Study Design. Retrospective, observational study of clinical outcomes at a single institution. Objective. To compare postoperative complication and readmission rates of payer groups in a cohort of patients undergoing anterior cervical discectomy and fusion (ACDF). Summary of Background Data. Studies examining associations between primary payer and outcomes in spine surgery have been equivocal. Methods. Patients at Mount Sinai having undergone ACDF from 2008 to 2016 were queried and assigned to one of five insurance categories: uninsured, managed care, commercial indemnity insurance, Medicare, and Medicaid, with patients in the commercial indemnity group serving as the reference cohort. Multivariable logistic regression equations for various outcomes with the exposure of payer were created, controlling for age, sex, American Society of Anesthesiology Physical Status Classification (ASA Class), the Elixhauser Comorbidity Index, and number of segments fused. A Bonferroni correction was utilized, such that alpha = 0.0125. Results. Two thousand three hundred eighty seven patients underwent ACDF during the time period. Both Medicare (P < 0.0001) and Medicaid (P < 0.0001) patients had higher comorbidity burdens than commercial patients when examining ASA Class. Managed care (2.86 vs. 2.72, P = 0.0009) and Medicare patients (2.99 vs. 2.72, P < 0.0001) had more segments fused on average than commercial patients. Medicaid patients had higher rates of prolonged extubation (odds ratio [OR]: 4.99; 95% confidence interval [CI]: 1.13–22.0; P = 0.007), and Medicare patients had higher rates of prolonged length of stay (LOS) (OR: 2.44, 95% CI: 1.13–5.27%, P = 0.004) than the commercial patients. Medicaid patients had higher rates of 30- (OR: 4.12; 95% CI: 1.43–11.93; P = 0.0009) and 90-day (OR: 3.28; 95% CI: 1.34–8.03; P = 0.0009) Emergency Department (ED) visits than the commercial patients, and managed care patients had higher rates of 30-day readmission (OR: 3.41; 95% CI: 1.00–11.57; P = 0.0123). Conclusion. Medicare and Medicaid patients had higher rates of prolonged LOS and postoperative ED visits, respectively, compared with commercial patients. Level of Evidence: 3

Study Design: Narrative review. Objectives: Artificial intelligence (AI) and machine learning (ML) have emerged as disruptive technologies with the potential to drastically affect clinical decision making in spine surgery. AI can enhance the delivery of spine care in several arenas: (1) preoperative patient workup, patient selection, and outcome prediction; (2) quality and reproducibility of spine research; (3) perioperative surgical assistance and data tracking optimization; and (4) intraoperative surgical performance. The purpose of this narrative review is to concisely assemble, analyze, and discuss current trends and applications of AI and ML in conventional and robotic-assisted spine surgery. Methods: We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2019 examining AI, ML, and robotics in spine surgery. Key findings were then compiled and summarized in this review. Results: The majority of the published AI literature in spine surgery has focused on predictive analytics and supervised image recognition for radiographic diagnosis. Several investigators have studied the use of AI/ML in the perioperative setting in small patient cohorts; pivotal trials are still pending. Conclusions: Artificial intelligence has tremendous potential in revolutionizing comprehensive spine care. Evidence-based, predictive analytics can help surgeons improve preoperative patient selection, surgical indications, and individualized postoperative care. Robotic-assisted surgery, while still in early stages of development, has the potential to reduce surgeon fatigue and improve technical precision.

Sports-related concussion has emerged as a public health crisis due to increased diagnosis of the condition and increased participation in organized and recreational athletics worldwide. Under-recognition of concussions can lead to premature clearance for athletic participation, leaving athletes vulnerable to repeat injury and subsequent short- and long-term complications. There is overwhelming evidence that assessment and management of sports-related concussions should involve a multifaceted approach. A number of assessment criteria have been developed for this purpose. It is important to understand the available and emerging diagnostic testing modalities for sports-related concussions. The most commonly used tools for evaluating individuals with concussion are the Post-Concussion Symptom Scale (PCSS), Standard Assessment of Concussion (SAC), Standard Concussion Assessment Tool (SCAT3), and the most recognized computerized neurocognitive test, the Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT). The strengths and limitations of each of these tools, and the Concussion Resolution Index (CRI), CogSport, and King-Devick tests were evaluated. Based on the data, it appears that the most sensitive and specific of these is the ImPACT test. Additionally, the King-Devick test is an effective adjunct due to its ability to test eye movements and brainstem function.

With a growing trend in medicine towards individualized, patient-centric care, traditional health information technology limits progress. With high administrative costs and the lack of universal data access, contemporary electronic medical records serve more the institution rather than the patient. Blockchain technology, as presently described, was initially developed for use in financial markets, serving as a decentralized, distributed ledger of transactions. However, certain inherent characteristics of this technology suit it for use in the healthcare sector. Potential applications of the blockchain in medicine include interoperable health data access, data storage and security, value-based payment mechanisms, medical supply chain efficiency, amongst others. While the technology remains in nascent stages, it is essential that members of the healthcare community understand the fundamental concepts behind blockchain, and recognize its potential impact on the future of medical care.