Session Type
Meeting
Search Results for Machine-learning
Abstract Number: 246
SHM Converge 2021
Background: Advance care planning (ACP) helps patients plan end of life care in accordance with their goals and values, but is often performed too late and infrequently. Hospitalists play an important role in delivering ACP for patients admitted for serious illnesses, but often cite competing inpatient priorities, lack of time and training, and uncertainty about […]
Abstract Number: 267
SHM Converge 2024
Background: There is growing interest in the use of artificial intelligence (AI) predictive models in hospital medicine. However, real-world implementation and evaluation of AI models lags the development of such models, with many such models being developed but never used in live practice. (1) Therefore, relatively less is known about the performance of these models […]
Abstract Number: 280
SHM Converge 2024
Background: Inadequate assessment and recognition of barriers to discharge at time of admission leads to delays in the discharge process and prolongation of hospital admissions. These delays are associated with multiple negative outcomes such as increased length of stay, decreased patient satisfaction, strain on hospital bed capacity, and higher readmission rates. Prior studies have shown […]
Abstract Number: 286
Hospital Medicine 2020, Virtual Competition
Background: Observation status was designed to reduce health care costs for patients in need of short hospital stays. CMS suggests that observation care should typically require less than 24 hours, and only rarely last more than 48 hours. However, studies suggest that length of stay for observation patients is often longer than CMS guidance.[1,2] An […]
Abstract Number: 336
Hospital Medicine 2020, Virtual Competition
Background: Older adults commonly lose mobility during hospitalization. This loss of mobility may be preventable if it is identified and addressed early in a patient’s hospital course. However, currently there is no systematic method to identify these patients early, and current practices are guided by physician experience and intuition. To this end, we used machine […]
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: 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 […]