Does ACDF Utilization and Reimbursement Change Based on Certificate of Need Status?

Achieving the United Nations Sustainable Development Goals in Spinal Surgery: Patient-Centered Care and Social Contribution

BACKGROUND

In recent years, achieving a sustainable society has become an international challenge. The medical field is also being called upon to contribute to the United Nations Sustainable Development Goals (SDGs). However, awareness of the SDGs in Japan is low, and relevant efforts in the healthcare sector are particularly lagging behind. This study aimed to describe the relationship between spinal surgery and the SDGs and to raise awareness regarding it in Japan.

METHODS

1) Literature review: PubMed was used to search for literature using the 2 keywords "spinal surgery" and "SDGs." Additional literature search was conducted using the 2 keywords "spinal surgery" and "climate change" and "spinal surgery" and "environmental impact." 2) Data analysis: We analyzed the energy consumption, medical waste management, and carbon dioxide emissions related to surgery at Makita General Hospital. Surgery-related carbon dioxide emissions were evaluated in a study on gas anesthesia among general anesthetics and in a study on the travel patterns of patients who underwent lumbar spinal stenosis surgery. 3) Discussion: Based on the results of the literature review and data analysis, we discussed the potential of spinal surgery to contribute to achieving the SDGs.

RESULTS

Spinal surgery is an important medical field that contributes to improving people's health and quality of daily living. It can potentially contribute to at least 10 of the 17 SDGs. However, awareness of the SDGs is low in Japan and lags far behind that in developed countries.

CONCLUSIONS

Spinal surgery can potentially contribute significantly to achieving the SDGs. Japanese spinal surgeons can help in achieving a sustainable society by raising awareness of the SDGs and taking proactive action.

Prediction of Admission Costs Following Anterior Cervical Discectomy and Fusion Utilizing Machine Learning

STUDY DESIGN

Retrospective case series.

OBJECTIVE

Predict cost following anterior cervical discectomy and fusion (ACDF) within the 90-day global period using machine learning models.

BACKGROUND

The incidence of ACDF has been increasing with a disproportionate decrease in reimbursement. As bundled payment models become common, it is imperative to identify factors that impact the cost of care.

MATERIALS AND METHODS

The Nationwide Readmissions Database (NRD) was accessed in 2018 for all primary ACDFs by the International Classification of Diseases 10th Revision (ICD-10) procedure codes. Costs were calculated by utilizing the total hospital charge and each hospital's cost-to-charge ratio. Hospital characteristics, such as volume of procedures performed and wage index, were also queried. Readmissions within 90 days were identified, and cost of readmissions was added to the total admission cost to represent the 90-day healthcare cost. Machine learning algorithms were used to predict patients with 90-day admission costs >1 SD from the mean.

RESULTS

There were 42,485 procedures included in this investigation with an average age of 57.7±12.3 years with 50.6% males. The average cost of the operative admission was $24,874±25,610, the average cost of readmission was $25,371±11,476, and the average total cost was $26,977±28,947 including readmissions costs. There were 10,624 patients who were categorized as high cost. Wage index, hospital volume, age, and diagnosis-related group severity were most correlated with the total cost of care. Gradient boosting trees algorithm was most predictive of the total cost of care (area under the curve=0.86).

CONCLUSIONS

Bundled payment models utilize wage index and diagnosis-related groups to determine reimbursement of ACDF. However, machine learning algorithms identified additional variables, such as hospital volume, readmission, and patient age, that are also important for determining the cost of care. Machine learning can improve cost-effectiveness and reduce the financial burden placed upon physicians and hospitals by implementing patient-specific reimbursement.