Successful implementation of a nurse-navigator-run program using natural language processing identifying patients with an abdominal aortic aneurysm
Copyright © 2022 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved..
BACKGROUND: Abdominal aortic aneurysms (AAA) are often identified incidentally on imaging studies. Patients and/or providers are frequently unaware of these AAA and the need for long-term follow-up. We sought to evaluate the outcome of a nurse-navigator-run AAA program that uses a natural language processing (NLP) algorithm applied to the electronic medical record (EMR) to identify patients with imaging report-identified AAA not being followed actively.
METHODS: A commercially available AAA-specific NLP system was run on EMR data at a large, academic, tertiary hospital with an 11-year historical look back (January 1, 2010, to June 2, 2021), to identify and characterize AAA. Beginning June 3, 2021, a direct link between the NLP system and the EMR enabled for real-time review of imaging reports for new AAA cases. A nurse-navigator (1.0 full-time equivalent) used software filters to categorize AAA according to predefined metrics, including repair status and adherence to Society for Vascular Surgery imaging surveillance protocol. The nurse-navigator then interfaced with patients and providers to reestablish care for patients not being followed actively. The nurse-navigator characterized patients as case closed (eg, deceased, appropriate follow-up elsewhere, refuses follow-up), cases awaiting review, and cases reviewed and placed in ongoing surveillance using AAA-specific software. The primary outcome measures were yield of surveillance imaging performed or scheduled, new clinic visits, and AAA operations for patients not being followed actively.
RESULTS: During the prospective study period (January 1, 2021, to December 30, 2021), 6,340,505 imaging reports were processed by the NLP. After filtering for studies likely to include abdominal aorta, 243,889 imaging reports were evaluated, resulting in the identification of 6495 patients with AAA. Of these, 2937 cases were reviewed and closed, 1183 were reviewed and placed in ongoing surveillance, and 2375 are awaiting review. When stratifying those reviewed and placed in ongoing surveillance by maximum aortic diameter, 258 were 2.5 to 3.4 cm, 163 were 3.5 to 3.9 cm, 213 were 4 to 5 cm, and 49 were larger than 5 cm; 36 were saccular, 86 previously underwent open repair, 274 previously underwent endovascular repair, and 104 were other. This process yielded 29 new patient clinic visits, 40 finalized imaging studies, 29 scheduled imaging studies, and 4 AAA operations in 3 patients among patients not being followed actively.
CONCLUSIONS: The application of an AAA program leveraging NLP successfully identifies patients with AAA not receiving appropriate surveillance or counseling and repair. This program offers an opportunity to improve best practice-based care across a large health system.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:77 |
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| Enthalten in: |
Journal of vascular surgery - 77(2023), 3 vom: 01. März, Seite 922-929 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Boitano, Laura T [VerfasserIn] |
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| Links: |
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| Themen: |
Abdominal aortic aneurysm |
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| Anmerkungen: |
Date Completed 27.02.2023 Date Revised 27.02.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.jvs.2022.10.034 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM348420684 |
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| 245 | 1 | 0 | |a Successful implementation of a nurse-navigator-run program using natural language processing identifying patients with an abdominal aortic aneurysm |
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| 500 | |a Date Completed 27.02.2023 | ||
| 500 | |a Date Revised 27.02.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2022 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved. | ||
| 520 | |a BACKGROUND: Abdominal aortic aneurysms (AAA) are often identified incidentally on imaging studies. Patients and/or providers are frequently unaware of these AAA and the need for long-term follow-up. We sought to evaluate the outcome of a nurse-navigator-run AAA program that uses a natural language processing (NLP) algorithm applied to the electronic medical record (EMR) to identify patients with imaging report-identified AAA not being followed actively | ||
| 520 | |a METHODS: A commercially available AAA-specific NLP system was run on EMR data at a large, academic, tertiary hospital with an 11-year historical look back (January 1, 2010, to June 2, 2021), to identify and characterize AAA. Beginning June 3, 2021, a direct link between the NLP system and the EMR enabled for real-time review of imaging reports for new AAA cases. A nurse-navigator (1.0 full-time equivalent) used software filters to categorize AAA according to predefined metrics, including repair status and adherence to Society for Vascular Surgery imaging surveillance protocol. The nurse-navigator then interfaced with patients and providers to reestablish care for patients not being followed actively. The nurse-navigator characterized patients as case closed (eg, deceased, appropriate follow-up elsewhere, refuses follow-up), cases awaiting review, and cases reviewed and placed in ongoing surveillance using AAA-specific software. The primary outcome measures were yield of surveillance imaging performed or scheduled, new clinic visits, and AAA operations for patients not being followed actively | ||
| 520 | |a RESULTS: During the prospective study period (January 1, 2021, to December 30, 2021), 6,340,505 imaging reports were processed by the NLP. After filtering for studies likely to include abdominal aorta, 243,889 imaging reports were evaluated, resulting in the identification of 6495 patients with AAA. Of these, 2937 cases were reviewed and closed, 1183 were reviewed and placed in ongoing surveillance, and 2375 are awaiting review. When stratifying those reviewed and placed in ongoing surveillance by maximum aortic diameter, 258 were 2.5 to 3.4 cm, 163 were 3.5 to 3.9 cm, 213 were 4 to 5 cm, and 49 were larger than 5 cm; 36 were saccular, 86 previously underwent open repair, 274 previously underwent endovascular repair, and 104 were other. This process yielded 29 new patient clinic visits, 40 finalized imaging studies, 29 scheduled imaging studies, and 4 AAA operations in 3 patients among patients not being followed actively | ||
| 520 | |a CONCLUSIONS: The application of an AAA program leveraging NLP successfully identifies patients with AAA not receiving appropriate surveillance or counseling and repair. This program offers an opportunity to improve best practice-based care across a large health system | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Abdominal aortic aneurysm | |
| 650 | 4 | |a Diagnostic screening programs | |
| 650 | 4 | |a Natural language processing | |
| 650 | 4 | |a Process assessment (health care) | |
| 700 | 1 | |a DeVivo, Gabrielle |e verfasserin |4 aut | |
| 700 | 1 | |a Robichaud, Devon I |e verfasserin |4 aut | |
| 700 | 1 | |a Okuhn, Steven |e verfasserin |4 aut | |
| 700 | 1 | |a Steppacher, Robert C |e verfasserin |4 aut | |
| 700 | 1 | |a Simons, Jessica P |e verfasserin |4 aut | |
| 700 | 1 | |a Aiello, Francesco A |e verfasserin |4 aut | |
| 700 | 1 | |a Jones, Douglas |e verfasserin |4 aut | |
| 700 | 1 | |a Judelson, Dejah |e verfasserin |4 aut | |
| 700 | 1 | |a Nguyen, Tammy |e verfasserin |4 aut | |
| 700 | 1 | |a Sorensen, Caitlin |e verfasserin |4 aut | |
| 700 | 1 | |a Schanzer, Andres |e verfasserin |4 aut | |
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