The effect of myelopathic symptoms on hospital costs, length of stay, and discharge location in anterior cervical discectomy and fusion.

OBJECTIVE: Cervical spondylotic myelopathy (CSM) is a common clinical degenerative disease treated with anterior cervical discectomy and fusion (ACDF), which seriously impacts quality of life and causes severe disability. The objective of the study was to determine the effect of different characteristics of the neurological deficit found in myelopathic patients undergoing ACDFs on hospital cost, length of stay (LOS), and discharge location. METHODS: This is a retrospective review of ACDF cases performed at a single institution by multiple surgeons from 2011 to 2017. Patient symptomatology, complications, comorbidities, demographics, surgical time, LOS, and discharge location were collected. Patients with readmissions or reoperations were excluded. Symptoms evaluated were based on clinical diagnosis, Japanese Orthopaedic Association classification, Ranawat grade, and Cooper scales. Symptoms were further grouped using principal component analysis. Cost was defined as surgical episode hospital stay costs plus outpatient clinic costs plus discharge disposition cost. Multivariate linear regression models were created to evaluate correlations with outcomes. The primary outcome was total 90-day hospital costs. Secondary outcomes were discharge location and LOS. RESULTS: A total of 250 patients were included in the analyses. Discharge location, neuromonitoring use, number of surgical vertebral levels, cage use, LOS, surgical time, having a complication, and sex were all found to be predictive of total 90-day costs. Myelopathic symptomatology was not found to be associated with increased 90-day costs (p ≥ 0.131) when correcting for these other factors. Lower-extremity functionality was found to be associated with increased LOS (p < 0.0001). Upper-extremity myelopathy was found to be associated with increased discharge location needs (p < 0.0001). CONCLUSIONS: Cervical myelopathy was not found to be predictive of total 90-day costs using symptomatology based on multiple myelopathy grading systems. Lower-extremity functionality was, however, found to predict LOS, while upper-extremity myelopathy was found to predict increased discharge location needs. This implies that preoperative deficits from myelopathy should not be considered in a bundled payment system; however, certain myelopathic symptoms should be considered when determining the cost of care.

Understanding Biases and Disparities in Radiology AI Datasets: A Review.

Artificial intelligence (AI) continues to show great potential in disease detection and diagnosis on medical imaging with increasingly high accuracy. An important component of AI model creation is dataset development for training, validation, and testing. Diverse and high-quality datasets are critical to ensure robust and unbiased AI models that maintain validity, especially in traditionally underserved populations globally. Yet publicly available datasets demonstrate problems with quality and inclusivity. In this literature review, the authors evaluate publicly available medical imaging datasets for demographic, geographic, genetic, and disease representation or lack thereof and call for an increase emphasis on dataset development to maximize the impact of AI models.

Reimbursement patterns for neurosurgery: Analysis of the NERVES survey results from 2011-2016.

OBJECTIVE: In response to rising national health expenditures, the Patient Protection and Affordable Care Act (ACA) was passed in 2010, with major provisions implemented in 2014. Due to increasing concerns about workload and compensation among neurosurgeons, we evaluated trends in neurosurgical reimbursement, productivity and compensation before and after the implementation of the major provisions of the ACA. PATIENTS AND METHODS: Results from Neurosurgery Executives' Resource Value and Education Society (NERVES) annual surveys were collected, representing data from 2011 to 2016. Responses from different practice settings across the six years were categorized into groups, and inverse variance-weighted averaging was performed within the frameworks of a one-way ANOVA model with year. Data from 2011 to 2013 and 2014-2016 were analyzed similarly for differences among practice setting and region. RESULTS: The NERVES survey response rates ranged from 20% to 36%. Median values for compensation decreased by 3.66%, 6.42%, and 10.34% within private, hospital, and academic practices respectively after 2014 although these trends did not reach statistical significance. Median work RVUs had a trend to decrease by 5.67%, 13.08%, and 19.44% within private, hospital, and academic practices respectively after 2014. Academic practices showed statistically significant decreases in annual total RVUs, total gross charges and collections. CONCLUSION: These data demonstrate neurosurgical reimbursement and productivity have trended down during a time that increases in productivity and reimbursement were predicted. This phenomenon is most notable in academic practices compared to private or hospital based practices. Prospective analyses of the impact of healthcare policy reform on neurosurgical productivity are urgently needed.