Public health informatics

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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] . It is one of the subdomains of health informatics, data management applied to medical systems.

Contents

The structure of public health informatics data collection and management in the United States is divided among both the federal and state levels. The Centers for Disease Control and Prevention (CDC) is the department at the federal level, and locally, it belongs to the state departments of health [2] . These programs have standardized the reporting of digital health data by hospitals and clinics. The government departments can then gather this data, analyze it, and use it for a variety of purposes. Such purposes typically fall under the three major domains of public health informatics: understanding more about complex processes that occur, storing a record of public health data, and analyzing and publicizing a general version of gathered data for public consumption. Additionally, data collected from social media can also be included in these processes, refining its accuracy [3] .

Job opportunities in this field include positions with the CDC and the American Medical Informatics Association, which provides more information about informatics for professionals in medical fields.

Health Informatics in the United States

In developed countries like 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.

At the federal Centers for Disease Control and Prevention in US states like Atlanta, Georgia, the Public Health Surveillance and Informatics Program Office (PHSIPO) 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. [1]

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. [2] 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, hospitals, and 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. [4]

Collection of public health data

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 National Electronic Disease Surveillance System (NEDSS), [5] built and provided free to states a comprehensive web and message-based reporting system called the NEDSS Base System (NBS). [6] 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 PA-NEDSS. [7] These do not provide timely full intestate notification services causing an increase in disease rates versus the NEDSS federal product.

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. [8]

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. [9] 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. [10]

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. [11]

Public health informatics can be thought of or divided into three categories.

Studying health data models

The first category is to discover and study models of complex systems, such as disease transmission. This can be done through different types of data collections, such as hospital surveys, or electronic surveys submitted to the organization (such as the CDC). [12] Transmission rates or disease incidence rates/surveillance can be obtained through government organizations, such as the CDC, or global organizations, such as WHO. Not only disease transmission/rates can be looked at. Public health informatics can also delve into people with/without health insurance and the rates at which they go to the doctor. [13] 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. [14]

Storing public health data

The second category is to find ways to improve the efficiency of different public health systems. This is done through various collections methods, storage of data and how the data is used to improve current health problems. In order to keep everything standardized, vocabulary and word usage needs to be consistent throughout all systems. Finding new ways to link together and share new data with current systems is important to keep everything up to date. [15]

Storage of public health data shares the same data management issues as other industries. Like other industries, the details of how these issues play out are affected by the nature of the data being managed. [16]

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. [17] 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 Public Health Logical Data Model, and simplistic, ad hoc models that untrained public health practitioners come up with and feel capable of working with. [18]

Due to the variability of the incoming data to public health jurisdictions, data quality assurance is also a major issue. [19]

Maintaining current public health data

Finally, the last category can be thought as maintaining and enriching current systems and models to adapt to overflow of data and storing/sorting of this new data. This can be as simple as connecting directly to an electronic data collection source, such as health records from the hospital, or can go public information (CDC) about disease rates/transmission. Finding new algorithms that will sort through large quantities of data quickly and effectively is necessary as well. [20]

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 DAP/SAS and PSPP/SPSS, to Geographical Information Systems (GIS) to expose the geographical dimension of public health trends. Such analyses usually require methods that appropriately secure the privacy of the health data. One approach is to separate the individually identifiable variables of the data from the rest. [21] Another broader approach is to use social media to analyze health trends. Since the late 2000s, data from social media websites such as Twitter and Facebook, as well as search engines such as Google and Bing, have been used extensively in detecting trends in public health. [3]

The health informatics industry

There are a few organizations out there that provide useful information for those professionals that want to be more involved in public health informatics. Such as the American Medical Informatics Association (AMIA). AMIA is for professions that are involved in health care, informatics research, biomedical research, including physicians, scientists, researchers, and students. The main goals of AMIA are to move from 'bench to bedside', help improve the impact of health innovations and advance the public health informatics field. They hold annual conferences, online classes and webinars, which are free to their members. There is also a career center specific for the biomedical and health informatics community. [22]

Many jobs or fellowships in public health informatics are offered. The CDC (Center for Disease Control) has various fellowship programs, while multiple colleges/companies offer degree programs or training in this field. [23]

For more information on these topics, follow the links below:

See also

Related Research Articles

<span class="mw-page-title-main">Health informatics</span> Computational approaches to health care

Health informatics is the study and implementation of computer structures and algorithms to improve communication, understanding, and management of medical information. It can be viewed as a branch of engineering and applied science.

Health Level Seven, abbreviated to HL7, is a range of global standards for the transfer of clinical and administrative health data between applications with the aim to improve patient outcomes and health system performance. The HL7 standards focus on the application layer, which is "layer 7" in the Open Systems Interconnection model. The standards are produced by Health Level Seven International, an international standards organization, and are adopted by other standards issuing bodies such as American National Standards Institute and International Organization for Standardization. There are a range of primary standards that are commonly used across the industry, as well as secondary standards which are less frequently adopted.

<span class="mw-page-title-main">Electronic health record</span> Digital collection of patient and population electronically stored health information

An electronic health record (EHR) is the systematized collection of patient and population electronically stored health information in a digital format. These records can be shared across different health care settings. Records are shared through network-connected, enterprise-wide information systems or other information networks and exchanges. EHRs may include a range of data, including demographics, medical history, medication and allergies, immunization status, laboratory test results, radiology images, vital signs, personal statistics like age and weight, and billing information.

Public health surveillance is, according to the World Health Organization (WHO), "the continuous, systematic collection, analysis and interpretation of health-related data needed for the planning, implementation, and evaluation of public health practice." Public health surveillance may be used to track emerging health-related issues at an early stage and find active solutions in a timely manner. Surveillance systems are generally called upon to provide information regarding when and where health problems are occurring and who is affected.

<span class="mw-page-title-main">SNOMED CT</span> System for medical classification

SNOMED CT or SNOMED Clinical Terms is a systematically organized computer-processable collection of medical terms providing codes, terms, synonyms and definitions used in clinical documentation and reporting. SNOMED CT is considered to be the most comprehensive, multilingual clinical healthcare terminology in the world. The primary purpose of SNOMED CT is to encode the meanings that are used in health information and to support the effective clinical recording of data with the aim of improving patient care. SNOMED CT provides the core general terminology for electronic health records. SNOMED CT comprehensive coverage includes: clinical findings, symptoms, diagnoses, procedures, body structures, organisms and other etiologies, substances, pharmaceuticals, devices and specimens.

The HL7 Clinical Document Architecture (CDA) is an XML-based markup standard intended to specify the encoding, structure and semantics of clinical documents for exchange. In November 2000, HL7 published Release 1.0. The organization published Release 2.0 with its "2005 Normative Edition".

Disease Informatics (also infectious disease informatics) studies the knowledge production, sharing, modeling, and management of infectious diseases. It became a more studied field as a by-product of the rapid increases in the amount of biomedical and clinical data widely available, and to meet the demands for useful data analyses of such data.

The Public Health Information Network (PHIN) is a US national initiative, developed by the Centers for Disease Control and Prevention (CDC), for advancing fully capable and interoperable information systems in public health organizations. The initiative involves establishing and implementing a framework for public health information systems.

Medcin, is a system of standardized medical terminology, a proprietary medical vocabulary and was developed by Medicomp Systems, Inc. MEDCIN is a point-of-care terminology, intended for use in Electronic Health Record (EHR) systems, and it includes over 280,000 clinical data elements encompassing symptoms, history, physical examination, tests, diagnoses and therapy. This clinical vocabulary contains over 38 years of research and development as well as the capability to cross map to leading codification systems such as SNOMED CT, CPT, ICD-9-CM/ICD-10-CM, DSM, LOINC, CDT, CVX, and the Clinical Care Classification (CCC) System for nursing and allied health.

<span class="mw-page-title-main">OpenMRS</span>

OpenMRS is a collaborative open-source project to develop software to support the delivery of health care in developing countries.

CEN ISO/IEEE 11073 Health informatics - Medical / health device communication standards enable communication between medical, health care and wellness devices and external computer systems. They provide automatic and detailed electronic data capture of client-related and vital signs information, and of device operational data.

Health information technology (HIT) is health technology, particularly information technology, applied to health and health care. It supports health information management across computerized systems and the secure exchange of health information between consumers, providers, payers, and quality monitors. Based on a 2008 report on a small series of studies conducted at four sites that provide ambulatory care – three U.S. medical centers and one in the Netherlands, the use of electronic health records (EHRs) was viewed as the most promising tool for improving the overall quality, safety and efficiency of the health delivery system.

International HL7 implementations is a collection of known implementations of the HL7 Interoperability standard. These do not necessarily refer to cross-border health information systems.

The Fast Healthcare Interoperability Resources standard is a set of rules and specifications for exchanging electronic health care data. It is designed to be flexible and adaptable, so that it can be used in a wide range of settings and with different health care information systems. The goal of FHIR is to enable the seamless and secure exchange of health care information, so that patients can receive the best possible care. The standard describes data formats and elements and an application programming interface (API) for exchanging electronic health records (EHR). The standard was created by the Health Level Seven International (HL7) health-care standards organization.

Medical device connectivity is the establishment and maintenance of a connection through which data is transferred between a medical device, such as a patient monitor, and an information system. The term is used interchangeably with biomedical device connectivity or biomedical device integration. By eliminating the need for manual data entry, potential benefits include faster and more frequent data updates, diminished human error, and improved workflow efficiency.

Health Level Seven International (HL7) is a non-profit ANSI-accredited standards development organization that develops standards that provide for global health data interoperability.

<span class="mw-page-title-main">Dipak Kalra</span>

Dipak Kalra is President of the European Institute for Health Records and of the European Institute for Innovation through Health Data. He undertakes international research and standards development, and advises on adoption strategies, relating to Electronic Health Records.

Clinical data standards are used to store and communicate information related to healthcare so that its meaning is unambiguous. They are used in clinical practice, in activity analysis and finding, and in research and development.

<span class="mw-page-title-main">Morris F. Collen</span> A founder of medical informatics

Morris Frank Collen was founder of the Kaiser Permanente Division of Research and an original member of the Permanente Medical Group, pioneering developer of Automated Multiphasic Health Testing (AMHT) systems, and Electronic Health Records (EHRs) for Public Health and Clinical Screening, serving as a model for pre-paid healthcare at the national level. Collen was a Founder of the American College of Medical Informatics (ACMI) in 1984, and the American Medical Informatics Association (AMIA) in 1989. The Morris F. Collen Award of Excellence was established in his honor by ACMI in 1993. In 1971 Collen was elected a member of the Institute of Medicine of the National Academy of Sciences.

References

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  2. 1 2 Massoudi, B L, and K G Chester. “Public Health, Population Health, and Epidemiology Informatics: Recent Research and Trends in the United States.” Yearbook of medical informatics vol. 26,1 (2017): 241-247. doi:10.15265/IY-2017-035
  3. 1 2 Ayers, John W.; Althouse, Benjamin M.; Dredze, Mark (9 April 2014). "Could Behavioral Medicine Lead the Web Data Revolution?". JAMA. 311 (14): 1399–1400. doi:10.1001/jama.2014.1505. ISSN   0098-7484. PMC   4670613 . PMID   24577162.
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  5. ↑ Group, The National Electronic Disease Surveillance System Working (2001). "National Electronic Disease Surveillance System (NEDSS): A Standards-Based Approach To Connect Public Health and Clinical Medicine". Journal of Public Health Management and Practice. 7 (6): 43–50. ISSN   1078-4659.{{cite journal}}: |last= has generic name (help)
  6. ↑ CDC (21 February 2024). "About National Electronic Disease Surveillance System Base System (NBS)". National Electronic Disease Surveillance System Base System (NBS). Retrieved 17 November 2024.
  7. ↑ "PA-NEDSS | Department of Health | Commonwealth of Pennsylvania". www.pa.gov. Retrieved 17 November 2024.
  8. ↑ CDC (30 September 2024). "Data Interchange Standards". PHIN Tools and Resources. Retrieved 17 November 2024.
  9. ↑ CDC (10 June 2024). "PHIN Tools & Resources for Public Health". PHIN Tools and Resources. Retrieved 17 November 2024.
  10. ↑ "NATIONAL CENTER FOR PUBLIC HEALTH INFORMATICS (CPE)". stacks.cdc.gov. Retrieved 17 November 2024.
  11. ↑ CDC (24 October 2024). "About Public Health Data Interoperability". Public Health Data Interoperability. Retrieved 17 November 2024.
  12. ↑ CDC (11 September 2024). "Surveys and Data Collection Systems". National Center for Health Statistics. Retrieved 17 November 2024.
  13. ↑ Felix, Suad El Burai (2024). "A Standard Framework for Evaluating Large Health Care Data and Related Resources". MMWR Supplements. 73. doi:10.15585/mmwr.su7303a1. ISSN   2380-8950.
  14. ↑ "Portfolio:Public health informatics - Write Edit Teach". www.writediteach.com. Retrieved 17 November 2024.
  15. ↑ "Using Technologies for Data Collection and Management | Epidemic Intelligence Service | CDC". www.cdc.gov. 25 September 2019. Retrieved 17 November 2024.
  16. ↑ "What is Health Data Management? Benefits, Challenges and Storage". Cloudian. Retrieved 17 November 2024.
  17. ↑ Walker, Daniel M et al. “Perspectives on Challenges and Opportunities for Interoperability: Findings From Key Informant Interviews With Stakeholders in Ohio.” JMIR medical informatics vol. 11 e43848. 24 Feb. 2023, doi:10.2196/43848
  18. ↑ Priyatna, Freddy et al. “Querying clinical data in HL7 RIM based relational model with morph-RDB.” Journal of biomedical semantics vol. 8,1 49. 5 Oct. 2017, doi:10.1186/s13326-017-0155-8
  19. ↑ Chen, Hong et al. “A review of data quality assessment methods for public health information systems.” International journal of environmental research and public health vol. 11,5 5170-207. 14 May. 2014, doi:10.3390/ijerph110505170
  20. ↑ "Data Modernization Initiative | CDC". www.cdc.gov. 18 October 2024. Retrieved 17 November 2024.
  21. ↑ CDC (13 September 2024). "Data and Analysis Tools". National Center for Health Statistics. Retrieved 17 November 2024.
  22. ↑ "Programs | Johns Hopkins | Bloomberg School of Public Health".
  23. ↑ "What We Do". www.phii.org. Retrieved 12 September 2023.
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