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Clinical surveillance — or syndromic surveillance — is the surveillance (collection and analysis) of health data about a clinical syndrome that has a significant impact on public health, which is then used to drive decisions about health policy and health education. The term applies to surveillance of populations and is distinct from active surveillance, which applies to individuals. Techniques of clinical surveillance have been used in particular to study infectious diseases. Many large institutions, such as the WHO and the CDC, have created databases and modern computer systems (public health informatics) that can track and monitor emerging outbreaks of illnesses such as influenza, SARS, HIV, and even bioterrorism, such as the 2001 anthrax attacks in the United States. Many regions and countries have their own cancer registry, one function of which is to monitor the incidence of cancers to determine the prevalence and possible causes of these illnesses. Other illnesses such as one-time events like stroke and chronic conditions such as diabetes, as well as social problems such as domestic violence, are increasingly being integrated into epidemiologic databases called disease registries that are being used in cost-benefit analysis in determining governmental funding for research and prevention. Many see this health outcomes data as greatly beneficial, but this kind of work is often controversial because many of measures such as quality-adjusted life years and disability-adjusted life years, which involve quantifying benefit according to subjective concepts such as survival, quality of life, and productivity measures. In addition, civil-libertarians believe that without disclosure or provisions to enable a way of opting out of such registries is a violation of both personal civil liberties and the doctor-patient privilege. Population-based healthcare is being promoted as registries are integrated, and health outcomes are increasingly being monitored. Systems that can automate the process of identifying adverse drug events, are currently being used, and are being compared to traditional written reports of such events. These systems intersect with the field of medical informatics, and are rapidly becoming adapted by hospitals and endorsed by institutions that oversee healthcare providers (such as JCAHO in the United States). Issues in regards to healthcare improvement are evolving around the surveillance of medication errors within institutions.〔(disa.mil ) PDF〕 ==Syndromic surveillance== Syndromic surveillance is the analysis of medical data to detect or anticipate disease outbreaks. According to a CDC definition, "the term 'syndromic surveillance' applies to surveillance using health-related data that precede diagnosis and signal a sufficient probability of a case or an outbreak to warrant further public health response. Though historically syndromic surveillance has been utilized to target investigation of potential cases, its utility for detecting outbreaks associated with bioterrorism is increasingly being explored by public health officials."〔(【引用サイトリンク】title=webcitation.org )〕 The first indications of disease outbreak or bioterrorist attack may not be the definitive diagnosis of a physician or a lab. Using a normal influenza outbreak as an example, once the outbreak begins to affect the population, some people may call in sick for work/school, others may visit their drug store and purchase medicine over the counter, others will visit their doctor's office and other's may have symptoms severe enough that they call the emergency telephone number or go to an emergency room. Syndromic surveillance systems monitor data from school absenteeism logs, emergency call systems, hospitals' over-the-counter drug sale records, Internet searches, and other data sources to detect unusual patterns. When a spike in activity is seen in any of the monitored systems disease epidemiologists and public health professionals are alerted that may be an issue. An early awareness and response to a bioterrorist attack could save many lives and potentially stop or slow the spread of the outbreak. The most effective syndromic surveillance systems automatically monitor these systems in real-time, do not require individuals to enter separate information (secondary data entry), include advanced analytical tools, aggregate data from multiple systems, across geo-political boundaries and include an automated alerting process.〔(United States Centers for Disease Control and Prevention ) ((WebCited/cached version ))〕 A syndromic surveillance system based on search queries was first proposed by Gunther Eysenbach, who began work on such a system in 2004. Inspired by these early, encouraging experiences, Google launched Google Flu Trends〔(【引用サイトリンク】title=Google Flu Trends )〕 in 2008. More flu-related searches are taken to indicate higher flu activity. The results closely match CDC data, and lead it by - 1–2 weeks. The results appeared in Nature. More recently, a series of more advanced linear and nonlinear approaches to influenza modelling from Google search queries have been proposed. Extending Google's work researchers from the Intelligent Systems Laboratory (University of Bristol, UK) created Flu Detector;〔(【引用サイトリンク】title=GeoPatterns - Flu Detector - Tracking Epidemics on Twitter )〕 an online tool which based on Information Retrieval and Statistical Analysis methods uses the content of Twitter to nowcast flu rates in the UK. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「clinical surveillance」の詳細全文を読む スポンサード リンク
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