| Autor: |
Shieh, Lisa, Smith, Margaret, Westphal, Jerri, Ron Li |
| Zdroj: |
American Journal of Medical Quality; 2023 Supplement, Vol. 38, pS51-S52, 2p |
| Abstrakt: |
This document is a report on the 2022 Vizient Connections Summit. It discusses two separate interventions aimed at improving patient outcomes in a hospital setting. The first intervention focused on reducing hospital-acquired infections (HAIs), specifically catheter-associated urinary tract infections (CAUTIs). The intervention involved implementing a culture of leadership accountability and ownership, as well as introducing a checklist and conducting "plane crash investigations" to identify areas for improvement. The intervention resulted in a significant reduction in CAUTIs, as well as cost savings and shorter hospital stays. The second intervention focused on using artificial intelligence (AI) and machine learning (ML) models to predict and prevent clinical deterioration in hospitalized patients. The intervention involved implementing an AI-enabled system that included an ML model for predicting deterioration, an alerting system, and a collaborative workflow between physicians and nurses. The pilot project showed promising results, with increased workflow adoption and positive feedback from nursing staff and residents. The document also discusses the challenges of translating and integrating AI/ML models into healthcare settings and proposes a new approach that combines quality improvement and design thinking methods. [Extracted from the article] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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