Case Study

A Clinical Decision Support System Powered by Gen AI

A renal care organization improves patient outcomes by personalizing and optimizing dialysis prescriptions for patients with multiple comorbidities

A leading provider of renal care services harnesses the potential of a Gen AI-driven Clinical Decision Support System (CDSS) to personalize dialysis prescriptions through effective comorbidity mapping.

Industry & Region: Healthcare, US
Tech Stack & Techniques: 
Web Development Framework: Django Web UI, Database: MongoDB, Data Extraction and Parsing: OCR for PDF Extraction, JSON Parsing, AI Models: Named Entity Recognition using OpenAI, Sentence Detection, Text Similarity & Classification using Gemini LLM models
Client Overview
Our client is a reputed renal care services provider, committed to delivering better value to patients through data-driven decision-making enabled by cutting-edge technologies and analytics.
Business Challenge
Our client’s traditional approach to dialysis prescriptions did not adequately address the diverse needs of patients with multiple comorbidities. Their standardized method overlooked the complexity of individual patient profiles. This led to suboptimal treatment outcomes and a lack of personalization in the dialysis prescriptions crucial for positive patient outcomes.
Solution Offered

KANINI was brought on board for its expertise in artificial intelligence and healthcare data analytics. Close collaboration between KANINI’s healthcare analytics experts and the client’s nephrology and IT teams ensured the successful development and deployment of a Clinical Decision Support System to optimize dialysis prescriptions.

This system leverages health record integration tools to extract and organize patient data. The extracted data is then fed into a large language model (LLM) powered by Generative AI to identify and map relevant comorbidities to improve treatment outcomes and patient satisfaction

A few challenges during the project included data integration from disparate sources and algorithm validation. These were addressed through meticulous data preprocessing including data identification, cleansing, transformation and reduction, and validation. Implementing advanced analytics algorithms, data integration techniques, and user-friendly decision support interfaces marked the project’s success.
Value Delivered
  • Strengthened the company’s position as a leader in personalized medicine and reinforced its commitment to patient-centric care.
  • Enhanced healthcare provider decision-making significantly through AI-powered data-driven insights.
  • Improved dialysis prescription accuracy, enhanced patient outcomes, and increased healthcare provider satisfaction.
  • Enabled the utilization of advanced analytics and decision-support tools for personalized patient care, opening new opportunities for modernization in the future.
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A Clinical Decision Support System Powered by Gen AI