The potential of artificial intelligence to improve patient safety: a scoping review
Abstract Artificial intelligence (AI) represents a valuable tool that could be used to improve the safety of care. Major adverse events in healthcare include: healthcare-associated infections, adverse drug events, venous thromboembolism, surgical complications, pressure ulcers, falls, decompensation...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-03-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-021-00423-6 |
| _version_ | 1797643101355900928 |
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| author | David W. Bates David Levine Ania Syrowatka Masha Kuznetsova Kelly Jean Thomas Craig Angela Rui Gretchen Purcell Jackson Kyu Rhee |
| author_facet | David W. Bates David Levine Ania Syrowatka Masha Kuznetsova Kelly Jean Thomas Craig Angela Rui Gretchen Purcell Jackson Kyu Rhee |
| author_sort | David W. Bates |
| collection | DOAJ |
| description | Abstract Artificial intelligence (AI) represents a valuable tool that could be used to improve the safety of care. Major adverse events in healthcare include: healthcare-associated infections, adverse drug events, venous thromboembolism, surgical complications, pressure ulcers, falls, decompensation, and diagnostic errors. The objective of this scoping review was to summarize the relevant literature and evaluate the potential of AI to improve patient safety in these eight harm domains. A structured search was used to query MEDLINE for relevant articles. The scoping review identified studies that described the application of AI for prediction, prevention, or early detection of adverse events in each of the harm domains. The AI literature was narratively synthesized for each domain, and findings were considered in the context of incidence, cost, and preventability to make projections about the likelihood of AI improving safety. Three-hundred and ninety-two studies were included in the scoping review. The literature provided numerous examples of how AI has been applied within each of the eight harm domains using various techniques. The most common novel data were collected using different types of sensing technologies: vital sign monitoring, wearables, pressure sensors, and computer vision. There are significant opportunities to leverage AI and novel data sources to reduce the frequency of harm across all domains. We expect AI to have the greatest impact in areas where current strategies are not effective, and integration and complex analysis of novel, unstructured data are necessary to make accurate predictions; this applies specifically to adverse drug events, decompensation, and diagnostic errors. |
| first_indexed | 2024-03-11T14:09:46Z |
| format | Article |
| id | doaj.art-ffc7611ccde44d46b91c9a2d9807c66e |
| institution | Directory Open Access Journal |
| issn | 2398-6352 |
| language | English |
| last_indexed | 2024-03-11T14:09:46Z |
| publishDate | 2021-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj.art-ffc7611ccde44d46b91c9a2d9807c66e2023-11-02T00:40:34ZengNature Portfolionpj Digital Medicine2398-63522021-03-01411810.1038/s41746-021-00423-6The potential of artificial intelligence to improve patient safety: a scoping reviewDavid W. Bates0David Levine1Ania Syrowatka2Masha Kuznetsova3Kelly Jean Thomas Craig4Angela Rui5Gretchen Purcell Jackson6Kyu Rhee7Division of General Internal Medicine, Brigham and Women’s HospitalDivision of General Internal Medicine, Brigham and Women’s HospitalDivision of General Internal Medicine, Brigham and Women’s HospitalHarvard Business School, Harvard UniversityIBM Watson HealthDivision of General Internal Medicine, Brigham and Women’s HospitalIBM Watson HealthIBM Watson HealthAbstract Artificial intelligence (AI) represents a valuable tool that could be used to improve the safety of care. Major adverse events in healthcare include: healthcare-associated infections, adverse drug events, venous thromboembolism, surgical complications, pressure ulcers, falls, decompensation, and diagnostic errors. The objective of this scoping review was to summarize the relevant literature and evaluate the potential of AI to improve patient safety in these eight harm domains. A structured search was used to query MEDLINE for relevant articles. The scoping review identified studies that described the application of AI for prediction, prevention, or early detection of adverse events in each of the harm domains. The AI literature was narratively synthesized for each domain, and findings were considered in the context of incidence, cost, and preventability to make projections about the likelihood of AI improving safety. Three-hundred and ninety-two studies were included in the scoping review. The literature provided numerous examples of how AI has been applied within each of the eight harm domains using various techniques. The most common novel data were collected using different types of sensing technologies: vital sign monitoring, wearables, pressure sensors, and computer vision. There are significant opportunities to leverage AI and novel data sources to reduce the frequency of harm across all domains. We expect AI to have the greatest impact in areas where current strategies are not effective, and integration and complex analysis of novel, unstructured data are necessary to make accurate predictions; this applies specifically to adverse drug events, decompensation, and diagnostic errors.https://doi.org/10.1038/s41746-021-00423-6 |
| spellingShingle | David W. Bates David Levine Ania Syrowatka Masha Kuznetsova Kelly Jean Thomas Craig Angela Rui Gretchen Purcell Jackson Kyu Rhee The potential of artificial intelligence to improve patient safety: a scoping review npj Digital Medicine |
| title | The potential of artificial intelligence to improve patient safety: a scoping review |
| title_full | The potential of artificial intelligence to improve patient safety: a scoping review |
| title_fullStr | The potential of artificial intelligence to improve patient safety: a scoping review |
| title_full_unstemmed | The potential of artificial intelligence to improve patient safety: a scoping review |
| title_short | The potential of artificial intelligence to improve patient safety: a scoping review |
| title_sort | potential of artificial intelligence to improve patient safety a scoping review |
| url | https://doi.org/10.1038/s41746-021-00423-6 |
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