Predictive Analytics: Mighty AI Tool to Avert Care Emergencies

Clinical Transform

Predictive Analytics:
Mighty AI Tool to Avert Care Emergencies

Overview

Outcome-based digital care delivery is backed with equally effective predictive analytics, pushing for improved patient care, enhancing treatment outcomes, and curtailing high costs. Recognizing this potential, we partnered with a Michigan-based healthcare organization needing an AI-powered analytics engine that shifts through their massive amounts of patient data to predict individual patient outcomes like hospital readmissions, disease progression, and adverse events. In answer, we leveraged the immense capabilities of SyncMesh and DataSteroid to subsequently engineer a state-of-the-art predictive analytics engine fortified by some client-partnered proprietary algorithms, successfully augmented clinical care, streamlined organizational performance, and boosted operational efficiency.

Challenges

Ensuring the quality and reliability of data: Machine learning algorithms necessitate high-quality, reliable data to deliver accurate predictions. Unfortunately, data often appears in multiple formats or may be incomplete or inaccurate, impeding seamless integration and analysis.
Time and resource-intensive training process: A significant chunk of data is useless unless fed into the model, allowing it to learn patterns and relationships. This training process consumes substantial time and resources.
Validating the trained model: The model must undergo meticulous validation and inference processes to ensure its accuracy, reliability, and unbiased predictive ability. This necessitates the development of intricate standardized protocols for algorithm validation and benchmarking.

Solutions

Safe collection and storage of data: Using SyncMesh, we gathered sensitive healthcare data from diverse sources like electronic health records (EHRs), medical claims, and patient-generated data. Stringent security standards were enforced to safeguard privacy.
Ensuring data quality and accessibility: We leveraged the capabilities of DataSteroid to supplement the acquired data using ETL processes and pre-process it by cleaning and formatting it to ensure its readiness for the model.
Training and validating the ML model: Our team identified relevant columns and trained the ML model, and implemented protocols and algorithms to guarantee accurate, unbiased predictions. We monitored the model's performance, retraining it periodically to maintain optimum efficiency.

Result

Thanks to the concerted efforts of our expert team and our fully customizable solutions SyncMesh and DataSteroid, we developed a tailor-made AI-powered predictive analytics engine for our client. This engine, trained on a massive patient dataset, could accurately forecast patient outcomes, pinpoint potential risks, and propose personalized treatment strategies. As a direct outcome of the project, the organization witnessed a 60% surge in doctor engagement and a staggering 85% increment in productivity and care delivery rate. Nonetheless, it’s important to remember that machine-learning models are designed to adapt and learn over time and thus should not be considered a 100% reliable source of prognosis and analysis.

Clinical Transform