Use of vasopressors in patients with acute kidney injury on continuous kidney replacement therapy.

Objective: To investigate whether the use of a specific vasopressor was associated with increased mortality or adverse outcomes in patients with acute kidney injury (AKI) receiving continuous kidney replacement therapy (CKRT).

Methods: Patients with AKI who underwent CKRT between 1/1/2012-1/1/2021 at a tertiary academic hospital were included. Cox proportional hazard model was used to assess the relationship between time-dependent vasopressor dose and in-hospital mortality.

Extracting International Classification of Diseases Codes from Clinical Documentation using Large Language Models.

Background: Large language models (LLMs) have shown promise in various professional fields, including medicine and law. However, their performance in highly specialized tasks, such as extracting ICD-10-CM codes from patient notes, remains underexplored.

Objective: The primary objective was to evaluate and compare the performance of ICD-10-CM code extraction by different LLMs with that of human coder.

Benchmarking Large Language Models for Extraction of International Classification of Diseases Codes from Clinical Documentation.

Background: Healthcare reimbursement and coding is dependent on accurate extraction of International Classification of Diseases-tenth revision - clinical modification (ICD-10-CM) codes from clinical documentation. Attempts to automate this task have had limited success. This study aimed to evaluate the performance of large language models (LLMs) in extracting ICD-10-CM codes from unstructured inpatient notes and benchmark them against human coder.

A Primer on Reinforcement Learning in Medicine for Clinicians.

Reinforcement Learning (RL) is a machine learning paradigm that enhances clinical decision-making for healthcare professionals by addressing uncertainties and optimizing sequential treatment strategies. RL leverages patient-data to create personalized treatment plans, improving outcomes and resource efficiency. This review introduces RL to a clinical audience, exploring core concepts, potential applications, and challenges in integrating RL into clinical practice, offering insights into efficient, personalized, and effective patient care.

A strategy for cost-effective large language model use at health system-scale.

Large language models (LLMs) can optimize clinical workflows; however, the economic and computational challenges of their utilization at the health system scale are underexplored. We evaluated how concatenating queries with multiple clinical notes and tasks simultaneously affects model performance under increasing computational loads. We assessed ten LLMs of different capacities and sizes utilizing real-world patient data.