Case Study: Using AI to Structure Data to Power Quality Assurance Automation in Healthcare
Case Study: Using AI to Structure Data to Power Quality Assurance Automation in Healthcare
Case Study: Using AI to Structure Data to Power Quality Assurance Automation in Healthcare
Case Study: Using AI to Structure Data to Power Quality Assurance Automation in Healthcare
Case Study: Using AI to Structure Data to Power Quality Assurance Automation in Healthcare
Case Study: Using AI to Structure Data to Power Quality Assurance Automation in Healthcare
Case Study: Using AI to Structure Data to Power Quality Assurance Automation in Healthcare
Maintaining the highest standard of patient care is crucial for healthcare providers, especially when it comes to counseling, where detailed notes are a vital part of the process. A large counseling network based in the US, supporting over 2,000 providers, faced significant challenges as they looked to migrate their existing data from their legacy Electronic Medical Records (EMR) system over to a more advanced platform. However since the data is both raw and unstructured, an enormous amount of effort and time is generally required to ensure that the data is ready for transfer. Additionally, ensuring the quality of patient care is and always will be the highest priority for the team. However the current quality assurance process is not only labor-intensive, but can be prone to bias if quality checks are inconsistent. Partnering with Gradient, the network leveraged Gradient’s AI data platform to transform raw data into structured formats and automate the quality assurance process, to ensure high-quality patient care.
Upgrading to a New EMR System
Compared to their old platform, the new EMR system will support the team in harnessing the full potential from their data to help serve their patients. However the process of transfer data from the old legacy EMR system to the new advanced platform, proved to be more challenging than expected.
Time-Consuming and Manual Process: Since the legacy system contains a vast amount of unstructured and raw data, the team took this opportunity to structure the data in order to make the most out of the data going into the new platform. However the process to migrate data from the outdated system to the advanced platform required considerable amount of time and resources since it must be manually interpreted, formatted, and structured. This is not only time-consuming but also increases the risk of errors.
Varied, Inconsistent Data: The data within the legacy system is often unstructured and varies widely in format, including written notes, forms, and PDFs. Despite being rich in context, the lack of standardization and organization makes it difficult to analyze and utilize effectively.
Quality Assurance Process
In order to ensure the highest quality of patient care, clinical notes and records from each session are anonymized and reviewed at random. However, this process can be quite challenging.
Labor Intensive and Prone to Bias: The existing quality assurance process was labor-intensive, requiring manual organization and interpretation of clinical notes. This process not only consumed a considerable amount of time, but it also carried the risk of biased results due to the subjective nature of the process.
The AI-Driven Solution
To address these challenges, the network provider implemented Gradient’s AI data platform to automate the structuring of raw data and enhance their quality assurance process.
AI-Powered Data Extraction
Gradient’s AI system was deployed to automate the extraction and structuring of vast amounts of raw data stored within the provider’s legacy EMR system. The AI system identified key entities, relationships, and themes within the unstructured data, transforming it into a structured format compatible with the new EMR system. This process significantly reduced the manual effort required and minimized the risk of errors.
Automated AI-Powered Quality Assurance
After structuring the raw data, Gradient’s AI system was leveraged to perform advanced sentiment analysis on clinical notes. This analysis was applied to randomly selected sessions, providing an unbiased quality check at a higher frequency. The AI system used both predefined standards and allowed the provider to define specific criteria for quality assurance, ensuring that the highest standard of patient care was maintained consistently and efficiently.
The Impact
The implementation of Gradient’s AI data platform had a significant impact on the network provider’s operations:
Increased Accuracy: Automating the data extraction and quality assurance processes led to a 35% increase in end-to-end accuracy. This improvement not only reduced errors caused by data misinterpretation but also ensured a more thorough and frequent quality check.
Reduction in Workload: The automation drastically reduced the manual effort required for data migration and quality assurance, cutting the overall workload by approximately 85%. This allowed the provider to reallocate resources to more strategic tasks.
Cost Savings: By leveraging Gradient’s AI data platform, the provider achieved a 30% reduction in overall costs. These savings were directly attributed to the efficiencies gained through the automation of previously labor-intensive processes.
By partnering with Gradient and their data platform, the mental health provider successfully transformed their data management and quality assurance processes. The results improved accuracy, reduced workload, and improved cost savings - enabling the provider to maintain the highest standards of patient care while continuing to scale their operations.
