Publications by authors named "R Kohli-Seth"

Background:  Nephrotoxin exposure may worsen kidney injury and impair kidney recovery if continued in patients with acute kidney injury (AKI).

Objectives:  This study aimed to determine if tiered implementation of a clinical decision support system (CDSS) would reduce nephrotoxin use in cardiac surgery patients with AKI.

Methods:  We assessed patients admitted to the cardiac surgery intensive care unit at a tertiary care center from January 2020 to December 2021, and August 2022 to September 2023.

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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.

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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.

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Navigating medical care at the end of life can be a challenging experience for patients. There are also significant resource burdens, including intensive care unit (ICU) admissions, accompanying terminal illness. For actively dying patients, developing a care plan based on patient goals and delivering care at the bedside can enhance patient well-being, avoid inappropriate transfers or interventions, and improve resource management.

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Article Synopsis
  • Intravenous fluids are crucial for managing acute kidney injury (AKI) after sepsis, but they can lead to fluid overload, prompting a need for a restrictive fluid strategy for certain patients.
  • A machine learning algorithm was developed and validated to identify sepsis patients with AKI who would benefit from receiving less than 500mL of fluids within 24 hours.
  • The algorithm suggested that 88.2% of patients in the validation cohort would benefit from a restrictive fluid approach, leading to higher rates of early and sustained AKI reversal and lower major adverse kidney events compared to those receiving more fluids.
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