This document provides information about quality management models and tools. It discusses data quality management and measurement, and the importance of data governance in healthcare. It also outlines several common quality management tools used in healthcare including check sheets, control charts, Pareto charts, scatter plots, and Ishikawa diagrams.
This document provides information about quality management system models, including definitions of key terms, tools and strategies for quality management. It discusses data quality management and measurement, and the importance of data governance in healthcare. Quality management tools described include check sheets, control charts, Pareto charts, scatter plots, and Ishikawa diagrams. The document emphasizes the role of accurate, high-quality data and the need for rigorous data quality practices in healthcare.
The document discusses the AHIMA data quality management model. It provides definitions for data quality management and data quality measurement. The model aims to ensure integrity of healthcare data during collection, use, storage and analysis. It addresses challenges from initiatives like EHR adoption, ICD-10 implementation, and quality reporting. The model evaluates data quality across collection, application, warehousing and analysis. HIM professionals can use the model and its assessment tools to improve data governance and take on expanded roles in healthcare data management.
This document provides an overview of various tools and concepts related to data quality management models. It discusses key aspects of data quality including accuracy, accessibility, comprehensiveness, consistency, currency, definition, granularity, precision, relevancy, and timeliness. Specific quality management tools are also outlined, such as check sheets, control charts, Pareto charts, and scatter plots. Examples are given for how these data quality principles and tools apply across different domains like healthcare records and data warehousing.
HIMSS Analytics, with a goal of helping healthcare organizations understand and advance healthcare analytics, has developed the Adoption Model for Analytics Maturity (AMAM) published here on www.SlideShare.net for healthcare industry reference.
This 8 stage international prescriptive analytics oriented maturity model offers an easy assessment and a detailed industry specific road map to help healthcare providers interested in analytics advance their capabilities.
For further information please see www.HIMSSAnalytics.org
NCQA’s Accreditation process provides payers with a comprehensive framework to improve quality of care and services. It allows members and employers to compare health plan performance across various plans and against industry benchmarks. NCQA accreditation has 3 parts – HEDIS, Patient experience CAHPS measures and NCQA standards
POV Healthcare Payer Medical Informatics and AnalyticsFrank Wang
1) Healthcare payers are facing increasing pressures to reduce costs while improving quality of care and consumer engagement. This is driving needs for modernized claims processing, population health management, and business intelligence solutions.
2) The document discusses various healthcare informatics and analytics solutions that can help payers address these challenges, including solutions for claims processing, care management, utilization management, disease management, wellness programs, and compliance and regulatory reporting.
3) It provides examples of how analytics can be used for cost containment, care coordination, and enabling accountable care through risk stratification, predictive modeling, and outcomes measurement.
Driving Home Health Efficiency through Data AnalyticsCitiusTech
This whitepaper highlights how data analytics can help track key performance indicators to drive clinical, financial and operational efficiency to improve quality of home health in an efficient manner.
CMS’ New Interoperability and Patient Access Proposed Rule - Top 5 Payer ImpactsCitiusTech
The recently proposed rule by the CMS introduces new policies to expand access to healthcare information and improve the seamless exchange of data in healthcare. This increased data sharing is a critical component of healthcare transformational efforts, and this eBook highlights the rules’ possible impact on payer systems and steps they need to take to manage this change effectively.
This document provides information about quality management system models, including definitions of key terms, tools and strategies for quality management. It discusses data quality management and measurement, and the importance of data governance in healthcare. Quality management tools described include check sheets, control charts, Pareto charts, scatter plots, and Ishikawa diagrams. The document emphasizes the role of accurate, high-quality data and the need for rigorous data quality practices in healthcare.
The document discusses the AHIMA data quality management model. It provides definitions for data quality management and data quality measurement. The model aims to ensure integrity of healthcare data during collection, use, storage and analysis. It addresses challenges from initiatives like EHR adoption, ICD-10 implementation, and quality reporting. The model evaluates data quality across collection, application, warehousing and analysis. HIM professionals can use the model and its assessment tools to improve data governance and take on expanded roles in healthcare data management.
This document provides an overview of various tools and concepts related to data quality management models. It discusses key aspects of data quality including accuracy, accessibility, comprehensiveness, consistency, currency, definition, granularity, precision, relevancy, and timeliness. Specific quality management tools are also outlined, such as check sheets, control charts, Pareto charts, and scatter plots. Examples are given for how these data quality principles and tools apply across different domains like healthcare records and data warehousing.
HIMSS Analytics, with a goal of helping healthcare organizations understand and advance healthcare analytics, has developed the Adoption Model for Analytics Maturity (AMAM) published here on www.SlideShare.net for healthcare industry reference.
This 8 stage international prescriptive analytics oriented maturity model offers an easy assessment and a detailed industry specific road map to help healthcare providers interested in analytics advance their capabilities.
For further information please see www.HIMSSAnalytics.org
NCQA’s Accreditation process provides payers with a comprehensive framework to improve quality of care and services. It allows members and employers to compare health plan performance across various plans and against industry benchmarks. NCQA accreditation has 3 parts – HEDIS, Patient experience CAHPS measures and NCQA standards
POV Healthcare Payer Medical Informatics and AnalyticsFrank Wang
1) Healthcare payers are facing increasing pressures to reduce costs while improving quality of care and consumer engagement. This is driving needs for modernized claims processing, population health management, and business intelligence solutions.
2) The document discusses various healthcare informatics and analytics solutions that can help payers address these challenges, including solutions for claims processing, care management, utilization management, disease management, wellness programs, and compliance and regulatory reporting.
3) It provides examples of how analytics can be used for cost containment, care coordination, and enabling accountable care through risk stratification, predictive modeling, and outcomes measurement.
Driving Home Health Efficiency through Data AnalyticsCitiusTech
This whitepaper highlights how data analytics can help track key performance indicators to drive clinical, financial and operational efficiency to improve quality of home health in an efficient manner.
CMS’ New Interoperability and Patient Access Proposed Rule - Top 5 Payer ImpactsCitiusTech
The recently proposed rule by the CMS introduces new policies to expand access to healthcare information and improve the seamless exchange of data in healthcare. This increased data sharing is a critical component of healthcare transformational efforts, and this eBook highlights the rules’ possible impact on payer systems and steps they need to take to manage this change effectively.
The document discusses how digitizing healthcare can transform the industry by moving from standalone systems to integrated systems that provide real-time access to data. It notes healthcare is moving from paper-based systems with data silos to integrated electronic systems that can improve quality of care through features like alerts and collaboration. The document also discusses how capturing unstructured data from sources like clinical notes using technologies like natural language processing can provide insights to help monitor metrics, identify conditions, and support research.
Information Management in Pharmaceutical IndustryFrank Wang
This document discusses the challenges and opportunities for information management in the biopharma industries. It covers topics like challenges in R&D, clinical trials, sales and marketing productivity. It also discusses the evolving biopharma value chain and how information management can help in areas like pharmaceutical R&D, clinical operations, sales, marketing, manufacturing and supply chain optimization. Finally, it discusses how the pharmaceutical industry is at a crossroads with challenges like declining R&D productivity and changing market landscapes in emerging markets and developed nations.
Healthcare ito in healthcare payer - annual report - preview deck - july 2013Everest Group
This report provides an overview of the ITO market for the healthcare payer industry. Analysis includes key trends in market size & growth, demand drivers, adoption & scope trends, emerging themes, key areas of investment, and implications for key stakeholders. The report also provides specific updates on the readiness of the various stakeholders from the perspective of payer reform mandates
The convergence of health plans and healthcare providers has led to the growing importance for provider-led health plans (Payviders). This eBook highlights the data and technology capabilities necessary for Payvider organizations to optimize performance and drive operational efficiencies.
Providers need to move towards real-time analytics that have become critical to demonstrate their quality of care, as reimbursement by government programs can be contingent upon how providers are measured in “Quality of Care”. For example, the Medicare Access and CHIP Reauthorization Act (MACRA) of 2015, also called the Permanent Doc Fix, changes the way Medicare doctors are reimbursed with the implementation of a merit based incentive. The performance-based pressure is huge, which makes it imperative that every provider consider technology solutions. Read more at https://www.solix.com/solutions/data-driven-solutions/healthcare/
The document discusses using health management groups (HMGs) to integrate self-reported data from health risk assessments (HRAs) with clinical and administrative claims data to further describe population health. It presents the development of standardized HMGs for conditions like smoking, physical activity, nutrition, and obesity. The HMGs stratify individuals into levels based on their risk profiles to help identify intervention opportunities. Results shown indicate HMGs can distinguish groups by costs, risks, and health outcomes to better target programming and resources.
MIPS APM for ACOs: A Hybrid Reimbursement ModelCitiusTech
This document discusses MIPS APM scoring for ACOs that do not meet the patient and payment thresholds to be classified as Advanced APMs. It provides an overview of MIPS APM reporting requirements and timelines, the measures ACOs can report through various methods like surveys and claims, and how payment adjustments will be determined based on a composite performance score. Key advantages of MIPS APM scoring include reduced reporting burdens and greater weight given to quality over cost measures.
Quality and Outcome Framework (QOF) is a voluntary annual incentive programme for GPs in England, detailing practice achievement results. The primary objective of QOF is to drive the quality of primary care and reduce variations in the quality of care amongst GPs
This document provides an overview of clinical data management processes. It discusses the goals of clinical data management which are to provide high-quality, accurate data through processes like case report form design, data entry, validation, and coding. It describes some commonly used clinical data management software and standards/guidelines like 21 CFR Part 11 and SCDM's Good Clinical Data Management Practices. The document is a project report submitted by students to fulfill requirements for a degree at Apollo Hospitals, New Delhi.
This document discusses trends in digitizing healthcare, including adopting electronic medical records and mobile health technologies. It describes investment in healthcare IT in various countries and regions, focusing on China. The opportunities of transforming healthcare through more integrated systems with better quality of care and outcomes are outlined. The document promotes using multifunction printers and analytics platforms to capture paper documents, simplify workflows, and unlock insights from structured and unstructured clinical data through searching, automated outputs, and personalized patient engagement.
The healthcare industry in India is facing a complex, competitive environment driven by disruptive technology. For healthcare organizations to survive, data-driven management is needed. As payment reforms increase pressure to improve quality while lowering costs, some hospitals have adopted quality improvement methodologies from other industries, like data analytics. Experts believe healthcare providers that invest in analytics through partnerships will have success. The article outlines the types of data analytics used in healthcare - descriptive analytics of past data, predictive analytics to forecast future needs, and prescriptive analytics to provide recommendations. It also discusses challenges to implementing data-driven management and provides steps for healthcare organizations to become more data-driven.
Caresoft Hospital Information System is a customizable, integrated hospital management software that offers standard, premium, and basic modules. It provides features like patient registration, billing, reporting, doctor management, lab, pharmacy, and other modules. The system aims to improve hospital management with a performance-based, intelligent approach through automation, integration, and analytics.
Healthcare Data Quality & Monitoring PlaybookCitiusTech
The healthcare industry has made significant strides across the care continuum, but incomplete and poor data quality still remains a challenge. In this brief playbook, we share key challenges, important quality checks, and a 4 step approach to enhance data quality.
Data-driven Healthcare for the Pharmaceutical IndustryLindaWatson19
The tremendous opportunity of a data-driven strategy is apparent to the pharmaceutical industry, as all these informational assets exhibiting volume, variety, and velocity need to be ingested and analyzed for enhanced insight leading to better business decisions to address proactively the needs of patient care, while getting to market cheaper, faster, with better products.
The Future of Healthcare in Consumerism WorldCitiusTech
The main aim of this document is to provide an overview of healthcare consumerism, its growth drivers and challenges / barriers providers and payers face while adopting it. The document provides insights on how providers and payers can tackle the rising wave of consumerism in healthcare industry. The document also provides some real-life examples on market trends which emphasize the need to brace consumerism in healthcare
Importance of URAC Accreditation for Health PlansCitiusTech
Utilization Review accreditation Commission (URAC) is one of the two major organizations which accredit health plans on various healthcare quality measures.It is a Washington DC-based, nonprofit, independent organization founded in 1990, recognized by 46 states, District of Columbia, and 6 federal agencies
Meditech - Healthcare Information System - Sunil Nair Health Informatics Dalh...Sunil Nair
Meditech is a healthcare IT company that provides an integrated software system for healthcare organizations. Their system handles various functions like patient management, clinical documentation, financial management, and more. It allows for things like computerized physician order entry, management of clinical and financial data, budgeting and cost analysis. The system aims to facilitate information sharing across different departments and providers. It collects data in a centralized data repository for reporting and decision making. Meditech competes with other large healthcare IT vendors and their system is used widely, though full integration across the healthcare system has yet to be achieved.
THE TECHNOLOGY OF USING A DATA WAREHOUSE TO SUPPORT DECISION-MAKING IN HEALTH...ijdms
This paper describes the technology of data warehouse in healthcare decision-making and tools for support
of these technologies, which is used to cancer diseases. The healthcare executive managers and doctors
needs information about and insight into the existing health data, so as to make decision more efficiently
without interrupting the daily work of an On-Line Transaction Processing (OLTP) system. This is a
complex problem during the healthcare decision-making process. To solve this problem, the building a
healthcare data warehouse seems to be efficient. First in this paper we explain the concepts of the data
warehouse, On-Line Analysis Processing (OLAP). Changing the data in the data warehouse into a
multidimensional data cube is then shown. Finally, an application example is given to illustrate the use of
the healthcare data warehouse specific to cancer diseases developed in this study. The executive managers
and doctors can view data from more than one perspective with reduced query time, thus making decisions
faster and more comprehensive
This document discusses electronic clinical quality measures (eCQMs) which are designed to leverage health information technology (HIT) to improve quality measurement. eCQMs use standardized data elements and terminology to measure care quality based on information in electronic health records. Effective eCQM reporting requires structured, coded data and use of standards for measure specification, calculation, and reporting. Widespread use of eCQMs could revolutionize quality measurement by facilitating automated reporting and improving data quality.
5 Inovasi Cobroketeam untuk Agen PropertiTom Hardi
CobrokeTeam adalah jejaring sosial agen properti di Indonesia yang dirancang khusus untuk perangkat seluler dan tablet. Aplikasi ini memungkinkan agen untuk menginput, mencari, dan mempromosikan properti secara mudah dan cepat serta membangun jaringan dengan agen lain.
1. The document describes a new approach to endodontic instrumentation using modified reamers with a reciprocating motion rather than continuous rotation to shape root canals.
2. This approach is proposed to have advantages such as being faster, causing less instrument separation and tooth structure removal, and producing fewer dentinal cracks compared to traditional rotary instrumentation.
3. The key elements of the approach are using relieved vertically-fluted reamers in a reciprocating motion at 3000-4000 cycles per minute to efficiently shape canals while preserving tooth structure and integrity.
The document discusses how digitizing healthcare can transform the industry by moving from standalone systems to integrated systems that provide real-time access to data. It notes healthcare is moving from paper-based systems with data silos to integrated electronic systems that can improve quality of care through features like alerts and collaboration. The document also discusses how capturing unstructured data from sources like clinical notes using technologies like natural language processing can provide insights to help monitor metrics, identify conditions, and support research.
Information Management in Pharmaceutical IndustryFrank Wang
This document discusses the challenges and opportunities for information management in the biopharma industries. It covers topics like challenges in R&D, clinical trials, sales and marketing productivity. It also discusses the evolving biopharma value chain and how information management can help in areas like pharmaceutical R&D, clinical operations, sales, marketing, manufacturing and supply chain optimization. Finally, it discusses how the pharmaceutical industry is at a crossroads with challenges like declining R&D productivity and changing market landscapes in emerging markets and developed nations.
Healthcare ito in healthcare payer - annual report - preview deck - july 2013Everest Group
This report provides an overview of the ITO market for the healthcare payer industry. Analysis includes key trends in market size & growth, demand drivers, adoption & scope trends, emerging themes, key areas of investment, and implications for key stakeholders. The report also provides specific updates on the readiness of the various stakeholders from the perspective of payer reform mandates
The convergence of health plans and healthcare providers has led to the growing importance for provider-led health plans (Payviders). This eBook highlights the data and technology capabilities necessary for Payvider organizations to optimize performance and drive operational efficiencies.
Providers need to move towards real-time analytics that have become critical to demonstrate their quality of care, as reimbursement by government programs can be contingent upon how providers are measured in “Quality of Care”. For example, the Medicare Access and CHIP Reauthorization Act (MACRA) of 2015, also called the Permanent Doc Fix, changes the way Medicare doctors are reimbursed with the implementation of a merit based incentive. The performance-based pressure is huge, which makes it imperative that every provider consider technology solutions. Read more at https://www.solix.com/solutions/data-driven-solutions/healthcare/
The document discusses using health management groups (HMGs) to integrate self-reported data from health risk assessments (HRAs) with clinical and administrative claims data to further describe population health. It presents the development of standardized HMGs for conditions like smoking, physical activity, nutrition, and obesity. The HMGs stratify individuals into levels based on their risk profiles to help identify intervention opportunities. Results shown indicate HMGs can distinguish groups by costs, risks, and health outcomes to better target programming and resources.
MIPS APM for ACOs: A Hybrid Reimbursement ModelCitiusTech
This document discusses MIPS APM scoring for ACOs that do not meet the patient and payment thresholds to be classified as Advanced APMs. It provides an overview of MIPS APM reporting requirements and timelines, the measures ACOs can report through various methods like surveys and claims, and how payment adjustments will be determined based on a composite performance score. Key advantages of MIPS APM scoring include reduced reporting burdens and greater weight given to quality over cost measures.
Quality and Outcome Framework (QOF) is a voluntary annual incentive programme for GPs in England, detailing practice achievement results. The primary objective of QOF is to drive the quality of primary care and reduce variations in the quality of care amongst GPs
This document provides an overview of clinical data management processes. It discusses the goals of clinical data management which are to provide high-quality, accurate data through processes like case report form design, data entry, validation, and coding. It describes some commonly used clinical data management software and standards/guidelines like 21 CFR Part 11 and SCDM's Good Clinical Data Management Practices. The document is a project report submitted by students to fulfill requirements for a degree at Apollo Hospitals, New Delhi.
This document discusses trends in digitizing healthcare, including adopting electronic medical records and mobile health technologies. It describes investment in healthcare IT in various countries and regions, focusing on China. The opportunities of transforming healthcare through more integrated systems with better quality of care and outcomes are outlined. The document promotes using multifunction printers and analytics platforms to capture paper documents, simplify workflows, and unlock insights from structured and unstructured clinical data through searching, automated outputs, and personalized patient engagement.
The healthcare industry in India is facing a complex, competitive environment driven by disruptive technology. For healthcare organizations to survive, data-driven management is needed. As payment reforms increase pressure to improve quality while lowering costs, some hospitals have adopted quality improvement methodologies from other industries, like data analytics. Experts believe healthcare providers that invest in analytics through partnerships will have success. The article outlines the types of data analytics used in healthcare - descriptive analytics of past data, predictive analytics to forecast future needs, and prescriptive analytics to provide recommendations. It also discusses challenges to implementing data-driven management and provides steps for healthcare organizations to become more data-driven.
Caresoft Hospital Information System is a customizable, integrated hospital management software that offers standard, premium, and basic modules. It provides features like patient registration, billing, reporting, doctor management, lab, pharmacy, and other modules. The system aims to improve hospital management with a performance-based, intelligent approach through automation, integration, and analytics.
Healthcare Data Quality & Monitoring PlaybookCitiusTech
The healthcare industry has made significant strides across the care continuum, but incomplete and poor data quality still remains a challenge. In this brief playbook, we share key challenges, important quality checks, and a 4 step approach to enhance data quality.
Data-driven Healthcare for the Pharmaceutical IndustryLindaWatson19
The tremendous opportunity of a data-driven strategy is apparent to the pharmaceutical industry, as all these informational assets exhibiting volume, variety, and velocity need to be ingested and analyzed for enhanced insight leading to better business decisions to address proactively the needs of patient care, while getting to market cheaper, faster, with better products.
The Future of Healthcare in Consumerism WorldCitiusTech
The main aim of this document is to provide an overview of healthcare consumerism, its growth drivers and challenges / barriers providers and payers face while adopting it. The document provides insights on how providers and payers can tackle the rising wave of consumerism in healthcare industry. The document also provides some real-life examples on market trends which emphasize the need to brace consumerism in healthcare
Importance of URAC Accreditation for Health PlansCitiusTech
Utilization Review accreditation Commission (URAC) is one of the two major organizations which accredit health plans on various healthcare quality measures.It is a Washington DC-based, nonprofit, independent organization founded in 1990, recognized by 46 states, District of Columbia, and 6 federal agencies
Meditech - Healthcare Information System - Sunil Nair Health Informatics Dalh...Sunil Nair
Meditech is a healthcare IT company that provides an integrated software system for healthcare organizations. Their system handles various functions like patient management, clinical documentation, financial management, and more. It allows for things like computerized physician order entry, management of clinical and financial data, budgeting and cost analysis. The system aims to facilitate information sharing across different departments and providers. It collects data in a centralized data repository for reporting and decision making. Meditech competes with other large healthcare IT vendors and their system is used widely, though full integration across the healthcare system has yet to be achieved.
THE TECHNOLOGY OF USING A DATA WAREHOUSE TO SUPPORT DECISION-MAKING IN HEALTH...ijdms
This paper describes the technology of data warehouse in healthcare decision-making and tools for support
of these technologies, which is used to cancer diseases. The healthcare executive managers and doctors
needs information about and insight into the existing health data, so as to make decision more efficiently
without interrupting the daily work of an On-Line Transaction Processing (OLTP) system. This is a
complex problem during the healthcare decision-making process. To solve this problem, the building a
healthcare data warehouse seems to be efficient. First in this paper we explain the concepts of the data
warehouse, On-Line Analysis Processing (OLAP). Changing the data in the data warehouse into a
multidimensional data cube is then shown. Finally, an application example is given to illustrate the use of
the healthcare data warehouse specific to cancer diseases developed in this study. The executive managers
and doctors can view data from more than one perspective with reduced query time, thus making decisions
faster and more comprehensive
This document discusses electronic clinical quality measures (eCQMs) which are designed to leverage health information technology (HIT) to improve quality measurement. eCQMs use standardized data elements and terminology to measure care quality based on information in electronic health records. Effective eCQM reporting requires structured, coded data and use of standards for measure specification, calculation, and reporting. Widespread use of eCQMs could revolutionize quality measurement by facilitating automated reporting and improving data quality.
5 Inovasi Cobroketeam untuk Agen PropertiTom Hardi
CobrokeTeam adalah jejaring sosial agen properti di Indonesia yang dirancang khusus untuk perangkat seluler dan tablet. Aplikasi ini memungkinkan agen untuk menginput, mencari, dan mempromosikan properti secara mudah dan cepat serta membangun jaringan dengan agen lain.
1. The document describes a new approach to endodontic instrumentation using modified reamers with a reciprocating motion rather than continuous rotation to shape root canals.
2. This approach is proposed to have advantages such as being faster, causing less instrument separation and tooth structure removal, and producing fewer dentinal cracks compared to traditional rotary instrumentation.
3. The key elements of the approach are using relieved vertically-fluted reamers in a reciprocating motion at 3000-4000 cycles per minute to efficiently shape canals while preserving tooth structure and integrity.
Rongde Qiu is a .NET developer with experience developing web applications using technologies like ASP.NET MVC, C#, HTML, CSS, JavaScript, and hosting applications on Windows Azure and Linux/Mono. He has skills in programming languages, .NET development, databases, data access, development tools, and web deployment. His experience includes projects leading the development of an e-commerce site and deploying LAMP and ASP.NET applications on Azure virtual machines.
This short document promotes creating presentations using Haiku Deck, a tool for making slideshows. It encourages the reader to get started making their own Haiku Deck presentation and sharing it on SlideShare. In just one sentence, it pitches the idea of using Haiku Deck to easily create engaging slideshows.
Software Security in DevOps: Synthesizing Practitioners’ Perceptions and Prac...Akond Rahman
In organizations that use DevOps practices, software changes can be deployed as fast as 500 times or more per day. Without adequate involvement of the security team, rapidly deployed software changes are more likely to contain vulnerabilities due to lack of adequate reviews. The goal of this paper is to aid software practitioners in integrating security and DevOps by summarizing experiences in utilizing security practices in a DevOps environment. We analyzed a selected set of Internet artifacts and surveyed representatives of nine organizations that are using DevOps to systematically explore experiences in utilizing security practices. We observe that the majority of the software practitioners have expressed the potential of common DevOps activities, such as automated monitoring, to improve the security of a system. Furthermore, organizations that integrate DevOps and security utilize additional security activities, such as security requirements analysis and performing security configurations. Additionally, these teams also have established collaboration between the security team and the development and operations teams.
IBM Summer Internship - Summary of ExperienceDavid Gregan
David Gregan interned at IBM in the summer of 2014 where he primarily worked in Central Business Services creating newsletters and managing assets. He also assisted the Test Automation Development Team by writing test case scenarios and shadowed a Mobile Developer. Through his internship, Gregan learned about business operations, workplace security, using high-end software systems, and working as part of a large team. Highlights included touring IBM server rooms and shadowing a Mobile Developer, while workplace laptop issues presented a low point.
El documento describe las características de un buen orador. Un orador es alguien que se expresa públicamente, generalmente a través de discursos. Para lograr su objetivo de persuadir a la audiencia, un orador debe dominar el arte de la oratoria y poseer ciertas características intelectuales, morales y de comunicación como ser sincero, honrado, tener buena memoria e imaginarción, y hablar de manera clara y adecuada para la audiencia. El documento también menciona algunos grandes oradores históricos como Abraham
George Vamos has over 30 years of experience as a lead software engineer developing mission critical systems. He has expertise in medical data management systems, aerospace command and control systems, and business databases. Some of his accomplishments include developing medical data warehouses that can adapt to changing data formats, and databases shared by spacecraft tracking systems where much of the schema is auto-generated from requirements. He holds patents related to neural implant management technology.
Ejercicios: Identifica en Metáfora o SímilNydia González
Este documento presenta varios ejemplos de poemas modernistas escritos por autores como Rubén Darío, Federico García Lorca, Juan Ramón Jiménez y Pablo Neruda. Cada poema contiene ejemplos de lenguaje figurado como la metáfora o el símil que ilustran características del modernismo en la literatura hispanoamericana.
This document provides an overview of quality and operations management. It discusses various quality management tools like check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, histograms. It also lists additional topics related to quality and operations management such as quality management systems, courses, standards and strategies. The document contains information that would be useful for someone looking to learn more about quality and operations management.
El documento resume los principios del taylorismo, un método de organización industrial desarrollado por Frederick W. Taylor que buscaba maximizar la eficiencia y productividad mediante la estandarización y minucioso estudio de los procesos productivos, la especialización de tareas, y la remuneración en función de la productividad. Algunas de las ideas clave del taylorismo incluyen la división extrema del trabajo, la cronometración de tareas, la reducción de tiempos muertos, y priorizar la eficiencia sobre la satisfacción de los trabaj
Synthesizing Continuous Deployment Practices in Software DevelopmentAkond Rahman
Continuous deployment speeds up the process of existing agile methods, such as Scrum, and Extreme Programming (XP) through the automatic deployment of software changes to end-users upon passing of automated tests. Continuous deployment has become an emerging software engineering process amongst numerous software companies, such as Facebook, Github, Netflix, and Rally Software. A systematic analysis of software practices used in continuous deployment can facilitate a better understanding of continuous deployment as a software engineering process. Such analysis can also help software practitioners in having a shared vocabulary of practices and in choosing the software practices that they can use to implement continuous deployment. The goal of this paper is to aid software practitioners in implementing continuous deployment through a systematic analysis of software practices that are used by software companies. We studied the continuous deployment practices of 19 software companies by performing a qualitative analysis of Internet artifacts and by conducting follow-up inquiries. In total, we found 11 software practices that are used by 19 software companies. We also found that in terms of use, eight of the 11 software practices are common across 14 software companies. We observe that continuous deployment necessitates the consistent use of sound software engineering practices such as automated testing, automated deployment, and code review.
1. The document describes a new approach to endodontic instrumentation using modified reamers with a reciprocating motion rather than continuous rotation to shape root canals.
2. This approach is proposed to have advantages such as being faster, causing less instrument separation and tooth structure removal, and producing fewer dentinal cracks compared to traditional rotary instrumentation.
3. The key elements of the approach are using relieved vertically-fluted reamers in a reciprocating motion at 3000-4000 cycles per minute to efficiently shape canals while preserving tooth structure and integrity.
Este documento presenta información sobre el texto argumentativo. Explica que el objetivo de un párrafo argumentativo es expresar opiniones o rebatirlas con el fin de persuadir al lector. Describe las características de un texto argumentativo como la introducción, el desarrollo de la tesis con argumentos, y la conclusión. También define el término "argumentar" y da ejemplos de cómo y dónde se usa comúnmente la argumentación.
This document contains 12 questions related to electrical machines including transformers, induction motors, synchronous machines, and DC machines. The questions cover topics like determining equivalent circuits, calculating voltages, currents, power, efficiency, slip, and more for various electrical machines and operating conditions. Multiple questions involve calculating values for motors and generators given circuit parameters and operating conditions.
The document discusses healthcare analytics and data management. It begins by outlining the typical evolution of data collection, sharing, and analysis that occurs in industries. It then discusses key principles for healthcare analytics including regularly evaluating goals, measures, and how to achieve them. The remainder of the document discusses challenges around data binding, governance, and adoption models for healthcare analytics. It emphasizes the importance of analytics for return on investment and outlines strategic options and considerations for healthcare organizations evaluating their analytic capabilities.
While Healthcare 1.0 was broadly defined by a focus on defensive medicine, billing, and fee-for-service, culminating in the mass adoption of EMRs, Healthcare 2.0 is a new wave focused on improving clinical efficiency, quality of care, affordability, and fee-for-value; culminating in a new age of healthcare analytics. This new age of analytics will require a new set of organizational skills and a foundational set of analytic information systems that many executives have not anticipated.
Join Dale Sanders, a 20-year healthcare CIO veteran and the industry's leading analytics expert, as he discusses his lessons learned, best practices in analytics, and what the C-level suite needs to know about this topic, now. Listen to Dale discuss 1) A step-by-step curriculum for analytic adoption and maturity in healthcare organizations, 2) the basic approach to a late-binding data warehouse, 3) pros and cons of early versus late binding, 4) the volatility in vocabulary and business rules in healthcare, 5) how to engineer your data to accommodate volatility in the future
The document discusses how accurate, consistent data is essential for healthcare organizations to succeed in the transition to value-based care. It explains that population health management, care coordination, predictive analytics, performance improvement, and clinical quality reporting all require reliable data captured across settings. Ensuring data quality requires establishing governance and processes to standardize, monitor, and protect this valuable asset. When structured properly, healthcare data can be leveraged for strategic decision making, care management, and financial success under new payment models.
The document discusses how healthcare organizations can improve patient outcomes through better integration of their systems, data, and governance. It emphasizes that integrating different sources of patient information, standardizing data, and using analytics tools are key to measuring performance and providing predictive analytics. The document recommends developing strategies for integration, data management, and choosing technology platforms that simplify workflows while focusing on the patient experience rather than just the systems.
Three Steps to Prioritize Clinical Quality Improvement in HealthcareHealth Catalyst
Healthcare organizations today have access to so much data from across their systems that they may struggle to know where to focus quality improvement efforts. An analytic framework and a stepwise process ensures organizations have broad data access and can identify the most significant opportunities for impact. With a strategic, data-informed approach to clinical quality improvement, health systems can consume fewer resources, discover cost savings, and improve ROI and the quality of care.
Three steps comprise an effective quality improvement process:
1. Adopt a healthcare-specific, open, scalable data platform.
2. Identify improvement priorities using the 80-20 rule.
3. Gain consensus from clinical teams on specific projects and goals.
HealthCare System EHR Governance Information Discussion.pdfsdfghj21
This document discusses establishing an information governance program to optimize an electronic health record (EHR) system. It provides an example of Hawaii Pacific Health, which focused on improving data quality and standardizing definitions to decrease costs and improve clinical quality. The summary discusses that an information governance program sets accountability and priorities for managing health system data and information assets. It also notes that executive support is important to establish a successful long-term information governance program.
6/29/2016 library.ahima.org/PB/DataStandards#appxA
http://library.ahima.org/PB/DataStandards#appxA 1/20
Data Standards, Data Quality, and Interoperability (2013
update)
Remove from myBoK
Editor's note: This update replaces the 2007 practice brief "Data Standards, Data Quality, and Interoperability."
Data quality and consistency are critical to ensuring patient safety, communicating delivery of health services, coordinating
care, and healthcare reporting. Assessing the quality and consistency of data requires data standards. This practice brief
provides health information management (HIM) professionals with a clear understanding of data standards as a tool to
enable interoperability and promote data quality.
The online version of this practice brief [...] is accompanied by an appendix that provides HIM professionals with a list of
standards to reference in data dictionary development, electronic health records, the exchange of health information, and
general data management processes to ensure information integrity and reliability. Evaluation of data validity, reliability,
completeness, and timeliness are accomplished through a combination of human and machine processes in healthcare, and
the list of data standard sources is a helpful reference guide when more detailed information is required.
Data Standards and Regulatory Framework
Data standards are "documented agreements on representations, formats, and definitions of common data. Data standards
provide a method to codify invalid, meaningful, comprehensive, and actionable ways, information captured in the course of
doing business." Rules to describe how the data is recorded to ensure consistency across multiple sources is another way to
think of data standards. Without data standards and data quality, the future of interoperability is bleak. Data fields and the
content of those fields need to be standardized.
Standards development organizations (SDOs) address a variety of aspects of health information and informatics. For
example, the American Society for Testing and Materials (ASTM) and Health Level Seven (HL7) target clinical data
standards. Insurance and remittance standards are a focus of the Accredited Standards Committee (ASC) X12. Standards to
transmit diagnostic images are developed through Digital Imaging and Communications in Medicine (DICOM). The
National Council for Prescription Drug Programs (NCPDP) represents pharmacy messages.
The Institute of Electrical and Electronics Engineers (IEEE), HL7, ASTM, and others develop data models and
frameworks. See the table on page 65 for a breakdown of regulatory agencies responsible for working with the American
National Standards Institute (ANSI) to drive data standards to achieve interoperability.
The AHIMA Leadership Model states that HIM professionals should serve as the leaders in healthcare organizations and in
their professional community for ensuring that data content standards are identified, understood, implemented, a.
6/29/2016 library.ahima.org/PB/DataStandards#appxA
http://library.ahima.org/PB/DataStandards#appxA 1/20
Data Standards, Data Quality, and Interoperability (2013
update)
Remove from myBoK
Editor's note: This update replaces the 2007 practice brief "Data Standards, Data Quality, and Interoperability."
Data quality and consistency are critical to ensuring patient safety, communicating delivery of health services, coordinating
care, and healthcare reporting. Assessing the quality and consistency of data requires data standards. This practice brief
provides health information management (HIM) professionals with a clear understanding of data standards as a tool to
enable interoperability and promote data quality.
The online version of this practice brief [...] is accompanied by an appendix that provides HIM professionals with a list of
standards to reference in data dictionary development, electronic health records, the exchange of health information, and
general data management processes to ensure information integrity and reliability. Evaluation of data validity, reliability,
completeness, and timeliness are accomplished through a combination of human and machine processes in healthcare, and
the list of data standard sources is a helpful reference guide when more detailed information is required.
Data Standards and Regulatory Framework
Data standards are "documented agreements on representations, formats, and definitions of common data. Data standards
provide a method to codify invalid, meaningful, comprehensive, and actionable ways, information captured in the course of
doing business." Rules to describe how the data is recorded to ensure consistency across multiple sources is another way to
think of data standards. Without data standards and data quality, the future of interoperability is bleak. Data fields and the
content of those fields need to be standardized.
Standards development organizations (SDOs) address a variety of aspects of health information and informatics. For
example, the American Society for Testing and Materials (ASTM) and Health Level Seven (HL7) target clinical data
standards. Insurance and remittance standards are a focus of the Accredited Standards Committee (ASC) X12. Standards to
transmit diagnostic images are developed through Digital Imaging and Communications in Medicine (DICOM). The
National Council for Prescription Drug Programs (NCPDP) represents pharmacy messages.
The Institute of Electrical and Electronics Engineers (IEEE), HL7, ASTM, and others develop data models and
frameworks. See the table on page 65 for a breakdown of regulatory agencies responsible for working with the American
National Standards Institute (ANSI) to drive data standards to achieve interoperability.
The AHIMA Leadership Model states that HIM professionals should serve as the leaders in healthcare organizations and in
their professional community for ensuring that data content standards are identified, understood, implemented, a ...
Meaningful Use Stage Two: The Future of Care CoordinationGreenway Health
The future of Meaningful Use has many over-arching effects on the health care industry beyond Stage Two measures. Care coordination teams, technology partnerships, data capture, practice redesign, and provider assessment are a few others to be considered when moving forward.
As per EU MDR, Post Marketing Clinical Follow-up (PMCF) is a continuous process where device manufacturers need to proactively collect and evaluate clinical data of the device when it is used as per the intended purpose. EU MDR gives more emphasize on PMCF data to confirm the safety and performance of the device throughout its expected lifetime, ensure continued acceptability of identified risks and detect emerging risks based on factual evidence.
Healthcare Analytics Adoption Model -- UpdatedHealth Catalyst
The Healthcare Analytics Adoption Model is the result of a collaboration of healthcare industry veterans over the last 15 years. The model borrows lessons learned from the HIMSS EMR Adoption Model, and describes an analogous approach for assessing the adoption of analytics in healthcare.
The Healthcare Analytics Adoption Model provides:
1) A framework for evaluating the industry’s adoption of analytics
2) A roadmap for organizations to measure their own progress toward analytic adoption
3) A framework for evaluating vendor products
This Analytics Adoption Model will enable healthcare organizations to fully understand and leverage the capabilities of analytics and so achieve the ultimate goal that has eluded most provider organizations – that of improving the quality of care while lowering costs and enhancing clinician and patient satisfaction.
This document discusses clinical information systems and their role in healthcare. It begins with background on healthcare and how information technology has helped address issues with declining resources and rapid knowledge growth. It then defines and discusses hospital information systems, clinical information systems, clinical decision support systems, and electronic medical records. It explains how these systems help with tasks like data management, decision making, and improving quality of care. The document also covers healthcare strategy making and how clinical information systems are developed and integrated.
Individual physician performance has a direct impact on a health system’s financial, patient safety, and care quality initiatives. It is also a key performance indicator, integral to helping hospitals deliver better care at lower costs. As the healthcare industry implements ICD-10 and continues the shift towards reimbursement tied to value, efficiency, and clinical quality of care, the need to enlist physicians to help drive clinical practice changes and improve documentation is urgent. Forward-thinking hospitals are looking for strategies and tools to help manage the change and to align physicians with organizational goals; they are finding that implementing a physician scorecard is a must.
A crucial stage in clinical research is clinical data management CDM , which produces high quality, reliable, and statistically sound data from clinical trials. This results in a significantly shorter period of time between drug development and marketing. Team members of CDM are laboriously involved in all stages of clinical trials right from commencement to completion. They should be able to sustain the quality standards set by CDM processes by having sufficient process expertise. colorful procedures in CDM including Case Report Form CRF designing, CRF reflection, database designing, data entry, data confirmation, distinction operation, medical coding, data birth, and database locking are assessed for quality at regular intervals during a trial. In the present script, theres an increased demand to ameliorate the CDM norms to meet the nonsupervisory conditions and stay ahead of the competition by means of brisk commercialization of products. With the perpetration of nonsupervisory biddable data operation tools, the CDM platoon can meet these demands. also, its getting obligatory for companies to submit the data electronically. CDM professionals should meet applicable prospects and set norms for data quality and also have the drive to acclimatize to the fleetly changing technology. This composition highlights the processes involved and provides the anthology an overview of the tools and norms espoused as well as the places and liabilities in CDM. Syed Shahnawaz Quadri | Syeda Saniya Ifteqar | Syed Shafa Raoof "Data Management in Clinical Research" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-2 , April 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd55050.pdf Paper URL: https://www.ijtsrd.com.com/pharmacy/other/55050/data-management-in-clinical-research/syed-shahnawaz-quadri
Health IT Summit Houston 2014 - Case Study "EHR Optimization for Organizational Value in a Changing Healthcare Environment"
Luis Saldana, MD, MBA, FACEP
CMIO
Texas Health Resources
iHT2 case studies and presentations illustrate challenges, successes and various factors in the outcomes of numerous types of health IT implementations. They are interactive and dynamic sessions providing opportunity for dialogue, debate and exchanging ideas and best practices. This session will be presented by a thought leader in the provider, payer or government space.
We have spent a lot of time this semester talking about various as.docxmelbruce90096
We have spent a lot of time this semester talking about various aspects of the health care industry -- cost, access, utilization, strategy. Another important aspect that needs to be balanced with all these other concerns is QUALITY!
What does QUALITY mean in health care?
How do you go about defining quality in health care? Is there just one measure of quality, or more?!
Find one outside article that addresses health care quality. Tell us about the article and how they define quality.
Be sure to post your citations
Alicia AliendreCOLLAPSE
Top of Form
Parent Post
In the health care industry quality of care means everyone participating in ways to improve health care such as health care professionals, patients and their families, researchers, payers, planners and educators. These changes lead to better outcomes in health, a better system performance in care, as well as better professional development.
When you describe quality, it’s the process for making strategic choices in health systems for quality assurance in health care and decision making. Although there are many outcomes to improve quality of care, the main concern is accomplishing a goal that will be beneficial for the future.
Good quality means providing patients with appropriate services in a technically competent manner, with good communication, shared decision making, and cultural sensitivity. In practical terms, poor quality can mean too much care (e.g., providing unnecessary tests, medications, and procedures, with associated risks and side effects), too little care (e.g., not providing an indicated diagnostic test or a lifesaving surgical procedure), or the wrong care (e.g., prescribing medicines that should not be given together, using poor surgical technique).
Quality can be evaluated based on structure, process, and outcomes (Donabedian 1980). Structural quality evaluates health system characteristics, process quality assesses interactions between clinicians and patients, and outcomes offer evidence about changes in patients' health status. All three dimensions can provide valuable information for measuring quality, but the published quality-of-care literature reveals that there is more experience with measuring processes of care.
Marie Savino
To many health care consumers quality of health care can mean several different things, including wait times, doctors professionalism, the courtesy of the medical staff and use of updated medical technology, which can all effect how people judge the quality of health care they are receiving. These characteristics may be important to the patient but they do not add up to a quality health care system. Quality health care can be defined as levels of superiority which distinguish the health care provided based on accepted standards of quality. Several factors help measure quality of care:
* Safety- health care does not cause harm
* Effective- health care service is based on scientific and medical knowledge and is right for the.
Evaluation of a clinical information system (cis)nikita024
This power point presentation provides an overview of a clinical information system (CIS). It discusses what a CIS is, how CIS have evolved, and the key players involved in designing CIS. It also examines the electronic health record component of a CIS and discusses the eight basic components that make up an EHR. Additional topics covered include clinical decision making systems, safety, costs, and education regarding CIS. The presentation was created by four students with each student covering specific slides and aspects of the topic.
This document provides an overview of ITIL quality management. It discusses various quality management tools like check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. It also lists additional quality management topics and resources like quality management systems, courses, techniques, standards, policies and strategies. The document recommends reviewing additional external articles on using the Theory of Constraints approach to identify and eliminate bottlenecks in ITIL processes for continuous improvement.
The document discusses supplier quality management systems. It provides an overview of the benefits of the MetricStream supplier quality management solution, including enabling supplier access, real-time quality analysis, issue tracking, streamlined corrective actions, supplier charge-backs, supplier scorecards, and supplier audits. It also lists and briefly describes several quality management tools: check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. Finally, it lists additional topics related to supplier quality management systems.
This document discusses quality risk management tools. It provides definitions and background on risk analysis and management tools. Key criteria for selecting the right tool are that it must be aligned with risk analysis objectives, support decision making, be accessible to users, have available data, and integrate with other processes. The document describes MITRE-developed tools like RiskNav that facilitate risk management. It also discusses common quality management tools like check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. Other related quality topics are also listed.
This document provides information about quality operations management including definitions and examples of common quality management tools. It describes quality operations management strategies and resources for continuous process improvement. Key quality management tools discussed include check sheets, control charts, Pareto charts, and scatter plots. These tools help analyze processes, identify sources of variation, determine if processes are in statistical control, and highlight most important factors for improvement. The document emphasizes the importance of quality and process improvement for business competitiveness.
The document discusses a quality management statement for St John Ambulance. It outlines their commitment to continuous quality improvement and implementing quality management systems. It lists their objectives to work with customers, conduct business reflecting core values, promote continuous improvement, ensure compliance, and provide training to support high quality services. The statement is supported by individual policies on corporate governance, clinical governance, people management, first aid training, event cover, and child protection.
This document provides an overview of quality management standards and tools. It discusses how quality management standards can help businesses improve efficiency and meet customer expectations. The document then lists and describes six common quality management tools: check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. It also provides additional links and resources related to quality management standards.
This document discusses quality management qualifications. It provides information on the typical education, skills, and experience required for quality management roles. A bachelor's degree is typically required, with some pursuing MBAs. Analytical skills, documentation skills, and management experience are important. The document also lists several quality management tools, including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, histograms, and provides links to additional quality management resources.
This document discusses project management quality management. It provides definitions and concepts related to quality management including customer satisfaction, prevention over inspection, and continuous improvement. It also outlines three key processes for project quality management: plan quality, perform quality assurance, and perform quality control. Finally, it describes several quality management tools including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, histograms, and others. If more assistance is needed with project management quality management, the reader is invited to leave a comment.
This document discusses quality management tools and techniques that can be used for the PMP exam, including check sheets, control charts, Pareto charts, and scatter plots. It provides detailed descriptions of each tool and how they are used to collect and analyze quality data. The document is from a course that thoroughly prepares students for the PMP certification exam by covering all aspects of quality management and other key project management topics.
This document provides information about a PG Diploma in Food Safety and Quality Management program offered by IGNOU and APEDA in India. The 1-year program aims to develop professionals for food safety and quality management. It covers topics like food laws, quality systems, auditing, and chemical analysis. Eligible candidates include science or allied science graduates with experience in food processing or quality control. Graduates will be qualified for jobs in food quality assurance and auditing. The program also lists free online resources on quality management tools, systems, and standards.
This document provides information about a Master's Degree in Quality Management, including:
- The program covers quality management concepts and theories to help design, implement, and manage quality systems.
- Students complete core subjects in quality management and choose electives to gain specialized skills for quality roles.
- Graduates can pursue careers in quality assurance, auditing, engineering, and management consulting across many industries.
The document discusses the key components of quality management systems. It identifies six main components: management support, customer focus, process approach, continual improvement, quality management tools, and other related topics. It provides details on each component and describes several common quality management tools, including check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams, and histograms. The goal of quality management is to systematically meet or exceed customer expectations through continual improvement.
1. Quality management model
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• qualitymanagement123.com/top-84-quality-management-KPIs
• qualitymanagement123.com/top-18-quality-management-job-descriptions
• qualitymanagement123.com/86-quality-management-interview-questions-and-answers
I. Contents of quality management model
==================
Healthcare leaders face many challenges, from the ICD-10-CM/PCS transition to achieving
meaningful use, launching accountable care organizations (ACOs) and value-based purchasing
programs, assuring the sustainability of health information exchanges (HIEs), and maintaining
compliance with multiple health data reporting and clinical documentation requirements—
among others. These initiatives impacting the health information management (HIM) and
healthcare industries have a common theme: data.
As electronic health record (EHR) systems have become more widely implemented in all
healthcare settings, these systems have developed and employed various methods of supporting
documentation for electronic records. Complaints and concerns are often voiced—whether via
blogs, online newsletters, or listservs—regarding the integrity, reliability, and compliance
capabilities of automated documentation processes. Several articles in the Journal of AHIMA
establish guidelines for preventing fraud in EHR systems. Documentation practices are within
the domain of the HIM profession, for both paper and electronic records.1
As a result, the need for more rigorous data quality governance, stewardship, management, and
measurement is greater than ever.
This practice brief will use the following definitions:
Data Quality Management: The business processes that ensure the integrity of an
organization's data during collection, application (including aggregation), warehousing, and
analysis.2 While the healthcare industry still has quite a journey ahead in order to reach the
robust goal of national healthcare data standards, the following initiatives are a step in the right
direction for data exchange and interoperability:
Continuity of Care Document (CCD), Clinical Documentation Architecture (CDA)
Data Elements for Emergency Department Systems (DEEDS)
2. Uniform Hospital Discharge Data Set (UHDDS)
Minimum Data Set (MDS) for long-term care
ICD-10-CM/PCS, Systemized Nomenclature of Medicine—Clinical Terms (SNOMED
CT), Logical Observation Identifiers Names and Codes (LOINC), RxNorm
Data Quality Measurement: A quality measure is a mechanism to assign a quantity to quality
of care by comparison to a criterion. Quality measurements typically focus on structures or
processes of care that have a demonstrated relationship to positive health outcomes and are under
the control of the healthcare system.3 This is evidenced by the many initiatives to capture
quality/performance measurement data, including:
The Joint Commission Core Measures
Outcomes and Assessment Information Set (OASIS) for home health care
National Committee for Quality Assurance's (NCQA) Health Plan Employer Data and
Information Set (HEDIS)
Meaningful Use-defined core and menu sets
Key Terms
Understanding of the following term definitions is key to ensure clarity in this article.
Data Quality Management: The business processes that ensure the integrity of an
organization's data during collection, application (including aggregation), warehousing,
and analysis.
Data Quality Measurement: A quality measure is a mechanism to assign a quantity to
quality of care by comparison to a criterion. Quality measurements typically focus on
structures or processes of care that have a demonstrated relationship to positive health
outcomes and are under the control of the healthcare system.
These data sets will be used within organizations for continuous quality improvement efforts and
to improve outcomes. They draw on data as raw material for research and comparing providers
and institutions with one another.
Payment reform and quality measure reporting initiatives increase a healthcare organization's
data needs for determining achievement of program goals, as well as identifying areas in need of
improvement. The introduction of new classification and terminology systems—with their
increased specificity and granularity—reinforce the importance of consistency, completeness,
and accuracy as key characteristics of data quality.4 The implementation of ICD-10 CM/PCS will
impact anyone using diagnosis or inpatient procedure codes, which are pervasive throughout
reimbursement systems, quality reporting, healthcare research and epidemiology, and public
health reporting. SNOMED CT, RxNorm, and LOINC terminologies have detailed levels for a
variety of healthcare needs, ranging from laboratory to pharmacy, and require awareness of the
underlying quality from the data elements.
Healthcare data serves many purposes across many settings, primarily directed towards patient
care. The industry is also moving towards an increased focus on ensuring that collected data is
available for many other purposes. The use of new technologies such as telemedicine, remote
monitoring, and mobile devices is also changing the nature of access to care and the manner in
which patients and their families interact with caregivers. The rates of EHR adoption and
3. development of HIEs continue to rise, which brings attention to assuring the integrity of the data
regardless of the practice setting, collection method, or system used to capture, store, and
transmit data across the healthcare continuum of care.
The main outcome of data quality management (DQM) is knowledge regarding the quality of
healthcare data and its fitness for applicable use in the intended purposes. DQM functions
involve continuous quality improvement for data quality throughout the enterprise (all healthcare
settings) and include data application, collection, analysis, and warehousing. DQM skills and
roles are not new to HIM professionals. As use of EHRs becomes widespread, however, data are
shared and repurposed in new and innovative ways, thus making data quality more important
than ever.
Data quality begins when EHR applications are planned. For example, data dictionaries for
applications should utilize standards for definitions and acceptable values whenever possible. For
additional information on this topic, please refer to the practice brief "Managing a Data
Dictionary."5
The quality of collected data can be affected by both the software—in the form of value labels or
other constraints around data entry—and the data entry mechanism whether it be automated or
manual. Automated data entry originates from various sources, such as clinical lab machines and
vital sign tools like blood pressure cuffs. All automated tools must be checked regularly to
ensure appropriate operation. Likewise, any staff entering data manually should be trained to
enter the data correctly and monitored for quality assurance.
For example, are measurements recorded in English or metric intervals? Does the organization
use military time? What is the process if the system cannot accept what the person believes is the
correct information?
Meaningful data analysis must be built upon high-quality data. Provided that underlying data is
correct, the analysis must use data in the correct context. For example, many organizations do
not collect external cause data if it is not required. Gunshot wounds would require external cause
data, whereas slipping on a rug would not. Developing an analysis around external causes and
representing it as complete would be misleading in many facilities. Additionally, the copy
capabilities available as a result of electronic health data are likely to proliferate as EHR
utilization expands. Readers can refer to AHIMA's Copy Functionality Toolkit for more
information on this topic.6Finally, with many terabytes of data generated by EHRs, the quality of
the data in warehouses will be paramount. The following are just some of the determinations that
need to be addressed to ensure a high-quality data warehouse:
Static data (date of birth, once entered correctly, should not change)
Dynamic data (patient temperature should fluctuate throughout the day)
When and how data updates (maintenance scheduling)
Versioning (DRGs and EHR systems change across the years—it is important to know
which grouper or EHR version was used)
Consequently, the healthcare industry needs data governance programs to help manage the
growing amount of electronic data.
Data Governance
Data governance is the high-level, corporate, or enterprise policies and strategies that define the
purpose for collecting data, the ownership of data, and the intended use of data. Accountability
4. and responsibility flow from data governance, and the data governance plan is the framework for
overall organizational approach to data governance.7
Information Governance and Stewardship
Information governance provides a foundation for the other
data-driven functions in AHIMA's HIM Core Model by providing parameters based on
organizational and compliance policies, processes, decision rights, and responsibilities.
Governance functions and stewardship ensure the use and management of health information is
compliant with jurisdictional law, regulation, standards, and organizational policies. As stewards
of health information, HIM professionals strive to protect and ensure the ethical use of health
information.8
The DQM model was developed to illustrate the different data quality challenges. The table
"Data Quality Management Model" includes a graphic of the DQM domains as they relate to the
characteristics of data integrity, and "Appendix A" includes examples of each characteristic
within each domain. The model is generic and adaptable to any care setting and for any
application. It is a tool or a model for HIM professionals to transition into enterprise-wide DQM
roles.
Assessing Data Quality Management Efforts
Traditionally, healthcare data quality practices were coordinated by HIM professionals using
paper records and department-based systems. These practices have evolved and now utilize data
elements, electronic searches, comparative and shared databases, data repositories, and
continuous quality improvement. As custodians of health records, HIM professionals have
historically performed warehousing functions such as purging, indexing, and editing data on all
types of media: paper, images, optical disk, computer disk, microfilm, and CD-ROM. In
addition, HIM professionals are experts in collecting and classifying data to support a variety of
needs. Some examples include severity of illness, meaningful use, pay for performance, data
mapping, and registries. Further, HIM professionals have encouraged and fostered the use of data
by ensuring its timely availability, coordinating its collection, and analyzing and reporting
collected data. To support these efforts, "Checklist to Assess Data Quality Management Efforts"
on page 67 outlines basic tenets in data quality management for healthcare professionals to
follow.
With AHIMA members fulfilling a wide variety of roles within the healthcare industry, HIM
professionals are expanding their responsibilities in data governance and stewardship.
Leadership, management skills, and IT knowledge are all required for effective expansion into
these areas.
Roles such as clinical data manager, terminology asset manager, and health data analyst
positions will continue to evolve into opportunities for those HIM professionals ready to upgrade
their expertise to keep pace with changing practice.
==================
III. Quality management tools
1. Check sheet
5. The check sheet is a form (document) used to collect data
in real time at the location where the data is generated.
The data it captures can be quantitative or qualitative.
When the information is quantitative, the check sheet is
sometimes called a tally sheet.
The defining characteristic of a check sheet is that data
are recorded by making marks ("checks") on it. A typical
check sheet is divided into regions, and marks made in
different regions have different significance. Data are
read by observing the location and number of marks on
the sheet.
Check sheets typically employ a heading that answers the
Five Ws:
Who filled out the check sheet
What was collected (what each check represents,
an identifying batch or lot number)
Where the collection took place (facility, room,
apparatus)
When the collection took place (hour, shift, day
of the week)
Why the data were collected
2. Control chart
Control charts, also known as Shewhart charts
(after Walter A. Shewhart) or process-behavior
charts, in statistical process control are tools used
to determine if a manufacturing or business
process is in a state of statistical control.
If analysis of the control chart indicates that the
process is currently under control (i.e., is stable,
with variation only coming from sources common
to the process), then no corrections or changes to
process control parameters are needed or desired.
In addition, data from the process can be used to
predict the future performance of the process. If
the chart indicates that the monitored process is
not in control, analysis of the chart can help
6. determine the sources of variation, as this will
result in degraded process performance.[1] A
process that is stable but operating outside of
desired (specification) limits (e.g., scrap rates
may be in statistical control but above desired
limits) needs to be improved through a deliberate
effort to understand the causes of current
performance and fundamentally improve the
process.
The control chart is one of the seven basic tools of
quality control.[3] Typically control charts are
used for time-series data, though they can be used
for data that have logical comparability (i.e. you
want to compare samples that were taken all at
the same time, or the performance of different
individuals), however the type of chart used to do
this requires consideration.
3. Pareto chart
A Pareto chart, named after Vilfredo Pareto, is a type
of chart that contains both bars and a line graph, where
individual values are represented in descending order
by bars, and the cumulative total is represented by the
line.
The left vertical axis is the frequency of occurrence,
but it can alternatively represent cost or another
important unit of measure. The right vertical axis is
the cumulative percentage of the total number of
occurrences, total cost, or total of the particular unit of
measure. Because the reasons are in decreasing order,
the cumulative function is a concave function. To take
the example above, in order to lower the amount of
late arrivals by 78%, it is sufficient to solve the first
three issues.
The purpose of the Pareto chart is to highlight the
most important among a (typically large) set of
factors. In quality control, it often represents the most
common sources of defects, the highest occurring type
of defect, or the most frequent reasons for customer
7. complaints, and so on. Wilkinson (2006) devised an
algorithm for producing statistically based acceptance
limits (similar to confidence intervals) for each bar in
the Pareto chart.
4. Scatter plot Method
A scatter plot, scatterplot, or scattergraph is a type of
mathematical diagram using Cartesian coordinates to
display values for two variables for a set of data.
The data is displayed as a collection of points, each
having the value of one variable determining the position
on the horizontal axis and the value of the other variable
determining the position on the vertical axis.[2] This kind
of plot is also called a scatter chart, scattergram, scatter
diagram,[3] or scatter graph.
A scatter plot is used when a variable exists that is under
the control of the experimenter. If a parameter exists that
is systematically incremented and/or decremented by the
other, it is called the control parameter or independent
variable and is customarily plotted along the horizontal
axis. The measured or dependent variable is customarily
plotted along the vertical axis. If no dependent variable
exists, either type of variable can be plotted on either axis
and a scatter plot will illustrate only the degree of
correlation (not causation) between two variables.
A scatter plot can suggest various kinds of correlations
between variables with a certain confidence interval. For
example, weight and height, weight would be on x axis
and height would be on the y axis. Correlations may be
positive (rising), negative (falling), or null (uncorrelated).
If the pattern of dots slopes from lower left to upper right,
it suggests a positive correlation between the variables
being studied. If the pattern of dots slopes from upper left
to lower right, it suggests a negative correlation. A line of
best fit (alternatively called 'trendline') can be drawn in
order to study the correlation between the variables. An
equation for the correlation between the variables can be
determined by established best-fit procedures. For a linear
8. correlation, the best-fit procedure is known as linear
regression and is guaranteed to generate a correct solution
in a finite time. No universal best-fit procedure is
guaranteed to generate a correct solution for arbitrary
relationships. A scatter plot is also very useful when we
wish to see how two comparable data sets agree with each
other. In this case, an identity line, i.e., a y=x line, or an
1:1 line, is often drawn as a reference. The more the two
data sets agree, the more the scatters tend to concentrate in
the vicinity of the identity line; if the two data sets are
numerically identical, the scatters fall on the identity line
exactly.
5.Ishikawa diagram
Ishikawa diagrams (also called fishbone diagrams,
herringbone diagrams, cause-and-effect diagrams, or
Fishikawa) are causal diagrams created by Kaoru
Ishikawa (1968) that show the causes of a specific
event.[1][2] Common uses of the Ishikawa diagram are
product design and quality defect prevention, to identify
potential factors causing an overall effect. Each cause or
reason for imperfection is a source of variation. Causes
are usually grouped into major categories to identify these
sources of variation. The categories typically include
People: Anyone involved with the process
Methods: How the process is performed and the
specific requirements for doing it, such as policies,
procedures, rules, regulations and laws
Machines: Any equipment, computers, tools, etc.
required to accomplish the job
Materials: Raw materials, parts, pens, paper, etc.
used to produce the final product
Measurements: Data generated from the process
that are used to evaluate its quality
Environment: The conditions, such as location,
time, temperature, and culture in which the process
operates
6. Histogram method
9. A histogram is a graphical representation of the
distribution of data. It is an estimate of the probability
distribution of a continuous variable (quantitative
variable) and was first introduced by Karl Pearson.[1] To
construct a histogram, the first step is to "bin" the range of
values -- that is, divide the entire range of values into a
series of small intervals -- and then count how many
values fall into each interval. A rectangle is drawn with
height proportional to the count and width equal to the bin
size, so that rectangles abut each other. A histogram may
also be normalized displaying relative frequencies. It then
shows the proportion of cases that fall into each of several
categories, with the sum of the heights equaling 1. The
bins are usually specified as consecutive, non-overlapping
intervals of a variable. The bins (intervals) must be
adjacent, and usually equal size.[2] The rectangles of a
histogram are drawn so that they touch each other to
indicate that the original variable is continuous.[3]
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