Title: A Communication Breakdown in Healthcare Referrals
After three months of developing Carenector’s inter-facility platform, I received a call that highlighted the flaws in healthcare referrals. A hospital social worker, who was utilizing our platform for individual patients, faced challenges coordinating the placement of an 82-year-old stroke patient. Despite numerous attempts, including 23 phone calls and 14 faxes, the patient remained stranded in a costly acute care bed due to the lack of information on available skilled nursing facilities that accepted her Medicaid plan and offered stroke rehabilitation services.
Expressing her frustration, she pointed out the disparity between our patient-focused platform and the outdated process of facility-to-facility transfers. In the year 2025, despite substantial investments in health IT and AI technology, the referral process still felt archaic. This sentiment was echoed by THCB editor Matthew Holt’s experience with specialist referrals through Blue Shield of California earlier this year.
Although clinicians make millions of specialty referrals annually in the U.S., research indicates that a significant portion of these referrals are left incomplete. While our consumer-facing platform has been successful in aiding individuals and families in finding suitable care providers, addressing institutional referral workflows remains a critical challenge.
The root cause does not lie in advanced algorithms or flashy interfaces but rather in the disconnect between AI implementation and the practical aspects of care coordination. Primary care physicians frequently send complete referral notes to specialists, but a significant percentage never receive them. This data fragmentation extends beyond missing referrals to instances where patients are referred to providers not covered by their insurance plans.
Furthermore, existing payment models fail to incentivize providers to ensure referral loops are closed efficiently, leading to persistent care fragmentation. The reliance on outdated communication methods such as fax machines and paper-based handoffs further exacerbates these challenges.
Despite these issues, most AI vendors have approached the problem with superficial solutions that fail to address the underlying complexities of healthcare coordination. By focusing on isolated tools rather than integrated systems, these solutions have inadvertently worsened inefficiencies within the healthcare system.
In response to these shortcomings, we are developing a facility-facing platform designed to streamline institutional referral processes comprehensively. By incorporating structured patient needs without revealing personal information initially, our AI engine facilitates real-time matching based on clinical and logistical criteria.
Instead of patching existing chaos with AI enhancements, we are reimagining the entire institutional referral workflow from end to end. Through secure communication channels and outcome tracking mechanisms, we aim to improve transparency and efficiency in facility placements.
Our ongoing collaboration with partner hospitals and skilled nursing facilities is providing valuable insights into refining our facility platform. By prioritizing transparency and privacy controls while emphasizing adoption strategies over technological advancements, we are working towards rebuilding trust within healthcare referrals.
Ultimately, our goal is not just to implement smarter algorithms but to reshape healthcare workflows based on user feedback and real-world needs. Success will be measured not by the sophistication of our technology but by the tangible improvements in care coordination outcomes.