Nebraska Hospital Information System (NHIS) overview of the new claims data management system for market analysis, pricing guide, and self-service analytics.
The document describes Claims Data Navigator, a product from Analytical Wizards that uses real-world claims data to provide targeted insights. It can map patient journeys, profile and target patients, inform clinical trial design, and perform advanced modeling like outcome prediction. The platform processes vast amounts of claims data from various sources using cloud-based technologies like Google BigQuery and machine learning to extract useful information for applications in areas like understanding patients, clinical trials, and commercial strategies.
Analytical Wizards' Claims Data Navigator for Patient Journey and MoreEric Levin
AW uses state-of-the art big data technologies, expert analytical methodologies, and deep healthcare industry expertise to mine massive claims databases to derive targeted insights for Patient Journey Analysis, Physician Targeting, Outcome Prediction, and more.
In this presentation, Shaheen Gauher talks about two things: (1) How data science and machine learning can be used to manage and control escalating healthcare costs, and (2) How to create a Population Health Management Solution using state of the art Azure Data Lake Analytics and Population Health Report with real time visualization capability using Power BI. The solution presented can be deployed on Azure through a one-click deployment option in https://gallery.cortanaintelligence.com/
Read
Discuss
Data mining is the process of extracting useful information from large sets of data. It involves using various techniques from statistics, machine learning, and database systems to identify patterns, relationships, and trends in the data. This information can then be used to make data-driven decisions, solve business problems, and uncover hidden insights. Applications of data mining include customer profiling and segmentation, market basket analysis, anomaly detection, and predictive modeling. Data mining tools and technologies are widely used in various industries, including finance, healthcare, retail, and telecommunications.
Electronic medical records (EMRs) can replace paper-based records by providing a single, up-to-date source of patient information accessible from any location. EMRs require less physical storage space and staff resources compared to paper records. They allow for fast access to test results, integrated viewing of medical images, prescription templates, and immunization histories. However, barriers to EMR adoption include upfront costs, technical challenges, privacy/security concerns, resistance to changes in workflows, and lack of interoperability between different EMR systems. Once implemented, an EMR system can manage appointments, patient demographics, clinical notes, test results, prescriptions, medical imaging, and other aspects of patient care.
Consumers' Checkbook Submission to RWJF & HHS Provider Network Challengehealth2dev
This document describes Consumers' Checkbook's proposal to provide an all-plan provider directory tool to help consumers using health insurance exchanges. It notes Checkbook's experience producing similar directories and plan comparison tools. The proposed tool would consolidate doctor data from all carriers into an easy-to-use interface showing which doctors participate in each plan. It would provide search and filter options, quality information on doctors, and has been successfully implemented for other exchanges. The business model involves an annual licensing fee from exchanges.
The document discusses the importance of statistics and statistical methods for analyzing large datasets and gaining business insights. It covers three events that have increased data and computing power: 1) technological developments producing large amounts of data, 2) advances in computing to process massive data, and 3) large data storage and cloud/parallel computing. Statistical methods like classification, pattern recognition, association analysis, and predictive modeling can help segment customers, recognize patterns, discover relationships between products purchased, and predict customer behavior and targeting. The document also defines key statistical concepts like population, parameter, sample, and statistic.
HMIS is an integrated Hospital management system, which addresses All requirements of hospitals. It is a powerful, flexible and easy to use application designed and developed to convey real conceivable benefits to hospitals and clinics which reduce the paper overload.
The document describes Claims Data Navigator, a product from Analytical Wizards that uses real-world claims data to provide targeted insights. It can map patient journeys, profile and target patients, inform clinical trial design, and perform advanced modeling like outcome prediction. The platform processes vast amounts of claims data from various sources using cloud-based technologies like Google BigQuery and machine learning to extract useful information for applications in areas like understanding patients, clinical trials, and commercial strategies.
Analytical Wizards' Claims Data Navigator for Patient Journey and MoreEric Levin
AW uses state-of-the art big data technologies, expert analytical methodologies, and deep healthcare industry expertise to mine massive claims databases to derive targeted insights for Patient Journey Analysis, Physician Targeting, Outcome Prediction, and more.
In this presentation, Shaheen Gauher talks about two things: (1) How data science and machine learning can be used to manage and control escalating healthcare costs, and (2) How to create a Population Health Management Solution using state of the art Azure Data Lake Analytics and Population Health Report with real time visualization capability using Power BI. The solution presented can be deployed on Azure through a one-click deployment option in https://gallery.cortanaintelligence.com/
Read
Discuss
Data mining is the process of extracting useful information from large sets of data. It involves using various techniques from statistics, machine learning, and database systems to identify patterns, relationships, and trends in the data. This information can then be used to make data-driven decisions, solve business problems, and uncover hidden insights. Applications of data mining include customer profiling and segmentation, market basket analysis, anomaly detection, and predictive modeling. Data mining tools and technologies are widely used in various industries, including finance, healthcare, retail, and telecommunications.
Electronic medical records (EMRs) can replace paper-based records by providing a single, up-to-date source of patient information accessible from any location. EMRs require less physical storage space and staff resources compared to paper records. They allow for fast access to test results, integrated viewing of medical images, prescription templates, and immunization histories. However, barriers to EMR adoption include upfront costs, technical challenges, privacy/security concerns, resistance to changes in workflows, and lack of interoperability between different EMR systems. Once implemented, an EMR system can manage appointments, patient demographics, clinical notes, test results, prescriptions, medical imaging, and other aspects of patient care.
Consumers' Checkbook Submission to RWJF & HHS Provider Network Challengehealth2dev
This document describes Consumers' Checkbook's proposal to provide an all-plan provider directory tool to help consumers using health insurance exchanges. It notes Checkbook's experience producing similar directories and plan comparison tools. The proposed tool would consolidate doctor data from all carriers into an easy-to-use interface showing which doctors participate in each plan. It would provide search and filter options, quality information on doctors, and has been successfully implemented for other exchanges. The business model involves an annual licensing fee from exchanges.
The document discusses the importance of statistics and statistical methods for analyzing large datasets and gaining business insights. It covers three events that have increased data and computing power: 1) technological developments producing large amounts of data, 2) advances in computing to process massive data, and 3) large data storage and cloud/parallel computing. Statistical methods like classification, pattern recognition, association analysis, and predictive modeling can help segment customers, recognize patterns, discover relationships between products purchased, and predict customer behavior and targeting. The document also defines key statistical concepts like population, parameter, sample, and statistic.
HMIS is an integrated Hospital management system, which addresses All requirements of hospitals. It is a powerful, flexible and easy to use application designed and developed to convey real conceivable benefits to hospitals and clinics which reduce the paper overload.
This document discusses REDCap, a web-based application for researchers and clinicians to create and manage databases and surveys. It describes REDCap features such as different data field types, data validation, user access controls, data export capabilities, and support for multi-center and longitudinal research. The document also provides statistics on REDCap usage at Kasr Al Ainy, including 46 projects, 114 users across several departments, and offers a live demo of the REDCap system and its features.
UCSF Informatics Day 2014 - Dana Ludwig, "Research Data Browser"CTSI at UCSF
The document summarizes the Research Data Browser, a tool that enables UCSF investigators to rapidly query de-identified clinical data to generate hypotheses and profile selected patients. It allows visualizing data through different views and tabs, applying filters, and exporting results. Access requires submitting a request through the Account Request Form system. Some limitations include only including data after June 2012 and orders being limited. Feedback is encouraged to improve the tool and data.
Introduction to Djhgchigchg kjfouhvlHIS2.pptxAronMozart1
DHIS2 is a flexible, web-based open source platform for collecting, managing, and analyzing health and other data. It supports data capture, validation, and visualization through charts, maps, and pivot tables. DHIS2 was first implemented in 1997 in South Africa and is now used in over 67 countries. It allows for both online and offline use on computers, tablets, and mobile devices. DHIS2 facilitates the entire data management cycle from data entry and validation to analysis, reporting, and decision-making.
This document provides an introduction to customer relationship management (CRM). It defines customers as those who buy goods or services, and identifies two types of customers: external and internal. It then describes four categories of customers based on personality traits. The document defines CRM as strategies that integrate people, processes, and technology to optimize relationships with customers. It lists features of CRM like central databases and customer data analysis. The document outlines advantages of CRM such as increased sales and customer loyalty. It then provides examples of the types of products and services a company might offer to help customers achieve their objectives.
HealthCare Management System by Softech.com.pkAppsGenii
This document summarizes HealthConnect, a comprehensive healthcare management system created by Softech Systems. It includes modules for patient registration, inpatient and outpatient departments, billing, surgery, pathology, radiology, cardiology, medical records management, and more. Each module is described in 1-2 paragraphs on features and functionality. The document also provides information on Softech Systems, the developer, including its experience, clients, and organizational structure to ensure quality software delivery.
This document provides guidance on conducting competitive landscape analysis through market research. It outlines key steps such as identifying competitors, analyzing them based on features and customer segments, and visualizing the analysis. Recommendations are given for both primary research like interviews and surveys, and secondary research from industry reports, government data, social media, and more. Common mistakes like relying solely on free online sources are highlighted. The goal of competitive analysis is to better understand the market and differentiate your own offering.
Tailoring machine learning practices to support prescriptive analyticsAdam Doyle
Slides from the November St. Louis Big Data IDEA. Anthony Melson talked about how to engineer machine learning practices to better support prescriptive analytics.
A Comparison of Non-Dictionary Based Approaches to Automate Cancer Detection Using Plaintext Medical Data with Dr. Shaun Grannis, Dr. Brian Dixon et. al. presented at the Regenstrief WIP (7th Jan 2015)
Chapter 3 SERVICE AREA COMPETITION ANALYSIS.pptxErum50
This document outlines the process of conducting a service area competitor analysis for a healthcare organization. It begins with defining the services and geographic service area. Next, the service area is profiled using economic, demographic and health data. A structural analysis of the service area examines factors like barriers to entry, substitute services, and bargaining powers. Competitors are then analyzed and mapped into strategic groups. Lastly, the various analyses are synthesized to identify key issues and trends to inform strategy formulation. The goal of service area competitor analysis is to understand the competitive landscape and identify opportunities for competitive advantage.
Chapter 3 SERVICE AREA COMPETITION ANALYSIS.pptxErum50
This document provides an overview of service area competition analysis. It discusses how analyzing competitors within a service area emerged as healthcare became more competitive. It defines a service area and lists the objectives of service area analysis. The document outlines the process of conducting service area analysis, including defining service categories and area boundaries, profiling the area, analyzing industry forces, conducting competitor evaluations, and mapping strategic groups. The goal is to understand the competitive landscape to inform strategic decision making.
Data mining (DM) in the pharmaceutical industrylurdhu agnes
Data mining techniques can help pharmaceutical companies analyze large datasets to identify hidden patterns and relationships. This allows companies to make more informed decisions. Specifically, data mining allows analysis of clinical, financial, and organizational data to support clinicians, manage treatment pathways, and efficiently use resources. Techniques like classification, prediction, clustering, and association rule mining can be applied to areas like drug discovery, predicting patient responses, and optimizing operations.
This document discusses DataActiva's approach to customer segmentation. It describes different levels and types of segmentation including mass, segmented, individual, and targeted segmentation. It also covers segmentation description techniques like a priori, cluster-based, and hybrid models. The document provides an overview of cluster analysis techniques including hierarchical, non-hierarchical, k-means, and Ward's methods. It discusses best practices for variable selection, standardization, response style effects, number of clusters, and validity checks in segmentation analysis. Finally, it notes how segmentation can be activated through customer relationship management processes and linked to other data sources.
This document describes a proposed big data solution to help patients find affordable healthcare options for procedures like knee replacements. It offers a price search tool that allows filtering by location, procedure, doctor, and insurance to find cost estimates. It also provides customized data analyses and reports to various stakeholders, using crowdsourced data combined with public and private sources. The goal is to increase price and quality transparency to help patients and organizations make more informed healthcare decisions.
Plan S Price Transparency requirements and frameworkAlicia Wise
This document summarizes a presentation on Plan S's price transparency framework for publishers. It includes an agenda for the presentation which will provide an overview of the requirements and frameworks, include experiences from publishers implementing them (PLOS and Springer Nature), and allow for discussion and questions. It then outlines the key aspects of the price transparency framework, including a two-part implementation guide and data collection spreadsheet. Publishers are encouraged to provide data according to this framework by July 2022 to remain eligible for Coalition S funding.
This document discusses various clinical applications of nursing informatics (NI), including assessment, documentation, planning, decision support systems, implementation, and evaluation. It focuses on using technology like electronic health records and computerized documentation to improve the efficiency and quality of nursing care. Key applications mentioned include computerized patient monitoring, point-of-care documentation systems, automated care planning, and decision support systems to aid nursing judgments. The summary emphasizes the importance of nurses identifying essential data for patient care decisions and evaluating technology to best serve nursing needs.
A marketing information system collects, analyzes, and distributes market data to support marketing decisions. It includes internal reporting on sales, customers, and operations; marketing intelligence on competitors and the external environment; decision support tools; and a marketing research function. The marketing research process involves 6 steps: defining the problem, developing a research plan, collecting information, analyzing the data, presenting findings, and making decisions based on the findings. Effective marketing information systems provide timely, accurate insights to improve strategy, offerings, and performance.
This document provides an overview of healthcare data analysis, including the types of data collected in healthcare and common analysis techniques. It discusses the differences between primary and secondary data analysis and describes various types of data like structured, unstructured, qualitative, and quantitative data. Common statistical analysis techniques are explained such as descriptive statistics, inferential statistics, data mining, predictive modeling, and exploratory data analysis. Opportunities for health information management professionals in data analysis are highlighted. The responsibilities of entry-level, mid-level, and senior-level health data analysts are outlined.
This document provides an overview of healthcare data analysis, including the types of data collected in healthcare and common analysis techniques. It discusses the differences between primary and secondary data analysis and describes various types of data like structured, unstructured, qualitative, and quantitative data. Common statistical analysis techniques are explained such as descriptive statistics, inferential statistics, data mining, predictive modeling, and exploratory data analysis. Opportunities for health information management professionals in data analysis are highlighted. The responsibilities of entry-level, mid-level, and senior-level health data analysts are outlined.
Cost-Quality Aim Analyzer for Ambulatory Carepscisolutions
The PSCI Cost-Quality (Triple Aim) Analyzer for Ambulatory Care module brings together three streams of customer data ― financial, clinical quality, and patient experience. The Cost-Quality Analyzer module analyzes cost in context of quality and patient experience. Optionally, a fourth stream ― State-of-Health (SOH) risk scores ― can be added through the Population SOH Analyzer module.
This document discusses UCAS's exhibitor data collection service, which allows exhibitors at higher education events to collect visitor data through barcode scanning. It benefits exhibitors by enabling them to collect visitor details in a simple, time-saving way and to directly communicate with prospective students after events. The process involves visitors opting-in and having their tickets scanned at events and stands. Exhibitors then receive the visitors' names, contact details, subject preferences, and other fields within 3 days for marketing purposes. Feedback from exhibitors and students is positive about the easy data collection and quality of information provided. The service also offers enhanced data analysis of exhibitor audiences.
This document discusses REDCap, a web-based application for researchers and clinicians to create and manage databases and surveys. It describes REDCap features such as different data field types, data validation, user access controls, data export capabilities, and support for multi-center and longitudinal research. The document also provides statistics on REDCap usage at Kasr Al Ainy, including 46 projects, 114 users across several departments, and offers a live demo of the REDCap system and its features.
UCSF Informatics Day 2014 - Dana Ludwig, "Research Data Browser"CTSI at UCSF
The document summarizes the Research Data Browser, a tool that enables UCSF investigators to rapidly query de-identified clinical data to generate hypotheses and profile selected patients. It allows visualizing data through different views and tabs, applying filters, and exporting results. Access requires submitting a request through the Account Request Form system. Some limitations include only including data after June 2012 and orders being limited. Feedback is encouraged to improve the tool and data.
Introduction to Djhgchigchg kjfouhvlHIS2.pptxAronMozart1
DHIS2 is a flexible, web-based open source platform for collecting, managing, and analyzing health and other data. It supports data capture, validation, and visualization through charts, maps, and pivot tables. DHIS2 was first implemented in 1997 in South Africa and is now used in over 67 countries. It allows for both online and offline use on computers, tablets, and mobile devices. DHIS2 facilitates the entire data management cycle from data entry and validation to analysis, reporting, and decision-making.
This document provides an introduction to customer relationship management (CRM). It defines customers as those who buy goods or services, and identifies two types of customers: external and internal. It then describes four categories of customers based on personality traits. The document defines CRM as strategies that integrate people, processes, and technology to optimize relationships with customers. It lists features of CRM like central databases and customer data analysis. The document outlines advantages of CRM such as increased sales and customer loyalty. It then provides examples of the types of products and services a company might offer to help customers achieve their objectives.
HealthCare Management System by Softech.com.pkAppsGenii
This document summarizes HealthConnect, a comprehensive healthcare management system created by Softech Systems. It includes modules for patient registration, inpatient and outpatient departments, billing, surgery, pathology, radiology, cardiology, medical records management, and more. Each module is described in 1-2 paragraphs on features and functionality. The document also provides information on Softech Systems, the developer, including its experience, clients, and organizational structure to ensure quality software delivery.
This document provides guidance on conducting competitive landscape analysis through market research. It outlines key steps such as identifying competitors, analyzing them based on features and customer segments, and visualizing the analysis. Recommendations are given for both primary research like interviews and surveys, and secondary research from industry reports, government data, social media, and more. Common mistakes like relying solely on free online sources are highlighted. The goal of competitive analysis is to better understand the market and differentiate your own offering.
Tailoring machine learning practices to support prescriptive analyticsAdam Doyle
Slides from the November St. Louis Big Data IDEA. Anthony Melson talked about how to engineer machine learning practices to better support prescriptive analytics.
A Comparison of Non-Dictionary Based Approaches to Automate Cancer Detection Using Plaintext Medical Data with Dr. Shaun Grannis, Dr. Brian Dixon et. al. presented at the Regenstrief WIP (7th Jan 2015)
Chapter 3 SERVICE AREA COMPETITION ANALYSIS.pptxErum50
This document outlines the process of conducting a service area competitor analysis for a healthcare organization. It begins with defining the services and geographic service area. Next, the service area is profiled using economic, demographic and health data. A structural analysis of the service area examines factors like barriers to entry, substitute services, and bargaining powers. Competitors are then analyzed and mapped into strategic groups. Lastly, the various analyses are synthesized to identify key issues and trends to inform strategy formulation. The goal of service area competitor analysis is to understand the competitive landscape and identify opportunities for competitive advantage.
Chapter 3 SERVICE AREA COMPETITION ANALYSIS.pptxErum50
This document provides an overview of service area competition analysis. It discusses how analyzing competitors within a service area emerged as healthcare became more competitive. It defines a service area and lists the objectives of service area analysis. The document outlines the process of conducting service area analysis, including defining service categories and area boundaries, profiling the area, analyzing industry forces, conducting competitor evaluations, and mapping strategic groups. The goal is to understand the competitive landscape to inform strategic decision making.
Data mining (DM) in the pharmaceutical industrylurdhu agnes
Data mining techniques can help pharmaceutical companies analyze large datasets to identify hidden patterns and relationships. This allows companies to make more informed decisions. Specifically, data mining allows analysis of clinical, financial, and organizational data to support clinicians, manage treatment pathways, and efficiently use resources. Techniques like classification, prediction, clustering, and association rule mining can be applied to areas like drug discovery, predicting patient responses, and optimizing operations.
This document discusses DataActiva's approach to customer segmentation. It describes different levels and types of segmentation including mass, segmented, individual, and targeted segmentation. It also covers segmentation description techniques like a priori, cluster-based, and hybrid models. The document provides an overview of cluster analysis techniques including hierarchical, non-hierarchical, k-means, and Ward's methods. It discusses best practices for variable selection, standardization, response style effects, number of clusters, and validity checks in segmentation analysis. Finally, it notes how segmentation can be activated through customer relationship management processes and linked to other data sources.
This document describes a proposed big data solution to help patients find affordable healthcare options for procedures like knee replacements. It offers a price search tool that allows filtering by location, procedure, doctor, and insurance to find cost estimates. It also provides customized data analyses and reports to various stakeholders, using crowdsourced data combined with public and private sources. The goal is to increase price and quality transparency to help patients and organizations make more informed healthcare decisions.
Plan S Price Transparency requirements and frameworkAlicia Wise
This document summarizes a presentation on Plan S's price transparency framework for publishers. It includes an agenda for the presentation which will provide an overview of the requirements and frameworks, include experiences from publishers implementing them (PLOS and Springer Nature), and allow for discussion and questions. It then outlines the key aspects of the price transparency framework, including a two-part implementation guide and data collection spreadsheet. Publishers are encouraged to provide data according to this framework by July 2022 to remain eligible for Coalition S funding.
This document discusses various clinical applications of nursing informatics (NI), including assessment, documentation, planning, decision support systems, implementation, and evaluation. It focuses on using technology like electronic health records and computerized documentation to improve the efficiency and quality of nursing care. Key applications mentioned include computerized patient monitoring, point-of-care documentation systems, automated care planning, and decision support systems to aid nursing judgments. The summary emphasizes the importance of nurses identifying essential data for patient care decisions and evaluating technology to best serve nursing needs.
A marketing information system collects, analyzes, and distributes market data to support marketing decisions. It includes internal reporting on sales, customers, and operations; marketing intelligence on competitors and the external environment; decision support tools; and a marketing research function. The marketing research process involves 6 steps: defining the problem, developing a research plan, collecting information, analyzing the data, presenting findings, and making decisions based on the findings. Effective marketing information systems provide timely, accurate insights to improve strategy, offerings, and performance.
This document provides an overview of healthcare data analysis, including the types of data collected in healthcare and common analysis techniques. It discusses the differences between primary and secondary data analysis and describes various types of data like structured, unstructured, qualitative, and quantitative data. Common statistical analysis techniques are explained such as descriptive statistics, inferential statistics, data mining, predictive modeling, and exploratory data analysis. Opportunities for health information management professionals in data analysis are highlighted. The responsibilities of entry-level, mid-level, and senior-level health data analysts are outlined.
This document provides an overview of healthcare data analysis, including the types of data collected in healthcare and common analysis techniques. It discusses the differences between primary and secondary data analysis and describes various types of data like structured, unstructured, qualitative, and quantitative data. Common statistical analysis techniques are explained such as descriptive statistics, inferential statistics, data mining, predictive modeling, and exploratory data analysis. Opportunities for health information management professionals in data analysis are highlighted. The responsibilities of entry-level, mid-level, and senior-level health data analysts are outlined.
Cost-Quality Aim Analyzer for Ambulatory Carepscisolutions
The PSCI Cost-Quality (Triple Aim) Analyzer for Ambulatory Care module brings together three streams of customer data ― financial, clinical quality, and patient experience. The Cost-Quality Analyzer module analyzes cost in context of quality and patient experience. Optionally, a fourth stream ― State-of-Health (SOH) risk scores ― can be added through the Population SOH Analyzer module.
This document discusses UCAS's exhibitor data collection service, which allows exhibitors at higher education events to collect visitor data through barcode scanning. It benefits exhibitors by enabling them to collect visitor details in a simple, time-saving way and to directly communicate with prospective students after events. The process involves visitors opting-in and having their tickets scanned at events and stands. Exhibitors then receive the visitors' names, contact details, subject preferences, and other fields within 3 days for marketing purposes. Feedback from exhibitors and students is positive about the easy data collection and quality of information provided. The service also offers enhanced data analysis of exhibitor audiences.
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- Video recording of this lecture in English language: https://youtu.be/Pt1nA32sdHQ
- Video recording of this lecture in Arabic language: https://youtu.be/uFdc9F0rlP0
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
These lecture slides, by Dr Sidra Arshad, offer a simplified look into the mechanisms involved in the regulation of respiration:
Learning objectives:
1. Describe the organisation of respiratory center
2. Describe the nervous control of inspiration and respiratory rhythm
3. Describe the functions of the dorsal and respiratory groups of neurons
4. Describe the influences of the Pneumotaxic and Apneustic centers
5. Explain the role of Hering-Breur inflation reflex in regulation of inspiration
6. Explain the role of central chemoreceptors in regulation of respiration
7. Explain the role of peripheral chemoreceptors in regulation of respiration
8. Explain the regulation of respiration during exercise
9. Integrate the respiratory regulatory mechanisms
10. Describe the Cheyne-Stokes breathing
Study Resources:
1. Chapter 42, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 36, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 13, Human Physiology by Lauralee Sherwood, 9th edition
DECLARATION OF HELSINKI - History and principlesanaghabharat01
This SlideShare presentation provides a comprehensive overview of the Declaration of Helsinki, a foundational document outlining ethical guidelines for conducting medical research involving human subjects.
Histololgy of Female Reproductive System.pptxAyeshaZaid1
Dive into an in-depth exploration of the histological structure of female reproductive system with this comprehensive lecture. Presented by Dr. Ayesha Irfan, Assistant Professor of Anatomy, this presentation covers the Gross anatomy and functional histology of the female reproductive organs. Ideal for students, educators, and anyone interested in medical science, this lecture provides clear explanations, detailed diagrams, and valuable insights into female reproductive system. Enhance your knowledge and understanding of this essential aspect of human biology.
Cell Therapy Expansion and Challenges in Autoimmune DiseaseHealth Advances
There is increasing confidence that cell therapies will soon play a role in the treatment of autoimmune disorders, but the extent of this impact remains to be seen. Early readouts on autologous CAR-Ts in lupus are encouraging, but manufacturing and cost limitations are likely to restrict access to highly refractory patients. Allogeneic CAR-Ts have the potential to broaden access to earlier lines of treatment due to their inherent cost benefits, however they will need to demonstrate comparable or improved efficacy to established modalities.
In addition to infrastructure and capacity constraints, CAR-Ts face a very different risk-benefit dynamic in autoimmune compared to oncology, highlighting the need for tolerable therapies with low adverse event risk. CAR-NK and Treg-based therapies are also being developed in certain autoimmune disorders and may demonstrate favorable safety profiles. Several novel non-cell therapies such as bispecific antibodies, nanobodies, and RNAi drugs, may also offer future alternative competitive solutions with variable value propositions.
Widespread adoption of cell therapies will not only require strong efficacy and safety data, but also adapted pricing and access strategies. At oncology-based price points, CAR-Ts are unlikely to achieve broad market access in autoimmune disorders, with eligible patient populations that are potentially orders of magnitude greater than the number of currently addressable cancer patients. Developers have made strides towards reducing cell therapy COGS while improving manufacturing efficiency, but payors will inevitably restrict access until more sustainable pricing is achieved.
Despite these headwinds, industry leaders and investors remain confident that cell therapies are poised to address significant unmet need in patients suffering from autoimmune disorders. However, the extent of this impact on the treatment landscape remains to be seen, as the industry rapidly approaches an inflection point.
8 Surprising Reasons To Meditate 40 Minutes A Day That Can Change Your Life.pptxHolistified Wellness
We’re talking about Vedic Meditation, a form of meditation that has been around for at least 5,000 years. Back then, the people who lived in the Indus Valley, now known as India and Pakistan, practised meditation as a fundamental part of daily life. This knowledge that has given us yoga and Ayurveda, was known as Veda, hence the name Vedic. And though there are some written records, the practice has been passed down verbally from generation to generation.
2. Nebraska Hospital Information System
• Overview
• Login/Security
• Homepage
• Settings
• User Defined Market Areas
• Market Analysis
• Pricing Guide
• Self-Service Analytics
2
3. Login/Security
Web Based Secure login access from anywhere
Permissions will be given at three levels:
• Full Access: At this level, the user will be able to see all details of each claim, including private health information (PHI).
• Limited Access: At this level, the user will be able to view the details of each claim except for private health
information (PHI).
• Aggregate Access: At this level, the user will not be able to see individual claim data, but only summarized
information.
5. USER-DEFINE MARKET AREA
3 Market Areas per User
• Saved to User Profile
• Zip Code and County filters
6. MARKET ANALYSIS
Inpatient, Outpatient Ambulatory, Outpatient Emergency Room, Outpatient Therapeutic and Diagnostic
• Filters by Time Frame, Market Area, Date Range.
• Interactive Dashboard by Hospitals, Primary MDs, Diagnosis Related Group (DRG), Payers ($)
7.
8. MARKET ANALYSIS
View Trend Analysis
• Gross Case Counts, Total $, Average Length of Stay (LOS)
• Export Data
9. PRICING GUIDE
• Enter CPT CODE
• Select Peer Group (Rural, CAH, CMI<1.5, CMI> 1.5, Non DRG Based, Referral, Region 1-6, Urban)
10. • Select Data Fields
• Create Analysis
• Select Visualization
• Build Dashboard
• Use Report Builder
• Save and Share your work
with other users.
• Schedule Reports to auto
run.
Self-Service Reporting,
Dashboards & Analytics
11. SELF SERVICE ANALYTICS
• Create your own analysis, Dashboards and Reports
• Folders to save and share analysis
12. SELF SERVICE ANALYTICS – Data Tab
• Select Data Source (Claims, Pricing Guide, or
Reference)
• Select Data Model
• Claims = Inpatient, Outpatient Claim Lines,
Outpatient Claims
• Pricing Guide = Outpatient, Surgery
• Reference = CPT APC Codes, CPT HCPCS
Codes, Diagnosis Codes, Hospitals, ICD Codes,
Payer Codes, Peer Groups, Providers, Zip
Codes/Counties
• Note Outpatient Claims and Outpatient Claim Lines
can be linked together.
13. SELF SERVICE ANALYTICS – Filter Tab
• Once data is selected you can filter on the data.
• Select comparisons (greater, less than, =, in list, not in list etc)
• Common example is date ranges.
14. SELF SERVICE ANALYTICS – Chart Tab
• Select the fields you want to use in chart
• Choose from Bar, Line, Curved Line, Pie, Scatter, Gauge, and Crosstabs
• Rename and Save chart
15. Coming Soon
• NHA Member hospitals will send files to H4 Technology SFTP.
• H4 Technology will process claims data.
• Creating output production files for NHIS.
Pause. Answer questions. Go back to any slides or elaborate? Set up dates and times for follow up…demo, who else needs to be on the next call. Timeframe for a decision.