Artificial intelligence-based clinical decision support for liver transplant evaluation and considerations about fairness : A qualitative study
Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Association for the Study of Liver Diseases..
BACKGROUND: The use of large-scale data and artificial intelligence (AI) to support complex transplantation decisions is in its infancy. Transplant candidate decision-making, which relies heavily on subjective assessment (ie, high variability), provides a ripe opportunity for AI-based clinical decision support (CDS). However, AI-CDS for transplant applications must consider important concerns regarding fairness (ie, health equity). The objective of this study was to use human-centered design methods to elicit providers' perceptions of AI-CDS for liver transplant listing decisions.
METHODS: In this multicenter qualitative study conducted from December 2020 to July 2021, we performed semistructured interviews with 53 multidisciplinary liver transplant providers from 2 transplant centers. We used inductive coding and constant comparison analysis of interview data.
RESULTS: Analysis yielded 6 themes important for the design of fair AI-CDS for liver transplant listing decisions: (1) transparency in the creators behind the AI-CDS and their motivations; (2) understanding how the AI-CDS uses data to support recommendations (ie, interpretability); (3) acknowledgment that AI-CDS could mitigate emotions and biases; (4) AI-CDS as a member of the transplant team, not a replacement; (5) identifying patient resource needs; and (6) including the patient's role in the AI-CDS.
CONCLUSIONS: Overall, providers interviewed were cautiously optimistic about the potential for AI-CDS to improve clinical and equitable outcomes for patients. These findings can guide multidisciplinary developers in the design and implementation of AI-CDS that deliberately considers health equity.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:7 |
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| Enthalten in: |
Hepatology communications - 7(2023), 10 vom: 01. Okt. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Strauss, Alexandra T [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 12.09.2023 Date Revised 22.03.2024 published: Electronic-eCollection Citation Status MEDLINE |
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| doi: |
10.1097/HC9.0000000000000239 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM361925271 |
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| 100 | 1 | |a Strauss, Alexandra T |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Artificial intelligence-based clinical decision support for liver transplant evaluation and considerations about fairness |b A qualitative study |
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| 500 | |a Date Revised 22.03.2024 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Association for the Study of Liver Diseases. | ||
| 520 | |a BACKGROUND: The use of large-scale data and artificial intelligence (AI) to support complex transplantation decisions is in its infancy. Transplant candidate decision-making, which relies heavily on subjective assessment (ie, high variability), provides a ripe opportunity for AI-based clinical decision support (CDS). However, AI-CDS for transplant applications must consider important concerns regarding fairness (ie, health equity). The objective of this study was to use human-centered design methods to elicit providers' perceptions of AI-CDS for liver transplant listing decisions | ||
| 520 | |a METHODS: In this multicenter qualitative study conducted from December 2020 to July 2021, we performed semistructured interviews with 53 multidisciplinary liver transplant providers from 2 transplant centers. We used inductive coding and constant comparison analysis of interview data | ||
| 520 | |a RESULTS: Analysis yielded 6 themes important for the design of fair AI-CDS for liver transplant listing decisions: (1) transparency in the creators behind the AI-CDS and their motivations; (2) understanding how the AI-CDS uses data to support recommendations (ie, interpretability); (3) acknowledgment that AI-CDS could mitigate emotions and biases; (4) AI-CDS as a member of the transplant team, not a replacement; (5) identifying patient resource needs; and (6) including the patient's role in the AI-CDS | ||
| 520 | |a CONCLUSIONS: Overall, providers interviewed were cautiously optimistic about the potential for AI-CDS to improve clinical and equitable outcomes for patients. These findings can guide multidisciplinary developers in the design and implementation of AI-CDS that deliberately considers health equity | ||
| 650 | 4 | |a Multicenter Study | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, U.S. Gov't, P.H.S. | |
| 700 | 1 | |a Sidoti, Carolyn N |e verfasserin |4 aut | |
| 700 | 1 | |a Sung, Hannah C |e verfasserin |4 aut | |
| 700 | 1 | |a Jain, Vedant S |e verfasserin |4 aut | |
| 700 | 1 | |a Lehmann, Harold |e verfasserin |4 aut | |
| 700 | 1 | |a Purnell, Tanjala S |e verfasserin |4 aut | |
| 700 | 1 | |a Jackson, John W |e verfasserin |4 aut | |
| 700 | 1 | |a Malinsky, Daniel |e verfasserin |4 aut | |
| 700 | 1 | |a Hamilton, James P |e verfasserin |4 aut | |
| 700 | 1 | |a Garonzik-Wang, Jacqueline |e verfasserin |4 aut | |
| 700 | 1 | |a Gray, Stephen H |e verfasserin |4 aut | |
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| 700 | 1 | |a Gurakar, Ahmet |e verfasserin |4 aut | |
| 700 | 1 | |a Segev, Dorry L |e verfasserin |4 aut | |
| 700 | 1 | |a Levin, Scott |e verfasserin |4 aut | |
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