收入
报销
医学
收入保证
医疗保健
患者安全
放射科
财务
收益模型
业务
经济
经济增长
作者
Bradley J. Roth,Rony Kampalath,Kayla Nakashima,Stephanie Shieh,Thanh-Lan Bui,Roozbeh Houshyar
标识
DOI:10.1067/j.cpradiol.2023.05.008
摘要
Radiology reports often contain recommendations for follow-up imaging, Provider adherence to these radiology recommendations can be incomplete, which may result in patient harm, lost revenue, or litigation. This study sought to perform a revenue assessment of a hybrid natural language processing (NLP) and human follow-up system. Reports generated from January 2020 to April 2021 that were indexed as overdue from follow-up recommendations by mPower Follow-Up Recommendation Algorithm (Nuance Communications Inc., Burlington, MA), were assessed for follow up and revenue. Follow-up exams completed because of the hybrid system were tabulated and given revenue amounts based on Medicare national reimbursement rates. These rates were then summated. A total of n =3011 patients were flagged via the mPower algorithm as having not received a timely follow-up indicated for procedure. Of these, n = 427 required the quality nurse to contact their healthcare provider to place orders. The follow-up imaging of these patients accounted for $62,937.66 of revenue. This revenue was calculated as higher than personnel cost (based on national average quality and safety nurse salary and time allotted on follow-ups). Our results indicate that a hybrid human-artificial intelligence follow-up system can be profitable, while potentially adding to patient safety. Our revenue figure likely significantly underestimates the true revenue obtained at our institution. This was due to the use of Medicare national reimbursement rates to calculate revenue, for the purposes of generalizability.
科研通智能强力驱动
Strongly Powered by AbleSci AI