Skilled nursing facilities (SNFs) are increasingly turning to artificial intelligence (AI) to optimize reimbursements and improve patient care. AI-powered software is revolutionizing the way SNFs identify and document conditions that impact reimbursement rates, saving facilities time and resources while ensuring accurate and comprehensive patient files.
One of the primary challenges faced by SNFs is the complexity of the reimbursement landscape. Under the Patient-Driven Payment Model (PDPM), reimbursement rates are determined by a combination of factors, including patient characteristics, diagnoses, and treatments (CMS, 2021). AI-powered software can quickly and accurately analyze patient data to identify conditions that warrant higher reimbursements, ensuring facilities receive appropriate compensation for the care they provide (Davenport & Kalakota, 2019).
AI algorithms can process vast amounts of structured and unstructured data, including clinical notes, diagnostic reports, and medication records (Jiang et al., 2017). By leveraging natural language processing (NLP) and machine learning techniques, AI-powered software can extract relevant information and identify patterns that may be missed by manual review (Sheikhalishahi et al., 2019). This not only saves time but also reduces the risk of human error, leading to more accurate and consistent documentation.
In addition to improving reimbursement accuracy, AI-powered software can also enhance patient care by identifying potential issues and gaps in documentation. For example, if a patient's file lacks information about their mobility status or nutritional needs, the software can alert staff to assess and document these factors (Rantz et al., 2018). This proactive approach ensures that patients receive the appropriate level of care and support, ultimately leading to better outcomes.
As the healthcare industry continues to evolve, the adoption of AI-powered software in skilled nursing facilities is becoming increasingly crucial. By leveraging the power of AI to optimize reimbursements and improve patient care, SNFs can remain competitive and financially viable while providing the highest quality of care to their residents.
References:CMS. (2021). Patient-Driven Payment Model. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/PDPM
Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94-98. https://doi.org/10.7861/futurehosp.6-2-94
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology, 2(4), 230-243. https://doi.org/10.1136/svn-2017-000101
Rantz, M. J., Popejoy, L., Vogelsmeier, A., Galambos, C., Alexander, G., Flesner, M., Crecelius, C., Ge, B., & Petroski, G. (2018). Impact of Advanced Practice Registered Nurses on Quality Measures: The Missouri Quality Initiative Experience. Journal of the American Medical Directors Association, 19(6), 541-550. https://doi.org/10.1016/j.jamda.2017.10.014
Sheikhalishahi, S., Miotto, R., Dudley, J. T., Lavelli, A., Rinaldi, F., & Osmani, V. (2019). Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review. JMIR Medical Informatics, 7(2), e12239. https://doi.org/10.2196/12239
