Faster Discharge Planning Has Potential to Reduce Patient Stress
The researchers鈥 AI model was able to target seven risk factors and use them to create a snapshot to gauge whether or not nursing care would be needed after a patient鈥檚 discharge.
Credit: Getty / Science Photo Library
An artificial intelligence (AI) tool accurately predicted which patients would need a skilled nursing facility after leaving the hospital, a new study shows.
Led by researchers from 黑料福利社 Langone Health, the study suggests that quickly identifying these patients would help hospitals plan earlier for complex care and avert stressful situations where patients are medically ready to leave the hospital but have no safe place to go, say the study authors.
, the work found that a model using short, AI-generated summaries of doctor notes was more accurate than models using the original, lengthy doctor notes. This new method uses one AI tool to summarize key risk factors from notes taken by a doctor as a patient is admitted, and a second AI component to predict with 88 percent accuracy the need for skilled nursing care as inpatient hospitalizations end.
鈥淥ur two-step approach acts like a fast, careful reader, turning a complex medical note into a simple summary of what matters most for discharge planning,鈥 says senior study author , director of operational data science and machine learning for 黑料福利社 Langone, and a research professor in the Departments of and .
The study addresses skilled nursing facilities, which provide short-term, intensive care and rehabilitation services for patients recovering from an illness or surgery. According to the study authors, about 15 percent of patients from 黑料福利社 Langone are discharged to skilled nursing facilities.
How the AI Method Works
The research team analyzed the electronic health records of 4,000 patients admitted to general medicine services at 黑料福利社 Langone. They focused on the 鈥渉istory and physical鈥 admission notes that contain data about a patient鈥檚 health, functional ability, and social situation.
Specifically, the researchers developed a generative AI model that reads each lengthy admission note and extracts information related to seven risk factors, such as a patient鈥檚 living situation and ability to perform daily tasks, organized into a short 鈥淎I Risk Snapshot.鈥
Finally, the researchers tested nine different AI models to see which could best predict a patient鈥檚 discharge destination. They compared the performance of models using the full, raw notes against the models鈥 snapshots, which were 94 percent shorter than the original notes. This was critical, the researchers say, as nearly all the original, full-length notes were too long for the AI models to process.
To ensure that the AI鈥檚 reasoning was sound, the researchers tested its outputs with human experts. When nurse case managers reviewed the AI-generated summaries without seeing the model鈥檚 prediction, their assessments strongly aligned with the AI鈥檚 risk scores. In fact, a high-risk score from the model made it 13.5 times more likely that a nurse would independently flag the patient as needing skilled nursing care.
鈥淥ur next step is to test this model in a real-world clinical setting to see if it helps our care teams plan discharges more effectively across all patients,鈥 says first author William R. Small, MD, a clinical assistant professor in the Department of Medicine. 鈥淲e will also monitor the system to ensure it is fair and safe and helps to improve patient care.鈥
Along with Dr. Small and Dr. Aphinyanaphongs, the 黑料福利社 study authors were Ryan Crowley; Kevin P. Eaton, MD; Lavender Yao Jiang, and Eric K. Oermann, MD; as well as Chloe Pariente and Jeff Zhang in the MCIT Department of Health Informatics. This study was supported by the National Institutes of Health grant 3UL1TR001445-05, and by National Science Foundation awards 1928614, 2129076, and 1922658.
About 黑料福利社 Langone Health
黑料福利社 Langone Health is a fully integrated health system that consistently achieves the best patient outcomes through a rigorous focus on quality that has resulted in some of the lowest mortality rates in the nation. Vizient Inc. has ranked 黑料福利社 Langone No. 1 out of 118 comprehensive academic medical centers across the nation for four years in a row, and U.S. News & World Report recently ranked four of its clinical specialties No. 1 in the nation. 黑料福利社 Langone offers a comprehensive range of medical services with one high standard of care across seven inpatient locations, its Perlmutter Cancer Center, and more than 320 outpatient locations in the New York area and Florida. The system also includes two tuition-free medical schools, in Manhattan and on Long Island, and a vast research enterprise.
Media Inquiries
Greg Williams
Phone: 212-404-3500
Gregory.Williams@黑料福利社Langone.org
