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Nursing Home Admissions Co-Pilot

Healthcare admissions AI Co-pilot


In the critical process of discharging patients from hospitals to skilled nursing facilities (SNFs), time is of the essence. Hospitals typically reach out to all nearby SNFs to find a suitable placement, and these facilities must quickly decide whether to accept a referral based on a comprehensive review of the patient's file. 

This decision-making process is fraught with challenges, as facilities operate under the pressure of filling beds promptly while ensuring they can provide the necessary care without incurring financial losses. Medicare's flat-rate reimbursement model adds complexity, as some patients' care needs may exceed the facility's financial intake. The need to sift through extensive patient files, sometimes up to 100 pages, to identify potential red flags or specific care requirements compounds the difficulty of making rapid, informed admissions decisions.


We developed an admissions co-pilot tool that aids the admissions teams in making swift and well-informed decisions. Upon uploading the patient file to a secure portal, our RAG-based (Retrieval Augmented Generation) AI technology meticulously extracts key information critical for review, such as potential yellow and red flags, the cost implications of medications or treatments, and the patient's overall condition in relation to the facility's care capabilities. 

We also built an admin portal for the client's leadership to set and update criteria for triggering yellow and red flags, enabling the client to continuously refine the tool to meet their specific needs. 

This tool not only automates the extraction of patient information into a CRM system but also generates a summary cover sheet for each patient, thereby optimizing the decision-making process.


The implementation of the admissions co-pilot tool has markedly enhanced the efficiency and effectiveness of the admissions process for SNFs. Facilities are now able to respond to hospital referrals more promptly, thereby increasing their chances of filling beds and optimizing their occupancy rates. The tool has also improved organizational visibility regarding incoming referrals and the basis for admission or denial decisions. Notably, the system facilitated cross-coverage capabilities; for example, in its first week post-deployment, a team member successfully admitted a new patient while the admissions director was out of office, showcasing the tool's potential to sustain operations seamlessly. Looking ahead, there is potential for the tool to evolve further, potentially offering recommendations for patient admissions, while currently employing a hybrid approach to decision-making. This advancement signifies a significant stride toward enhancing operational efficiency, decision-making quality, and patient care coordination within the skilled nursing facility sector.

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