AI solutions that boosted the hospital’s operational efficiency and patient care.
US-based hospital looking to leverage AI to optimize its patient care process.
Client tasked our team with developing an AI-driven system that can identify the most promising cancer patients for treatment in a hospital overwhelmed by a patient load ten times greater than its capacity. The AI will also predict the probability of successful treatment for each patient.
Our approach to this extensive project was divided into three key phases:
Phase 1: Data extraction and preparation
The hospital stores patient information in the Cerner EMR system, but much of this data exists in unstructured PDF format. This format hinders the effective use of AI. Therefore, the initial crucial step is to extract patient data from these PDFs and make it accessible for AI applications. To tackle this challenge, we implemented the following steps:
For cases where AWS Comprehend Medical provided insufficient results, we developed and managed a custom AI model using BioBERT, a neural network trained on medical texts. This model was trained on the hospital’s historical data to enhance accuracy.
Phase 2: AI-powered patient admissions module development
Having enriched patient data, we moved forward with developing a smart patient admissions module:
Phase 3: Treatment success prediction module development
In the final phase, we focused on developing a module for predicting treatment success and determining the optimal timing for hospice transitions:
Our innovative solution tackled the hospital’s complex challenges and delivered measurable improvements:
These strategic advancements not only addressed the immediate challenges but also set the stage for future improvements in hospital operations and patient care.
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