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The primary objective of this project was to enable MedHeal Hospital Network to offer more personalized healthcare services. The focus was to track events and align them with the predefined KPI and alert the authorities if any department is not inline with the defined TAT using AI and data analytics to predict staff efficiency for improved customer service and support for patient engagement and healthcare outcomes.
Decision Trees, Neural Networks
R, Python libraries like TensorFlow and PyTorch
HIPAA-compliant data encryption
Electronic Health Records (EHR), wearables data
MedHeal Hospital Network faced several challenges
Low levels of patient engagement were leading to less effective treatments and lower patient satisfaction.
The existing healthcare services were generic and not tailored to individual patient needs.
The hospital had access to vast amounts of data but lacked the tools to utilize it effectively.
We implemented a Consumer Behavior Prediction model to address MedHeal Hospital Network's challenges
Utilized machine learning algorithms like Decision Trees and Neural Networks to predict patient behavior and needs.
Employed data analytics tools such as R and Python libraries like TensorFlow and PyTorch for data modeling and analysis.
Integrated real-time data from Electronic Health Records and wearables to provide a more comprehensive view of patient health.
Ensured HIPAA-compliant data encryption to protect patient information.
The implementation of AIVEDA’s Consumer Behavior Prediction solutions led to significant improvements:
Ready to offer more personalized and effective healthcare services? Our specialized Consumer Behavior Prediction solutions are tailored to meet the unique challenges of your healthcare institution. Engage with our experts to find out how AIVEDA can provide a customized solution for you.
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