Difference between revisions of "Public health informatics"

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(Updated intro.)
(Updated content formatting. Need to go back and add references later.)
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# "reflect[s] the governmental context in which public health is practiced."
# "reflect[s] the governmental context in which public health is practiced."


==History==
Before the advent of the Internet, public health data, like other healthcare and business data, were collected on paper forms and stored centrally at the relevant public health agency. As computers became more commonplace, some data and information would be computerized, requiring a distinct data entry process, storage in various file formats, and analysis by mainframe computers using standard batch processing. With the coming of the Internet and cheaper large-scale storage technologies, public health agencies with sufficient resources began transitioning to web-accessible collections of public health data, and, more recently, to automated messaging of the same information.


==United States==
==Application==
In the United States and other parts of the world, public health informatics is practiced by individuals in public health agencies at the national, state, and larger local health jurisdiction levels. Additionally, research and training in public health informatics takes place at a variety of academic institutions. In the United States, the bulk of public health informatics activities takes place at the state and local level, in the state departments of health and the county or parish departments of health. In other parts of the world the bulk of activities may occur at a national level, with local jurisdictions reporting directly to an appropriate government or health-related entity. Activities may include:


In the United States, public health informatics is practiced by individuals in public health agencies at the federal and state levels and in the larger local health jurisdictions. Additionally, research and training in public health informatics takes place at a variety of academic institutions.
* collecting and storing vital statistics such as birth and death records.
* collecting reported communicable disease cases from doctors, [[Hospital|hospitals]], and [[Laboratory|laboratories]] for infectious disease surveillance.
* sharing infectious disease statistics and trends with other entities, including the public.
* collecting child immunization and lead screening information.
* collecting and analyzing emergency room data to detect early evidence of biological threats.
* collecting hospital capacity information to allow for planning of responses in case of emergencies.


At the [[Centers for Disease Control and Prevention]] (CDC) in Atlanta, Georgia, the [http://www.cdc.gov/osels/ph_informatics_technology/index.html Public Health Informatics and Technology Program Office (PHITPO)] focuses on advancing the state of information science and applies digital information technologies to aid in the detection and management of diseases and syndromes in individuals and populations.  The three sub-units within PHITPO include Informatics Practice, Policy and Coordination; Informatics Solutions and Operations; and Informatics Research and Development.
As part of the collection and application of public health data, several challenges still exist. Some entities may simply not be aware they need to report data to other entities. A lack of resources of either the reporter or collector may also hinder reporting and sharing of data. In some parts of the world, a lack of interoperability of data interchange formats (which can be at the purely syntactic or at the semantic level) may lead to under- or non-reported public health data. Finally, variations in reporting requirements across the states, territories, and localities pose challenges, which itself may lead to variability of incoming data to public health jurisdictions, requiring greater data quality standards.


The bulk of the work of public health informatics in the United States, as with public health generally, takes place at the state and local level, in the state departments of health and the county or parish departments of health.  At a state health department the activities may include: collection and storage of ''vital statistics'' (birth and death records); collection of reports of communicable disease cases from doctors, [[Hospital|hospitals]], and [[Laboratory|laboratories]], used for infectious disease surveillance; display of infectious disease statistics and trends; collection of child immunization and lead screening information; daily collection and analysis of emergency room data to detect early evidence of biological threats; collection of hospital capacity information to allow for planning of responses in case of emergencies.  Each of these activities presents its own information processing challenge.
==Informatics==
Due to the complexity and variability of public health data, like health care data generally, the issue of data modeling presents a particular challenge. Flat data sets for statistical analysis were the norm; however, today's requirements of interoperability and integrated sets of data across the public health enterprise require more sophistication. The relational database is increasingly the norm in public health informatics. Designers and implementers of the many sets of data required for various public health purposes must find a workable balance between very complex and abstract data models and simplistic, ''ad hoc'' models that untrained public health practitioners come up with and feel capable of working with.


===Collection of public health data===
Another challenge is found in the need to extract usable public health information from the mass of available heterogeneous data. The public health informaticist is thus required to become familiar with a variety of analysis tools, ranging from business intelligence tools to produce routine or ''ad hoc'' reports, to sophisticated statistical analysis tools and geographical information systems (GIS) to expose the geographical dimension of public health trends.


Before the advent of the internet, public health data in the United States, like other healthcare and business data, were collected on paper forms and stored centrally at the relevant public health agency.  If the data were to be computerized they required a distinct data entry process, were stored in the various file formats of the day and analyzed by mainframe computers using standard batch processing.
===In the United States===
The [[Centers for Disease Control and Prevention]] (CDC) in Atlanta, Georgia has played an important role in public health and [[infectious disease informatics]]. The agency's Public Health Informatics and Technology Program Office (PHITPO) has focused on advancing the state of information science in these realms, applying digital information technologies to aid in the detection and management of diseases and syndromes in individuals and populations. The CDC also created the National Electronic Disease Surveillance System (NEDSS), which includes a free comprehensive web and message-based reporting system called the NEDSS Base System (NBS), used for managing and transmitting reportable disease data.  


(TODO: describe CDC-provided DOS/desktop-based systems like TIMSS (TB), STDMIS (Sexually transmitted  diseases); Epi-Info for epidemiology investigations; and others )
Since about 2005, the CDC has promoted the idea of the Public Health Information Network to facilitate the transmission of data from various partners in the health care industry and elsewhere (hospitals, clinical and environmental laboratories, doctors' practices, pharmacies) to local health agencies, then to state health agencies, and then to the CDC. At each stage the entity must be capable of receiving the data, storing it, aggregating it appropriately, and transmitting it to the next level. To promote interoperability between the NBS and other informatics systems, the CDC has also encouraged the adoption of several standard vocabularies and messaging formats from the health care world. The most prominent of these are the [[Health Level 7]] (HL7) standards for health care messaging, the LOINC system for encoding laboratory test and result information, and the Systematized Nomenclature of Medicine (SNOMED) vocabulary of health care concepts.


Since the beginning of the World Wide Web, public health agencies with sufficient information technology resources have been transitioning to web-based collection of public health data, and, more recently, to automated messaging of the same information.  In the years roughly 2000 to 2005 the Centers for Disease Control and Prevention, under its [http://www.cdc.gov/nedss/ National Electronic Disease Surveillance System] (NEDSS), built and provided free to states a comprehensive web and message-based reporting system called the [http://www.cdc.gov/nedss/ NEDSS Base System] (NBS).  Due to the funding being limited and it not being wise to have fiefdom-based systems, only a few states and larger counties have built their own versions of electronic disease surveillance systems, such as Pennsylvania's [https://www.nedss.state.pa.us/nedss/ PA-NEDSS].  These do not provide timely full intestate notification services causing an increase in disease rates versus the NEDSS federal product.
A typical example of data transmissions to the CDC would be infectious disease data, which hospitals, labs, and doctors are legally required to report to local health agencies. The local health agencies must then report to their state public health department and the states must report in aggregate form to the CDC. Among other uses of this received data, the CDC publishes the ''Morbidity and Mortality Weekly Report'' (MMWR), "the agency’s primary vehicle for scientific publication of timely, reliable, authoritative, accurate, objective, and useful public health information and recommendations."
 
To promote interoperability, the CDC has encouraged the adoption in public health data exchange of several standard vocabularies and messaging formats from the health care world. The most prominent of these are: the [[Health Level 7]] (HL7) standards for health care messaging; the LOINC system for encoding laboratory test and result information; and the Systematized Nomenclature of Medicine (SNOMED) vocabulary of health care concepts.
 
Since about 2005, the CDC has promoted the idea of the Public Health Information Network to facilitate the transmission of data from various partners in the health care industry and elsewhere (hospitals, clinical and environmental laboratories, doctors' practices, pharmacies) to local health agencies, then to state health agencies, and then to the CDC.  At each stage the entity must be capable of receiving the data, storing it, aggregating it appropriately, and transmitting it to the next level.  A typical example would be infectious disease data, which hospitals, labs, and doctors are legally required to report to local health agencies; local health agencies must report to their state public health department; and which the states must report in aggregate form to the CDC. Among other uses, the CDC publishes the Morbidity and Mortality Weekly Report (MMWR) based on these data acquired systematically from across the United States.
 
Major issues in the collection of public health data are: awareness of the need to report data; lack of resources of either the reporter or collector; lack of interoperability of data interchange formats, which can be at the purely syntactic or at the semantic level; variation in reporting requirements across the states, territories, and localities.
 
===Storage of public health data===
 
Storage of public health data shares the same [[data management]] issues as other industries.  And like other industries, the details of how these issues play out are affected by the nature of the data being managed.
 
Due to the complexity and variability of public health data, like health care data generally, the issue of [[data modeling]] presents a particular challenge.  While a generation ago flat data sets for statistical analysis were the norm, today's requirements of interoperability and integrated sets of data across the public health enterprise require more sophistication.  The relational database is increasingly the norm in public health informatics.  Designers and implementers of the many sets of data required for various public health purposes must find a workable balance between very complex and abstract data models such as HL7's Reference Information Model (RIM) or CDC's [http://www.cdc.gov/phin/library/documents/pdf/PHIN_LDM_User_Guide_v1.0.pdf Public Health Logical Data Model], and simplistic, ad hoc models that untrained public health practitioners come up with and feel capable of working with.
 
Due to the variability of the incoming data to public health jurisdictions, data quality assurance is also a major issue.
 
===Analysis of public health data===
 
The need to extract usable public health information from the mass of data available requires the public health informaticist to become familiar with a range of analysis tools, ranging from business intelligence tools to produce routine or ad hoc reports, to sophisticated statistical analysis tools such as SAS and PSPP/SPSS, to Geographical Information Systems (GIS) to expose the geographical dimension of public health trends.
 
===Applications in health surveillance and epidemiology===
 
* SAPPHIRE (Health care) or ''Situational Awareness and Preparedness for Public Health Incidences and Reasoning Engines'' is a semantics-based health information system capable of tracking and evaluating situations and occurrences that may affect public health.


==Notes==
==Notes==


Many portions of this article are reused from [http://en.wikipedia.org/wiki/Public_health_informatics the Wikipedia article].
Some portions of this article are reused from [http://en.wikipedia.org/wiki/Public_health_informatics the Wikipedia article].


==References==
==References==
<references/>
<references/>
* Public Health Informatics and Information Systems by D.A. Ross, A.R. Hinman, K. Saarlas, and W.H. Foege (Hardcover - Oct 16, 2002)  ISBN 0-387-95474-0
* [http://papers.ssrn.com/sol3/papers.cfm?abstract_id=962532 A Vision for More Effective Public Health Information Technology] on SSRN
* Olmeda, Christopher J. (2000). Information Technology in Systems of Care. Delfin Press. ISBN 978-0-9821442-0-6
* http://www.fda.gov/fdac/features/596_info.html on FDA
* [http://phpartners.org/health_stats.html Health Data Tools and Statistics]


<!---Place all category tags here-->
[[Category:Informatics]]
[[Category:Informatics]]

Revision as of 22:57, 11 June 2014

Public health informatics has been defined as "the systematic application of information and computer science and technology to public health practice, research, and learning."[1][2] Like other types of informatics, public health informatics is a multidisciplinary field, involving the studies of informatics, computer science, psychology, law, statistics, epidemiology, and microbiology.

In 2000, researcher William A. Yasnoff and his colleagues identified four key aspects that differentiate public health informatics from medical informatics and other informatics specialty areas. Public health informatics[1]:

  1. focuses on "applications of information science and technology that promote the health of populations as opposed to the health of specific individuals."
  2. focuses on "applications of information science and technology that prevent disease and injury by altering the conditions or the environment that put populations of individuals at risk."
  3. "explore[s] the potential for prevention at all vulnerable points in the causal chains leading to disease, injury, or disability; applications should not be restricted to particular social, behavioral, or environmental contexts."
  4. "reflect[s] the governmental context in which public health is practiced."

History

Before the advent of the Internet, public health data, like other healthcare and business data, were collected on paper forms and stored centrally at the relevant public health agency. As computers became more commonplace, some data and information would be computerized, requiring a distinct data entry process, storage in various file formats, and analysis by mainframe computers using standard batch processing. With the coming of the Internet and cheaper large-scale storage technologies, public health agencies with sufficient resources began transitioning to web-accessible collections of public health data, and, more recently, to automated messaging of the same information.

Application

In the United States and other parts of the world, public health informatics is practiced by individuals in public health agencies at the national, state, and larger local health jurisdiction levels. Additionally, research and training in public health informatics takes place at a variety of academic institutions. In the United States, the bulk of public health informatics activities takes place at the state and local level, in the state departments of health and the county or parish departments of health. In other parts of the world the bulk of activities may occur at a national level, with local jurisdictions reporting directly to an appropriate government or health-related entity. Activities may include:

  • collecting and storing vital statistics such as birth and death records.
  • collecting reported communicable disease cases from doctors, hospitals, and laboratories for infectious disease surveillance.
  • sharing infectious disease statistics and trends with other entities, including the public.
  • collecting child immunization and lead screening information.
  • collecting and analyzing emergency room data to detect early evidence of biological threats.
  • collecting hospital capacity information to allow for planning of responses in case of emergencies.

As part of the collection and application of public health data, several challenges still exist. Some entities may simply not be aware they need to report data to other entities. A lack of resources of either the reporter or collector may also hinder reporting and sharing of data. In some parts of the world, a lack of interoperability of data interchange formats (which can be at the purely syntactic or at the semantic level) may lead to under- or non-reported public health data. Finally, variations in reporting requirements across the states, territories, and localities pose challenges, which itself may lead to variability of incoming data to public health jurisdictions, requiring greater data quality standards.

Informatics

Due to the complexity and variability of public health data, like health care data generally, the issue of data modeling presents a particular challenge. Flat data sets for statistical analysis were the norm; however, today's requirements of interoperability and integrated sets of data across the public health enterprise require more sophistication. The relational database is increasingly the norm in public health informatics. Designers and implementers of the many sets of data required for various public health purposes must find a workable balance between very complex and abstract data models and simplistic, ad hoc models that untrained public health practitioners come up with and feel capable of working with.

Another challenge is found in the need to extract usable public health information from the mass of available heterogeneous data. The public health informaticist is thus required to become familiar with a variety of analysis tools, ranging from business intelligence tools to produce routine or ad hoc reports, to sophisticated statistical analysis tools and geographical information systems (GIS) to expose the geographical dimension of public health trends.

In the United States

The Centers for Disease Control and Prevention (CDC) in Atlanta, Georgia has played an important role in public health and infectious disease informatics. The agency's Public Health Informatics and Technology Program Office (PHITPO) has focused on advancing the state of information science in these realms, applying digital information technologies to aid in the detection and management of diseases and syndromes in individuals and populations. The CDC also created the National Electronic Disease Surveillance System (NEDSS), which includes a free comprehensive web and message-based reporting system called the NEDSS Base System (NBS), used for managing and transmitting reportable disease data.

Since about 2005, the CDC has promoted the idea of the Public Health Information Network to facilitate the transmission of data from various partners in the health care industry and elsewhere (hospitals, clinical and environmental laboratories, doctors' practices, pharmacies) to local health agencies, then to state health agencies, and then to the CDC. At each stage the entity must be capable of receiving the data, storing it, aggregating it appropriately, and transmitting it to the next level. To promote interoperability between the NBS and other informatics systems, the CDC has also encouraged the adoption of several standard vocabularies and messaging formats from the health care world. The most prominent of these are the Health Level 7 (HL7) standards for health care messaging, the LOINC system for encoding laboratory test and result information, and the Systematized Nomenclature of Medicine (SNOMED) vocabulary of health care concepts.

A typical example of data transmissions to the CDC would be infectious disease data, which hospitals, labs, and doctors are legally required to report to local health agencies. The local health agencies must then report to their state public health department and the states must report in aggregate form to the CDC. Among other uses of this received data, the CDC publishes the Morbidity and Mortality Weekly Report (MMWR), "the agency’s primary vehicle for scientific publication of timely, reliable, authoritative, accurate, objective, and useful public health information and recommendations."

Notes

Some portions of this article are reused from the Wikipedia article.

References

  1. ↑ 1.0 1.1 Yasnoff, William A.; O’Carroll, Patrick W.; Koo, Denise; Linkins, Robert W.; Kilbourne, Edwin M. (2000). "Public Health Informatics: Improving and Transforming Public Health in the Information Age" (PDF). Journal of Public Health Management and Practice 6 (6): 67–75. PMID 18019962. http://people.dbmi.columbia.edu/~rik7001/Yasnoff.pdf. Retrieved 11 June 2014. 
  2. ↑ Friede, Andrew; Blum, Henrik L.; McDonald, Mike (1995). "Public Health Informatics: How Information-Age Technology Can Strengthen Public Health". Annual Review of Public Health 16: 239–252. doi:10.1146/annurev.pu.16.050195.001323. http://www.annualreviews.org/doi/abs/10.1146/annurev.pu.16.050195.001323. Retrieved 11 June 2014. 
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