Our increasingly connected world continues to face an ever-growing number of network-based attacks. An Intrusion Detection System (IDS) is an essential security technology used for detecting these attacks. Although numerous Machine Learning-based IDSs have been proposed for the detection of malicious network traffic, the majority have difficulty properly detecting and classifying the more uncommon attack types. In this paper, we implement a novel hybrid technique using synthetic data produced by a Generative Adversarial Network (GAN) to use as input for training a Deep Reinforcement Learning (DRL) model. Our GAN model is trained on the NSL-KDD dataset, a publicly available collection of labeled network traffic data specifically designed to support the evaluation and benchmarking of IDSs. Ultimately, our findings demonstrate that training the DRL model on synthetic datasets generated by specific GAN models can result in better performance in correctly classifying minority classes over training on the true imbalanced dataset.

Background: Occupancy rates within skilled nursing facilities (SNFs) in the United States have reached a record low. Understanding drivers of occupancy, including admission decisions, is critical for assessing the recovery of the long-term care sector as a whole. We provide the first comprehensive analysis of financial, clinical, and operational factors that impact whether a patient referral to an SNF is accepted or denied, using a large health informatics database. Objective: Our key objectives were to describe the distribution of referrals sent to SNFs in terms of key referral- and facility-level features; analyze key financial, clinical, and operational variables and their relationship to admission decisions; and identify the key potential reasons behind referral decisions in the context of learning health systems. Methods: We extracted and cleaned referral data from 627 SNFs from January 2020 to March 2022, including information on SNF daily operations (occupancy and nursing hours), referral-level factors (insurance type and primary diagnosis), and facility-level factors (overall 5-star rating and urban versus rural status). We computed descriptive statistics and applied regression modeling to identify and describe the relationships between these factors and referral decisions, considering them individually and controlling for other factors to understand their impact on the decision-making process. Results: When analyzing daily operation values, no significant relationship between SNF occupancy or nursing hours and referral acceptance was observed (P>.05). By analyzing referral-level factors, we found that the primary diagnosis category and insurance type of the patient were significantly related to referral acceptance (P<.05). Referrals with primary diagnoses within the category “Diseases of the Musculoskeletal System” are least often denied whereas those with diagnoses within the “Mental Illness” category are most often denied (compared with other diagnosis categories). Furthermore, private insurance holders are least often denied whereas “medicaid” holders are most often denied (compared with other insurance types). When analyzing facility-level factors, we found that the overall 5-star rating and urban versus rural status of an SNF are significantly related to referral acceptance (P<.05). We found a positive but nonmonotonic relationship between the 5-star rating and referral acceptance rates, with the highest acceptance rates found among 5-star facilities. In addition, we found that SNFs in urban areas have lower acceptance rates than their rural counterparts. Conclusions: While many factors may influence a referral acceptance, care challenges associated with individual diagnoses and financial challenges associated with different remuneration types were found to be the strongest drivers. Understanding these drivers is essential in being more intentional in the process of accepting or denying referrals. We have interpreted our results using an adaptive leadership framework and suggested how SNFs can be more purposeful with their decisions while striving to achieve appropriate occupancy levels in ways that meet their goals and patients’ needs.

BACKGROUND Occupancy rates within skilled nursing facilities (SNFs) in the United States have reached a record low. Understanding drivers of occupancy, including admission decisions, is critical for assessing the recovery of the long-term care sector as a whole. We provide the first comprehensive analysis of financial, clinical, and operational factors that impact whether a patient referral to an SNF is accepted or denied, using a large health informatics database. OBJECTIVE Our key objectives were to describe the distribution of referrals sent to SNFs in terms of key referral- and facility-level features; analyze key financial, clinical, and operational variables and their relationship to admission decisions; and identify the key potential reasons behind referral decisions in the context of learning health systems. METHODS We extracted and cleaned referral data from 627 SNFs from January 2020 to March 2022, including information on SNF daily operations (occupancy and nursing hours), referral-level factors (insurance type and primary diagnosis), and facility-level factors (overall 5-star rating and urban versus rural status). We computed descriptive statistics and applied regression modeling to identify and describe the relationships between these factors and referral decisions, considering them individually and controlling for other factors to understand their impact on the decision-making process. RESULTS When analyzing daily operation values, no significant relationship between SNF occupancy or nursing hours and referral acceptance was observed (P>.05). By analyzing referral-level factors, we found that the primary diagnosis category and insurance type of the patient were significantly related to referral acceptance (P<.05). Referrals with primary diagnoses within the category “Diseases of the Musculoskeletal System” are least often denied whereas those with diagnoses within the “Mental Illness” category are most often denied (compared with other diagnosis categories). Furthermore, private insurance holders are least often denied whereas “medicaid” holders are most often denied (compared with other insurance types). When analyzing facility-level factors, we found that the overall 5-star rating and urban versus rural status of an SNF are significantly related to referral acceptance (P<.05). We found a positive but nonmonotonic relationship between the 5-star rating and referral acceptance rates, with the highest acceptance rates found among 5-star facilities. In addition, we found that SNFs in urban areas have lower acceptance rates than their rural counterparts. CONCLUSIONS While many factors may influence a referral acceptance, care challenges associated with individual diagnoses and financial challenges associated with different remuneration types were found to be the strongest drivers. Understanding these drivers is essential in being more intentional in the process of accepting or denying referrals. We have interpreted our results using an adaptive leadership framework and suggested how SNFs can be more purposeful with their decisions while striving to achieve appropriate occupancy levels in ways that meet their goals and patients’ needs.

We introduce SNFsim, an open-source discrete-event simulator for developing and evaluating reinforcement learning (RL) methods for multi-dimensional sequential decision support in Skilled Nursing Facilities (SNFs). SNFs play a vital role in the United States healthcare system, delivering specialized care to individuals with ongoing medical needs. Decision-making within SNFs is often complex due to their fast-paced and stochastic nature. SNFsim provides a modular and extendable simulation of major decision-making processes within SNFs, capturing many of the complexities and uncertainties existing in healthcare environments while still being flexible enough to allow for easy customization. Its potential uses are two-fold: First, as a test bed for the development and comparison of RL algorithms, and second, as the basis of a decision-support system that can be tailored to individual SNFs.