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Search Results for Learning
Abstract Number: 371
SHM Converge 2024
Background: In-hospital patient deterioration, often unpredictable and multifaceted, presents a significant challenge in hospital medicine. Despite existing measures like illness severity scoring systems and rapid response teams (RRT), patient outcomes remain suboptimal. Delays in recognizing and treating worsening conditions lead to adverse effects and increased healthcare costs. Purpose: In our large healthcare system, covering two […]
Abstract Number: 372
SHM Converge 2024
Background: To address the risk of missed or delayed diagnoses, organizations need to identify and learn from their diagnostic opportunities. However, current approaches to identifying diagnostic opportunities are insensitive, resource intensive and often have low yield.(1,2) Evaluation of diagnostic trajectories can highlight diagnostic opportunities. For example, a patient may re-present to the healthcare system with […]
Abstract Number: 393
Hospital Medicine 2019, March 24-27, National Harbor, Md.
Background: Sepsis is one of the top causes of inpatient mortality and rapid detection presents numerous challenges. In March, 2016, an interdisciplinary team consisting of top clinicians, data scientists and machine learning experts at a large academic medical center (AMC) embarked on an innovation pilot to develop a novel machine learning model to detect sepsis. […]
Abstract Number: 393
Hospital Medicine 2020, Virtual Competition
Background: Neonatal abstinence syndrome (NAS) occurs when an infant is exposed to chemical substances in utero and consequently withdrawals from the substance(s) after birth. The long-term impact of NAS remains unclear due to confounding by other factors that impact infant health and development, but literature suggests that infants with NAS have motor and cognitive delays […]
Abstract Number: 422
SHM Converge 2024
Background: Goal-concordant care is an ongoing challenge in hospital settings. Failures in communication with patients and caregivers can lead to unwanted usage of hospital resources, including the ICU. This leads to lower quality care for patients and increased burden on the hospital. Goals of care conversations can be utilized to ensure that care aligns with […]
Abstract Number: 448
Hospital Medicine 2020, Virtual Competition
Background: Clinical reasoning is a core component of medical training yet learners receive very little formative feedback on their clinical reasoning documentation. We hypothesize that this is related to the lack of a shared assessment rubric and faculty time constraints. Purpose: Here we describe the process of developing a machine learning algorithm for feedback on […]
Abstract Number: H18
SHM Converge 2022
Background: Residents receive little feedback on their clinical reasoning (CR) documentation due to time constraints of supervisors and lack of a shared mental model. We developed an innovative workplace-based assessment tool using machine learning (ML) to provide feedback on CR documentation. Here, we describe the impact of feedback using this assessment tool. Methods: In earlier […]