Overview of Health Informatics: survey of fundamentals of health information technology, Identify the forces behind health informatics, educational and career opportunities in health informatics.
module-8-ppt-session-1 for ehealth (1).pptxssuser2714fe
Explain key eHealth and mHealth concepts
Define commonly used eHealth and mHealth terms
Illustrate eHealth and mHealth applications
Describe limitations and considerations for eHealth and mHealth
Augmented Personalized Health: using AI techniques on semantically integrated...Amit Sheth
Keynote @ 2018 AAAI Joint Workshop on Health Intelligence (W3PHIAI 2018), 2 February 2018, New Orleans, LA [Video: https://youtu.be/GujvoWRa0O8]
Related article: https://ieeexplore.ieee.org/document/8355891/
Abstract
Healthcare as we know it is in the process of going through a massive change - from episodic to continuous, from disease-focused to wellness and quality of life focused, from clinic centric to anywhere a patient is, from clinician controlled to patient empowered, and from being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data-driven. While the ability to create and capture data is already here, the upcoming innovations will be in converting this big data into smart data through contextual and personalized processing such that patients and clinicians can make better decisions and take timely actions for augmented personalized health. In this talk, we will discuss how use of AI techniques on semantically integrated patient-generated health data (PGHD), environmental data, clinical data, and public social data is exploited to achieve a range of augmented health management strategies that include self-monitoring, self-appraisal, self-management, intervention, and Disease Progression Tracking and Prediction. We will review examples and outcomes from a number of applications, some involving patient evaluations, including asthma in children, bariatric surgery/obesity, mental health/depression, that are part of the Kno.e.sis kHealth personalized digital health initiative.
Background: Background: http://bit.ly/k-APH, http://bit.ly/kAsthma, http://j.mp/PARCtalk
Overview of Health Informatics: survey of fundamentals of health information technology, Identify the forces behind health informatics, educational and career opportunities in health informatics.
module-8-ppt-session-1 for ehealth (1).pptxssuser2714fe
Explain key eHealth and mHealth concepts
Define commonly used eHealth and mHealth terms
Illustrate eHealth and mHealth applications
Describe limitations and considerations for eHealth and mHealth
Augmented Personalized Health: using AI techniques on semantically integrated...Amit Sheth
Keynote @ 2018 AAAI Joint Workshop on Health Intelligence (W3PHIAI 2018), 2 February 2018, New Orleans, LA [Video: https://youtu.be/GujvoWRa0O8]
Related article: https://ieeexplore.ieee.org/document/8355891/
Abstract
Healthcare as we know it is in the process of going through a massive change - from episodic to continuous, from disease-focused to wellness and quality of life focused, from clinic centric to anywhere a patient is, from clinician controlled to patient empowered, and from being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data-driven. While the ability to create and capture data is already here, the upcoming innovations will be in converting this big data into smart data through contextual and personalized processing such that patients and clinicians can make better decisions and take timely actions for augmented personalized health. In this talk, we will discuss how use of AI techniques on semantically integrated patient-generated health data (PGHD), environmental data, clinical data, and public social data is exploited to achieve a range of augmented health management strategies that include self-monitoring, self-appraisal, self-management, intervention, and Disease Progression Tracking and Prediction. We will review examples and outcomes from a number of applications, some involving patient evaluations, including asthma in children, bariatric surgery/obesity, mental health/depression, that are part of the Kno.e.sis kHealth personalized digital health initiative.
Background: Background: http://bit.ly/k-APH, http://bit.ly/kAsthma, http://j.mp/PARCtalk
Please note that it is important to follow the instructi.docxrandymartin91030
Please note that it is important to follow the instructions given, write the essay as a review of what you have researched on crispr-cas9. (Do not critique)
Competencies
for Public Health Informaticians
2009
U.S. DEPARTMENT OF HEALTH AND HUMAN
SERVICES
Centers for Disease Control and Prevention
Office of Workforce and Career Development
and
University of Washington School of Public Health and Community Medicine's
Center for Public Health Informatics
Competencies for Public Health Informaticians—2009
U.S. Department of Health and Human Services (HHS), Centers for Disease Control and
Prevention (CDC)
The University of Washington School of Public Health and Community Medicine (UW SPHCM)
Working Group Members: Bryant Karras, MD; Jac Davies, MS, MPH; Denise Koo, MD, MPH;
Janise Richards, PhD, MPH, MS; Patrick O’Carroll, MD, MPH, FACPM, FACMI; Kathleen Miner,
PhD, MPH, CHES; Mark Oberle, MD, MPH; Milton Corn, MD; Don Detmer, MD, MA; Seth Foldy,
MD, MPH; Larry Hanrahan, PhD, MS; Ginny Hare, MS; Marty LaVenture, PhD, MPH; Cecil
Lynch, MD; Nancy Roderer, MLS, AHIP, FACMI; Dave Ross, ScD; Thomas Savel, MD; and
Edward Sondik, PhD
Acknowledgement: Thanks to Herman Tolentino, MD, and Sonya Arundar, MS for their careful
final review and editing of the list of competencies for public health informaticians.
The following organizations also participated in the development of these competencies:
American Medical Informatics Association (AMIA)
Association of Schools of Public Health (ASPH)
Association of State and Territorial Health Officials (ASTHO)
Council of State and Territorial Epidemiologists (CSTE)
National Association of County and City Health Officials (NACCHO)
National Association for Public Health Information Technology (NAPHIT)
Public Health Informatics Institute (PHII)
This work was carried out during 2005–2007 with Bryant Karras, MD, as principal investigator
and was facilitated by Jac Davies, MS, MPH. The project was convened by Denise Koo, MD,
MPH, director of the Career Development Division in the Office of Workforce and Career
Development (OWCD) at the Centers for Disease Control and Prevention (CDC). It was
supported through an Association of Schools of Public Health–CDC cooperative agreement with
the University of Washington's Center for Public Health Informatics (ASPH/CDC Project#S3447-
24/24).
The contents of this document are in the public domain, but we request that you cite or
acknowledge any use of this material. Suggested citation: Centers for Disease Control and
Prevention and University of Washington's Center for Public Health Informatics. Competencies
for Public Health Informaticians. Atlanta, GA: US Department of Health and Human Services,
Centers for Disease Control and Prevention. 2009.
This document is available online at http://www.cdc.gov/InformaticsCompetencies and at
http://.
Presentation “Harnessing EHRs and Health IT to Achieve Population Health”
Jonathan Weiner, DrPH
Professor Department of Health Policy and Management
Director of Center for Population Health IT
Johns Hopkins Bloomberg School of Public Health, Baltimore Maryland
Professor Weiner’s presentation will focus on how electronic health records and other e-health tools can be harnessed to move beyond providing medical care for a single patient episode towards the achievement of “population health.” This provocative presentation will offer new conceptual paradigms and will review “big data” opportunities and challenges. The emphasis of the talk will be on how population focused care transformation can be brought about through the integration and application of e-health/EHR systems and claims/MIS systems. The talk will offer examples of analytic tools and methods designed to increase the effectiveness, efficiency and equity of care provided at a geographic community level and to “populations” of consumers enrolled in health plans, ACOs and other integrated delivery systems.
Key goals of presentation:
∙ To offer frameworks and paradigms to better understand how EHRs and other HIT can improve population health
∙ To outline opportunities and challenges for communities, ACOs and other integrated delivery systems
∙ To offer some case studies on the application of health IT to population health
BRIEF COMMENTARY: USING A LOGIC MODEL TO INTEGRATE PUBLIC HEALTH INFORMATICS ...hiij
The COVID-19 pandemic has been a watershed moment in public health surveillance, highlighting the
crucial role of data-driven insights in informing health actions and policies. Revisiting key concepts—
public health, epidemiology in public health practice, public health surveillance, and public health
informatics—lays the foundation for understanding how these elements converge to create a robust public
health surveillance system framework. Especially during the COVID-19 pandemic, this integration was
exemplified by the WHO efforts in data dissemination and the subsequent global response. The role of
public health informatics emerged as instrumental in this context, enhancing data collection, management,
analysis, interpretation, and dissemination processes. A logic model for public health surveillance systems
encapsulates the integration of these concepts. It outlines the inputs and outcomes and emphasizes the
crucial actions and resources for effective system operation, including the imperative of training and
capacity development.
BRIEF COMMENTARY: USING A LOGIC MODEL TO INTEGRATE PUBLIC HEALTH INFORMATICS ...hiij
The COVID-19 pandemic has been a watershed moment in public health surveillance, highlighting the
crucial role of data-driven insights in informing health actions and policies. Revisiting key concepts—
public health, epidemiology in public health practice, public health surveillance, and public health
informatics—lays the foundation for understanding how these elements converge to create a robust public
health surveillance system framework. Especially during the COVID-19 pandemic, this integration was
exemplified by the WHO efforts in data dissemination and the subsequent global response. The role of
public health informatics emerged as instrumental in this context, enhancing data collection, management,
analysis, interpretation, and dissemination processes. A logic model for public health surveillance systems
encapsulates the integration of these concepts. It outlines the inputs and outcomes and emphasizes the
crucial actions and resources for effective system operation, including the imperative of training and
capacity development.
Data Science in Healthcare" by authors Sergio Consoli, Diego Reforgiato Recupero, and Milan Petkovic is an insightful guide that delves into the intersection of data science and healthcare. As a first-year student in Pharmaceutical Management, I found this book to be a valuable resource for understanding how data-driven approaches are transforming the healthcare industry, offering fresh perspectives and practical insights for future professionals like myself.
What you need to know about Meaningful Use 2 & interoperabilityCompliancy Group
Does this describe you?
·You are constantly challenged to stay abreast of the latest information on EHR integration and HIE interoperability, Meaningful Use stages, the Direct Project, clinician and patient portals, just to name a few.
·You walk a fine line between adopting health information technology for the good it can bring patient outcomes…….and for the good incentive dollars it can mean to your organization.
·You play a key role in ensuring your organization can attest for meaningful use.
Join Andy Nieto, Health IT Strategist at DataMotion where he’ll explain the key role that interoperability plays in Meaningful Use Stage 2 attestation including:
- What does interoperability really mean
- Why you can’t ignore interoperability
- How to achieve interoperability and make it meaningful
- What you need in order to attest
Reviewwww.thelancet.com Vol 395 May 16, 2020 1579Adessiechisomjj4
Review
www.thelancet.com Vol 395 May 16, 2020 1579
Artificial intelligence and the future of global health
Nina Schwalbe*, Brian Wahl*
Concurrent advances in information technology infrastructure and mobile computing power in many low and
middle-income countries (LMICs) have raised hopes that artificial intelligence (AI) might help to address challenges
unique to the field of global health and accelerate achievement of the health-related sustainable development goals. A
series of fundamental questions have been raised about AI-driven health interventions, and whether the tools,
methods, and protections traditionally used to make ethical and evidence-based decisions about new technologies can
be applied to AI. Deployment of AI has already begun for a broad range of health issues common to LMICs, with
interventions focused primarily on communicable diseases, including tuberculosis and malaria. Types of AI vary, but
most use some form of machine learning or signal processing. Several types of machine learning methods are
frequently used together, as is machine learning with other approaches, most often signal processing. AI-driven
health interventions fit into four categories relevant to global health researchers: (1) diagnosis, (2) patient morbidity
or mortality risk assessment, (3) disease outbreak prediction and surveillance, and (4) health policy and planning.
However, much of the AI-driven intervention research in global health does not describe ethical, regulatory, or
practical considerations required for widespread use or deployment at scale. Despite the field remaining nascent,
AI-driven health interventions could lead to improved health outcomes in LMICs. Although some challenges of
developing and deploying these interventions might not be unique to these settings, the global health community will
need to work quickly to establish guidelines for development, testing, and use, and develop a user-driven research
agenda to facilitate equitable and ethical use.
Introduction
AI is changing how health services are delivered in many
high-income settings, particularly in specialty care
(eg, radiology and pathology).1–3 This development has
been facilitated by the growing availability of large
datasets and novel analytical methods that rely on such
datasets. Concurrent advances in information technology
(IT) infrastructure and mobile computing power have
raised hopes that AI might also provide opportunities to
address health challenges in LMICs.4 These challenges,
including acute health workforce shortages and weak
public health surveillance systems, undermine global
progress towards achieving the health-related sustainable
development goals (SDGs).5,6 Although not unique to
such countries, these challenges are particularly relevant
given their contribution to morbidity and mortality.7,8
AI-driven health technologies could be used to address
many of these and other system-related challenges.4
For example, ...
February 10, 2011 BDPA Charlotte Program meeting.
Presented by:
Karen D. Hill, RHIA
Recruitment/Placement Specialist
ONC HIT Grant
Health Sciences Division
Central Piedmont Community College
Health Information Technology Workforce Development Program
Central Piedmont Community College
Health Education on prevention of hypertensionRadhika kulvi
Hypertension is a chronic condition of concern due to its role in the causation of coronary heart diseases. Hypertension is a worldwide epidemic and important risk factor for coronary artery disease, stroke and renal diseases. Blood pressure is the force exerted by the blood against the walls of the blood vessels and is sufficient to maintain tissue perfusion during activity and rest. Hypertension is sustained elevation of BP. In adults, HTN exists when systolic blood pressure is equal to or greater than 140mmHg or diastolic BP is equal to or greater than 90mmHg. The
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Please note that it is important to follow the instructi.docxrandymartin91030
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Competencies
for Public Health Informaticians
2009
U.S. DEPARTMENT OF HEALTH AND HUMAN
SERVICES
Centers for Disease Control and Prevention
Office of Workforce and Career Development
and
University of Washington School of Public Health and Community Medicine's
Center for Public Health Informatics
Competencies for Public Health Informaticians—2009
U.S. Department of Health and Human Services (HHS), Centers for Disease Control and
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The University of Washington School of Public Health and Community Medicine (UW SPHCM)
Working Group Members: Bryant Karras, MD; Jac Davies, MS, MPH; Denise Koo, MD, MPH;
Janise Richards, PhD, MPH, MS; Patrick O’Carroll, MD, MPH, FACPM, FACMI; Kathleen Miner,
PhD, MPH, CHES; Mark Oberle, MD, MPH; Milton Corn, MD; Don Detmer, MD, MA; Seth Foldy,
MD, MPH; Larry Hanrahan, PhD, MS; Ginny Hare, MS; Marty LaVenture, PhD, MPH; Cecil
Lynch, MD; Nancy Roderer, MLS, AHIP, FACMI; Dave Ross, ScD; Thomas Savel, MD; and
Edward Sondik, PhD
Acknowledgement: Thanks to Herman Tolentino, MD, and Sonya Arundar, MS for their careful
final review and editing of the list of competencies for public health informaticians.
The following organizations also participated in the development of these competencies:
American Medical Informatics Association (AMIA)
Association of Schools of Public Health (ASPH)
Association of State and Territorial Health Officials (ASTHO)
Council of State and Territorial Epidemiologists (CSTE)
National Association of County and City Health Officials (NACCHO)
National Association for Public Health Information Technology (NAPHIT)
Public Health Informatics Institute (PHII)
This work was carried out during 2005–2007 with Bryant Karras, MD, as principal investigator
and was facilitated by Jac Davies, MS, MPH. The project was convened by Denise Koo, MD,
MPH, director of the Career Development Division in the Office of Workforce and Career
Development (OWCD) at the Centers for Disease Control and Prevention (CDC). It was
supported through an Association of Schools of Public Health–CDC cooperative agreement with
the University of Washington's Center for Public Health Informatics (ASPH/CDC Project#S3447-
24/24).
The contents of this document are in the public domain, but we request that you cite or
acknowledge any use of this material. Suggested citation: Centers for Disease Control and
Prevention and University of Washington's Center for Public Health Informatics. Competencies
for Public Health Informaticians. Atlanta, GA: US Department of Health and Human Services,
Centers for Disease Control and Prevention. 2009.
This document is available online at http://www.cdc.gov/InformaticsCompetencies and at
http://.
Presentation “Harnessing EHRs and Health IT to Achieve Population Health”
Jonathan Weiner, DrPH
Professor Department of Health Policy and Management
Director of Center for Population Health IT
Johns Hopkins Bloomberg School of Public Health, Baltimore Maryland
Professor Weiner’s presentation will focus on how electronic health records and other e-health tools can be harnessed to move beyond providing medical care for a single patient episode towards the achievement of “population health.” This provocative presentation will offer new conceptual paradigms and will review “big data” opportunities and challenges. The emphasis of the talk will be on how population focused care transformation can be brought about through the integration and application of e-health/EHR systems and claims/MIS systems. The talk will offer examples of analytic tools and methods designed to increase the effectiveness, efficiency and equity of care provided at a geographic community level and to “populations” of consumers enrolled in health plans, ACOs and other integrated delivery systems.
Key goals of presentation:
∙ To offer frameworks and paradigms to better understand how EHRs and other HIT can improve population health
∙ To outline opportunities and challenges for communities, ACOs and other integrated delivery systems
∙ To offer some case studies on the application of health IT to population health
BRIEF COMMENTARY: USING A LOGIC MODEL TO INTEGRATE PUBLIC HEALTH INFORMATICS ...hiij
The COVID-19 pandemic has been a watershed moment in public health surveillance, highlighting the
crucial role of data-driven insights in informing health actions and policies. Revisiting key concepts—
public health, epidemiology in public health practice, public health surveillance, and public health
informatics—lays the foundation for understanding how these elements converge to create a robust public
health surveillance system framework. Especially during the COVID-19 pandemic, this integration was
exemplified by the WHO efforts in data dissemination and the subsequent global response. The role of
public health informatics emerged as instrumental in this context, enhancing data collection, management,
analysis, interpretation, and dissemination processes. A logic model for public health surveillance systems
encapsulates the integration of these concepts. It outlines the inputs and outcomes and emphasizes the
crucial actions and resources for effective system operation, including the imperative of training and
capacity development.
BRIEF COMMENTARY: USING A LOGIC MODEL TO INTEGRATE PUBLIC HEALTH INFORMATICS ...hiij
The COVID-19 pandemic has been a watershed moment in public health surveillance, highlighting the
crucial role of data-driven insights in informing health actions and policies. Revisiting key concepts—
public health, epidemiology in public health practice, public health surveillance, and public health
informatics—lays the foundation for understanding how these elements converge to create a robust public
health surveillance system framework. Especially during the COVID-19 pandemic, this integration was
exemplified by the WHO efforts in data dissemination and the subsequent global response. The role of
public health informatics emerged as instrumental in this context, enhancing data collection, management,
analysis, interpretation, and dissemination processes. A logic model for public health surveillance systems
encapsulates the integration of these concepts. It outlines the inputs and outcomes and emphasizes the
crucial actions and resources for effective system operation, including the imperative of training and
capacity development.
Data Science in Healthcare" by authors Sergio Consoli, Diego Reforgiato Recupero, and Milan Petkovic is an insightful guide that delves into the intersection of data science and healthcare. As a first-year student in Pharmaceutical Management, I found this book to be a valuable resource for understanding how data-driven approaches are transforming the healthcare industry, offering fresh perspectives and practical insights for future professionals like myself.
What you need to know about Meaningful Use 2 & interoperabilityCompliancy Group
Does this describe you?
·You are constantly challenged to stay abreast of the latest information on EHR integration and HIE interoperability, Meaningful Use stages, the Direct Project, clinician and patient portals, just to name a few.
·You walk a fine line between adopting health information technology for the good it can bring patient outcomes…….and for the good incentive dollars it can mean to your organization.
·You play a key role in ensuring your organization can attest for meaningful use.
Join Andy Nieto, Health IT Strategist at DataMotion where he’ll explain the key role that interoperability plays in Meaningful Use Stage 2 attestation including:
- What does interoperability really mean
- Why you can’t ignore interoperability
- How to achieve interoperability and make it meaningful
- What you need in order to attest
Reviewwww.thelancet.com Vol 395 May 16, 2020 1579Adessiechisomjj4
Review
www.thelancet.com Vol 395 May 16, 2020 1579
Artificial intelligence and the future of global health
Nina Schwalbe*, Brian Wahl*
Concurrent advances in information technology infrastructure and mobile computing power in many low and
middle-income countries (LMICs) have raised hopes that artificial intelligence (AI) might help to address challenges
unique to the field of global health and accelerate achievement of the health-related sustainable development goals. A
series of fundamental questions have been raised about AI-driven health interventions, and whether the tools,
methods, and protections traditionally used to make ethical and evidence-based decisions about new technologies can
be applied to AI. Deployment of AI has already begun for a broad range of health issues common to LMICs, with
interventions focused primarily on communicable diseases, including tuberculosis and malaria. Types of AI vary, but
most use some form of machine learning or signal processing. Several types of machine learning methods are
frequently used together, as is machine learning with other approaches, most often signal processing. AI-driven
health interventions fit into four categories relevant to global health researchers: (1) diagnosis, (2) patient morbidity
or mortality risk assessment, (3) disease outbreak prediction and surveillance, and (4) health policy and planning.
However, much of the AI-driven intervention research in global health does not describe ethical, regulatory, or
practical considerations required for widespread use or deployment at scale. Despite the field remaining nascent,
AI-driven health interventions could lead to improved health outcomes in LMICs. Although some challenges of
developing and deploying these interventions might not be unique to these settings, the global health community will
need to work quickly to establish guidelines for development, testing, and use, and develop a user-driven research
agenda to facilitate equitable and ethical use.
Introduction
AI is changing how health services are delivered in many
high-income settings, particularly in specialty care
(eg, radiology and pathology).1–3 This development has
been facilitated by the growing availability of large
datasets and novel analytical methods that rely on such
datasets. Concurrent advances in information technology
(IT) infrastructure and mobile computing power have
raised hopes that AI might also provide opportunities to
address health challenges in LMICs.4 These challenges,
including acute health workforce shortages and weak
public health surveillance systems, undermine global
progress towards achieving the health-related sustainable
development goals (SDGs).5,6 Although not unique to
such countries, these challenges are particularly relevant
given their contribution to morbidity and mortality.7,8
AI-driven health technologies could be used to address
many of these and other system-related challenges.4
For example, ...
February 10, 2011 BDPA Charlotte Program meeting.
Presented by:
Karen D. Hill, RHIA
Recruitment/Placement Specialist
ONC HIT Grant
Health Sciences Division
Central Piedmont Community College
Health Information Technology Workforce Development Program
Central Piedmont Community College
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Hypertension is a chronic condition of concern due to its role in the causation of coronary heart diseases. Hypertension is a worldwide epidemic and important risk factor for coronary artery disease, stroke and renal diseases. Blood pressure is the force exerted by the blood against the walls of the blood vessels and is sufficient to maintain tissue perfusion during activity and rest. Hypertension is sustained elevation of BP. In adults, HTN exists when systolic blood pressure is equal to or greater than 140mmHg or diastolic BP is equal to or greater than 90mmHg. The
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Professor Orazio Schillaci, Minister of Health, Italy
Dr Hans Groth, Chairman of the Board, World Demographic & Ageing Forum
Professor Ilona Kickbusch, Founder and Chair, Global Health Centre, Geneva Graduate Institute and co-chair, World Health Summit Council
Dr Natasha Azzopardi Muscat, Director, Country Health Policies and Systems Division, World Health Organisation EURO
Dr Marta Lomazzi, Executive Manager, World Federation of Public Health Associations
Dr Shyam Bishen, Head, Centre for Health and Healthcare and Member of the Executive Committee, World Economic Forum
Dr Karin Tegmark Wisell, Director General, Public Health Agency of Sweden
1. Public Health 101 Series
Introduction to Public
Health Informatics
Instructor name
Title
Organization
Note: This slide set is in the public domain and may be customized as needed by the
user for informational or educational purposes. Permission from the Centers for
Disease Control and Prevention is not required, but citation of the source is
appreciated.
2. Course Topics
Introduction to Public
Health Informatics
2
1. A Public Health Approach
2. Public Health Informatics Definition, Components,
and Functions
3. Creating a Public Health Information System
4. At the Intersection of the Informatician, the Public
Health Official, and the Information Technologist
3. Learning Objectives
After this course, you will be able to
• explain the importance of informatics to the public
health mission
• describe the role of the informatician in public
health practice
• differentiate between public health informatics
and information technology
3
8. 8
The Public Health Mission
CDC provides crucial scientific
information that protects our
nation against dangerous and
costly health threats
9. Public Health Informatics — Defined
Public health informatics is the
systematic application of
information, computer science,
and technology to public health
practice, research, and learning
Yasnoff WA, O’Carroll PW, Koo D, Linkins RW, Kilbourne EM. Public health informatics: improving and transforming public
health in the information age. J Public Health Manag Pract 2000;6:67–75.
Riegelman R, ed. Public health 101: healthy people—healthy populations. Sudbury, MA: Jones & Bartlett Learning; 2010: 40. 9
11. Building Your Public Health
Information System
Programmer
Database
Administrator
Web
Designer
Security
Specialist
Network Administrator 11
12. 12
Knowledge Check
A tuberculosis outbreak has occurred in 10 states across
the country. To increase knowledge of the health threat,
CDC uses computer science, technology, and applied
information methods that will inform the nation’s
population about important _________________.
A. research
B. health information
C. security measures
13. Knowledge Check
Informatics uses public health knowledge to
13
A. broaden the public health knowledge base
through learning
B. improve population health in daily practice
C. further knowledge in public health research
D. all of the above
16. 16
Creating a Public Health Information System
Creating a Public Health Information System
Public health
official
Informatician
Information
technology
professional
Step 1 — Vision and System Planning
Envision solutions, opportunities, and application of
information technology in public health
Step 2 — Health Data Standards and Integration
Define and design health data standards and
transformation (e.g., HL7, ICD, SNOMED) and health domain
integration (e.g., ELR, EHR, CMS, HIE, surveillance,
demographics, social media)
Design and implement databases, tables, columns, data
formats, and keys for linking tables and data to support
defined health data standards and integration
Step 3 — Data Privacy and Security
Define and implement health data privacy and HIPAA
regulations
Implement and enforce data, systems, and communication
security
Step 4 — Systems Design and Implementation
Define and design methods for public health functions, data
elements, data flow, case definitions, and message
mapping
Implement information technology for defined functions,
data elements, data flow, and case definitions
Step 5 — Visualization, Analysis, and Reporting of Health
Data
Broad knowledge of public health practice, proficiency in
information technology, and capacity for innovation
Expertise in health data standards, database design, and
data linking and integration across health systems
Expertise in relational/SQL
databases and unstructured
data design and management
Knowledge of health data privacy
Understanding information technology
security functions
Expertise in health systems
and data interoperability
Expertise in managing information
technology systems development
Expertise in public health practice,
business intelligence, decision
making, and use of analytic software
CMS = Centers for Medicare and Medicaid Services; EHR = electronic health record; ELR = electronic laboratory record; HIE = health
information exchange; HIPAA = Health Insurance Portability and Accountability Act; HL7 = Health Level 7; ICD = International Classification of
Diseases; SNOMED = Systematized Nomenclature of Human Medicine; SQL = structured query language.
17. 17
Step 1 — Vision and System Planning
Hardware
Software
Communication
Technology
18. 18
Step 2 — Health Data Standards
and Integration
Health data standards and
integration are required when
defining the data.
Centers for Disease Control and Prevention (CDC). Meaningful use—introduction. Atlanta, GA: US Department of Health and
Human Services, CDC; 2012. http://www.cdc.gov/ehrmeaningfuluse/introduction.html.
19. 19
Step 3 — Data Privacy and Security
Data privacy and security must
be identified, prescribed, and
implemented throughout the
data lifecycle.
20. 20
Step 4 — Systems Design
and Implementation
• Define or design methods for
public health functions, data
elements, data flow, case
definitions, and message
mapping
• Implement information
technology for defined public
health functions, data
elements, data flow, case
definition, and similar needs
21. 21
Step 5 — Visualization, Analysis,
and Reporting of Health Data
Visualization and implementation
of the required analysis, reporting,
and meaningful use of the data
collected and managed by the
system.
22. 22
Informatics in Action — CDC’s FluView
A clear-cut way to share
national influenza data was
needed by
• the public health community,
• clinicians,
• scientists, and
• the general public
23. Informatics in Action — FluView
Centers for Disease Control and Prevention (CDC). FluView. Atlanta, GA: US Department of Health and Human Services,
CDC; 2013. http://gis.cdc.gov/grasp/fluview/main.html. 23
24. Knowledge Check
24
On the basis of what you have learned about creating a
public health information system, which of the following
does an informatician consider first when identifying
technologies to use for sharing national malaria data?
A. Health data standards and integration
B. Vision and systems planning
C. System design and implementation
25. Knowledge Check
Informatics is used to create a program such as CDC’s
FluView. Which of the following three disciplines must work
together to visually represent the data in an effective
method?
25
A. Computer science, epidemiology, and
public health
B. Technology, computer science, and
applied information methods
C. Technology, surveillance systems, and
epidemiology
26. Topic 4
26
At the Intersection
of the Informatician,
the Public Health Official,
and the Information Technologist
27. 27
Common Knowledge and Skills
Creating a Public Health Information System
Public health
official
Informatician
Information
technology
professional
Step 1 — Vision and System Planning
Envision solutions, opportunities, and application of
information technology in public health
Step 2 — Health Data Standards and Integration
Define and design health data standards and
transformation (e.g., HL7, ICD, SNOMED) and health domain
integration (e.g., ELR, EHR, CMS, HIE, surveillance,
demographics, social media)
Design and implement databases, tables, columns, data
formats, and keys for linking tables and data to support
defined health data standards and integration
Step 3 — Data Privacy and Security
Define and implement health data privacy and HIPAA
regulations
Implement and enforce data, systems, and communication
security
Step 4 — Systems Design and Implementation
Define and design methods for public health functions, data
elements, data flow, case definitions, and message
mapping
Implement information technology for defined functions,
data elements, data flow, and case definitions
Step 5 — Visualization, Analysis, and Reporting of Health
Data
Broad knowledge of public health practice, proficiency in
information technology, and capacity for innovation
Expertise in health data standards, database design, and
data linking and integration across health systems
Expertise in relational/SQL
databases and unstructured
data design and management
Knowledge of health data privacy
Understanding information technology
security functions
Expertise in health systems
and data interoperability
Expertise in managing information
technology systems development
Expertise in public health practice,
business intelligence, decision making,
and use of analytic software
CMS = Centers for Medicare and Medicaid Services; EHR = electronic health record; ELR = electronic laboratory record; HIE = health
information exchange; HIPAA = Health Insurance Portability and Accountability Act; HL7 = Health Level 7; ICD = International Classification of
Diseases; SNOMED = Systematized Nomenclature of Human Medicine; SQL = structured query language.
28. 28
Step 4 — Creating a Public Health
Information System
Creating a Public Health Information System
Public health
official
Informatician
Information
technology
professional
Step 4 — Systems Design and Implementation
Define and design methods for public health
functions, data elements, data flow, case
definition, and message mapping
Implement information technology for
defined public health functions, data
elements, data flow, and case definition
Expertise in health systems
and data interoperability
Expertise in managing information
technology systems development
30. The Role of the Informatician
in Public Health
• Plans, designs, and defines functional
requirements for public health
information systems
• Evaluates the application and impact of
information systems in support of health
goals
• Serves as a liaison between
multidisciplinary teams
• Uses data standards to support
interoperability of data between systems
• Ensures confidentiality, security, and
integrity standards
• Is knowledgeable about health data
standards, sources, and meaningful use
of health data
Centers for Disease Control and Prevention (CDC). Public health informatics competencies. Atlanta, GA: US Department of Health and
Human Services, CDC; 2009. http://www.cdc.gov/informaticscompetencies/. 30
31. The Role of the Information Technologist
in Public Health
• Plans technology projects and
milestones, develops software, and
maintains and operates systems
• Evaluates the performance and
availability of information systems
• Designs, implements, and
administers database architecture,
privacy, security, and backup
procedures
Centers for Disease Control and Prevention (CDC). Public health informatics competencies. Atlanta, GA: US Department of Health and
Human Services, CDC; 2009. http://www.cdc.gov/informaticscompetencies/. 31
32. Knowledge Check
One of the United Nations’ Millennium Development Goals
is to substantially reduce infant mortality worldwide. A
system has been developed that will display the data and
track the progress of attaining this goal.
Which of the following professionals works with health data
standards and sources and ensures the integrity and
security of the standards?
A. The information technologist
B. The informatician
32
33. Knowledge Check
33
Which of the following is NOT a function of a public health
informatician?
A. Uses data standards to support interoperability of
data between systems
B. Ensures confidentiality, security, and integrity
standards
C. Designs, implements, and administers database
architecture, privacy, security, and backup
procedures
D. Is knowledgeable about health data standards,
sources, and meaningful use of health data
34. Learning Objectives
During this course, you learned to
• explain the importance of informatics to the public
health mission
• describe the role of the Informatician in public
health practice
• differentiate between public health informatics
and information technology
34
36. Resources and Additional Reading
• Yasnoff WA, O’Carroll PW, Koo D, Linkins RW, Kilbourne EM. Public health informatics: improving
and transforming public health in the information age. J Public Health Manag Pract 2000;6:67–75.
• Riegelman R, ed. Public health 101: healthy people—healthy populations. Sudbury, MA: Jones &
Bartlett Learning; 2010: 40.
• Centers for Disease Control and Prevention (CDC). Public health approach. Atlanta, GA: US
Department of Health and Human Services, CDC; 2008. http://www.cdc.gov/
ViolencePrevention/overview/publichealthapproach.html.
• Taylor RS. Value-added processes in the information life cycle. J Am Soc Inf Sci 1982:33:341–6.
http://asis.org/Publications/JASIS/Best_Jasist/1982Taylor.pdf.
• Centers for Disease Control and Prevention (CDC). Public health informatics competencies.
Atlanta, GA: US Department of Health and Human Services, CDC; 2009.
http://www.cdc.gov/informaticscompetencies/.
• Centers for Disease Control and Prevention (CDC). FluView. Atlanta, GA: US Department of
Health and Human Services, CDC; 2013. http://gis.cdc.gov/grasp/fluview/main.html.
• Environmental Protection Agency (EPA). Data standards. Washington, DC: EPA; 2013.
https://iaspub.epa.gov/sor_internet/registry/datastds/home/whatisadatastandard/.
• Centers for Disease Control and Prevention (CDC). Youth violence: state statistics; Texas. Atlanta,
GA: US Department of Health and Human Services, CDC; 2011.
http://www.cdc.gov/ViolencePrevention/youthviolence/stats_at-a_glance/TX.html.
36
37. Disclaimers
Links provided in this course to nonfederal organizations are provided solely as a
service to our users. These links do not constitute an endorsement of these
organizations nor their programs by the Centers for Disease Control and
Prevention (CDC) or the federal government, and none should be inferred. CDC
is not responsible for the content contained at these sites.
Use of trade names and commercial sources is for identification only and does
not imply endorsement by the Division of Scientific Education and Professional
Development, Center for Surveillance, Epidemiology, and Laboratory Services,
Centers for Disease Control and Prevention, the Public Health Service, or the U.S.
Department of Health and Human Services.
The findings and conclusions in this course are those of the authors and do not
necessarily represent the official position of the Centers for Disease Control and
Prevention.
37
38. For more information, please contact the Centers for Disease Control and Prevention
1600 Clifton Road, NE, Atlanta, GA 30333
Telephone: 1-800-CDC-INFO (232-4636)/TTY: 1-888-232-6348
Visit: http://www.cdc.gov | Contact CDC at: 1-800-CDC-INFO or http://www.cdc.gov/info
The findings and conclusions in this course are those of the authors and do not necessarily represent the official position of the
Centers for Disease Control and Prevention.
Center for Surveillance, Epidemiology, and Laboratory Services
Division of Scientific Education and Professional Development