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Accelerating QA Efficiency with CitiusTech Synthetic Quality Engineer
Using AI-driven quality engineering
Gen AI-driven QA transformation to accelerate delivery and quality with Shift-Left & Shift-Right
With a consulting-led approach, transformed QA process to accelerate delivery by 500+ hours, reduce rework, and turn fragmented test processes into a strategic, automation-first quality framework.
AI-powered expert routing for smarter, faster clinical consults
Built a Gen AI-powered solution to help physicians find the right expert using natural language queries. Delivered 85%+ accuracy and 50% higher response rates connecting care teams faster, with confidence.
Accelerated multi-modal medical research with an intelligent imaging platform
Developed a GxP-compliant, multi-tenant platform for medical imaging research. It enabled seamless AI/ML deployment, improved data accessibility, and faster clinical insights.
Gen AI-powered enterprise search to supercharge clinical and operational efficiency
Built an intelligent search app using Vertex AI and Gemini Pro to deliver real-time, relevant insights. It boosted productivity, reduced bottlenecks, and enabled faster decision-making across the enterprise.
Revolutionized patient discharge flow with Gen AI-powered task monitoring
Developed a smart solution to identify missed ICU/Step-down tasks using sparse clinical notes saving up to 8 hours per patient.
Enhanced physician efficiency and patient engagement with AI-powered Ambient Assist solution
Implemented a smart, EHR-integrated documentation assistant to cut admin time by 30% and enhance care quality. It enables physicians to focus more on patients with 80% fewer manual notes and higher satisfaction scores.
Transformed post-discharge care with virtual agents
Built an intelligent virtual agent using NLP, 3D avatars, and real-time pipelines to scale patient assessments with human-like interactions. Helping with early risk detection and reducing manual workload for care teams.
Intelligent HCC risk scoring with NLP to identify high-risk patients
Developed a robust NLP-based pipeline and OCR solution to extract clinical data from unstructured records for HCC scoring, cutting claim review time by 60%.
Streamlined claims prioritization for the largest PPO network provider in US
Deployed an ML-driven solution with stringent custom scoring metrics to settle FWA-suspect claims. Achieved 40% cost reduction and 95% success rate based on a Claim Priority Score.
AI-enabled imaging product suite for stroke and aneurysm detection
Developed intelligent mobile DICOM viewer solution to enable seamless 2D/3D visualization, real-time collaboration, and AI-powered insights driving smarter decisions and faster care at the point of need.
Operationalized an AI-driven clinical decision support system for early diagnosis
Helped the client handle the complexities of operationalizing the algorithm on a cloud-based platform that integrates seamlessly with the hospital's electronic health records (EHRs).
Developed an ML-based face morphing and age progression application
Enabled self-wrinkle and age progression analysis, remote consultation, and unbiased evaluation for end users of a leading bio-pharma company with 90% accuracy and high-speed processing in image analysis and resolution.
Improved claims auto-adjudication from 75% to 92%
Built an AI model to classify claims, resulting in a 75-92% improvement in auto-adjudication rates, ~$5 million in annual savings, and seamless automation of workflows and real-time performance dashboards integrated into existing systems.
Reimagined claims denial management with AI for an RCM company
Developed machine learning models to predict medical claims denials with 95% precision and 74% recall, using advanced algorithms and feature engineering to improve accuracy, based on 10.5M claim lines.
Transformed hearing care with machine learning
Developed a machine learning platform for a leading hearing benefit provider to predict sensorineural hearing loss risk, analyze utilization disparities, and build targeted care management cohorts using predictive modeling and AWS Sagemaker.
