Kindred Health partnered with Alitek to modernize its patient referral intake process using OpenText Capture technology. The initiative reduced processing time from days to minutes, cut labor costs by 90 percent, and improved patient placement accuracy across facilities.
Background
Kindred Health operates hospitals and care facilities across the United States, serving patients with complex and specialized needs. Timely and accurate referral intake is critical to ensure appropriate placement and optimal patient outcomes.
The organization received electronic referrals from physicians, hospitals, and partner organizations through multiple digital channels. However, inconsistent formats and manual processes limited efficiency and visibility across intake teams.
The Challenge
Kindred Health relied on a labor-intensive referral process that required printing, reviewing, organizing, categorizing, and manually routing documents for evaluation and acceptance. Long processing cycles led to lost referral opportunities, high error rates, and the risk of accepting patients outside coverage capabilities, impacting both revenue and care quality.
The Solution
Alitek implemented an automated referral intake solution powered by OpenText Capture and OCR technology. The platform automatically ingested referrals from multiple electronic channels and categorized them with 70 to 90 percent accuracy upon receipt.
Incoming referral packets were electronically organized and structured into standardized digital records. An intuitive dashboard enabled intake personnel to review, confirm, correct, and approve referral packets before final routing.
Automated workflows routed categorized referrals to the appropriate facility and decision maker based on predefined business rules. When new clinical or administrative information was added, the system automatically reprocessed and updated the existing referral without manual intervention.
The standardized ingestion framework also reduced the effort required to onboard new referral channels. Kindred Health established a scalable automation foundation capable of supporting additional document driven processes such as prior authorizations.
Results and Outcomes
The new automated process reduced referral processing time from days or hours to less than 15 minutes. This acceleration minimized lost referral opportunities and improved responsiveness to referring providers.
Categorization and routing labor costs decreased by 90 percent as manual sorting and document handling were eliminated. Improved data accuracy reduced intake errors and strengthened alignment between patient needs and facility capabilities.
By improving placement precision and intake visibility, Kindred Health enhanced both operational efficiency and patient outcomes across its network.
Project Highlights
Reduced referral processing time to under 15 minutes through automated ingestion and intelligent categorization
Lowered categorization and routing labor costs by 90 percent by eliminating manual document handling
Improved patient placement accuracy through structured digital workflows and real time validation
Conclusion
By modernizing referral intake with OpenText Capture, Kindred Health transformed a manual bottleneck into a streamlined digital workflow. The organization gained speed, accuracy, scalability, and improved patient alignment across its care network.
Alitek delivered a future ready automation platform that supports operational excellence and continued digital transformation.
