Health Systems最新文献

Falls are one of the most common cause of nonfatal and fatal injuries in the U.S. costing over an estimated $54 billion annually. A significant percentage of patients presenting to hospital emergency departments (ED) for falls are hospitalised. This paper analyzes a regional hospital data pertaining to adults presenting to the ED because of falls. We use patient demographics and medical conditions to help identify patients at risk for immediate undesirable outcomes after a fall. Furthermore, we determine the relative risk of patient hospitalisation and surgery and their characteristics. Our results indicate that older patient's, patients arriving by ambulance, patients with higher severity levels and patients with pre-existing comorbidities were at a higher relative risk of hospitalisation and surgery. Furthermore, patients with medical conditions pertaining to femur and tibia fractures, pelvis, renal failure, ambulatory dysfunction, and cellulitis, among others, and non-Hispanic whites were at a much higher relative risk of hospitalisation and surgery.

Home testing is an emerging innovation that can enable nations and health care systems to safely and efficiently test large numbers of patients to manage COVID-19 and other viral outbreaks.  In this position paper, we explore the process of moving home testing across the translational continuum from labs to households, and ultimately into practice and communities for optimal public health impact. We focus on the four translational science drivers to accelerate the implementation of systems-wide home testing programmes 1) collaboration and team science, 2) technology, 3) multilevel interventions, and 4) knowledge integration. We use the Socio Ecological Model (SEM) as a framework to illustrate our vision for the ideal future state of a comprehensive system of stakeholders utilising tech-enabled home testing for COVID-19 and other virus outbreaks, and we suggest SEM as a tool to address key translational readiness and response questions.

It has been established that high no-show rates of publicly supported health systems in economically depressed areas are largely due to a lack of inexpensive, reliable transportation. The purpose of this paper is to determine the financial feasibility of offering transportation and investigate the net cost savings by reducing no-show rates. The approach starts with a data analysis on 636 patients at the Family Health Center (FHC) in San Antonio, Texas, followed by logistic regression to determine the impact of various transportation factors on cancellations/no-shows and late arrivals. We then investigate the costs savings that could be realised by reducing the no-show rate from 24.3% by up to 60%. Finally, we analyse the expenses that would be incurred should the FHC provide transportation. The full analysis indicates a cost reduction of more than $15,000 per month can be achieved when the no-show rate is reduced by 25% down to 18.2%.

The combination of electronic health records (EHRs), health information exchange (HIE), and telehealthholds a high potential for improving the coordination of care and saving lives. As well, the benefits of the three HIT on hospitals' depend on the patterns of capabilities that are available and used by clinicians. However, little is known about how the three HIT, actually empirically coexist and about the strategies underlying the use of HIE in hospital settings. Based on data from a European Union survey, we use a combination of hierarchical and non-hierarchical clustering and discriminant analysis to identify patterns of hospitals' HIT capabilities. Five statistically significantly separated configurations were derived from a data set of 1038 acute care hospitals. The actual empirical coexistence of the three HIT capabilities and associated HIE strategies revealed by this study can be counter-intuitive and shed light on misalignments that may impede the realisation of the potential benefits.

Mixed registration type clinics accept both walk-in and scheduled patients. Such clinics provide patients with an additional option for how they access care while patient bookings help providers ensure a full workday. The model described in this paper determines how many patients to schedule (and when) in mixed registration type clinics. The methodology, simulation optimisation allows stochastic features found in such clinic to be modelled and provides optimisation techniques to identify solutions. A general simulation optimisation formulation for mixed registration type clinics is presented. Furthermore, the methodology is applied to a case study of a collaborative emergency centre in Nova Scotia, Canada. We demonstrate how the model can be used in clinics with multiple providers and multiple objectives. We compare the simulation optimisation generated schedule with existing schedules and show the advantages the collaborative emergency centre can expect when using schedules developed with the presented methods.

The hospital outpatient non-attendance imposes a substantial financial burden on hospitals and roots in multiple diverse reasons. This research aims to build an advanced predictive model for predicting non-attendance regarding the whole spectrum of probable contributing factors to non-attendance that could be collated from heterogeneous sources including electronic patients records and external non-hospital data. We proposed a new non-attendance prediction model based on deep neural networks and machine learning models. The proposed approach works upon sparse stacked denoising autoencoders (SDAEs) to learn the underlying manifold of data and thereby compacting information and providing a better representation that can be utilised afterwards by other learning models as well. The proposed approach is evaluated over real hospital data and compared with several well-known and scalable machine learning models. The evaluation results reveal the proposed approach with softmax layer and logistic regression outperforms other methods in practice.

This paper addresses the daily appointment scheduling (AS) of patients in a hospital-integrated facility where outpatients and inpatients are treated simultaneously and share critical resources. We propose a lean approach based on the pull-strategy "Constant Work in Process" (ConWIP) to develop robust and easy-to-implement AS rules. Our objective is to reduce patients' waiting time and maximise the use rate of resources while considering the global surgical process and stochastic service times. The AS rules based on ConWIP are evaluated using a Discrete-Event-Simulation model. Numerical experiments based on a real-life case study are carried out to assess the proposed appointment rules' performance and compare them to AS rules developed in the literature. The results highlight the robustness of our approach and demonstrate its usefulness in practice.

Since 1997, special paediatric intensive care retrieval teams (PICRTs) based in 11 locations across England and Wales have been used to transport sick children from district general hospitals to one of 24 paediatric intensive care units. We develop a location allocation optimisation framework to help inform decisions on the optimal number of locations for each PICRT, where those locations should be, which local hospital each location serves and how many teams should station each location. Our framework allows for stochastic journey times, differential weights for each journey leg and incorporates queuing theory by considering the time spent waiting for a PICRT to become available. We examine the average waiting time and the average time to bedside under different number of operational PICRT stations, different number of teams per station and different levels of demand. We show that consolidating the teams into fewer stations for higher availability leads to better performance.