Data management involves collecting, storing, and processing data to transform it into useful information. Ensuring data integrity is important to make appropriate decisions. Techniques to reduce errors include educating staff, system prompts, verification, data mining, and data cleansing. Nursing informatics involves managing nursing data and information to support patient care through roles like project manager, consultant, educator, researcher, and chief information officer.
Our regular Introduction to Data Management (DM) workshop (90-minutes). Covers very basic DM topics and concepts. Audience is graduate students from all disciplines. Most of the content is in the NOTES FIELD.
This document provides an introduction to data management. It discusses the importance of data management for making informed decisions and gaining a competitive advantage. It also outlines some key benefits of good data management, such as improved data quality and decision making, and costs of poor data management like wasted time and money. Additionally, it describes different approaches to data management like file-based and database management systems, and covers concepts such as data modeling, databases, and different database models.
This document discusses data management plans (DMPs), which are brief plans that define how research data will be created, documented, stored, shared, and preserved. DMPs are often required as part of grant applications. The document provides an overview of why DMPs are important, how they benefit researchers and institutions, and key aspects to address in a DMP such as data organization, stakeholders, and making data FAIR (findable, accessible, interoperable, and reusable). Examples of DMPs from real projects are also presented.
Health Information Technology & Nursing InformaticsJil Wright
This document discusses health information technology and nursing informatics. It begins with an introduction by Jil Wright who identifies herself as a nursing informatics "geek". The document then provides resources for more information on health IT and nursing informatics. It discusses how nursing informatics integrates nursing science, computer science, and information science to support patients, nurses, and healthcare providers. Examples of clinical information systems and technologies that can help transform nursing practice are also provided, such as electronic medical records, wireless systems, and RFID technologies. Meaningful use requirements and examples of how health IT can improve documentation and the nursing process are summarized as well.
This document discusses medical data and its importance. It defines key terms like data, information and knowledge. It explains how medical data is collected and used by various stakeholders in healthcare. It also outlines the peculiarities of medical data and challenges with traditional record keeping. Finally, it discusses important data sources, users, and agencies involved in medical data in India.
The document discusses healthcare information technology and its evolution. It defines common terms like EMR, EHR, HIS, HL7, DICOM and PACS. It states that computerized physician order entry (CPOE) can significantly reduce medication errors and preventable adverse drug events. The hospital information system (HIS) is described as an integrated system that manages administrative, financial and clinical data across different departments. Several standards organizations are working to develop standards for interoperability between different health IT tools and electronic medical records.
Overview of Health Informatics: survey of fundamentals of health information technology, Identify the forces behind health informatics, educational and career opportunities in health informatics.
Our regular Introduction to Data Management (DM) workshop (90-minutes). Covers very basic DM topics and concepts. Audience is graduate students from all disciplines. Most of the content is in the NOTES FIELD.
This document provides an introduction to data management. It discusses the importance of data management for making informed decisions and gaining a competitive advantage. It also outlines some key benefits of good data management, such as improved data quality and decision making, and costs of poor data management like wasted time and money. Additionally, it describes different approaches to data management like file-based and database management systems, and covers concepts such as data modeling, databases, and different database models.
This document discusses data management plans (DMPs), which are brief plans that define how research data will be created, documented, stored, shared, and preserved. DMPs are often required as part of grant applications. The document provides an overview of why DMPs are important, how they benefit researchers and institutions, and key aspects to address in a DMP such as data organization, stakeholders, and making data FAIR (findable, accessible, interoperable, and reusable). Examples of DMPs from real projects are also presented.
Health Information Technology & Nursing InformaticsJil Wright
This document discusses health information technology and nursing informatics. It begins with an introduction by Jil Wright who identifies herself as a nursing informatics "geek". The document then provides resources for more information on health IT and nursing informatics. It discusses how nursing informatics integrates nursing science, computer science, and information science to support patients, nurses, and healthcare providers. Examples of clinical information systems and technologies that can help transform nursing practice are also provided, such as electronic medical records, wireless systems, and RFID technologies. Meaningful use requirements and examples of how health IT can improve documentation and the nursing process are summarized as well.
This document discusses medical data and its importance. It defines key terms like data, information and knowledge. It explains how medical data is collected and used by various stakeholders in healthcare. It also outlines the peculiarities of medical data and challenges with traditional record keeping. Finally, it discusses important data sources, users, and agencies involved in medical data in India.
The document discusses healthcare information technology and its evolution. It defines common terms like EMR, EHR, HIS, HL7, DICOM and PACS. It states that computerized physician order entry (CPOE) can significantly reduce medication errors and preventable adverse drug events. The hospital information system (HIS) is described as an integrated system that manages administrative, financial and clinical data across different departments. Several standards organizations are working to develop standards for interoperability between different health IT tools and electronic medical records.
Overview of Health Informatics: survey of fundamentals of health information technology, Identify the forces behind health informatics, educational and career opportunities in health informatics.
Healthcare Data Management: Three Principles of Using Data to Its Full PotentialHealth Catalyst
Author Douglas Laney is now tackling the topic of Infonomics: the practice of information economics. In his 2017 book, Infonomics: How to Monetize, Manage, and Measure Information as an asset for competitive advantage, Laney provides detailed rationale as well as a thoughtful framework for treating information as a modern-day organization’s most valuable asset.
This article walks through how healthcare organizations can leverage data to its full potential using this framework and the three principles of infonomics:
Measure - How much data does the organization have? What is it worth?
Manage - What data does the organization have? Where is it stored?
Monetize - How does the organization use data?
A brief presentation outlining the concepts of data quality in the context of clinical data, and highlighting the importance of data quality for population health, health analytics, and other secondary uses of clinical data.
Data analysis involves editing, coding, sorting, and entering data to discover useful information. The process includes gathering data from various sources, reviewing it, and analyzing it to form conclusions. Specifically, editing ensures quality by reviewing collected data, coding categorizes data for computer analysis, sorting arranges data in a meaningful order for comprehension, and data entry inputs collected information into a computer. Finally, a master chart compiles all essential data in one format.
This document discusses various healthcare information systems and their functions. It identifies six main types of systems: electronic medical records, practice management software, master patient indexes, patient portals, remote patient monitoring, and clinical decision support. It also defines hospital information systems, administrative information systems, and clinical information systems. The document differentiates the nursing process from critical pathways in nursing system design. Finally, it outlines the five main components of a basic database system: hardware, software, data, procedures, and database access language.
The document provides an overview of health informatics by:
1. Defining key terms like informatics, biomedical informatics, health informatics, and discussing the relationships between related fields.
2. Explaining the data-information-knowledge-wisdom hierarchy and providing examples.
3. Describing health informatics as the optimal use of information, aided by technology, to improve health, healthcare, research, and more.
Public health information systems and data standards are essential for public health informatics. The birth of modern vital records systems in the 19th century in England and the U.S. established standards for collecting data on births, deaths, and diseases. This data has been critical for analyzing health trends, identifying disease outbreaks, and informing public health policy. Today, electronic systems have largely replaced paper-based reporting and allow more robust analysis and sharing of surveillance data. Standards ensure consistency and interoperability in collecting and aggregating this important public health information.
This document discusses data management and presentation. It begins by defining data as a collection of facts such as values or measurements. The document then discusses different types of data including numerical, categorical, qualitative, and quantitative data. Various methods of collecting and presenting data are also outlined, including tables, graphs, charts, and different types of each. The document provides examples and discusses how to interpret data findings by relating them to objectives and real-life contexts.
Business intelligence (BI) focuses on analyzing past business data to provide insights for decision-making, while data science aims to predict future trends through analyzing patterns in data using machine learning and other advanced analytics techniques. BI uses structured data to identify strengths and weaknesses, while data science can leverage both structured and unstructured data to develop forecasts. Data science requires stronger technical skills than BI and can manage more dynamic data sources.
This document provides an introduction to health informatics, including definitions of key terms, subdomains, and applications. It defines health informatics as the intersection of information science, computer science, and healthcare. The document outlines several subdomains including clinical informatics, medical informatics, nursing informatics, and bioinformatics. It also discusses some benefits and barriers to health information technology adoption, highlighting how tools like electronic health records and data warehouses can help improve decision making by generating and sharing health information.
Clinical decision support systems use information technology to reduce errors and improve clinical decision making by providing clinicians with patient data, knowledge resources, and clinical guidance at the point of care through tools such as diagnostic decision trees, drug databases, alerts, and reminders. However, implementing clinical decision support systems poses challenges around usability, workflow integration, and clarifying the relationship between machine recommendations and human clinical judgment.
A database is an organized collection of related data that can be used alone or combined with other data for multiple purposes. A database management system (DBMS) enables users to store, modify, and extract information from a database. Key characteristics of a DBMS include performance, sharing data access, security, removing redundancy, and concurrent access. DBMS were developed to address difficulties with typical file processing systems like data redundancy, inconsistent access, and security problems.
Public Health informatics, Consumer health informatics, mHealth & PHRs (Novem...Nawanan Theera-Ampornpunt
Presented at the M.S. and Ph.D. Programs in Data Science for Health Care, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on November 11, 2019
Health informatics is the interdisciplinary study of how to design, develop, apply and use information technology in healthcare to improve health services. It involves optimizing the acquisition, storage, retrieval and use of health information. Key applications include translational bioinformatics, clinical research informatics, clinical informatics, consumer health informatics and public health informatics. Health informatics uses mathematics and statistics to understand health data and probabilistic methods to determine clinical probabilities and integrate new data.
This document is a presentation by Raymond Gensinger on data analytics in healthcare. It discusses examples of analytics used in baseball to improve performance, the different types of analytics including descriptive, predictive, and prescriptive. It also covers how analytics have evolved, organizational readiness for analytics, and key factors for analytics success including data, enterprise integration, leadership, targets, and having the right analysts. The presentation provides a framework for healthcare to apply analytics and examples of how different types of analytics could be used.
The Use of Predictive Analytics in Health Carejetweedy
This document discusses the use of predictive analytics in healthcare. It describes how predictive analytics uses data and statistics to analyze massive amounts of patient information to predict outcomes. This can help with readmissions, triage, emergency care, detecting patient decompensation, and adverse events. Challenges to implementing predictive analytics in healthcare electronically include testing models, oversight, data quality, and ensuring interoperability between systems. When done correctly, predictive analytics has the potential to improve patient health and lower healthcare costs.
The document is a chapter from a textbook on data mining written by Akannsha A. Totewar, a professor at YCCE in Nagpur, India. It provides an introduction to data mining, including definitions of data mining, the motivation and evolution of the field, common data mining tasks, and major issues in data mining such as methodology, performance, and privacy.
The document discusses different types of data that can be collected in statistics including categorical vs. quantitative data, discrete vs. continuous data, and different levels of measurement for data including nominal, ordinal, interval, and ratio scales. It also discusses key concepts such as parameters, statistics, populations, and samples. Potential pitfalls in statistical analysis are outlined such as misleading conclusions, nonresponse bias, and issues with survey question wording and order.
This document provides information about a Nursing Informatics course, including the instructor, schedule, course description, objectives, outline, and definitions of key terms. The course deals with using information technology and standards in nursing, managing patient data, and clinical decision-making. It is worth 3 credits, includes both lecture and lab hours, and covers topics such as computer hardware and software, electronic health records, issues in informatics, and applications for practice, research, and the future of the field.
Leeann Sills presents on the topic of nursing informatics. Nursing informatics involves using computers and information technology to support nursing practice, education, research, and administration. It aims to help manage and process nursing data and information to support decision making. Some key benefits include improved access to patient records, decreased data entry redundancy, and increased time for patient care through automation. Challenges include upfront costs, need for training, and ensuring privacy and security of patient information. Nursing informatics is still emerging but will continue growing in importance as health care delivery increasingly relies on technology.
Healthcare Data Management: Three Principles of Using Data to Its Full PotentialHealth Catalyst
Author Douglas Laney is now tackling the topic of Infonomics: the practice of information economics. In his 2017 book, Infonomics: How to Monetize, Manage, and Measure Information as an asset for competitive advantage, Laney provides detailed rationale as well as a thoughtful framework for treating information as a modern-day organization’s most valuable asset.
This article walks through how healthcare organizations can leverage data to its full potential using this framework and the three principles of infonomics:
Measure - How much data does the organization have? What is it worth?
Manage - What data does the organization have? Where is it stored?
Monetize - How does the organization use data?
A brief presentation outlining the concepts of data quality in the context of clinical data, and highlighting the importance of data quality for population health, health analytics, and other secondary uses of clinical data.
Data analysis involves editing, coding, sorting, and entering data to discover useful information. The process includes gathering data from various sources, reviewing it, and analyzing it to form conclusions. Specifically, editing ensures quality by reviewing collected data, coding categorizes data for computer analysis, sorting arranges data in a meaningful order for comprehension, and data entry inputs collected information into a computer. Finally, a master chart compiles all essential data in one format.
This document discusses various healthcare information systems and their functions. It identifies six main types of systems: electronic medical records, practice management software, master patient indexes, patient portals, remote patient monitoring, and clinical decision support. It also defines hospital information systems, administrative information systems, and clinical information systems. The document differentiates the nursing process from critical pathways in nursing system design. Finally, it outlines the five main components of a basic database system: hardware, software, data, procedures, and database access language.
The document provides an overview of health informatics by:
1. Defining key terms like informatics, biomedical informatics, health informatics, and discussing the relationships between related fields.
2. Explaining the data-information-knowledge-wisdom hierarchy and providing examples.
3. Describing health informatics as the optimal use of information, aided by technology, to improve health, healthcare, research, and more.
Public health information systems and data standards are essential for public health informatics. The birth of modern vital records systems in the 19th century in England and the U.S. established standards for collecting data on births, deaths, and diseases. This data has been critical for analyzing health trends, identifying disease outbreaks, and informing public health policy. Today, electronic systems have largely replaced paper-based reporting and allow more robust analysis and sharing of surveillance data. Standards ensure consistency and interoperability in collecting and aggregating this important public health information.
This document discusses data management and presentation. It begins by defining data as a collection of facts such as values or measurements. The document then discusses different types of data including numerical, categorical, qualitative, and quantitative data. Various methods of collecting and presenting data are also outlined, including tables, graphs, charts, and different types of each. The document provides examples and discusses how to interpret data findings by relating them to objectives and real-life contexts.
Business intelligence (BI) focuses on analyzing past business data to provide insights for decision-making, while data science aims to predict future trends through analyzing patterns in data using machine learning and other advanced analytics techniques. BI uses structured data to identify strengths and weaknesses, while data science can leverage both structured and unstructured data to develop forecasts. Data science requires stronger technical skills than BI and can manage more dynamic data sources.
This document provides an introduction to health informatics, including definitions of key terms, subdomains, and applications. It defines health informatics as the intersection of information science, computer science, and healthcare. The document outlines several subdomains including clinical informatics, medical informatics, nursing informatics, and bioinformatics. It also discusses some benefits and barriers to health information technology adoption, highlighting how tools like electronic health records and data warehouses can help improve decision making by generating and sharing health information.
Clinical decision support systems use information technology to reduce errors and improve clinical decision making by providing clinicians with patient data, knowledge resources, and clinical guidance at the point of care through tools such as diagnostic decision trees, drug databases, alerts, and reminders. However, implementing clinical decision support systems poses challenges around usability, workflow integration, and clarifying the relationship between machine recommendations and human clinical judgment.
A database is an organized collection of related data that can be used alone or combined with other data for multiple purposes. A database management system (DBMS) enables users to store, modify, and extract information from a database. Key characteristics of a DBMS include performance, sharing data access, security, removing redundancy, and concurrent access. DBMS were developed to address difficulties with typical file processing systems like data redundancy, inconsistent access, and security problems.
Public Health informatics, Consumer health informatics, mHealth & PHRs (Novem...Nawanan Theera-Ampornpunt
Presented at the M.S. and Ph.D. Programs in Data Science for Health Care, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on November 11, 2019
Health informatics is the interdisciplinary study of how to design, develop, apply and use information technology in healthcare to improve health services. It involves optimizing the acquisition, storage, retrieval and use of health information. Key applications include translational bioinformatics, clinical research informatics, clinical informatics, consumer health informatics and public health informatics. Health informatics uses mathematics and statistics to understand health data and probabilistic methods to determine clinical probabilities and integrate new data.
This document is a presentation by Raymond Gensinger on data analytics in healthcare. It discusses examples of analytics used in baseball to improve performance, the different types of analytics including descriptive, predictive, and prescriptive. It also covers how analytics have evolved, organizational readiness for analytics, and key factors for analytics success including data, enterprise integration, leadership, targets, and having the right analysts. The presentation provides a framework for healthcare to apply analytics and examples of how different types of analytics could be used.
The Use of Predictive Analytics in Health Carejetweedy
This document discusses the use of predictive analytics in healthcare. It describes how predictive analytics uses data and statistics to analyze massive amounts of patient information to predict outcomes. This can help with readmissions, triage, emergency care, detecting patient decompensation, and adverse events. Challenges to implementing predictive analytics in healthcare electronically include testing models, oversight, data quality, and ensuring interoperability between systems. When done correctly, predictive analytics has the potential to improve patient health and lower healthcare costs.
The document is a chapter from a textbook on data mining written by Akannsha A. Totewar, a professor at YCCE in Nagpur, India. It provides an introduction to data mining, including definitions of data mining, the motivation and evolution of the field, common data mining tasks, and major issues in data mining such as methodology, performance, and privacy.
The document discusses different types of data that can be collected in statistics including categorical vs. quantitative data, discrete vs. continuous data, and different levels of measurement for data including nominal, ordinal, interval, and ratio scales. It also discusses key concepts such as parameters, statistics, populations, and samples. Potential pitfalls in statistical analysis are outlined such as misleading conclusions, nonresponse bias, and issues with survey question wording and order.
This document provides information about a Nursing Informatics course, including the instructor, schedule, course description, objectives, outline, and definitions of key terms. The course deals with using information technology and standards in nursing, managing patient data, and clinical decision-making. It is worth 3 credits, includes both lecture and lab hours, and covers topics such as computer hardware and software, electronic health records, issues in informatics, and applications for practice, research, and the future of the field.
Leeann Sills presents on the topic of nursing informatics. Nursing informatics involves using computers and information technology to support nursing practice, education, research, and administration. It aims to help manage and process nursing data and information to support decision making. Some key benefits include improved access to patient records, decreased data entry redundancy, and increased time for patient care through automation. Challenges include upfront costs, need for training, and ensuring privacy and security of patient information. Nursing informatics is still emerging but will continue growing in importance as health care delivery increasingly relies on technology.
The document discusses nursing informatics, which involves using computers and information technology to help manage and process nursing data and information to support nursing practice and education. It provides definitions of nursing informatics from various sources and outlines how it applies to different areas of nursing including clinical practice, administration, education, and research. Benefits include improved access to patient records, decreased documentation time, and decision support software to alert nurses of potential issues. Challenges include upfront costs and ensuring privacy and security of patient data. The conclusion states that nursing informatics is an emerging field and more educational programs are still needed.
Nursing informatics is the use of computers and information technology to support nursing practice, education, administration, research, and clinical care. It involves managing nursing data, information, and knowledge through technologies like electronic health records. The goal is to improve patient health outcomes and support nurses' decision-making. While nursing informatics is still emerging, national nursing organizations recommend nurses become computer literate as healthcare increasingly relies on digital tools and data.
Nursing informatics is a specialty that integrates nursing science, computer science, and information science to manage and communicate data, information, knowledge and wisdom in nursing practice. It aims to improve patient safety and quality of care through generating knowledge and clinical workflows, creating uniform guidelines, and using information technology in all aspects of nursing. Nurses play a key role in nursing informatics by assessing needs, developing efficient clinical workflows, and managing and interpreting data to improve clinical practice.
This document discusses nursing informatics. It begins by introducing nursing informatics and noting its importance for nurses to be computer literate. The goals of nursing informatics are to improve health through optimizing information management and communication using technology. Nursing informatics can be used in direct patient care, administration, education, research, and more. It provides benefits such as increased efficiency, more time for patient care, and improved communication. The document outlines components, competencies, and applications of nursing informatics.
This document outlines a nursing informatics course that teaches students to apply concepts of informatics in nursing care. The course covers topics such as the history of computers in nursing, computer hardware and software, clinical information systems, nursing data standards, and informatics applications in different nursing specialties and settings. Students are expected to create their own nursing informatics webpage and do an individual or group presentation to pass the course. The document emphasizes that as information doubles every few years, nurses need informatics skills to effectively manage patient data and provide evidence-based care using technology.
This document discusses the course Nursing Informatics. The course covers the use of information technology and data standards in nursing based on informatics principles. It deals with using clinical information systems to manage patient care and support decision making. The course objectives are to apply informatics concepts to nursing and discuss relevant issues and trends. Various topics are outlined including computers and nursing history, computer systems, issues in informatics, informatics theories, and applying informatics in different nursing practices and internationally. Students are expected to create an online nursing informatics page and do an individual or group presentation to pass the course.
BME 307 - HMIS - Data Management Systems 24112021 Final.pdfedwardlowassa1
This document outlines a course on health management information systems (HMIS). The course aims to equip students with knowledge and skills of information technology and its applications in health management. It will cover topics like electronic health records, integrated practice management systems, and health information technology interoperability. Teaching methods will include lectures, tutorials, group works, site visits and assignments. Students will be assessed through tests, assignments and an examination. The course references textbooks on biomedical informatics, health informatics, and information systems. It also provides context on HMIS in Tanzania and discusses frameworks for understanding well-functioning HMIS like PRISM and the 12 components framework.
Ehr by jessica austin, shaun baker, victoria blankenship and kayla borokayla_ann_30
This document provides an overview of electronic health records (EHR) including what they are, key components, considerations for implementation, and security and costs. It discusses that EHRs provide a centralized digital patient record accessible by healthcare providers. The eight essential components that must be included are things like health information, order entry, decision support, and administrative functions. Proper implementation requires input from various stakeholders like medical staff, IT, and leadership. Security and privacy are also important considerations, as are the financial costs of purchasing and maintaining an EHR system.
A Clinical Information System (CIS) is a computer-based system that collects, stores, and provides access to clinical information for healthcare delivery. A CIS includes features like electronic medical records, clinical data repositories, order entry, and decision support. Key stakeholders in selecting and implementing a CIS include clinical staff, administrative staff, and information technology staff. Implementing a CIS requires significant costs for equipment, IT support, training, and maintenance. Ongoing education is also needed to ensure staff can properly use and adapt to changes in the system.
Application of information technology in nursingSaurabh Gupta
This document discusses the use of information technology in nursing. It begins by defining information technology as the use of computers and software to manage information storage, retrieval, and transmission. It then lists several advantages of information technology in nursing, such as easier patient monitoring, reduced paperwork, and improved decision-making. Potential disadvantages include costs, security issues, and system failures. The document also outlines various software applications used in nursing, such as those for patient tracking and drug guides. Finally, it describes hospital management information systems and their benefits, such as connecting people and data in real-time across the hospital.
This document discusses nursing informatics, which integrates nursing science with information management and analytical sciences. It is the science of processing and managing nursing data, information, and knowledge to support various areas of nursing. The field has grown with the increasing use of technology in healthcare, such as the transition to electronic health records. The document outlines the history of computing in nursing and covers topics like clinical information systems and the nursing informatics model.
The document discusses several topics related to nursing informatics and clinical information systems:
1. It defines common computer inputs, outputs, and storage devices such as keyboards, monitors, hard drives, and flash drives.
2. It distinguishes between computer software and hardware, providing examples of each. Software is intangible programs while hardware are physical components like CPUs and keyboards.
3. It defines information systems, management information systems, and hospital information systems. Hospital information systems specifically manage administrative, financial, and clinical data in hospitals using both electronic and paper records.
4. It discusses how information technologies are being adopted in Philippine hospitals, though progress has been slow, and hopes for more advanced technologies to improve
This study developed a survey instrument to segment physicians into groups based on their preferences and opinions regarding information technology. The survey was developed using qualitative research including interviews and focus groups. It presented physicians with statements about IT to rank order.
The findings identified six distinct preference profiles among physicians: 1) "Full-Range Adopters" who saw many benefits of IT, 2) "Skills-Concerned Adopters" who saw benefits but had skills concerns, 3) "Technology-Critical Adopters" who saw benefits but had strong concerns about privacy, monitoring and skills, 4) "Independently-Minded and Concerned" who emphasized independent research uses of IT but also had strong privacy and skills concerns
This document provides an overview of nursing informatics. It discusses the historical context of nursing informatics from the 1970s to present day. Nursing informatics is defined as integrating nursing science, computer science, and information science to manage and communicate data to support patient care. Key areas covered include competencies, education, roles, skills, evidence-based practice, healthcare technology, socio-technical issues, foundations and committees, and the present and future agenda. The document references several sources and provides images of healthcare technology like alarms and monitoring systems.
Nursing informatics is defined as integrating nursing science, computer science, and information science to manage and communicate patient information to support nurses and other providers in decision making. The American Nurses Association recognizes trends in information technology like electronic medical records and defines nursing informatics roles like collecting, recording, interpreting, and using clinical data to produce information and knowledge. Healthcare information systems include clinical information systems to support patient care, administrative systems to manage billing and scheduling, and nursing information systems that provide nurses the information they need to practice and document care.
This document discusses hospital information systems and nursing informatics. It begins by defining the objectives of the presentation which are to define management information systems, discuss different information systems used in hospitals, discuss nursing informatics and its implications, discuss obstacles to nursing information systems, and the role of nursing managers in hospital information systems. It then defines hospital management systems and hospital information systems. It describes the types of data stored in hospital information systems and types of systems including nursing information systems, clinical information systems, pharmacy information systems, financial information systems, and laboratory information systems. The document discusses nursing informatics and its implications for patient charting, staff scheduling, clinical data integration, and decision support. It also discusses the contributions of information technology to the efficiency
The document provides an overview of Joanne Duffy's Quality Caring Model. It discusses Duffy's background, education, and career achievements. It then outlines the key concepts of the revised Quality Caring Model, including that humans exist in relationships, relationship-centered professional encounters, feeling cared for, and self-caring. The assumptions and propositions of the model are presented. The caring factors and relationships are explained, including with self, patients/families, healthcare team, and communities. The application and critique of the Quality Caring Model are also summarized.
This document discusses different methods of data collection, including self-reports, observations, and biophysiologic measures. Self-reports involve participants responding to researcher questions through methods like questionnaires and interviews. Observations involve directly observing participant behaviors and characteristics. Biophysiologic measures assess clinical variables. Each method has advantages, like the amount of data self-reports can provide, but also disadvantages, such as potential biases in self-reports. The document provides details on developing and administering various self-report instruments.
This document discusses caring for patients harmed by medical treatment. It notes that patients suffer physically and psychologically from their injuries as well as from poor handling of incidents. Injured patients need open disclosure, explanations, apologies and assurances to prevent future harm. However, disclosing errors is challenging for medical professionals due to fears of litigation and damage to reputation. The document recommends strategies like acknowledging the frequency of errors, providing peer and psychological support to staff, and training in disclosure and communication to help all parties cope after adverse events.
This document discusses the evolution of quality in nursing care from the past to present. In the past, quality focused on inspection and detection of defects. It later evolved to include statistical process control and quality assurance programs. Today, quality emphasizes total quality management through continuous improvement, innovation, error prevention, and staff development across all levels and divisions of an organization. The goal is to consistently meet and exceed customer needs and improve patient services and outcomes.
The document discusses groups and teams. It defines groups and identifies formal and informal groups. It outlines the five stages of group development and discusses the role of the leader in each stage. It also discusses types of teams like problem-solving teams, self-managed work teams, cross-functional teams, and virtual teams. Key differences between work groups and work teams are that work groups share information while work teams generate synergy through coordinated effort.
This document defines conflict and negotiation, outlines the conflict process, and describes negotiation strategies. It discusses:
- The 5 stages of conflict: latent, perceived, felt, manifest, and aftermath
- 3 types of conflict: task, relationship, process
- 2 approaches to negotiation: distributive vs integrative bargaining
- Factors that influence negotiation such as personality, culture, gender
- The roles of mediators, arbitrators, and conciliators in third-party negotiations
Managing conflict and building team, delegation andSara Aljanabi
Managing conflict and building teams involves defining conflict, understanding its causes and types, and employing effective conflict management and team building strategies. The document outlines conflict processes, causes, and management roles and functions. It also discusses team building, delegation strategies and common errors. Effective leaders model collaboration, set clear goals, empower and support delegates, and facilitate team processes to resolve conflicts and accomplish goals.
Introduction of nurses executive function in hospitalsSara Aljanabi
Nursing administration has evolved from overseeing clinical services to managing complex healthcare delivery systems. The role of the nurse administrator/executive involves designing, facilitating, supervising, and evaluating systems to educate and employ nurses according to standards of practice. Nurse administrators require graduate degrees and demonstrate leadership, communication, and business competencies. As members of senior healthcare leadership, they facilitate quality care delivery, build staff relationships, and advance the nursing profession.
This document defines conflict and outlines its types and common causes. It discusses intrapersonal, interpersonal, and intergroup conflicts. Common causes of organizational conflict include poor communication, unclear expectations, individual behaviors, conflicts of interest, and diversity. The document presents the GRID model for managing conflict and references leadership and management textbooks.
This document discusses work motivation and its key elements. It defines motivation and explores theories like need theory, expectancy theory, and equity theory. Motivation has three main elements: direction of behavior, level of effort, and level of persistence. Motivation alone does not determine performance, as other factors also influence it. The document also examines motivation strategies employers can use, like job rotation, job enlargement, and job enrichment. Money is an important motivating factor as it satisfies many needs, though it does not intrinsically motivate on its own. Overall, the document provides an overview of the concept of work motivation and the main factors that influence employee motivation and behavior in organizations.
Job analysis, job design, job descriptions, and job evaluations are important human resource processes. Job analysis identifies the tasks and requirements of a job. Job design determines how work is performed. Job descriptions define duties and expectations. Job specifications list qualifications. Job evaluation establishes the relative value of jobs through systematic analysis of factors like skills, responsibilities, and decisions. Together, these processes help define jobs and compensation.
This document outlines several prominent theories of human resource management. It discusses early developments in the field and defines human resources as the people who operate an organization. Several theories are then summarized, including resource-based theory, organizational life cycle theory, strategic contingency theory, and human capital theory. Overall, the theories aim to improve job performance and worker motivation through more efficient management practices.
Career development involves assessing one's skills and interests to further their work identity over various career stages. There are typically three career phases for nurses: promise (early career), momentum (middle career), and harvest (late career). Organizations should support career development through career paths, performance assessments, training, and challenging assignments. Effective career coaching involves gathering employee data, discussing future opportunities, and setting a career plan. Barriers to career development can include personal and financial factors, family obligations, age perceptions, and lack of organizational support.
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This document discusses ethical issues in nursing. It defines ethics and codes of ethics, including the American Nurses Association code which outlines 9 principles. Common nursing ethics dilemmas are discussed, such as informed consent, cultural diversity, and workplace issues. Ethical principles of autonomy, beneficence, non-maleficence, and justice are explained. The document also covers moral problems nurses may face like uncertainty, dilemmas, and distress. It provides examples of how nurses can clarify ethical decisions and discusses nurse executive behaviors and use of ethical power.
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This document outlines several prominent theories of human resource management. It discusses early developments in HR education and functions. Key theories summarized include resource-based theory, which views employees as valuable resources; organizational life cycle theory, which proposes organizations progress through stages; and strategic contingency theory, which argues organizational strategies depend on the environment. The document also examines transaction cost theory, general systems theory, human capital theory, and agency theory among others. Overall, the theories aim to improve job performance and worker motivation through more efficient and effective human resource practices and management.
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These vital statistics are invaluable for planning, monitoring and evaluating various programs related to primary health care, family planning, maternal and child health, education etc.
2. outline
• Definition of Data management and other concepts related.
• Management Information System (MIS) and Data integrity.
• Techniques to help alleviate erroneous data.
• Computer networking.
• Healthcare information systems
• Nursing informatics
• Nursing informatics role in health care
3. Data Management
Data collection of numbers, characters, or facts that are gathered according to
some perceived need for analysis and possibly action a later point of time
(Anderson, 1992).
4. Information is a set of data that has been interpreted covering some
aspect of time, such as over course of a day.
Knowledge Appling fact or ideas acquired by study, investigation,
observation, or experience.
Wisdom occurs when knowledge is used appropriately to manage.
Nurses acquire knowledge over time and use it extensively in their
daily task or direct patient care.
5. Data collection, with the aid of computer and information
technology, helps to provide evidence of best practice supported by
research.
Provide database of knowledge that can be applied to everyday
practice situations.
6. Management Information System (MIS) An integrated system for
collecting, storing, retrieving, and processing a collective set of data;
the data are transformed from storage into knowledge that is directly
useful and applicable in the process of directing and controlling
resources and their application to the achievement of specific
management objectives.
7. Data integrity
It is not enough to collect data, interpret it, and build a database of
knowledge. Data that make up the database of information must be
maintained with optimal assurance that good-quality data exist.
8. If data integrity is less than optimal, it could result in inappropriate
decisions that could possibly harm a patient.
Good quality data have characteristics that can be identified:
Data must be timely, accurate, rapidly and easily available, precise,
clear, comprehensive, reliable regardless of who collects it, easy to
interpret, current, and appropriate for the users need.
9. Techniques to help reduce data error
Educating
staff
System
prompts
Various
verification
techniques
Data
mining
Data
cleansing
10. Educating staff
educating staff is vital to having high-quality data and reducing
erroneous data.
Nurse should have a general understanding of how to use software,
enter data, extract data, print reports and evaluate data to make good
clinic decisions.
They need to know how to find information and procedures rather
than know they need to learn to question rather than answer.
Documentation take 13% to 28% of nurses time
11. System prompts
Manager, staff, and informatics nurses can work with the IS department
to develop system prompts that alert to user to recheck the data.
Check that the data fall in a specific range, or confirm that have been
entered correctly. This embedded in the application software that Is
developed by the IS department.
12. Verification techniques
verifying data is important to having high-quality data can be utilized
for clinical decision making.
Many verification including verbal questioning and visual verification.
Both techniques are very effective in the right environment.
System prompts certainly help, but other techniques may be used to
check and recheck data that are being entered at the point of care.
13. Data mining
Extracting data from databases is known as data mining. The process of
data mining has become known as knowledge discovery and data
mining or data to knowledge.
Nurse managers, especially, should be aware of the uses of techniques
such as data mining that allow them to extract, predict, evaluate, and
apply knowledge to daily task.
14. Data cleansing
Technique used to clean up erroneous data
that have been captured and stored in
databases.
Data cleansing software flags the erroneous
data and generates a report.
After the report is reviewed, the files or
records that have been flagged may be
deleted or corrected.
16. Computer networking
Intranets, extranets, and virtual private networks
Intranets are used by organization for internal use and employ a variety of
communication technologies. intranets usually are not geographically.
Extranets extend beyond intranets and an organization information providing
access to anywhere in the world.
They generally use multiple types of communication technologies and are
not geographically limited just as intranets.
Extranets are used to connect different parties that have common interests.
Virtual private that provide stronger security with flexible remote access.
17. Computer networking
Wireless network
Wireless networking has rapidly become the norm for most computer
users. Wireless access points are available in most areas such as
academic areas, …
19. Healthcare information systems
There are several types of healthcare information systems. Some of
these systems help to manage the daily operations of general
healthcare organizations.
Administrative information systems
Clinical information systems
20. Healthcare information systems
Administrative information systems
These include financial systems, human resource systems, nonclinical
patient systems.
Clinical information systems
Involve any system that is used in patient care and may not be nursing
information systems.
22. Nursing Informatics The management and processing of nursing data,
information, and knowledge to support the practice of nursing and the
delivery of nursing care.
23. Evolution of informatics
The first master’s degree in nursing informatics was offered by the
University of Maryland in 1989.
In 1992, that same university followed with the first doctoral program
in nursing informatics.
25. Nursing informatics role in health care
Nursing
informatics
Consultant
Project
manager
Educator
Researcher
Product
developer
Decision
support
Policy
developer
Chief
information
officer
Innovator
26. Nursing informatics role in health care
Informatics nurse as project manager:
Informatics nurses analyze, design, develop, select, test,
implement, and evaluate new of modified informatics projects that
support optimal data and delivery of quality patient care.
27. Nursing informatics role in health care
Informatics nurse as consultant :
Consultants may take on a variety of role, including project manager,
market research, planning conference, strategic, information
technology planning, reviewing clinical software, redesigning the
workplace, and other . They may work for an organization or with
consulting firm.
28. Nursing informatics role in health care
Informatics nurse as educator:
Informatics nurse may educate staff nurse,
managers, and others in using the
healthcare information system. Their role
would include educating all staff on
confidentiality and security matters as well.
29. Nursing informatics role in health care
Informatics nurse as researcher:
As a researcher, the informatics nurse may research clinical situation
that arise, help implement evidence-passed practice, evaluate the
current system for improved outcomes, and conduct research to
improve clinical information systems and clinical outcomes.
30. Nursing informatics role in health care
Informatics nurse as product developer:
The informatics nurse may develop software applications for clinical
and nonclinical healthcare environments.
31. Nursing informatics role in health care
Informatics nurse as decision support:
Nurses in this role use technology and other systems tools to maintain
data integrity and reliability, identify outcomes, and develop
performance measurements.
32. Nursing informatics role in health care
Informatics nurse as policy developer:
Nurses in this role help to develop policies for clinical and
administrative health care information systems.
33. Nursing informatics role in health care
Informatics nurse as chief information officer (CIO):
This role provides leadership and management at the executive level
for both the organization and vendors.
34. Nursing informatics role in health care
Informatics nurse as innovator:
informatics nurses may fit here by managing their own practice,
developing applications, or owning their own healthcare information
systems business.
35. Discuses the technique that help to reduced the data error.
Discuses the nursing informatics role.
36. references
Diane L. Huber ,(2010) Leadership And Nursing Care Management, 4th edition,
Saunders, an imprint of Elsevier Inc.
Linda Roussel, (2012) management and leadership for nurse administrators,
Ascend learning.
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