Issue link: https://beckershealthcare.uberflip.com/i/1233009
13 SPINE SURGEONS Wisconsin health system settles orthopedic surgeon's kickback violation allegations for $10M: 5 details By Laura Dyrda M ilwaukee-based Agnesian HealthCare and its physician group will pay $10 million to settle a whistleblower suit brought by orthopedic surgeon Clark Searle, MD, who alleged the health system paid kickbacks and other financial incentives for patient referrals, according to the Fond du Lac Reporter. Five things to know: 1. Dr. Searle filed the lawsuit in 2014, alleging that the health system adjusted physician pay from 1996 to 2015 based on the physician's specialty referrals to the health system's services, hospitals and other facilities. He also contended that the health system made deferred com- pensation payments that made it difficult for physicians who left the system to refer patients to competitors. 2. The complaint also describes an environment where Agnesian made deals on the side to procure additional compensation for physicians designated as high-referrals sources. 3. Agnesian denied claims in the lawsuit, and said it does not track referral leaks in the report. 4. Dr. Searle joined the health system in 2006 and served on the board of directors for three years. He ended his employment with the health system in July 2017 and re- ported that he had tried to raise concerns about Agne- sian's compensation methodology while there. 5. The federal government declined to intervene in the case. n Can AI reverse the 'unsustainable' trajectory of spine care? 7 research takeaways By Angie Stewart A rtificial intelligence could help im- prove the efficiencies and outcomes of spine surgery while reversing the field's "unsustainable" trajectory of rising costs in the U.S., according to a paper pub- lished Jan. 6 in the Global Spine Journal. Amid the shi to value-based care, there has been much emerging research on the use of AI to improve spine surgery outcomes while low- ering costs. Michelle Lee, Matthew Grabows- ki, MD, Ghaith Habboub, MD, and omas Mroz, MD, of the Cleveland Clinic Founda- tion reviewed work published in the past year to evaluate the use of AI in spine care. Seven takeaways from their paper: 1. Machine learning systems designed to accu- rately predict length of stay, discharge to non- home facility and early unplanned readmis- sions aer spine surgery could help identify high-risk patients and factors that contribute to risk. ese projections could enable hospi- tals to more efficiently allocate resources, re- duce costly lengths of stay and readmissions, and maintain or improve care quality. 2. Because spine surgeries aren't easily stud- ied through traditional methods, clinicians could use predictive algorithms derived from vast data repositories to standardize care. 3. Some researchers have developed machine learning models that accurately predict pos- itive outcomes aer surgery for degenerative cervical myelopathy, probability of failure of nonoperative management in spinal epidural abscesses, and 90-day post-discharge mortal- ity in patients with spinal metastatic disease. 4. Clinical researchers have also used AI technology to predict negative outcomes, such as surgical complications in patients un- dergoing elective anterior cervical discecto- my and fusion, posterior lumbar fusion, and adult spinal deformity surgeries. Some mod- els even predicted prolonged opioid prescrip- tion aer surgery for lumbar disc herniation. 5. Surgeons' subjectivity remains important for reviewing spine imaging and determining whether a patient should undergo surgery, but AI and machine learning algorithms are de- signed to help in that decision-making process. For instance, one model was able to predict the diagnosis and severity of cervical spondylotic myelopathy with high sensitivity and specificity. 6. A fully integrated AI platform can auto- mate burdensome administrative tasks such as conducting postsurgical checks and or- dering medications, thereby enhancing effi- ciencies, outcomes, patient satisfaction and employee engagement, the authors said. 7. e paper acknowledged that a lack of large, quality data sets and consensus about how to categorize issues could be barriers to using AI-driven platforms in spine care. ese models can make false associations if based upon inadequate data, and work must be done to make the algorithms transfer from one facility to another. n Surgeons' subjectivity remains important for reviewing spine imaging