic health records (EHR), personal health records (PHR), and legal health records (LHR). It discusses the definitions, components, benefits, and challenges of each system. The document emphasizes that health care information systems can improve patient care, administrative functions, and overall health care operations if implemented successfully. However, significant financial investment, user training, and overcoming resistance to change are necessary for full adoption and utilization of these systems.
Introduction to 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.
EHR Presentation-Jacksonville University
This document discusses the benefits of electronic health records (EHRs) and how to build consensus around their implementation in healthcare organizations. It outlines that EHRs provide complete patient information across providers, improve care coordination and clinical decision-making. The document also notes that EHRs can increase safety through features like computerized physician order entry and electronic medication administration. Finally, it emphasizes that proper training and support are essential to a successful EHR transition.
Building a consensus for the electronic health record
This document discusses building consensus for electronic health records (EHRs). It begins by defining EHRs and distinguishing them from electronic medical records (EMRs). The document outlines the benefits of EHRs, such as reducing medical errors, improving patient outcomes, and empowering patients. It also discusses meaningful use standards and key aspects of EHR implementation like computerized physician order entry. Overall, the document emphasizes that successful EHR adoption requires thorough preparation, customized training, and comprehensive security planning.
Electronic Health Record (EHR)
This presentation talks about the context of developing the Electronic Health records for India. the guidelines as mentioned in the GOI site is described vividly with examples, for better understanding.
N.B: Please download the ppt first, for the animations to work better.
Disease Management and Disease Registries
This document discusses disease management and disease registries. It defines disease management as a systematic approach to identify at-risk patients and provide care programs while measuring outcomes. Disease registries track chronic disease patients and help disease management programs provide reminders and appointments. The document compares manual, automatic, and integrated registry formats and provides examples like Chronic Disease Electronic Management Systems and Population Health Navigator. Overall, the document outlines how disease management relies on technology like registries and describes challenges in integrating registries into electronic health records.
Cis group project
A Clinical Information System (CIS) is a computer-based system that collects, stores, and provides access to clinical information for healthcare delivery. A CIS includes features like electronic medical records, clinical data repositories, order entry, and decision support. Key stakeholders in selecting and implementing a CIS include clinical staff, administrative staff, and information technology staff. Implementing a CIS requires significant costs for equipment, IT support, training, and maintenance. Ongoing education is also needed to ensure staff can properly use and adapt to changes in the system.
Terry cannestra resume 2016.07.30
Terry Cannestra is a registered nurse in Wisconsin with over 35 years of clinical experience. She has held roles as a clinical research nurse, data specialist, case coordinator, and bedside nurse. Her skills include research coordination, project management, data entry, clinical skills such as phlebotomy and IV placement, and strong computer and communication abilities. She is certified as a clinical research coordinator and in basic life support.
What's hot (19)
When Telemedicine Does Deliver!: IT-Strategist, County Council of Uppsala
When Telemedicine Does Deliver!: IT-Strategist, County Council of Uppsala
Big Data Analytics for Treatment Pathways John Cai
Big Data Analytics for Treatment Pathways John Cai
Health Information System (HIS) at Landspitali University Hospital
Health Information System (HIS) at Landspitali University Hospital
Major health care information systems (emr, ehr, phr, lhr)
Major health care information systems (emr, ehr, phr, lhr)
Building a consensus for the electronic health record
Building a consensus for the electronic health record
Similar to Application of business analytics in healthcare
Business Analytics in healthcare industry.pptx
Hey there!
Exciting news – we're diving into the fascinating world of Business Analytics in the Healthcare sector, and I've just uploaded a killer PowerPoint presentation on SlideShare that you won't want to miss!
🏥 Title: Unveiling the Power of Business Analytics in Healthcare
🚀 Description:
Hey, fellow data enthusiasts! 👋 Get ready to embark on a journey through the dynamic realm where business analytics meets healthcare. Our latest presentation explores the impactful synergy between data-driven insights and the healthcare sector's ever-evolving landscape.
👉 Key Highlights:
Uncovering the role of analytics in optimizing healthcare operations.
Real-world examples showcasing improved patient outcomes through data analysis.
Navigating the challenges and opportunities in healthcare analytics.
Future trends that promise to reshape the healthcare analytics landscape.
🌐 SlideShare Link: Business Analytics in Healthcare
👀 Why You Should Check it Out:
Whether you're a healthcare professional, data enthusiast, or just someone intrigued by the magic that happens when numbers meet healthcare, this presentation is tailor-made for you! Gain insights, spark discussions, and stay ahead of the curve in understanding how analytics is revolutionizing the healthcare game.
Ready to elevate your understanding of business analytics in healthcare? Click the link above and let the learning begin! 🚀
Feel free to share with your network and dive into the discussion. Let's amplify the conversation around data-driven healthcare together!
Cheers !!!
Health Informatics
Nursing informatics combines nursing science, computer science, and information science to manage and communicate data, helping to develop more efficient healthcare processes and provide high-quality patient care. It requires the use of information technology to collect evidence-based research, develop skills to use electronic medical records, create health policies through data collection, and facilitate interdisciplinary communication using technologies like telehealth. Effectively using data can improve patient care through more accurate diagnosis, engagement, and predictive analytics, as well as administrative functions like revenue management, appointments, and staffing.
83cSZGd1SggbKECL708.pptx
The document discusses health management information systems (HMIS) and nursing management information systems (NMIS). It defines HMIS as a system that collects, uses, analyzes, and transmits health information to support health services, training, and research. It outlines the objectives, characteristics, domains/fields, information processes, sources, and problems/constraints of HMIS in India. It also discusses subsystems of HMIS, challenges it faces, and its benefits. For NMIS, it discusses its applications in workload measurement, staffing, personnel management, and fiscal resource management as well as its advantages for nursing administration, practice, research, and education.
Health management information system
Health information system is that that system in which collection, utilization, analysis and transmission of information is done for conducting health services, training and research.
Babithas Notes on unit-3 Health/Nursing Informatics Technology
This document provides an overview of health information systems. It begins by defining a health information system as a system designed to manage healthcare data, including electronic medical records, hospital operations, and systems supporting healthcare policy decisions. It notes that health information systems commonly access, process, or maintain large volumes of sensitive data, so security is a primary concern. The document then discusses definitions of health care information systems and their components. It provides an example of how data might be recorded and where during a patient's visit. Finally, it outlines different architectures for health information systems, including stand-alone, centralized, decentralized, and federated systems.
MIES
The document discusses Management Information and Evaluation Systems (MIES). It defines key terms like information, systems, information systems, management information systems, and evaluation systems. It also describes the objectives, importance, classification, advantages, and limitations of management information systems. Specific types of health information systems and nursing information systems are explained. The major kinds of evaluation systems - process, output, effects, and short-term impact evaluation - are summarized. Finally, the conclusion states that an MIES helps managers make timely decisions by collecting and using information, and that an effective MIES provides accurate, complete and timely feedback at all organizational levels.
Clinical Analytics
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.
Unit 4 - 2.docx
This document discusses the importance of databases in healthcare information systems (HIS). Databases allow for efficient collection and storage of patient data, easy exchange of information between healthcare providers, and monitoring to improve quality of care. They enable quick access to patient records, reduce paperwork, and help with diagnosis, treatment, and billing. Overall, well-designed healthcare databases improve efficiency, care quality, and health outcomes.
Patient Blood Management: Impact of Quality Data on Patient Outcomes
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
PP_04_Hospital Information System.ppt
This document provides an overview of a hospital information system (HIS). It discusses the objective of managing hospital information effectively through computerization. The contents cover introduction to hospital information management, confidentiality and security, introduction to hospital software and IT projects. Evaluation involves modular exams and assignments. Effective information management is important for cost-effectiveness, quality care, and decision making. Implementation of a HIS requires identifying needs, customizing software solutions, training staff, and establishing infrastructure for an integrated system. New technologies like telemedicine, electronic records, and multimedia can further enhance a HIS.
D1 0950 big data in healthcare hongkong2016
Using big data and advanced analytics, hospitals can improve clinical care in several areas:
1) Identifying high-cost patients through claims data and developing targeted interventions to better manage their conditions and reduce costs.
2) Predicting patient readmissions and tailoring interventions for individual patients to reduce readmission rates.
3) Monitoring patients on general medical floors continuously through devices to detect early signs of deterioration and reduce ICU transfers and code events.
4) Developing predictive models to optimize staffing and resource allocation.
Roll no.40 Class Presentation.pptx
Data science has significantly changed the healthcare industry by enabling more informed decision making, better diagnostics, and predictive analysis. It has helped structure large amounts of medical data and allows doctors to make more educated decisions about patient health. Data science provides the foundation for drug development in the pharmaceutical sector by discovering statistical relationships between patient attributes and compounds. When applied to medical imaging, deep learning can detect small abnormalities that help doctors develop effective treatment plans. While data science holds great potential for improving healthcare, challenges remain around standardizing data and management practices.
Emerging Technologies and the Quality of Care
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
Chapter 11
This document discusses various administrative information systems used in healthcare organizations. It begins by introducing agency-based health information systems and core business systems. Case management information systems help identify care patterns, promote preventative care through decision support, and track patient information across visits. Communication, admission, financial, acuity, and scheduling systems are described as core business systems that enhance administrative tasks. Order entry, clinical documentation, pharmacy, laboratory, and radiology systems are presented as important patient care support systems. The document concludes by discussing managing healthcare data through data warehouses, marts, and mining to discover relationships and build knowledge.
Practice Application- Nursing Informatics
The document discusses the evolution of the definition of nursing informatics from 1980 to the present. It provides definitions from various nursing organizations over time that have defined nursing informatics as integrating nursing science, computer science, and information science to manage data and information to support nursing practice, administration, education, research, and expand nursing knowledge. The definitions focus on collecting, processing, and managing nursing data and information through technology to improve nursing care and outcomes.
Clinical decision support system
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.
Hmis
The document discusses the Health Management Information System (HMIS), including its definition, objectives, characteristics, domains/fields, sources, uses, and challenges. The key points are:
HMIS involves collecting, analyzing, and transmitting health information to support health services, training, and research. Its objectives include providing reliable health information to administrators and improving health policies based on feedback. Some challenges include a fragmented system with incomplete data and a lack of computerization. HMIS aims to measure population health and inform the planning, administration, and management of health services and programs.
Safti net kick off 12092010-mrs
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.
Advanced practice nurse led clinics March 2010
This document outlines how nurse-led clinics can be established in general practices to manage preventative health and chronic disease care through a team-based approach. It discusses recruiting target patient populations, conducting assessments, developing care plans, involving GPs, using software and templates, billing appropriately, and establishing recall systems. The goals are to expand services, improve outcomes, and utilize nurses' clinical expertise while enhancing practices' competitiveness. Close collaboration between nurses and GPs is emphasized.
Computer validation of e-source and EHR in clinical trials-Kuchinke
Clinical Trials in the Learning Health System (LHS): Computer System Validation of eSource and EHR Data.
The question that was addressed: How to make a clinical trial data management system that uses EHR data, Patient Reported Outcome (PRO) and eSource data as part of the Learning Health System compliant with regulations and with Good Clinical Practice (GCP)?
The Learning Health System (LHS) connects health care with translational and clinical research. It generates new medical knowledge as a by-product of the care process and its aim is to improve health and safety of patients. The LHS generates and applies knowledge. For this purpose, clinical research, which is research involving humans, must be part of the LHS. Two general types of research exists: observational studies and clinical trials.
Clinical data drive the LHS, because results from randomized controlled trials are seen as “gold standard” for medical evidence. For this reason the concept of using data gathered directly from the patient care environment has enormous potential for accelerating the rate at which useful knowledge is generated.
All computer systems involved in clinical trials must undergo Computer System Validation (CSV). For this process, a legal framework for the TRANSFoRm project was developed. It was used for data privacy analysis of the data flow in two research use cases: an epidemiological cohort study on Diabetes and a randomised clinical trial about different GORD treatment regimes.
Computerized system validation is the documented process to produce evidence that a computerized system does exactly what it is designed to do in a consistent and reproducible manner. The validation of electronic source data in clinical trials presents many challenges because of the blurring of the border between care and research. Here we present our approach for the validation of eSource data capture and the developed documentation for the CSV of the complete data flow in the LHS developed by the TRANSFoRm project. An important part hereby played the GORD Valuation Study.
Similar to Application of business analytics in healthcare (20)
Babithas Notes on unit-3 Health/Nursing Informatics Technology
Babithas Notes on unit-3 Health/Nursing Informatics Technology
Patient Blood Management: Impact of Quality Data on Patient Outcomes
Patient Blood Management: Impact of Quality Data on Patient Outcomes
Computer validation of e-source and EHR in clinical trials-Kuchinke
Computer validation of e-source and EHR in clinical trials-Kuchinke
Recently uploaded
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
原版制作【微信:41543339】【利兹贝克特大学毕业证(LeedsBeckett毕业证书)】【微信:41543339】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
Build applications with generative AI on Google Cloud
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Intelligence supported media monitoring in veterinary medicine
Media monitoring in veterinary medicien
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
毕业原版【微信:176555708】【(UCSF毕业证书)旧金山分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理
原版办【微信号:BYZS866】【兰加拉学院毕业证(Langara毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
University of New South Wales degree offer diploma Transcript
澳洲UNSW毕业证书制作新南威尔士大学假文凭定制Q微168899991做UNSW留信网教留服认证海牙认证改UNSW成绩单GPA做UNSW假学位证假文凭高仿毕业证申请新南威尔士大学University of New South Wales degree offer diploma Transcript
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
一比一原版(harvard毕业证书）哈佛大学毕业证如何办理
一模一样【微信：A575476】【(harvard毕业证书）哈佛大学毕业证成绩单Offer】【微信：A575476】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：A575476】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：A575476】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"
Embark on a captivating financial journey with 'Financial Odyssey,' our hackathon project. Delve deep into the past performance of two companies as we employ an array of financial statement analysis techniques. From ratio analysis to trend analysis, uncover insights crucial for informed decision-making in the dynamic world of finance."
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Global Situational Awareness of A.I. and where its headed
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
DSSML24_tspann_CodelessGenerativeAIPipelines
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
原版一模一样【微信：741003700 】【(uts毕业证书)悉尼科技大学毕业证学历证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Recently uploaded (20)
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Build applications with generative AI on Google Cloud
Build applications with generative AI on Google Cloud
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
Intelligence supported media monitoring in veterinary medicine
Intelligence supported media monitoring in veterinary medicine
Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdf
Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdf
A presentation that explain the Power BI Licensing
A presentation that explain the Power BI Licensing
University of New South Wales degree offer diploma Transcript
University of New South Wales degree offer diploma Transcript
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
Global Situational Awareness of A.I. and where its headed
Global Situational Awareness of A.I. and where its headed
Challenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more important
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Application of business analytics in healthcare
- 1. APPLICATION OF BUSINESS ANALYTICS IN HEALTHCARE
- 2. WHY IS IT THE NEED OF THE HOUR? • Value of services provided by healthcare is questionable • Increasing demand for healthcare • Constrained resources to meet the increasing demand • Evidence based diagnosis for patients
- 3. SCOPE OF DATA ANALYTICS IN HEALTHCARE • Claims and Billing Database: an organized billing system which is connected to a database that stores all the information. Provides quick access to details requested like the amount covered by claims, insurance provider, etc • Supply chain management: Maintaining inventories will be easier and systems can be set in place inside this. For example, different alerts for different level of storage of a drug which will help refill according to needs
- 4. Clinical Analysis: A database of patient details and history would help provide better services to the patients when they come back. Systems can again be put into place here where a patient’s next visit can be notified automatically according to the aliment • Epidemiologic Analysis: Using Statistical tools to help analyze important epidemiological problems and to understand the distribution of these diseases in the population which can pave way to Research
- 5. • Biostatistical Analysis: Using this, huge data can be broken down to solve issues of public health and will also be useful to conduct research in different fields in Healthcare • Human Resource Management: Analyzing data can help work out a more efficient schedule of doctors, nurses and other staffs for different point of time in the hospitals
- 6. • Telemedicine: Telemedicine reduces costs and saves time by keeping the patient away from the hospital. Data Analysis can help provider a better telemedicine service • Medical imaging: Linking medical imaging to data analysis will help read the image and provide outcomes through already set algorithms saving time and money