Public health informatics

Last updated

Public health informatics has been defined as the systematic application of information and computer science and technology to public health practice, research, and learning. It is one of the subdomains of health informatics.

Contents

Definition

Public health informatics is defined as the use of computers, clinical guidelines, communication and information systems, which apply to vast majority of public health-related professions, such as nursing, clinical, hospital care, public health and medical research. [1]

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) [2] 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 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, 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.

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), [3] built and provided free to states a comprehensive web and message-based reporting system called the NEDSS Base System (NBS). [4] 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. [5] 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.

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.

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

Study models of different systems

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). 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. 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. [6]

Storage of 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. [6]

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.

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 Public Health Logical Data Model, [7] 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

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. [6]

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 [8]

Applications in health surveillance and epidemiology

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. [1]

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. [9]

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

Social media analytics

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. [10]

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 branch of engineering and applied science.

The National Center for Health Statistics (NCHS) is a U.S. government agency that provides statistical information to guide actions and policies to improve the public health of the American people. It is a unit of the Centers for Disease Control and Prevention (CDC) and a principal agency of the U.S. Federal Statistical System. It is headquartered at University Town Center in Hyattsville, Maryland, just outside Washington, D.C.

A cancer registry is a systematic collection of data about cancer and tumor diseases. The data are collected by Cancer Registrars. Cancer Registrars capture a complete summary of patient history, diagnosis, treatment, and status for every cancer patient in the United States, and other countries.

<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.

Continuity of Care Record (CCR) is a health record standard specification developed jointly by ASTM International, the Massachusetts Medical Society (MMS), the Healthcare Information and Management Systems Society (HIMSS), the American Academy of Family Physicians (AAFP), the American Academy of Pediatrics (AAP), and other health informatics vendors.

<span class="mw-page-title-main">Disease surveillance</span> Monitoring spread of disease to establish patterns of progression

Disease surveillance is an epidemiological practice by which the spread of disease is monitored in order to establish patterns of progression. The main role of disease surveillance is to predict, observe, and minimize the harm caused by outbreak, epidemic, and pandemic situations, as well as increase knowledge about which factors contribute to such circumstances. A key part of modern disease surveillance is the practice of disease case reporting.

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 Association of Public Health Laboratories (APHL) is a membership organization in the United States representing the laboratories that protect the health and safety of the public. APHL serves as a liaison between public health laboratories and federal and international agencies. Membership consists of local, state, county, and territorial public health laboratories; public health environmental, agricultural and veterinary laboratories; and corporations and individuals with an interest in public health and laboratory science. APHL is a non-profit, 501(c)(3) organization with a history of over fifty years.

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.

<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.

The Continuity of Care Document (CCD) specification is an XML-based markup standard intended to specify the encoding, structure, and semantics of a patient summary clinical document for exchange.

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.

Health informatics in China is about the Health informatics or Medical informatics or Healthcare information system/technology in China.

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.

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.

Charles Safran is an American-born physician, biomedical informatician, and professor, who is known for his work regarding the use of health information technology (HIT) to improve the delivery and quality of healthcare, in particular clinical information systems.

References

  1. 1 2 "Programs | Johns Hopkins | Bloomberg School of Public Health".
  2. https://www.cdc.gov/osels/phsipo
  3. https://www.cdc.gov/nedss/
  4. https://www.cdc.gov/nedss/
  5. https://www.nedss.state.pa.us/nedss/
  6. 1 2 3 "The Role of Public Health Informatics in Enhancing Public Health Surveillance".
  7. https://www.cdc.gov/phin/library/documents/pdf/PHIN_LDM_User_Guide_v1.0.pdf
  8. Mazumdar S, Konings P, Hewett M, et al. (2014). "Protecting the privacy of individual general practice patient electronic records for geospatial epidemiology research". Australian and New Zealand Journal of Public Health. 38 (6): 548–552. doi:10.1111/1753-6405.12262. hdl: 1885/76167 . PMID   25308525. S2CID   41505067. http://onlinelibrary.wiley.com/doi/10.1111/1753-6405.12262/full
  9. "What We Do". www.phii.org. Retrieved 12 September 2023.
  10. 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.