About the Customer
The client runs one of the most prestigious healthcare organizations in the US. With the proliferation of new economic realities and evolving healthcare markets, the client is working on promoting digital healthcare throughout the organization.
With data and technology unveiling new insights and highlighting new ways to discover patients’ requirements and the delivery of quality care, the client wants to transform doctors’ decision-making process and anomaly detection.
The Challenge
Patients’ expectations to become increasingly involved in treatment decisions today pose significant challenges for doctors. Moreover, time-sensitive decisions are the most critical components in the healthcare organization.
Unpredictable work and critical conditions influenced clinical decisions.
Data quality and training data samples presented numerous challenges while designing an anomaly detection model.
What we did?
We designed an intelligent-based healthcare system to aid doctors’ decision-making processes by identifying anomalies in medical data. For instance, breast cancer and Parkinson’s disease-based intelligent AI systems were designed and tested on samples collected from hospitals.
The Solution
We designed an AI-based healthcare system that could detect anomalies in medical data to aid better decision-making. The process included.
Data Strategy: We created an effective data strategy to collect data while minimizing bias in the AI model. We collected and analyzed the medical data and incorporated equity and fairness into the data collection process.
Testing: The training and testing of AI algorithms were done equally across different data sets. We conducted internal audits to test the AI system and ensure the system was unbiased.
Monitoring: Since learning algorithms adjust to new data over time, we monitored them regularly to ensure they stayed within the acceptable control limits.
The following settings enabled enterprise-level anomaly detection.
Contextual Anomalies: Such anomalies indicate anomalies in one data set that may not be an anomaly in another dataset as they deviate from different data points within the same context.
Point Anomalies: Such anomalies appear far from the remaining data set.
Collective Outliers: It is a collection of data points appearing as an outlier in the entire dataset. These are neither contextual nor point anomalies.
We designed a well-thought model based on these settings and the normal behavior sets to locate outliers in the medical data.
Supervised ML algorithms, including neural networks, support vector machines, and k-nearest neighbors, were used to construct a predictive model based on a labeled training set with normal and anomalous samples.
Benefits & Outcomes
We were able to design an intelligent-based system that helped doctors make better decisions by detecting anomalies in medical data.
The system aided the client in improving clinical trial recruitment and patient identification.
Physicians leveraged AI to enhance diagnostics and therapeutic decisions, significantly improving clinician productivity and accuracy.
The organization was able to reduce the manual effort for executing administrative tasks through RPA and AI-based decision-making.
The Impact
Achieved time and cost savings by automating tasks
AI-based analytics support better decision-making
Accelerated scientific discovery processes
Enhanced patient experience due to conversational AI
Why Transpire?
Transpire is a global IT consultancy company with over 15 years of experience in digital solutions, providing businesses with a roadmap to digital transformation. Our team of experts works with clients to help them implement new data science and intelligent technologies across their business to transform it and achieve better outcomes that scale.
