AI-READY DATA
Fuel your AI with the highest quality data
THE CHALLENGE
Your AI is only as good as your data
High-quality data sets help AI models recognize patterns, make accurate predictions, and provide valuable insights. A robust data strategy that focuses on quantity, quality, and context is essential for unlocking the full potential of AI.
THE SOLUTION
Industry data model
Your AI engine can be trained more effectively when it can analyze data from multiple sources in a standardized model. The Coperor industry data model for healthcare ensures that the data feeding your AI engine will be aligned across sources, resulting in more reliable results and avoiding the much-feared hallucinations that can arise when data is taken out of context.
Authority weighting
It’s critical to invest the time to grade the importance of content sources (“authority weighting”), which helps the model understand the relative value of different sources. This weighting is a core function of the Coperor Master Data Management (MDM) engine where sophisticated rules calculate the relative authority assigned to each data field within each system.
Data quality
AI models rely on high-quality training data, and flaws like errors or inconsistencies can severely impact their performance. Poor data can lead to nonsensical outputs or deficiencies in generative models. Coperor ensures quality with automated validation pipelines, human review for nuanced errors, and continuous monitoring to address bias and maintain integrity.
Privacy and consent adherence
Protecting user privacy, obtaining proper consent, and maintaining data confidentiality are non-negotiable ethical obligations when developing generative AI systems. Organizations must implement robust mechanisms to adhere to all relevant data privacy regulations like GDPR, CCPA, and HIPAA. This involves comprehensive de-identification and anonymization of any personal or sensitive data used for model training. Coperor provides robust access controls, encryption, and consent management for all healthcare data.
Trust built through visibility
The inability to see how AI systems make their decisions is known as the “black box problem” and it is a major challenge to adoption where the outcomes have material impacts related to safety, finance, or ethics. The next best thing to having visibility into the AI reasoning is to provide complete visibility into the data on which the decisions are made. The Coperor data viewer allows users to interrogate the training data to better understand the AI output which leads to improved training and user trust.
CUSTOMER SUCCESS
Gaine for the win
Gaine is helping pharmaceutical manufacturers build a comprehensive patient profile that captures every interaction from initial engagement through omni marketing channels to patient services. This carefully curated patient profile fuels predictive analytics that assess the likelihood of prospect conversion, insurance coordination, adherence risk and many other key business drivers
