Predictive Analytics: Mighty AI Tool to Avert Care Emergencies






Decrease in Emergency Room Admission

Designing a Robust Tool to Revolutionize Patient Care

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.


Data Analytics, Healthcare


Python, Apache Kafka, Apache Spark, PostgreSQL, React, Tableau, Docker, AWS.


Michigan, USA

Challenges to Proper and Accurate Predictions

The process of creating the perfect machine-learning solution to the problem was challenging. 

  • Machine learning algorithms need high-quality, reliable data to be accurate. However, data can be incomplete, inaccurate, or in multiple formats, which makes it difficult to integrate and analyze.


  • Collected data has no impact if the model is not fed, allowing it to learn patterns and relationships. The training process is very time and resource intensive.


  • A trained model must undergo validation and inference to ensure its accuracy, reliability, and unbiased predictive ability. This demands the creation of complex standardized protocols for algorithm validation and benchmarking.

Working Through the Issues

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.

Modules implemented

ETL Pipelines

  • Extracted data from various sources, such as electronic health records (EHRs), medical claims data, and other patient-generated data.
  • Cleaned and validated the data to ensure it was accurate and complete.
  • Normalized the data to a standard format.
  • Loaded the data into a data warehouse for storage and analysis.

Data Privacy Measures

  • Encrypted the data at rest and in transit.
  • Implemented access controls to restrict access to authorized users only.
  • Anonymized the data to remove any personally identifiable information.

Model Training

  • Defined the features to be used in the model.
  • Implemented a machine learning algorithm to train the model.
  • Evaluated the model to ensure it was accurate and reliable.

Model Validation

  • Used a holdout data set to test the model’s accuracy and reliability.
  • Implemented various protocols and algorithms to ensure the predictions were accurate and unbiased.
  • Evaluated the model’s performance on a variety of metrics to ensure efficiency.

Platform for Inference

  • Created a platform for deploying the model and making predictions.
  • Implemented a user interface to allow users to interact with the model easily.
  • Optimized the platform for performance and scalability.

Performance monitoring and model retraining pipeline

  • Developed a pipeline to monitor the performance of the model.
  • Implemented alerts to notify users when the model’s performance degrades.
  • Retrained the model periodically to improve its accuracy and reliability.

High Level Design Architecture

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Creating a Perfect Machine Learning Solution

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.

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