Responsible artificial intelligence in healthcare: predicting and preventing insurance claim denials for economic and social wellbeing
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The use of Responsible Artificial Intelligence in healthcare to predict and prevent insurance claim denials could lead to significant cost savings and improved patient well-being.
“A concrete example of this is the use of Responsible Artificial Intelligence in healthcare to predict and prevent insurance claim denials, which could lead to significant cost savings and improved patient well-being (123).”
From The impact of generative artificial intelligence on socioeconomic inequalities and policy making (2024) · cites this paper as [123]
The citation lists haven’t been fetched yet
References 47 · Cited by 88
- Authors
- Johnson, M., Albizri, A., & Harfouche, A.
- Affiliations
- Montclair State University (US)
- Université Paris Nanterre (FR)
- Corresponding author
- Harfouche, Antoine
- Venue
- Information Systems Frontiers
- Published
- 2021-04-28
- Volume
- 25
- Issue
- 6
- Pages
- 2179-2195
- ISSN
- 1387-3326 (ISSN-L)
- 1572-9419
- OpenAlex
- W3157643309
- Cited by
- 88
- References
- 47
- Citations by year
- 2026: 11 · 2025: 21 · 2024: 26 · 2023: 15 · 2022: 13 · 2021: 2
- FWCI
- 32.26
- Type
- book chapter
- Language
- en
- Open access
- closed
- Licence
- Locations
- Information Systems Frontiers (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- responsible AI, hospital revenue cycle, hospital administrators, AdaBoost, patient well-being
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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