The Contributions of Biomedical Informatics to the Fight Against Bioterrorism

Kohane, Isaac S.

doi:10.1197/jamia.m1054

Cited by 23 publications

(8 citation statements)

References 34 publications

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“…Recently, the growing bioterrorist threat has led to the development of real-time syndromic surveillance systems built on top of existing information systems [5-7], with increased research activity in the biomedical engineering [8], public health [9], and general scientific [10] communities.…”

Section: Introductionmentioning

confidence: 99%

Time series modeling for syndromic surveillance

Reis

Mandl

2003

BMC Med Inform Decis Mak

205

147

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BackgroundEmergency department (ED) based syndromic surveillance systems identify abnormally high visit rates that may be an early signal of a bioterrorist attack. For example, an anthrax outbreak might first be detectable as an unusual increase in the number of patients reporting to the ED with respiratory symptoms. Reliably identifying these abnormal visit patterns requires a good understanding of the normal patterns of healthcare usage. Unfortunately, systematic methods for determining the expected number of (ED) visits on a particular day have not yet been well established. We present here a generalized methodology for developing models of expected ED visit rates.MethodsUsing time-series methods, we developed robust models of ED utilization for the purpose of defining expected visit rates. The models were based on nearly a decade of historical data at a major metropolitan academic, tertiary care pediatric emergency department. The historical data were fit using trimmed-mean seasonal models, and additional models were fit with autoregressive integrated moving average (ARIMA) residuals to account for recent trends in the data. The detection capabilities of the model were tested with simulated outbreaks.ResultsModels were built both for overall visits and for respiratory-related visits, classified according to the chief complaint recorded at the beginning of each visit. The mean absolute percentage error of the ARIMA models was 9.37% for overall visits and 27.54% for respiratory visits. A simple detection system based on the ARIMA model of overall visits was able to detect 7-day-long simulated outbreaks of 30 visits per day with 100% sensitivity and 97% specificity. Sensitivity decreased with outbreak size, dropping to 94% for outbreaks of 20 visits per day, and 57% for 10 visits per day, all while maintaining a 97% benchmark specificity.ConclusionsTime series methods applied to historical ED utilization data are an important tool for syndromic surveillance. Accurate forecasting of emergency department total utilization as well as the rates of particular syndromes is possible. The multiple models in the system account for both long-term and recent trends, and an integrated alarms strategy combining these two perspectives may provide a more complete picture to public health authorities. The systematic methodology described here can be generalized to other healthcare settings to develop automated surveillance systems capable of detecting anomalies in disease patterns and healthcare utilization.

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Section: Introductionmentioning

confidence: 99%

Time series modeling for syndromic surveillance

Reis

Mandl

2003

BMC Med Inform Decis Mak

205

147

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“…The potential risks attributable to lapses or errors in automated electronic reports are great, as are costs associated with misdiagnoses and treatment of healthy persons ( 16 ). Experiences in Hawaii and Pennsylvania indicate the need for continuing with existing reporting mechanisms during the first year while new systems are being refined.…”

Section: Discussionmentioning

confidence: 99%

Automated Laboratory Reporting of Infectious Diseases in a Climate of Bioterrorism

M’ikanatha

Southwell

Lautenbach

2003

Emerg. Infect. Dis.

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While newly available electronic transmission methods can increase timeliness and completeness of infectious disease reports, limitations of this technology may unintentionally compromise detection of, and response to, bioterrorism and other outbreaks. We reviewed implementation experiences for five electronic laboratory systems and identified problems with data transmission, sensitivity, specificity, and user interpretation. The results suggest a need for backup transmission methods, validation, standards, preserving human judgment in the process, and provider and end-user involvement. As illustrated, challenges encountered in deployment of existing electronic laboratory reporting systems could guide further refinement and advances in infectious disease surveillance.

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“…This is realized by an up-to-date continuity of operations plan (COOP), which has an important role in preparation for disasters 12 . This is achieved by collecting data, detecting a threat and responding to that threat correctly and suitable time 13 . Hence, the main function of public health is to monitor and detect the population who is at risk and prevent them from facing diseases and outbreaks.…”

Section: Applications Of Phimentioning

confidence: 99%

A review of the role of public health informatics in healthcare

Aziz

2017

Journal of Taibah University Medical Sciences

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Recognized as information intensive, healthcare requires timely, accurate information from many different sources generated by health information systems (HIS). With the availability of information technology in today's world and its integration in healthcare systems; the term “Public Health Informatics (PHI)” was coined and used. The main focus of PHI is the use of information science and technology for promoting population health rather than of individuals. PHI has a disease prevention rather than treatment focus in order to prevent chain of events or disease spread. Moreover, PHI often operates at the level of government rather than at the private sector. This review article provides an overview of the field of PHI and compares between paper based surveillance system and Public Health Information Networks (PHIN). The current trends and future challenges of applying PHI systems in KSA were also reported.

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The Contributions of Biomedical Informatics to the Fight Against Bioterrorism

Cited by 23 publications

References 34 publications

Time series modeling for syndromic surveillance

Time series modeling for syndromic surveillance

Automated Laboratory Reporting of Infectious Diseases in a Climate of Bioterrorism

A review of the role of public health informatics in healthcare

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