WorldView, a leading provider of integrated healthcare technology to the top home health and hospice EHR/EMR platforms, announced the upcoming launch of Referral AI, an enhancement to automate intake referrals using a custom AI/ML model built specific for the healthcare industry.
Referral AI uses AI/ML (Artificial Intelligence/Machine Learning) to scan and analyze dense referral document packets in seconds, detecting false positives and negatives, using custom rules to send confirmed referrals to the EHR/EMR system.
In a recent survey by WorldView, confidence in a referral being acted upon quickly was a top-ranking factor for 85 percent of referring partners. WorldView’s Referral AI was designed to help agencies win more business and eliminate manual workflows related to the overload of documents in their inbox.
Home health and hospice agencies receive many forms of electronic documents in their inbox, including referrals for new patient service. Referrals must be acted on quickly, but with documents being dozens of pages, they often sit unread or, worse, are missed or overlooked. Over time, the referral can become invalid, resulting in lost revenue for the agency and posing a risk of delayed service for patients.
Referral AI is a custom AI/ML model built specifically for the home-based care industry and trained on 22+ years of data, outperforming off-the-shelf AI/ML models for similar tasks in speed and accuracy.
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Referral AI benefits home health and hospice agencies through cutting-edge features:
- Precision Sensitivity Settings: Customizable sensitivity settings for detecting false positives and negatives, ensuring the highest level of accuracy tailored to specific needs.
- Continuous Learning Feedback Loop (CLFL): Users can tag and provide feedback on any document errors, enabling the AI system to learn and improve continuously on an agency’s unique data set, enhancing accuracy and reliability over time. Given referral packets vary widely in format and by referrer, the system leverages both WorldView’s 22+ years of data and the agency data for maximum learning potential.
- Expert In-House AI Team: WorldView’s dedicated AI team is at the forefront of innovation, constantly developing new use cases to integrate AI into workflows.
- Lightning-Fast Processing Speed: Referral AI scans and analyzes the pages of referral/intake documents in seconds across 120+ variables, accelerating the intake process and saving valuable time.
- Custom Document Classification Model: WorldView’s model is pre-trained and fine-tuned based on a unique dataset built from tens of thousands of referral documents, delivering exceptional performance and accuracy in document classification.
- Effortless Integration: Integrated into the EHR/EMR system, there is no change in workflow for existing WorldView customers. Referral AI will require minimal additional training and can be implemented within 24 hours. All documents are stored in one centralized location for easy access and management.
Why Referral AI matters:
“When we started developing our Referral AI technology, we saw first-hand how other solutions released features that inevitably created more downstream issues,” said Jared Robey, SVP at WorldView. “We leveraged our extensive dataset to build and train our AI/ML model, ensuring that referrals are identified accurately and routed to an intake team for prioritization. This investment allows WorldView to continue pushing automation limits to enhance user experience and increase financial success.”
WorldView‘s Referral AI prioritizes rapid patient care and reduces the burden on back-office staff. By drastically cutting down the time needed for the intake process, Referral AI enables care coordination to begin almost immediately. The solution provides an organized and insightful overview of the referral packet, ensuring clinicians have quick access to the patient’s clinical history, reasons for care, and critical findings. This clarity allows admitting clinicians to focus on delivering high-quality care without sifting through extensive documentation.
Source: PRNewswire
