The principles of whole-hospital predictive analytics monitoring for clinical medicine originated in the neonatal ICU
Abstract In 2011, a multicenter group spearheaded at the University of Virginia demonstrated reduced mortality from real-time continuous cardiorespiratory monitoring in the neonatal ICU using what we now call Artificial Intelligence, Big Data, and Machine Learning. The large, randomized heart rate c...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-03-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-022-00584-y |
| _version_ | 1827611244450807808 |
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| author | J. Randall Moorman |
| author_facet | J. Randall Moorman |
| author_sort | J. Randall Moorman |
| collection | DOAJ |
| description | Abstract In 2011, a multicenter group spearheaded at the University of Virginia demonstrated reduced mortality from real-time continuous cardiorespiratory monitoring in the neonatal ICU using what we now call Artificial Intelligence, Big Data, and Machine Learning. The large, randomized heart rate characteristics trial made real, for the first time that we know of, the promise that early detection of illness would allow earlier and more effective intervention and improved patient outcomes. Currently, though, we hear as much of failures as we do of successes in the rapidly growing field of predictive analytics monitoring that has followed. This Perspective aims to describe the principles of how we developed heart rate characteristics monitoring for neonatal sepsis and then applied them throughout adult ICU and hospital medicine. It primarily reflects the work since the 1990s of the University of Virginia group: the theme is that sudden and catastrophic deteriorations can be preceded by subclinical but measurable physiological changes apparent in the continuous cardiorespiratory monitoring and electronic health record. |
| first_indexed | 2024-03-09T08:03:18Z |
| format | Article |
| id | doaj.art-2af7c0d2dff4400487e5fb34f028b0eb |
| institution | Directory Open Access Journal |
| issn | 2398-6352 |
| language | English |
| last_indexed | 2024-03-09T08:03:18Z |
| publishDate | 2022-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj.art-2af7c0d2dff4400487e5fb34f028b0eb2023-12-03T00:33:53ZengNature Portfolionpj Digital Medicine2398-63522022-03-01511610.1038/s41746-022-00584-yThe principles of whole-hospital predictive analytics monitoring for clinical medicine originated in the neonatal ICUJ. Randall Moorman0Cardiovascular Division, Department of Internal Medicine, Center for Advanced Medical Analytics, University of VirginiaAbstract In 2011, a multicenter group spearheaded at the University of Virginia demonstrated reduced mortality from real-time continuous cardiorespiratory monitoring in the neonatal ICU using what we now call Artificial Intelligence, Big Data, and Machine Learning. The large, randomized heart rate characteristics trial made real, for the first time that we know of, the promise that early detection of illness would allow earlier and more effective intervention and improved patient outcomes. Currently, though, we hear as much of failures as we do of successes in the rapidly growing field of predictive analytics monitoring that has followed. This Perspective aims to describe the principles of how we developed heart rate characteristics monitoring for neonatal sepsis and then applied them throughout adult ICU and hospital medicine. It primarily reflects the work since the 1990s of the University of Virginia group: the theme is that sudden and catastrophic deteriorations can be preceded by subclinical but measurable physiological changes apparent in the continuous cardiorespiratory monitoring and electronic health record.https://doi.org/10.1038/s41746-022-00584-y |
| spellingShingle | J. Randall Moorman The principles of whole-hospital predictive analytics monitoring for clinical medicine originated in the neonatal ICU npj Digital Medicine |
| title | The principles of whole-hospital predictive analytics monitoring for clinical medicine originated in the neonatal ICU |
| title_full | The principles of whole-hospital predictive analytics monitoring for clinical medicine originated in the neonatal ICU |
| title_fullStr | The principles of whole-hospital predictive analytics monitoring for clinical medicine originated in the neonatal ICU |
| title_full_unstemmed | The principles of whole-hospital predictive analytics monitoring for clinical medicine originated in the neonatal ICU |
| title_short | The principles of whole-hospital predictive analytics monitoring for clinical medicine originated in the neonatal ICU |
| title_sort | principles of whole hospital predictive analytics monitoring for clinical medicine originated in the neonatal icu |
| url | https://doi.org/10.1038/s41746-022-00584-y |
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