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Use of Artificial Intelligence in Acute Care to Predict Impending Respiratory Failure.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Use of Artificial Intelligence in Acute Care to Predict Impending Respiratory Failure./
Author:	Little, Melissa L.
Description:	1 online resource (83 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
Contained By:	Dissertations Abstracts International85-01B.
Subject:	Nursing. -
Online resource:	click for full text (PQDT)
ISBN:	9798379896508

Use of Artificial Intelligence in Acute Care to Predict Impending Respiratory Failure.
Little, Melissa L.

Use of Artificial Intelligence in Acute Care to Predict Impending Respiratory Failure. - 1 online resource (83 pages)

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

Thesis (D.N.P.)--Oklahoma City University, 2023.

Includes bibliographical references

Respiratory distress syndrome results in more than 75,000 deaths in the United States annually. Predictive methods for identifying acute respiratory distress are extremely limited. The use of artificial intelligence devices is still being developed, but it can aid in the early identification of patients admitted to acute care hospitals who are at risk of respiratory decline during their hospital stay. In practice, artificial intelligence in healthcare will become essential. The ability to incorporate advanced technology into medical practice is still in its infancy, but it is being used in healthcare. The goal of this project is to identify patients who are at risk of respiratory decline upon admission. Early detection of respiratory decline can help reduce length of stay, code blue events, and death in the acute care setting. The advancing research and clinical practice through close collaboration (ARCC) model translational was used in the evaluation of selected variables over a four-week implementation time. The data from selected variables of patient status on admission based on the updated HACOR score were used to identify patients at risk of respiratory decline during their hospital stay. Using the updated HACOR score, patients were classified as low risk, moderate risk, high risk, or severe risk for respiratory decline during admission in a retrospective chart review of 100 patients. The average risk for possible respiratory decline during hospitalization was 2.43. The average length of stay of hospitalization was 9.3 days.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379896508Subjects--Topical Terms:

563081
Nursing.
Subjects--Index Terms:

Acute careIndex Terms--Genre/Form:

554714
Electronic books.

Use of Artificial Intelligence in Acute Care to Predict Impending Respiratory Failure.
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