Purpose Lumbar spinal stenosis (LSS) is a condition affecting several hundreds of thousands of adults in the United States each year and is associated with significant economic burden. The current decision-making practice to determine surgical candidacy for LSS is often subjective and clinician specific. In this study, we hypothesize that the performance of artificial intelligence (AI) methods could prove comparable in terms of prediction accuracy to that of a panel of spine experts. Methods We propose a novel hybrid AI model which computes the probability of spinal surgical recommendations for LSS, based on patient demographic factors, clinical symptom manifestations, and MRI findings. The hybrid model combines a random forest model trained from medical vignette data reviewed by surgeons, with an expert Bayesian network model built from peer-reviewed literature and the expert opinions of a multidisciplinary team in spinal surgery, rehabilitation medicine, interventional and diagnostic radiology. Sets of 400 and 100 medical vignettes reviewed by surgeons were used for training and testing. Results The model demonstrated high predictive accuracy, with a root mean square error (RMSE) between model predictions and ground truth of 0.0964, while the average RMSE between individual doctor's recommendations and ground truth was 0.1940. For dichotomous classification, the AUROC and Cohen's kappa were 0.9266 and 0.6298, while the corresponding average metrics based on individual doctor's recommendations were 0.8412 and 0.5659, respectively. Conclusions Our results suggest that AI can be used to automate the evaluation of surgical candidacy for LSS with performance comparable to a multidisciplinary panel of physicians.

The relevance of the study is that healthcare reform constitutes an important component of decentralisation. The old model of functioning of medical institutions is inefficient; therefore, there is a need for reorganisation, which is carried out by making appropriate decisions. The medical sector is one of the most problematic in Ukraine, especially in terms of organisation of medical care in local communities. Therefore, reform in a decentralised environment is a complex process to address and implement. Reforming the medical sector involves the creation of a new system of territorial organisation, the development of local self-government and the improvement of regional national policy. The development of the medical industry is one of the main factors in achieving the well-being of the population and national security. Most medical facilities are low-capacity hospitals with outdated technical equipment and worn-out fixed assets. New approaches to public health need to be introduced. The purpose of the study is to identify the main problems that negatively affect the development of the medical sector in Ukraine in the context of decentralisation reform. Decentralisation embodies the ability of communities to manage resources and have the authority to implement them. The mechanism of decentralisation reform involves the redistribution of financial resources and powers from central government to regional or local governments. The reform of the healthcare sector stipulates specific steps to improve the quality of life and services rendered, it has a conceptual content and a clear framework. In the process of decentralisation, a wide scope of reforms of regional policy and local self-government are being implemented. Its achievements are reducing corruption, improving governance and activating communities. However, this process is accompanied by numerous problems, challenges, and risks in the medical system, which need to be refined and taken into account for further successful implementation. The practical significance lies in identifying problematic factors in the development of the medical system and establishing relationships that hinder healthcare reform

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