Integrated monitoring and analysis for early warning of patient deterioration.
Recently there has been an upsurge of interest in strategies for detecting at-risk patients in order to trigger the timely intervention of a Medical Emergency Team (MET), also known as a Rapid Response Team (RRT). We review a real-time automated system, BioSign, which tracks patient status by combin...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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2006
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| _version_ | 1826268757253685248 |
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| author | Tarassenko, L Hann, A Young, D |
| author_facet | Tarassenko, L Hann, A Young, D |
| author_sort | Tarassenko, L |
| collection | OXFORD |
| description | Recently there has been an upsurge of interest in strategies for detecting at-risk patients in order to trigger the timely intervention of a Medical Emergency Team (MET), also known as a Rapid Response Team (RRT). We review a real-time automated system, BioSign, which tracks patient status by combining information from vital signs monitored non-invasively on the general ward. BioSign fuses the vital signs in order to produce a single-parameter representation of patient status, the Patient Status Index. The data fusion method adopted in BioSign is a probabilistic model of normality in five dimensions, previously learnt from the vital sign data acquired from a representative sample of patients. BioSign alerts occur either when a single vital sign deviates by close to +/-3 standard deviations from its normal value or when two or more vital signs depart from normality, but by a smaller amount. In a trial with high-risk elective/emergency surgery or medical patients, BioSign alerts were generated, on average, every 8 hours; 95% of these were classified as 'True' by clinical experts. Retrospective analysis has also shown that the data fusion algorithm in BioSign is capable of detecting critical events in advance of single-channel alerts. |
| first_indexed | 2024-03-06T21:14:29Z |
| format | Journal article |
| id | oxford-uuid:3f4c2547-7bf7-4d99-ad0f-27f04da4bf91 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T21:14:29Z |
| publishDate | 2006 |
| record_format | dspace |
| spelling | oxford-uuid:3f4c2547-7bf7-4d99-ad0f-27f04da4bf912022-03-26T14:31:07ZIntegrated monitoring and analysis for early warning of patient deterioration.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:3f4c2547-7bf7-4d99-ad0f-27f04da4bf91EnglishSymplectic Elements at Oxford2006Tarassenko, LHann, AYoung, DRecently there has been an upsurge of interest in strategies for detecting at-risk patients in order to trigger the timely intervention of a Medical Emergency Team (MET), also known as a Rapid Response Team (RRT). We review a real-time automated system, BioSign, which tracks patient status by combining information from vital signs monitored non-invasively on the general ward. BioSign fuses the vital signs in order to produce a single-parameter representation of patient status, the Patient Status Index. The data fusion method adopted in BioSign is a probabilistic model of normality in five dimensions, previously learnt from the vital sign data acquired from a representative sample of patients. BioSign alerts occur either when a single vital sign deviates by close to +/-3 standard deviations from its normal value or when two or more vital signs depart from normality, but by a smaller amount. In a trial with high-risk elective/emergency surgery or medical patients, BioSign alerts were generated, on average, every 8 hours; 95% of these were classified as 'True' by clinical experts. Retrospective analysis has also shown that the data fusion algorithm in BioSign is capable of detecting critical events in advance of single-channel alerts. |
| spellingShingle | Tarassenko, L Hann, A Young, D Integrated monitoring and analysis for early warning of patient deterioration. |
| title | Integrated monitoring and analysis for early warning of patient deterioration. |
| title_full | Integrated monitoring and analysis for early warning of patient deterioration. |
| title_fullStr | Integrated monitoring and analysis for early warning of patient deterioration. |
| title_full_unstemmed | Integrated monitoring and analysis for early warning of patient deterioration. |
| title_short | Integrated monitoring and analysis for early warning of patient deterioration. |
| title_sort | integrated monitoring and analysis for early warning of patient deterioration |
| work_keys_str_mv | AT tarassenkol integratedmonitoringandanalysisforearlywarningofpatientdeterioration AT hanna integratedmonitoringandanalysisforearlywarningofpatientdeterioration AT youngd integratedmonitoringandanalysisforearlywarningofpatientdeterioration |