This document provides definitions for various terms related to health informatics. It defines terms such as algorithm, bioinformatics, clinical coding system, clinical data system, clinical decision tool, communication, database, electronic health record, and medical knowledge. The definitions cover topics such as the use of informatics methods and technologies in research, clinical practice, public health, and consumer health contexts.
Medical Informatics: Computational Analytics in HealthcareNUS-ISS
Presented by Dr Liu Nan, Senior Research Scientist and Principal Investigator, Singapore General Hospital at ISS Seminar: How Analytics is Transforming Healthcare on 31 Oct 2014.
Medical informatics is the application of computers, communications, and information technology to medicine. It aims to improve patient care, medical education, and research. Key areas of medical informatics include telemedicine, knowledge management through databases and guidelines, decision support, and electronic health records. Implementation faces challenges like changing clinician behavior and raising awareness, but factors like increased technology, specialization, and costs are driving more use of informatics in healthcare.
Pathology informatics has evolved from early pioneers applying data analytics and computers to medicine. It involves applying information science principles to pathology practice and laboratory data. At UCDHS, pathology informatics manages laboratory information systems, implements digital pathology, performs data mining and analytics, and oversees clinical registries. Future trends include personalized medicine using "big data", wearable devices, learning healthcare systems, and tethered meta-registries that integrate multiple data sources to improve quality and lower costs.
This document discusses health informatics and compares it to medical informatics. Health informatics is a broader field that includes medical informatics as well as other areas like healthcare delivery, management, telemedicine, and patient education. Medical informatics focuses specifically on optimizing the storage, retrieval, and management of biomedical information. The document provides examples of areas within health informatics, such as electronic patient records, telemedicine, distance education, and how technologies in these areas can provide advantages like time savings and increased access to care.
The document provides an overview of biomedical informatics. It defines biomedical informatics as the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving, and decision making, motivated by efforts to improve human health. It notes that biomedical informatics develops, studies and applies theories, methods and processes for the generation, storage, retrieval, use, and sharing of biomedical data, information, and knowledge. Biomedical informatics investigates and supports reasoning, modeling, simulation, experimentation and translation across the spectrum from molecules to populations.
Overview and introduction to health informatics and dental informaticsEbtissam Al-Madi
This document provides an overview of health informatics and dental informatics. It defines health informatics, medical informatics, and dental informatics. It discusses the history and development of the fields, including the introduction of computers into medicine from the 1960s onward. It describes the scope of health informatics and dental informatics, covering areas like information science, computer science, and cognitive science. Finally, it discusses current issues in health informatics research, such as data acquisition, storage and retrieval, knowledge representation, and linking disparate systems.
Introduction to Health Informatics and Health Information Technology (Part 1)...Nawanan Theera-Ampornpunt
Presented at the Health Informatics and Health Information Technology Course, Doctor of Philosophy and Master of Science Programs in Data Science for Health Care (International Program), Faculty of Medicine Ramathibodi Hospital, Mahidol University on October 3, 2017
Presented at the 7th Healthcare CIO Program, Hospital Administration School, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Thailand on July 8, 2016
Medical Informatics: Computational Analytics in HealthcareNUS-ISS
Presented by Dr Liu Nan, Senior Research Scientist and Principal Investigator, Singapore General Hospital at ISS Seminar: How Analytics is Transforming Healthcare on 31 Oct 2014.
Medical informatics is the application of computers, communications, and information technology to medicine. It aims to improve patient care, medical education, and research. Key areas of medical informatics include telemedicine, knowledge management through databases and guidelines, decision support, and electronic health records. Implementation faces challenges like changing clinician behavior and raising awareness, but factors like increased technology, specialization, and costs are driving more use of informatics in healthcare.
Pathology informatics has evolved from early pioneers applying data analytics and computers to medicine. It involves applying information science principles to pathology practice and laboratory data. At UCDHS, pathology informatics manages laboratory information systems, implements digital pathology, performs data mining and analytics, and oversees clinical registries. Future trends include personalized medicine using "big data", wearable devices, learning healthcare systems, and tethered meta-registries that integrate multiple data sources to improve quality and lower costs.
This document discusses health informatics and compares it to medical informatics. Health informatics is a broader field that includes medical informatics as well as other areas like healthcare delivery, management, telemedicine, and patient education. Medical informatics focuses specifically on optimizing the storage, retrieval, and management of biomedical information. The document provides examples of areas within health informatics, such as electronic patient records, telemedicine, distance education, and how technologies in these areas can provide advantages like time savings and increased access to care.
The document provides an overview of biomedical informatics. It defines biomedical informatics as the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving, and decision making, motivated by efforts to improve human health. It notes that biomedical informatics develops, studies and applies theories, methods and processes for the generation, storage, retrieval, use, and sharing of biomedical data, information, and knowledge. Biomedical informatics investigates and supports reasoning, modeling, simulation, experimentation and translation across the spectrum from molecules to populations.
Overview and introduction to health informatics and dental informaticsEbtissam Al-Madi
This document provides an overview of health informatics and dental informatics. It defines health informatics, medical informatics, and dental informatics. It discusses the history and development of the fields, including the introduction of computers into medicine from the 1960s onward. It describes the scope of health informatics and dental informatics, covering areas like information science, computer science, and cognitive science. Finally, it discusses current issues in health informatics research, such as data acquisition, storage and retrieval, knowledge representation, and linking disparate systems.
Introduction to Health Informatics and Health Information Technology (Part 1)...Nawanan Theera-Ampornpunt
Presented at the Health Informatics and Health Information Technology Course, Doctor of Philosophy and Master of Science Programs in Data Science for Health Care (International Program), Faculty of Medicine Ramathibodi Hospital, Mahidol University on October 3, 2017
Presented at the 7th Healthcare CIO Program, Hospital Administration School, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Thailand on July 8, 2016
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.
Clinical Information Systems and Electronic Health Records (October 18, 2021)Nawanan Theera-Ampornpunt
This document discusses health IT in clinical settings and provides key points about IT decision making in hospitals. It outlines factors for successful IT implementation including system quality, information quality, use, and user satisfaction. Individual and organizational impacts are also discussed. Examples of important hospital IT are provided such as enterprise systems, departmental applications, and clinical IT. Clinical decision support systems are discussed as tools that can help improve clinical decision making when designed and implemented properly. Overall, the document emphasizes focusing on using health IT to improve patient care and outcomes rather than viewing technology as the end goal.
An AI-based Decision Platform built using unified data model, incorporating systems biology topics for unit analysis using semi-supervised learning models
This document discusses health informatics and patient safety. It covers visions for patient-centered care from Judge Cartwright in 1988 and the Bristol inquiry in 2001. Health IT use in New Zealand has increased, with nearly all doctors using electronic patient records by 2009. While health IT can help when designed properly, technology alone does not ensure patient safety or effective communication - a culture of safety and strong relationships are also required. Themes around continuous learning, responsibility, and communication are highlighted. A case example describes a surgeon who unknowingly removed a patient's gallbladder twice due to a missed scan report and unreviewed records.
Clinical Information Systems, Hospital Information Systems & Electronic Healt...Nawanan Theera-Ampornpunt
This document discusses clinical information systems (CIS), hospital information systems (HIS), and electronic health records (EHRs). It defines these terms and explains how they are used in hospitals to support various clinical and administrative functions. Key points include: CIS/HIS are used to manage patient data across departments; they integrate applications like electronic health records, laboratory information systems, pharmacy systems and more. EHRs allow longitudinal documentation of a patient's medical history and care. The use of these systems provides benefits like ubiquitous access to records, clinical decision support, and improved quality of care through functions like computerized physician order entry.
The document discusses departmental information systems and management information systems in healthcare organizations. It outlines different types of hospital departments and their information needs. It also describes the key components of hospital information systems, including those that support patient management, care delivery, clinical decision-making, department management, and financial functions. The document stresses the importance of integrating these different systems and ensuring data and processes are coordinated across departments. It further discusses management information systems and how they provide data for managing healthcare organizations effectively through business intelligence applications and analytics.
E-health technologies show promise in developing countriesInSTEDD
Three evaluations of e-health technologies in developing countries found promising results:
1) Systems that improved communication between institutions helped order and manage medications and monitor patients who may abandon care.
2) Personal digital assistants and mobile devices were effective at improving data collection time and quality.
3) Donors and funders should require and sponsor outside evaluations to ensure future e-health investments are well-targeted.
Panel: FROM SMALL TO BIG TO RICH DATA: Dealing with new sources of data in Biomedicine Precision and Participatory Medicine
Fernando J. Martin-Sanchez, Professor and Chair of Health Informatics at Melbourne Medical School, discusses new sources of data in biomedicine including small, big, and rich data. He describes how small data connects people with meaningful insights from big data to be understandable for everyday tasks. Martin-Sanchez also discusses precision medicine, participatory health, and how convergence between the two can help integrate multiple data sources including genomics, the exposome, and digital health to improve disease prevention and treatment outcomes.
Consumer Health Informatics, Mobile Health, and Social Media for Health: Part...Nawanan Theera-Ampornpunt
Presented at the Master of Science and Doctor of Philosophy Programs in Data Science for Healthcare and Clinical Informatics, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on November 10, 2021
The document discusses the features and advantages of a central patient monitoring system. It allows nurses to monitor multiple patients from a central location, monitor vital signs, receive alerts, integrate with other systems like EMRs. It provides clinical decision support tools, helps manage alarms to reduce alarm fatigue, and supports a continuous patient record.
Mobile Technology in Medical InformaticJAMES JACKY
1. Mobile Technology in Medical Informatic
2. Mobile Health
3. The Cloud
4. MediHome
5. Itareps
6. Advantages of Mobile Technology in Medical Informatic
7. Problems faced in implementing mobile technology in medical healthcare
8. How does the systems work?
This document discusses electronic health records (EHRs) and clinical decision support systems (CDS). It provides examples of different types of CDS, such as alerts and reminders, reference information, order sets, and diagnostic/treatment expert systems. The goal of health IT and CDS is to improve the quality of healthcare by making it safer, more timely, effective, efficient, patient-centered and equitable. However, human decisions are still necessary, so health IT should be designed to support, not replace, clinicians.
Legal and Ethical Considerations in Nursing InformaticsKimarie Brown
This document discusses legal and ethical considerations around information security and confidentiality in nursing informatics. It covers key concepts like privacy, confidentiality, and information security. It identifies threats to system security like hackers, viruses and human error. It also discusses security measures that can be implemented, including firewalls, antivirus software, authentication methods like passwords, and proper disposal of confidential information. The impact of internet technology on health information security is also addressed.
Departmental Information Systems and Management Information Systems in Health...Nawanan Theera-Ampornpunt
Theera-Ampornpunt N. Departmental information systems and management information systems in healthcare organizations. Presented at: Faculty of ICT, Mahidol University; 2012 Feb 8; Bangkok, Thailand.
Theera-Ampornpunt N. Informatics in emergency medicine: a brief introduction. In: The International Conference in Emergency Medicine: Challenges in Emergency Medicine: It’s Time for Change!; 2012 Jan 30 - Feb 1; Bangkok, Thailand. Bangkok (Thailand): Mahidol University, Faculty of Medicine Ramathibodi Hospital; 2012 Feb.
Adopting Information Systems in a Hospital - A Case Study & Lessons LearnedNawanan Theera-Ampornpunt
This document summarizes the journey of adopting health information technology (IT) at Ramathibodi Hospital in Thailand over four generations from 1987 to the present. It describes the hospital's transition from a file-based system built in-house to a more standardized, project-based approach integrating commercial and custom-built systems. Key lessons learned include the strategic advantage of early IT adoption, balancing customization with standardization, and making careful build vs. buy decisions that consider long-term sustainability. The goal of health IT should be improving care quality, efficiency and supporting clinical and organizational strategies.
Presented at the Master of Science and Doctor of Philosophy Programs in Data Science for Healthcare and Clinical Informatics, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on October 4, 2021
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.
Health informatics is the application of information science to address problems in healthcare. It involves using technology and data to improve individual health as well as healthcare systems. The adoption of health IT aims to enhance quality, safety, efficiency and reduce costs. Key health IT tools discussed include electronic health records, clinical decision support systems, computerized physician order entry, and health information exchange. The document outlines the benefits and challenges of implementing health IT to transform healthcare delivery.
1) The article discusses health informatics and how it helps doctors make better decisions by improving how patient data and medical knowledge is captured, processed, communicated and applied.
2) It focuses on how information is handled during routine clinical tasks like consultations and covers broader issues related to things like medical record keeping, communicating with colleagues and patients, and keeping clinical knowledge up to date.
3) It explains the differences between actual information, representations of that information, and how individuals may interpret it, noting that information is best captured and represented in a way that helps both human and computer users find and interpret it.
This document provides definitions for various terms related to health informatics. It defines terms such as algorithm, bioinformatics, clinical coding system, clinical data system, clinical decision tool, communication, database, electronic health record, and medical knowledge. The definitions cover topics such as the use of informatics methods and technologies in clinical care, research, public health, and consumer health contexts.
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.
Clinical Information Systems and Electronic Health Records (October 18, 2021)Nawanan Theera-Ampornpunt
This document discusses health IT in clinical settings and provides key points about IT decision making in hospitals. It outlines factors for successful IT implementation including system quality, information quality, use, and user satisfaction. Individual and organizational impacts are also discussed. Examples of important hospital IT are provided such as enterprise systems, departmental applications, and clinical IT. Clinical decision support systems are discussed as tools that can help improve clinical decision making when designed and implemented properly. Overall, the document emphasizes focusing on using health IT to improve patient care and outcomes rather than viewing technology as the end goal.
An AI-based Decision Platform built using unified data model, incorporating systems biology topics for unit analysis using semi-supervised learning models
This document discusses health informatics and patient safety. It covers visions for patient-centered care from Judge Cartwright in 1988 and the Bristol inquiry in 2001. Health IT use in New Zealand has increased, with nearly all doctors using electronic patient records by 2009. While health IT can help when designed properly, technology alone does not ensure patient safety or effective communication - a culture of safety and strong relationships are also required. Themes around continuous learning, responsibility, and communication are highlighted. A case example describes a surgeon who unknowingly removed a patient's gallbladder twice due to a missed scan report and unreviewed records.
Clinical Information Systems, Hospital Information Systems & Electronic Healt...Nawanan Theera-Ampornpunt
This document discusses clinical information systems (CIS), hospital information systems (HIS), and electronic health records (EHRs). It defines these terms and explains how they are used in hospitals to support various clinical and administrative functions. Key points include: CIS/HIS are used to manage patient data across departments; they integrate applications like electronic health records, laboratory information systems, pharmacy systems and more. EHRs allow longitudinal documentation of a patient's medical history and care. The use of these systems provides benefits like ubiquitous access to records, clinical decision support, and improved quality of care through functions like computerized physician order entry.
The document discusses departmental information systems and management information systems in healthcare organizations. It outlines different types of hospital departments and their information needs. It also describes the key components of hospital information systems, including those that support patient management, care delivery, clinical decision-making, department management, and financial functions. The document stresses the importance of integrating these different systems and ensuring data and processes are coordinated across departments. It further discusses management information systems and how they provide data for managing healthcare organizations effectively through business intelligence applications and analytics.
E-health technologies show promise in developing countriesInSTEDD
Three evaluations of e-health technologies in developing countries found promising results:
1) Systems that improved communication between institutions helped order and manage medications and monitor patients who may abandon care.
2) Personal digital assistants and mobile devices were effective at improving data collection time and quality.
3) Donors and funders should require and sponsor outside evaluations to ensure future e-health investments are well-targeted.
Panel: FROM SMALL TO BIG TO RICH DATA: Dealing with new sources of data in Biomedicine Precision and Participatory Medicine
Fernando J. Martin-Sanchez, Professor and Chair of Health Informatics at Melbourne Medical School, discusses new sources of data in biomedicine including small, big, and rich data. He describes how small data connects people with meaningful insights from big data to be understandable for everyday tasks. Martin-Sanchez also discusses precision medicine, participatory health, and how convergence between the two can help integrate multiple data sources including genomics, the exposome, and digital health to improve disease prevention and treatment outcomes.
Consumer Health Informatics, Mobile Health, and Social Media for Health: Part...Nawanan Theera-Ampornpunt
Presented at the Master of Science and Doctor of Philosophy Programs in Data Science for Healthcare and Clinical Informatics, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on November 10, 2021
The document discusses the features and advantages of a central patient monitoring system. It allows nurses to monitor multiple patients from a central location, monitor vital signs, receive alerts, integrate with other systems like EMRs. It provides clinical decision support tools, helps manage alarms to reduce alarm fatigue, and supports a continuous patient record.
Mobile Technology in Medical InformaticJAMES JACKY
1. Mobile Technology in Medical Informatic
2. Mobile Health
3. The Cloud
4. MediHome
5. Itareps
6. Advantages of Mobile Technology in Medical Informatic
7. Problems faced in implementing mobile technology in medical healthcare
8. How does the systems work?
This document discusses electronic health records (EHRs) and clinical decision support systems (CDS). It provides examples of different types of CDS, such as alerts and reminders, reference information, order sets, and diagnostic/treatment expert systems. The goal of health IT and CDS is to improve the quality of healthcare by making it safer, more timely, effective, efficient, patient-centered and equitable. However, human decisions are still necessary, so health IT should be designed to support, not replace, clinicians.
Legal and Ethical Considerations in Nursing InformaticsKimarie Brown
This document discusses legal and ethical considerations around information security and confidentiality in nursing informatics. It covers key concepts like privacy, confidentiality, and information security. It identifies threats to system security like hackers, viruses and human error. It also discusses security measures that can be implemented, including firewalls, antivirus software, authentication methods like passwords, and proper disposal of confidential information. The impact of internet technology on health information security is also addressed.
Departmental Information Systems and Management Information Systems in Health...Nawanan Theera-Ampornpunt
Theera-Ampornpunt N. Departmental information systems and management information systems in healthcare organizations. Presented at: Faculty of ICT, Mahidol University; 2012 Feb 8; Bangkok, Thailand.
Theera-Ampornpunt N. Informatics in emergency medicine: a brief introduction. In: The International Conference in Emergency Medicine: Challenges in Emergency Medicine: It’s Time for Change!; 2012 Jan 30 - Feb 1; Bangkok, Thailand. Bangkok (Thailand): Mahidol University, Faculty of Medicine Ramathibodi Hospital; 2012 Feb.
Adopting Information Systems in a Hospital - A Case Study & Lessons LearnedNawanan Theera-Ampornpunt
This document summarizes the journey of adopting health information technology (IT) at Ramathibodi Hospital in Thailand over four generations from 1987 to the present. It describes the hospital's transition from a file-based system built in-house to a more standardized, project-based approach integrating commercial and custom-built systems. Key lessons learned include the strategic advantage of early IT adoption, balancing customization with standardization, and making careful build vs. buy decisions that consider long-term sustainability. The goal of health IT should be improving care quality, efficiency and supporting clinical and organizational strategies.
Presented at the Master of Science and Doctor of Philosophy Programs in Data Science for Healthcare and Clinical Informatics, Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on October 4, 2021
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.
Health informatics is the application of information science to address problems in healthcare. It involves using technology and data to improve individual health as well as healthcare systems. The adoption of health IT aims to enhance quality, safety, efficiency and reduce costs. Key health IT tools discussed include electronic health records, clinical decision support systems, computerized physician order entry, and health information exchange. The document outlines the benefits and challenges of implementing health IT to transform healthcare delivery.
1) The article discusses health informatics and how it helps doctors make better decisions by improving how patient data and medical knowledge is captured, processed, communicated and applied.
2) It focuses on how information is handled during routine clinical tasks like consultations and covers broader issues related to things like medical record keeping, communicating with colleagues and patients, and keeping clinical knowledge up to date.
3) It explains the differences between actual information, representations of that information, and how individuals may interpret it, noting that information is best captured and represented in a way that helps both human and computer users find and interpret it.
This document provides definitions for various terms related to health informatics. It defines terms such as algorithm, bioinformatics, clinical coding system, clinical data system, clinical decision tool, communication, database, electronic health record, and medical knowledge. The definitions cover topics such as the use of informatics methods and technologies in clinical care, research, public health, and consumer health contexts.
This clinical review document provides an overview of health informatics and discusses key concepts around clinical information including how it is captured, represented, interpreted, stored, and used. It examines these issues through the example of a clinical encounter between Dr. McKay and patient Ms. Smith. The document also discusses where clinical information comes from, how to assess its quality, and the various costs associated with capturing and processing information.
This document summarizes key aspects of clinical review and use of health informatics in patient assessment. It discusses 1) using open questions rather than closed questions to understand the patient's reason for visiting, 2) considering symptoms and non-verbal cues to form an initial hypothesis, 3) gathering a medical history over time to understand recurring issues, 4) reviewing records such as laboratory results, prescriptions, and family history to inform the diagnosis, and 5) the importance of coding clinical data for storage and communication between healthcare providers and systems.
This document summarizes key concepts around using clinical data and informatics tools to improve healthcare services. It discusses how data from multiple sources can be linked and analyzed to provide intelligence to decision-makers. However, issues around data protection, privacy and ensuring data is used appropriately must be addressed. Effective presentation of data is important so clinicians view it as valid and are motivated to change practices. Feedback of performance data can improve quality when done constructively and by considering the local context.
Ms. Patel uses the internet to research her risk of breast cancer after her mother's diagnosis. The document discusses how eHealth tools allow people to research health issues without a doctor's consultation. It describes search engines, patient portals, and ways to access medical literature. Risk is often described in complex ways, so many people prefer professional help interpreting information. Guidelines suggest when to see a genetics specialist based on family cancer history.
Ms. Amulya Patel uses the internet to research her risk of breast cancer after her mother's diagnosis. She finds many resources online, including patient portals and medical literature, but may have difficulty interpreting information without professional help. The document describes how electronic tools can provide initial information for health issues, but a consultation is still beneficial for personalized risk assessment and advice. Online information prompted Ms. Patel to ask her family about their cancer history, finding a strong familial link, so she sought a genetics expert's opinion. While the internet can satisfy some health questions, patients may need a doctor's expertise to properly interpret data.
This document summarizes an article about how clinical informatics tools can help make patient consultations more efficient. It discusses how computers can help clinicians obtain relevant patient information, assess the patient's status, and take effective management steps. It also explains how recording consultation data can produce useful information to improve local healthcare services and inform broader health system policies. The document provides a flow chart outlining the acute asthma management process in emergency departments for children over 5 years old.
How decision support tools help define clinical problemseduardo guagliardi
1) The document discusses using informatics resources like decision support tools to consider issues beyond a patient's presenting problem during a clinical consultation, including management of continuing problems, opportunistic health promotion, and modification of help-seeking behaviors.
2) It provides an example of how these tools could help in the consultation of a patient, Mr. Evans, who presented with headaches and sleep issues but was also found to have high blood pressure and undisclosed alcohol use, which are relevant to understanding his overall health situation and treating his depression.
3) The document examines how electronic prompts can help bring hidden issues to light and make both doctors and patients aware of all factors that could impact the patient's health, though clinical judgement
This document summarizes key concepts around using clinical data and informatics tools to improve healthcare services. It discusses how data from multiple sources can be linked and analyzed to provide intelligence to decision-makers. However, issues around data protection, privacy and ensuring data is used appropriately must be addressed. Effective presentation of data is important so clinicians view it as valid and are motivated to change practices. Feedback of performance data can improve quality when done constructively and by considering the local context.
1) The document discusses the promise and potential perils of eHealth technologies like remote monitoring devices, virtual assistants, and personalized health records.
2) Factors driving eHealth include patient demand for convenient access to information, the ability to link separate health services, and using technology to address issues like staff shortages.
3) Potential benefits include improved patient information and choice, better communication between providers, and links to vetted external health resources. However, issues around privacy and control of personal data still need solutions.
The Indian Parliament passed the Companies Bill 2013, replacing the previous Companies Act of 1956. The new law aims to modernize corporate regulation in India and promote business-friendly initiatives. It provides for greater transparency, accountability, and protection of minority shareholders and investors. Some key changes include allowing more members in private companies, introducing one-person companies, mandatory CSR spending for large companies, restrictions on related party transactions and loans to directors, and establishing a new National Company Law Tribunal to handle corporate matters instead of the High Court. The new law is expected to facilitate business while improving governance.
1. Ms Smith developed symptomatic renal impairment that may have been avoided if her underlying medical issues were properly diagnosed and treated earlier at multiple points in her care.
2. Ensuring appropriate follow-up and continuity of care between primary care physicians and specialists can help reduce risks for patients with chronic conditions like Ms Smith.
3. Integrating medical records electronically across different parts of a healthcare system can improve information sharing, guide treatment according to best practices, and ultimately enhance patient outcomes.
How computers can help to share understanding with patientseduardo guagliardi
This document provides an overview of how computers and interactive health applications can help doctors share information and clinical understanding with patients. It discusses the case of Ms. Patel, who met with her doctor and genetics clinic team to discuss her family history of breast cancer and 30% lifetime risk of also developing breast cancer. The team created a genogram to visualize her family history and risks. The document then discusses the benefits of interactive tools for improving patient understanding, reducing uncertainty, and better supporting patients. It provides some examples of how images, videos and other multimedia can help explain clinical information to patients during consultations.
This document summarizes how clinical information can help doctors understand patients and their health issues. It provides examples of how a doctor uses a patient's medical history, including prior visits, prescriptions, lab results, and family history to inform their assessment of the patient's current issues. The document also contrasts experienced doctors' hypothetico-deductive approach to less experienced doctors' more exhaustive checklist method. Overall, it discusses how electronic medical records can provide doctors important context to help identify health problems and guide their consultation with patients.
1) The document discusses the promise and potential perils of eHealth technologies like remote monitoring devices, virtual assistants, and personalized health records.
2) Factors driving eHealth include patient demand for convenient access to information, the ability to link separate health services, and using technology to address staffing shortages. However, eHealth may also shift costs to patients and change the role of healthcare providers.
3) Potential benefits of eHealth include improved patient information and choice, better communication between providers, and links to vetted external health resources. However, ensuring privacy and appropriate access to personal health data is also discussed.
Team Sol2 01 Health Care Informatics Power PointMessner Angie
The document discusses clinical information systems and their components. It provides an overview of electronic health records and describes key parts of a clinical information system including health information, order entry, decision support, and clinical documentation. It also discusses clinical decision making systems and their importance in reducing variation, costs, and improving diagnosis. Safety, education and costs related to clinical information systems are also evaluated.
A clinical information system (CIS) is a technology-based system used at the point of care to support processing and storing patient information. It includes electronic health records, clinical data repositories, decision support, and communication tools. Implementing a CIS requires representation from all areas of healthcare to ensure success. Effective CIS can reduce errors, improve guideline-based care, and decrease healthcare utilization through components like clinical decision support systems. However, ensuring data security, accuracy, and privacy is important when using and networking CIS.
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 introduction to information, information science, and information systems. It defines key terms like data, information, knowledge, information science, and information systems. Information science is described as the science of studying how information and knowledge are used in organizations and how people, organizations, and information systems interact. The document outlines how data is acquired and processed to become valuable information.
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.
Cis evaluation final_presentation, nur 3563 sol1SBU
An overview of a Computer Information System (CIS) and considerations that need to be taken with implementing an Electronic Health Record (EHR) in a healthcare setting.
Machine Intellegance/ Multilayer Perceptrons/Function Approximation
Let’s consider the indicator function of the rational numbers to the set of real numbers (also
known as Dirichlet function), i.e. the function:
f: IR {0, 1}, x {1, if xQ 0, otherwise.
a) Can this function be approximated with arbitrary accuracy by a neural network (multilayer
perceptron)?
b) What is the result of part a) on the calculation abilities of neural Networks?
Solution
The care business traditionally has generated massive amounts of information, driven by record
keeping, compliance & regulative needs, and patient care [1]. whereas most knowledge is hold
on in text type, the present trend is toward fast conversion of those massive amounts of
information. Driven by obligatory needs and also the potential to enhance the standard of health
care delivery in the meantime reducing the prices, these huge quantities of information (known
as ‘big data’) hold the promise of supporting a good vary of medical and care functions, as well
as among others clinical call support, illness police work, and population health management [2,
3, 4, 5]. Reports say knowledge from the U.S. care system alone reached, in 2011, one hundred
fifty exabytes. At this rate of growth, huge knowledge for U.S. care can before long reach the
zettabyte (1021 gigabytes) scale and, shortly when, the yottabyte (1024 gigabytes) [6]. Kaiser
Permanente, the California-based health network, that has over nine million members, is
believed to possess between twenty six.5 and forty four petabytes of doubtless made knowledge
from EHRs, as well as pictures and annotations [6].
By definition, huge knowledge in care refers to electronic health knowledge sets therefore
massive and complicated that they\'re tough (or impossible) to manage with ancient computer
code and/or hardware; nor will they be simply managed with ancient or common knowledge
management tools and strategies [7]. huge knowledge in care is overwhelming not solely due to
its volume however additionally due to the range of information varieties and also the speed at
that it should be managed [7]. The totality of information associated with patient care and well-
being compose “big data” within the care business. It includes clinical knowledge from CPOE
and clinical call support systems (physician’s written notes and prescriptions, medical imaging,
laboratory, pharmacy, insurance, and alternative body knowledge); patient knowledge in
electronic patient records (EPRs); machine generated/sensor data, like from observance
important signs; social media posts, as well as Twitter feeds (so-called tweets) [8], blogs [9],
standing updates on Facebook and alternative platforms, and net pages; and fewer patient-
specific data, as well as emergency care knowledge, news feeds, and articles in medical journals.
For the massive knowledge person, there is, amongst this large quantity and array of information,
chance. By discovering associations and understanding patterns.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
The document discusses the development and importance of Nursing Minimum Data Sets (NMDS) systems. It notes that the identification of NMDS in the 1980s spurred the development of similar nursing data sets around the world. The chapter provides a historical overview and synthesis of NMDS systems, and discusses how they can increase nursing data and information capacity to support knowledge building for the nursing discipline and profession. This data can help inform the development of electronic health record systems.
In this full-day tutorial, you will learn basic overview of electronic medical records systems, health data management and how you can use the OpenMRS system for data and information management. We will cover basics of installation, user management, location management, patient dashboards and some interesting features that are provided by different modules. You can see how OpenMRS can be customized with different modules that are suitable for different contexts. This tutorial is helpful for new users and developers who would like to know the features of OpenMRS. Individuals who would like to evaluate and try to see if OpenMRS fits their healthcare needs will also benefit from this tutorial.
The document discusses clinical decision support systems (CDSS), which apply medical knowledge to patient data to generate recommendations for healthcare providers. It notes challenges in implementing CDSS, including lack of technical expertise, cost, integration issues, and physician acceptance. The document then provides examples of how CDSS could be used in different medical domains like diagnosis, electronic records, and disease management. It lists some benefits of using CDSS in hospital emergency departments and concludes that the group's research paper will focus on challenges involved in implementing different types of CDSS.
This document discusses using ontologies to simplify semantic solutions for biomedical applications. It provides examples of how ontologies can be used to integrate medical expertise and knowledge from different sources. It also describes challenges in representing biomedical information with ontologies and introduces MedMaP, a medical management portal that aims to simplify access to ontology-based reasoning and analytics using graphical visualizations and self-service tools. MedMaP allows users to customize their experience and gain insights from subject matter experts.
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This document discusses decision support systems (DSS) in healthcare. It begins by defining DSS as computer-based systems that aid decision-making. It then explains how DSS can help ensure correct medical diagnoses and reduce risks of drug reactions. The document also discusses how data warehousing and data mining are important components of DSS, allowing trends to be discovered in large datasets. Finally, it outlines some challenges in implementing DSS, such as high costs and debates around calculating costs and benefits.
This document provides an overview of nursing informatics, which integrates nursing science with information management and analytical sciences. It discusses how the role of nursing has changed with technology, including the transition to electronic health records. Nursing informatics is defined as processing and managing nursing data, information and knowledge to support various aspects of nursing practice. The document also describes clinical information systems, which collect and store clinical information to support healthcare delivery, and how these systems can provide benefits like improved access to patient data and drug safety, while also facing barriers such as costs and clinician resistance.
This document defines clinical decision support systems (CDSS) and outlines their key components and challenges. It begins by defining CDSS as computer programs that help health professionals make clinical decisions. It then describes the main categories of CDSS, including diagnostic assistance, therapy planning, and image recognition. The document outlines the typical system architecture of CDSS including tools for information management, focusing attention, and patient-specific consultation. It also discusses the need for CDSS, potential applications, disadvantages, and challenges to implementation. Throughout, it provides examples to illustrate different types of CDSS.
1. The document discusses the advantages and disadvantages of implementing an electronic health record (EHR) system to replace a paper-based system.
2. A key disadvantage is the high cost of implementation, with the cost of Alberta's new clinical information system estimated at $1.6 billion over 10 years.
3. Another disadvantage is a lack of interoperability between existing EHR systems, which prevents patient information from being shared and understood across health settings.
Similar to Glossary of health informatics terms (20)
Curso de introdução à informática em saúde apresentação ao colegiadoeduardo guagliardi
Este documento descreve um curso introdutório sobre informática em saúde ministrado por Renato Sabbatini na UNIPLAC em 2012. O curso será online e assíncrono, dividido em 10 módulos sobre diversos tópicos como prontuário eletrônico, telemedicina e sistemas de informação hospitalar. Os alunos serão avaliados por questionários, exercícios e discussões em fórum, podendo obter certificado se alcançarem nota mínima de 70%.
O documento discute a importância do prontuário médico para proteger médicos éticos, ameaçar aqueles sem escrúpulos, e proteger contra reclamações de pacientes insatisfeitos.
1. Ms Smith developed symptomatic renal impairment that may have been avoided if her underlying medical issues were properly diagnosed and treated earlier at multiple points in her care.
2. Ensuring appropriate follow-up and continuity of care between primary and secondary healthcare providers is challenging but important to reduce risks to patients.
3. The integration of clinical records and guidelines across healthcare locations and providers could help improve diagnosis, investigation, referral and follow-up to reduce risks to patients like Ms Smith.
1) Doctors must continue learning after medical school to keep up with changing knowledge, but traditional continuing education methods may not optimally impact clinical practice.
2) Workplace learning, where doctors learn by answering clinical questions that arise during patient care, is more effective for retaining knowledge and applying it.
3) However, workplace learning faces barriers like lack of time and clear questioning skills. Prioritizing urgent questions and using efficient knowledge resources can help address these barriers.
How decision support tools help define clinical problemseduardo guagliardi
1) The document discusses how decision support tools can help doctors consider issues beyond a patient's presenting problem during a consultation, including management of ongoing medical problems, opportunistic health promotion, and modifying patient help-seeking behaviors.
2) It provides an example consultation with patient Mr. Evans, who presented with headaches and sleep issues, but is also found to have high blood pressure and an alcohol problem complicating his known diabetes.
3) The document examines how electronic prompts can help bring hidden health issues to light to provide more effective treatment that addresses underlying problems driving a patient's symptoms.
How computers can help to share understanding with patientseduardo guagliardi
This document provides an overview of how computers and interactive health applications can help doctors share information and clinical understanding with patients. It discusses the case of Ms. Patel, who met with her doctor and genetics clinic team to discuss her family history of breast cancer and 30% lifetime risk of also developing breast cancer. The team created a genogram using software to visualize her family history and risk factors. The document then discusses the benefits of interactive tools for improving patient understanding, reducing uncertainty, and supporting patients. It also notes challenges in using these tools during medical consultations due to time constraints.
Directory information is vital for navigating healthcare services but is often inadequate. It provides contact details of local services, specialists' preferences, and how to access tests and referrals. However, directory information is difficult to maintain as it is locally specific and changes frequently. While new technologies may help, current solutions are fragmented without a consistent format or centralized responsibility. Improving the collection and use of accurate, up-to-date directory information could support clinicians and reduce patient frustrations when navigating the healthcare system.
Este documento discute o prontuário eletrônico do paciente (PEP) na assistência, informação e conhecimento médico. Ele apresenta o PEP e sua importância, discute padrões de registro e transmissão de dados em saúde, e examina aplicações do PEP em diversas áreas como educação, pesquisa, telemedicina e gestão da qualidade. O documento é direcionado a profissionais de saúde e tem como objetivo difundir os conceitos e benefícios do PEP.
O documento discute as diferenças entre depressão unipolar e bipolar, enfatizando que: 1) a depressão é frequentemente a apresentação inicial do transtorno bipolar, mas é erroneamente diagnosticada como unipolar; 2) os bipolares passam mais tempo deprimidos do que maníacos/hipomaníacos; 3) vários sintomas e características sugerem um diagnóstico de bipolaridade ao invés de unipolar.
O documento discute a importância da informática na saúde e propõe várias iniciativas de colaboração entre a Sociedade Brasileira de Informática em Saúde (SBIS) e a Uniplac, incluindo cursos online sobre o tema para alunos e professores e a exibição de webconferências gravadas.
Este documento apresenta os resultados de uma pesquisa realizada pelo Conselho Regional de Medicina do Estado de São Paulo (Cremesp) para avaliar 85 Centros de Atenção Psicossocial (CAPS) no estado de São Paulo, representando cerca de 40% da rede instalada. A pesquisa identificou lacunas no funcionamento dos CAPS, como falta de profissionais, ausência de atendimento médico e retaguarda para emergências. As conclusões apontam para a necessidade de aprimorar a supervisão, capacitação de equipes, atividades comunitárias e
Este documento discute como os coordenadores dos Centros de Atenção Psicossocial Infantojuvenil (CAPSi) percebem o valor e a utilidade dos prontuários de pacientes. Os coordenadores veem os prontuários como valiosas ferramentas de trabalho, importantes para intervenção clínica e acompanhamento. No entanto, eles não percebem qual seria a utilidade dos prontuários para os próprios pacientes.
This document summarizes an article about how clinical informatics tools can help make patient consultations more efficient. It provides guidelines for assessing the severity of asthma exacerbations in children and outlines the steps for acute asthma management in an emergency department. Recording clinical data from patient encounters enables clinicians to better understand local service needs, compare performance to other health systems, and continuously improve care over time.
NAVIGATING THE HORIZONS OF TIME LAPSE EMBRYO MONITORING.pdfRahul Sen
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Pictorial and detailed description of patellar instability with sign and symptoms and how to diagnose , what investigations you should go with and how to approach with treatment options . I have presented this slide in my 2nd year junior residency in orthopedics at LLRM medical college Meerut and got good reviews for it
After getting it read you will definitely understand the topic.
Know the difference between Endodontics and Orthodontics.Gokuldas Hospital
Your smile is beautiful.
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Osvaldo Bernardo Muchanga-GASTROINTESTINAL INFECTIONS AND GASTRITIS-2024.pdfOsvaldo Bernardo Muchanga
GASTROINTESTINAL INFECTIONS AND GASTRITIS
Osvaldo Bernardo Muchanga
Gastrointestinal Infections
GASTROINTESTINAL INFECTIONS result from the ingestion of pathogens that cause infections at the level of this tract, generally being transmitted by food, water and hands contaminated by microorganisms such as E. coli, Salmonella, Shigella, Vibrio cholerae, Campylobacter, Staphylococcus, Rotavirus among others that are generally contained in feces, thus configuring a FECAL-ORAL type of transmission.
Among the factors that lead to the occurrence of gastrointestinal infections are the hygienic and sanitary deficiencies that characterize our markets and other places where raw or cooked food is sold, poor environmental sanitation in communities, deficiencies in water treatment (or in the process of its plumbing), risky hygienic-sanitary habits (not washing hands after major and/or minor needs), among others.
These are generally consequences (signs and symptoms) resulting from gastrointestinal infections: diarrhea, vomiting, fever and malaise, among others.
The treatment consists of replacing lost liquids and electrolytes (drinking drinking water and other recommended liquids, including consumption of juicy fruits such as papayas, apples, pears, among others that contain water in their composition).
To prevent this, it is necessary to promote health education, improve the hygienic-sanitary conditions of markets and communities in general as a way of promoting, preserving and prolonging PUBLIC HEALTH.
Gastritis and Gastric Health
Gastric Health is one of the most relevant concerns in human health, with gastrointestinal infections being among the main illnesses that affect humans.
Among gastric problems, we have GASTRITIS AND GASTRIC ULCERS as the main public health problems. Gastritis and gastric ulcers normally result from inflammation and corrosion of the walls of the stomach (gastric mucosa) and are generally associated (caused) by the bacterium Helicobacter pylor, which, according to the literature, this bacterium settles on these walls (of the stomach) and starts to release urease that ends up altering the normal pH of the stomach (acid), which leads to inflammation and corrosion of the mucous membranes and consequent gastritis or ulcers, respectively.
In addition to bacterial infections, gastritis and gastric ulcers are associated with several factors, with emphasis on prolonged fasting, chemical substances including drugs, alcohol, foods with strong seasonings including chilli, which ends up causing inflammation of the stomach walls and/or corrosion. of the same, resulting in the appearance of wounds and consequent gastritis or ulcers, respectively.
Among patients with gastritis and/or ulcers, one of the dilemmas is associated with the foods to consume in order to minimize the sensation of pain and discomfort.
Breast cancer: Post menopausal endocrine therapyDr. Sumit KUMAR
Breast cancer in postmenopausal women with hormone receptor-positive (HR+) status is a common and complex condition that necessitates a multifaceted approach to management. HR+ breast cancer means that the cancer cells grow in response to hormones such as estrogen and progesterone. This subtype is prevalent among postmenopausal women and typically exhibits a more indolent course compared to other forms of breast cancer, which allows for a variety of treatment options.
Diagnosis and Staging
The diagnosis of HR+ breast cancer begins with clinical evaluation, imaging, and biopsy. Imaging modalities such as mammography, ultrasound, and MRI help in assessing the extent of the disease. Histopathological examination and immunohistochemical staining of the biopsy sample confirm the diagnosis and hormone receptor status by identifying the presence of estrogen receptors (ER) and progesterone receptors (PR) on the tumor cells.
Staging involves determining the size of the tumor (T), the involvement of regional lymph nodes (N), and the presence of distant metastasis (M). The American Joint Committee on Cancer (AJCC) staging system is commonly used. Accurate staging is critical as it guides treatment decisions.
Treatment Options
Endocrine Therapy
Endocrine therapy is the cornerstone of treatment for HR+ breast cancer in postmenopausal women. The primary goal is to reduce the levels of estrogen or block its effects on cancer cells. Commonly used agents include:
Selective Estrogen Receptor Modulators (SERMs): Tamoxifen is a SERM that binds to estrogen receptors, blocking estrogen from stimulating breast cancer cells. It is effective but may have side effects such as increased risk of endometrial cancer and thromboembolic events.
Aromatase Inhibitors (AIs): These drugs, including anastrozole, letrozole, and exemestane, lower estrogen levels by inhibiting the aromatase enzyme, which converts androgens to estrogen in peripheral tissues. AIs are generally preferred in postmenopausal women due to their efficacy and safety profile compared to tamoxifen.
Selective Estrogen Receptor Downregulators (SERDs): Fulvestrant is a SERD that degrades estrogen receptors and is used in cases where resistance to other endocrine therapies develops.
Combination Therapies
Combining endocrine therapy with other treatments enhances efficacy. Examples include:
Endocrine Therapy with CDK4/6 Inhibitors: Palbociclib, ribociclib, and abemaciclib are CDK4/6 inhibitors that, when combined with endocrine therapy, significantly improve progression-free survival in advanced HR+ breast cancer.
Endocrine Therapy with mTOR Inhibitors: Everolimus, an mTOR inhibitor, can be added to endocrine therapy for patients who have developed resistance to aromatase inhibitors.
Chemotherapy
Chemotherapy is generally reserved for patients with high-risk features, such as large tumor size, high-grade histology, or extensive lymph node involvement. Regimens often include anthracyclines and taxanes.
Travel Clinic Cardiff: Health Advice for International TravelersNX Healthcare
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Nano-gold for Cancer Therapy chemistry investigatory projectSIVAVINAYAKPK
chemistry investigatory project
The development of nanogold-based cancer therapy could revolutionize oncology by providing a more targeted, less invasive treatment option. This project contributes to the growing body of research aimed at harnessing nanotechnology for medical applications, paving the way for future clinical trials and potential commercial applications.
Cancer remains one of the leading causes of death worldwide, prompting the need for innovative treatment methods. Nanotechnology offers promising new approaches, including the use of gold nanoparticles (nanogold) for targeted cancer therapy. Nanogold particles possess unique physical and chemical properties that make them suitable for drug delivery, imaging, and photothermal therapy.
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Glossary of health informatics terms
1. Glossary of health informatics terms
Algorithm-A process for carrying out a complex task broken Confidentiality (protecting privacy)-The policies restricting
down into simple decision and action steps. Often assists the access to a person’s data to those whom the patient agrees need
requirements analysis process carried out before programming. access to it, except rarely in emergency and for the public good
Bioinformatics-The use of health informatics methods to aid or (e.g. to contain epidemics, allow important research to be under-
facilitate research in molecular biology. taken or solve serious crime). In addition, other regulatory and
Checklist-A type of clinical decision tool: A form listing one or institutional approval may be needed (e.g. the need to seek con-
more items of patient data to be collected before, during or after sent from medical ethics committees or relevant national
an encounter; can be paper or computer based. authorities). In recent years, leading public health researchers
Clinical coding system (clinical thesaurus, controlled have warned that legislation enacted to protect patients’ medical
vocabulary)-A limited list of preferred terms from which the data in the UK, Europe and US could potentially hamper obser-
user can draw one or more to express a concept such as patient vational research and medical record linkage studies.5 6
data, a disease or drug name, etc. An alphanumeric code Consumer health informatics-The use of health informatics
corresponding to the term is then stored by the computer. Syno- methods to facilitate the study and development of paper and
nyms or close matches to each preferred term are usually also electronic systems which support public access to and the use of
available and map onto the same internal codes. This approach health and lifestyle information. For additional discussion on the
makes it easier for a computer to analyse data than the use of scope of consumer health informatics, see Eysenbach.7 See also
free text words or phrases. Examples of clinical coding systems eHealth.
include SNOMED-CT (divergent codes used to capture patient Data quality-The degree to which data items are accurate, com-
data), MeSH (terms used to index biomedical literature) and plete, relevant, timely, sufficiently detailed, appropriately
ICD-10 (convergent codes for international comparisons, with represented (e.g. consistently coded using a clinical coding system)
specific rules to guide coders). Clinical coding systems play a key and retain sufficient contextual information to support decision
role in epidemiological studies and health service research, from making.
the use of MeSH terms to conduct literature searches for system- Database-A collection of data in machine readable format
atic reviews to numerous studies which use ICD codes to classify organised so that it can be retrieved or processed automatically
and compare diseases. To prevent information loss, it is vital that by computer. A flat file database is organised like a card file, with
the terms and codes are never changed or dropped, only added many records (cards) each including one or more fields (data
to. Obsolete terms can be marked as such to deter inappropriate items). A relational database is organised as one or more related
use. Continuing maintenance is needed to incorporate new tables, each containing columns and rows. Data are organised in
terms and codes for new concepts and new synonyms as they a database according to a schema or data model; some items are
arise. often coded using a clinical coding system.
Clinical data system-Any information system concerned with Decision support system (computer decision aid)-A type of
the capture, processing or communication of patient data.1 clinical decision tool: a computer system that uses two or more
Clinical decision tool-Any mechanical, paper or electronic aid items of patient data to generate case or encounter-specific
that collects data from an individual patient to generate output advice.8 Examples include computer risk assessors to estimate
that aids clinical decisions during the doctor-patient encounter.2 cardiovascular disease risk9 and the Leeds Acute Abdominal
Examples include decision support systems, paper or computer Pain system, which aids the diagnosis of conditions causing such
reminders and checklists, which are potentially useful tools in public pain.10 Evidence-adaptive decision support systems are a type of
health informatics, as well as other branches of health informatics. decision aid with a knowledge base that is constructed from and
Clinical information-Organised patient data or clinical knowledge continually adapts to new research- and practice-based
used to make clinical decisions (adapted from Shortliffe et al3); evidence.11
may also include directory information. Many activities in public Decision tree-A way to model a complex decision process as a
health and epidemiology (e.g. surveillance systems, cohort stud- tree with branches representing all possible intermediate states
ies to assess the effects of a risk factor of disease and clinical tri- or final outcomes of an event. The probabilities of each interme-
als to estimate efficacies of new treatments) involve the diate state or final outcome and the perceived utilities of each are
organisation of such data (e.g. case report forms for individual combined to attach expected utilities to each outcome. The
patients) into useable information (e.g. incidence of notifiable science of drawing decision trees and assessing utilities is called
cases of disease from surveillance programmes and summary decision analysis.
evidence from cohort studies or clinical trials, expressed as odds Directory information-Information specific to an organisation or
ratios for certain harmful and beneficial outcomes). See also: service which is useful in managing public health services, health
information. care services or patients. Examples include a phone directory, a
Clinical informatics-The use of health informatics methods to aid lab handbook listing available tests and tubes to use, and a list of
management of patients, employing an interdisciplinary the drugs in the local formulary.
approach, including the clinical and information sciences.3 eHealth-The use of internet technology by the public, health
Communication-The exchange of information between agents workers and others to access health and lifestyle information,
(human or automated) face to face or using paper or electronic services and support; it encompasses telemedicine, telecare etc. For
media.4 Requires the use of a shared language and understand- in-depth discussion on the scope and security issues of eHealth,
ing or common ground. see a report by the National HealthKey Collaborative.12
Computer vision (image interpretation)-The use of computer Electronic health record (EHR)-In the UK, the lifelong
techniques to assist the interpretation of images, such as summary of a person’s health episodes, assembled from summa-
mammograms. ries of individual electronic patient records and other relevant data.13
2. Electronic patient record (EPR)-A computer-based clinical data actually used by the physician. This entails clinical practices inno-
system designed to replace paper patient records. vation24 or narrowing the gap between what we know and what
Evaluating health information systems: Measuring or describ- we do. The NHS is developing a programme of knowledge codi-
ing the key characteristics of an information system, such as its fication to inform routine problem solving, e.g. through the
quality, usability, accuracy, clinical impact or cost effectiveness.14 National Electronic Library of Health, guidelines from the
Generally, information systems can be evaluated using standard National Institute of Clinical Excellence (NICE), and care
health technology assessment methods. pathways and triage algorithms used in the NHS Direct Clinical
Explicit knowledge-Knowledge that can be communicated on Advice System.25
paper or electronically, without person-to-person contact.15 Medical knowledge (clinical knowledge)-Information about dis-
Health workers and physicians cannot use explicit knowledge if eases, therapies, interpretation of lab tests etc., and potentially
they cannot access it. There is thus a need to identify, capture, applicable to decisions about multiple patients and public health
index and make available explicit knowledge to professionals, a policies, unlike patient data. This information should where pos-
process called codification. Much of the work done by the sible be based on sound evidence from clinical and
Cochrane Collaboration involves codification of explicit epidemiological studies, using valid and reliable methods. See
knowledge. See also: tacit knowledge. also: explicit knowledge, tacit knowledge, knowledge management.
Geographical information system (GIS)-Computer software Minimum data set-A list of the names, definitions and sources of
which captures, stores, processes and displays location as well as data items needed to support a specific purpose, such as surveil-
other data. The display may preserve distance ratios between lance of the health of a community, investigation of a research
data objects (e.g. true scale maps) or link similar objects, ignoring hypothesis or monitoring the quality of care in a registry.
distance (e.g. topological maps such as that distributed to the Patient data-Information about an individual patient and poten-
public for the London Underground). GIS software is used in tially relevant to decisions about her current or future health or
many ecological studies of disease. A famous example is Peto’s illness. Patient data should be collected using methods that mini-
study of diet, mortality and lifestyle in rural China.16 Disease mise systematic and random error. See also: medical knowledge.
mapping studies have also been conducted to assess childhood Public health informatics-the use of health informatics methods
leukaemia in areas with different radon levels,17 the clustering of to promote “public health practice, research and learning”,
respiratory cancer cases in areas with a steel foundry18 and socio-
employing an interdisciplinary approach, including the public
economic gradients in infant mortality.19 GISs are also used for
health sciences, e.g. epidemiology and health services research,
public health planning and surveillance purposes at local or
and the information sciences, e.g. computing science and
national health departments. Care should be taken by policy
technology (adapted from Yasnoff et al26). In a recent paper out-
makers in interpreting maps produced by GIS software, particu-
lining an agenda for developing this branch of informatics,
larly in regard to the ecologic fallacy.20
Yasnoff et al27 argued for the need to construct, implement and
Health informatics (medical informatics)-The study and appli-
integrate public health surveillance systems at national and local
cation of methods to improve the management of patient data,
levels, to enable rapid identification and response to disease
medical knowledge, population data and other information relevant
hotspots (and more topically, bioterrorism). As Yasnoff rightly
to patient care and community health. Unlike some other defini-
points out, methods of assessing costs and benefits of such
tions of health or medical informatics (e.g. Greenes and
systems are needed. Public health informatics can also contribute
Shortliffe21), this definition puts the emphasis on information
in other areas, e.g. reminders have played an important role in
management rather than technology. Branches of health
prevention programmes such as smoking cessation advice to
informatics include bioinformatics, clinical informatics, consumer
health informatics and public health informatics. smokers28 and the use of preventive care for patients.29
Information-Organised data or knowledge used by human and Registry -A database and associated applications which collects a
computer agents to reduce uncertainty, take decisions and guide minimum data set on a specified group of patients (often those
actions (adapted from Shortliffe et al3 and Wyatt22). See also: with a certain disease or who have undergone a specific
clinical information, patient data, medical knowledge. procedure), health professionals, organisations or even clinical
Information design-The science and practice of designing trials. Registries can be used to explore and improve the quality
forms, reports, books etc. so that the information they contain can of care or to support research, for example to monitor long term
be found rapidly and interpreted without error (adapted from outcomes or rare complications of procedures. Key issues in reg-
Sless23). Information design is based on psychological and istries are maintaining confidentiality, coverage of the target
graphical design theories and a large number of empirical stud- population and data quality.
ies of human perception and decision making using alternative Reminder-A type of clinical decision tool which reminds a doctor
formats for information. about some item of patient data or clinical knowledge relevant to an
Knowledge base-A store of knowledge which is represented individual patient that they would be expected to know. Can be
explicitly so that a computer can search and reason with it auto- paper- or computer-based; includes checklists, sticky labels on
matically; often uses a clinical coding system to label the concepts. front of notes, an extract from a guideline placed inside notes or
See also decision support system. computer-based alerts. There has been much interest in remind-
Knowledge based system (expert system)-A computer decision ers as an innovation method recently because of the poor uptake of
support system with an explicit knowledge base and separate practice guidelines, even those based on good quality evidence.
reasoner program which uses this to give advice or interpret An example is in the treatment of dyslipidaemia in primary care,
data, often patient data. where there is a big gap between recommendations and actual
Knowledge management-The identification, mobilisation and practice.30
use of knowledge to improve decisions and actions. In public Requirements analysis-The process of understanding and cap-
health and medicine, much of this work involves the turing user needs, skills, and wishes before developing an infor-
management of medical knowledge (from epidemiological studies, mation system (adapted from Somerville31). See software
randomised-controlled trials and systematic reviews) so that it is engineering.
3. Security-The technical methods by which confidentiality is Telecare-A kind of telemedicine with the patient located in the
achieved.12 community (e.g. their own home); see also eHealth.
Software engineering-The process of system development, Telemedicine-The use of any electronic medium to mediate or
documentation, implementation and upgrading (adapted from augment clinical consultations. Telemedicine can be simultaneous
Somerville31). In the classical or “waterfall” model of software (e.g. telephone, videoconference) or store and forward (e.g. an
engineering, requirements analysis leads to a document which email with an attached image).
serves as the basis for a system specification and database schema,
Additional resources: Readers who are interested in general
from which programmers work to develop the software.
coverage of the field of health informatics are encouraged to
However, increasingly, users and software designers work
refer to standard texts.32 33 Those who are interested in alternative
together from the start to develop and refine a prototype system.
This helps to engage the users, educate the software or complementary definitions of the above terms can look up
development team, brings the requirements documents alive various sources.3 4 34–36
and allows users to explore how their requirements might Notes to the list of concepts:
change due to interaction with the new software. Italic means “see also”
Tacit knowledge (intuition)-Knowledge that requires person-to- Synonyms are mentioned in brackets, after the core term
person contact to transfer and cannot be communicated on paper AcknowledgementJoe Liu, Centre for Statistics in Medicine, Oxford contributed
or electronically.15–25 Over time, some tacit knowledge can be ana- many definitions to an earlier version of this glossary and Ameen Abu Hanna,
lysed, decomposed and made explicit. See also: explicit knowledge. KIK, AMC Amsterdam provided useful comments.
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