Engineers
Months
Predictive analytics is overtaking a chunk of the healthcare industry, helping deliver better care, improve patient outcomes, and reduce costs incurred. Keeping in mind the need and demand, predictive analytics can change the face of healthcare – diagnosing possible future risks, predicting outcomes of a particular treatment procedure, and even suggesting post-treatment care.
A Michigan-based healthcare organization approached us with the need for its analytics engine to go through its scores of patient data and predict individual patient outcomes, such as hospital readmissions, disease progression, and adverse events. With a collaborative partnership, we build an AI-based predictive analytics engine based on proprietary algorithms designed to improve clinical care, organizational performance, and operational efficiency.
The process of creating the perfect machine-learning solution to the problem was challenging.
Sensitive healthcare data was collected from various sources, including electronic health records (EHRs), medical claims, and other patient-generated data. To safeguard the privacy of sensitive data, strong security standards were implemented.
The acquired data was supplemented using ETL processes to ensure data quality and accessibility. Data was fed into the model, which was then preprocessed.
Preprocessing involved cleaning the data, removing any errors or inconsistencies, and formatting it for the model to understand.
The ML model was then trained by detecting relevant columns. Protocols and algorithms were implemented to ensure predictions were accurate and unbiased. Additionally, the model’s performance was constantly monitored and retrained periodically.
Throughout the project timeline, the project manager successfully kept the cross-functional team motivated, discussed roadblocks, and managed goals all within a given time frame.
Our team of experts successfully created an AI-predictive analytics engine for our client serving their unique demands. The engine was trained on a large patient dataset and could accurately predict patient outcomes, identify potential risks, and suggest personalized treatment plans. As a result of the project, the organization saw a 60% increase in doctor engagement and an 85% increase in productivity and care delivery rate. Despite everything, machine-learning models are meant to adapt and learn through time. Hence, it is ignorance to believe they are a 100% reliable source of prognosis and analysis.
