Background: In heart data mining and machine learning, dimension reduction is needed to remove multicollinearity. Meanwhile, it has been proven to improve the interpretation of the parameter model. In addition, dimension reduction can also increase the time of computing in high dimensional data. Methods: In this paper, we perform high dimensional ordination towards event counts in intensive care hospital for Emergency Department (ED 1), First Intensive Care Unit (ICU1), Second Intensive Care Unit (ICU2), Respiratory Care Intensive Care Unit (RICU), Surgical Intensive Care Unit (SICU), Subacute Respiratory Care Unit (RCC), Trauma and Neurosurgery Intensive Care Unit (TNCU), Neonatal Intensive Care Unit (NICU) which use the Generalized Linear Latent Variable Models (GLLVM’s). Results: During the analysis, we measure the performance and calculate the time computing of GLLVM by employing variational approximation and Laplace approximation, and compare the different distributions, including Negative Binomial, Poisson, Gaussian, ZIP, and Tweedie, respectively. GLLVMs (Generalized Linear Latent Variable Models), an extended version of GLMs (Generalized Linear Models) with latent variables, have fast computing time. The major challenge in latent variable modelling is that the function $$f\left(\varTheta \right)=\int f\left(u\varTheta \right)h\left(u\right)du$$f Θ = ∫ f u Θ h u d u is not trivial to solve since the marginal likelihood involves integration over the latent variable u. Conclusions: In a nutshell, GLLVMs lead as the best performance reaching the variance of 98% comparing other methods. We get the best model negative binomial and Variational approximation, which provides the best accuracy by accuracy value of AIC, AICc, and BIC. In a nutshell, our best model is GLLVM-VA Negative Binomial with AIC 7144.07 and GLLVM-LA Negative Binomial with AIC 6955.922.

During the COVID-19 pandemic, outsourcing non-core services like cleaning, security, and logistics allowed healthcare facilities to focus on patient care. In Taiwan, the Ministry of Health and Welfare’s guidelines ensured outsourced staff were properly trained to minimize infection risks, supporting epidemic control. Our hospital’s “Epidemic Response Command System” highlighted the critical role of outsourced personnel in maintaining operational continuity, enabling medical staff to focus on patient care. Outsourcing was essential for supporting logistics and operations, ensuring the smooth functioning of the hospital while healthcare professionals focused on direct patient care. Simultaneously, stringent auditing mechanisms were established to enable outsourced colleagues to perform tasks within a safety-controlled framework. Emphasis was placed on prioritized and enhanced management for personnel engaged in cleaning, transportation, and access security, including personnel segregation and diversion management. In-depth on-site training by infection control nurses covered cleaning, disinfection operations, health management, and counseling mechanisms. It was executed meticulously, given the paramount importance of operations auditing in infection control and epidemic prevention effectiveness. The COVID-19 pandemic highlighted the need for stricter infection control. Reducing outsourced personnel ensures standardized hygiene protocols, better staff protection, and reduced legal risks, improving overall healthcare response. In future planning for specialized units, medical institutions should consider minimizing outsourced personnel or implementing measures, such as enhancing salary and benefits, for dedicated on-site staff.