Evaluating the Patient Boarding during Omicron Surge in Hong Kong: Time Series Analysis.

The fifth wave of COVID-19 outbreaks in Hong Kong (HK) from January to March 2022 has the highest confirmed cases and deaths compared with previous waves. Severe hospital boarding (to inpatient wards) was noted in various Emergency Departments (EDs). Our objective is to identify factors associated with hospital boarding during Omicron surge in HK. We conducted a retrospective cohort study including all ED visits and inpatient (IP) ward admissions from January 1st to March 31st, 2022. Vector Autoregression model evaluated the effects of a single variable on the targeted hospital boarding variables. Admissions from elderly homes with 6 lag days held the highest positive value of statistical significance (t-stat = 2.827, P < .05) caused prolonged admission waiting time, while medical patients with 4 lag days had the highest statistical significance (t-stat = 2.530, P < .05) caused an increased number of boarding patients. Within one week after impulses, medical occupancy's influence on the waiting time varied from 0.289 on the 1st day to -0.315 on the 7th day. While occupancy of medical wards always positively affected blocked number of patients, and its response was maximized at 0.309 on the 2nd day. Number of confirmed COVID-19 cases was not the sole significant contributor, while occupancy of medical wards was still a critical factor associated with patient boarding. Increasing ward capacity and controlling occupancy were suggested during the outbreak. Moreover, streamlining elderly patients in ED could be an approach to relieve pressure on the healthcare system.

Clustering-based iterative heuristic framework for a non-emergency patients transportation problem.

Introduction: Non-emergency patient transportation (NEPT) services are particularly important nowadays due to the aging population and contagious disease outbreaks (e.g., Covid-19 and SARS). In this work, we study a NEPT problem with a case study of patient transportation services in Hong Kong. The purpose of this work is to study the discomfort and inconvenience measures (e.g., waiting time and extra ride time) associated with the transportation of non-emergency patients while optimizing the operational costs and utilization of NEPT ambulances. Methods: A mixed-integer linear programming (MILP) formulation is developed to model the NEPT problem. This MILP model contributes to the existing literature by not only including the patient inconvenience measures in the objective function but also illustrating a better trade-off among different performance measures through its specially customized formulation and real-life characteristics. CPLEX is used to find the optimal solutions for the test instances. To overcome the computational complexity of the problem, a clustering-based iterative heuristic framework is designed to solve problems of practical sizes. The proposed framework distinctively exploits the problem-specific structure of the considered NEPT problem in a novel way to enhance and improve the clustering mechanism by repeatedly updating cluster centers. Results: The computational experiments on 19 realistic problem instances show the effective execution of the solution method and demonstrate the applicability of our approach. Our heuristic framework observes an optimality gap of less than 5% for all those instances where CPLEX delivered the result. The weighted objective function of the proposed model supports the analysis of different performance measures by setting different preferences for these measures. An extensive sensitivity analysis performed to observe the behavior of the MILP model shows that when operating costs are given a weightage of 0.05 in the objective function, the penalty value for user inconvenience measures is the lowest; when the weightage value for operating costs varies between 0.8 and 1.0, the penalty value for the same measures is the highest. Conclusions: This research can assist decision-makers in improving service quality by balancing operational costs and patient discomfort during transportation.

Outbreak minimization v.s. influence maximization: an optimization framework.

BACKGROUND: An effective approach to containing epidemic outbreaks (e.g., COVID-19) is targeted immunization, which involves identifying "super spreaders" who play a key role in spreading disease over human contact networks. The ultimate goal of targeted immunization and other disease control strategies is to minimize the impact of outbreaks. It shares similarity with the famous influence maximization problem studied in the field of social network analysis, whose objective is to identify a group of influential individuals to maximize the influence spread over social networks. This study aims to establish the equivalence of the two problems and develop an effective methodology for targeted immunization through the use of influence maximization. METHODS: We present a concise formulation of the targeted immunization problem and show its equivalence to the influence maximization problem under the framework of the Linear Threshold diffusion model. Thus the influence maximization problem, as well as the targeted immunization problem, can be solved by an optimization approach. A Benders' decomposition algorithm is developed to solve the optimization problem for effective solutions. RESULTS: A comprehensive computational study is conducted to evaluate the performance and scalability of the optimization approach on real-world large-scale networks. Computational results show that our proposed approaches achieve more effective solutions compared to existing methods. CONCLUSIONS: We show the equivalence of the outbreak minimization and influence maximization problems and present a concise formulation for the influence maximization problem under the Linear Threshold diffusion model. A tradeoff between computational effectiveness and computational efficiency is illustrated. Our results suggest that the capability of determining the optimal group of individuals for immunization is particularly crucial for the containment of infectious disease outbreaks within a small network. Finally, our proposed methodology not only determines the optimal solutions for target immunization, but can also aid policymakers in determining the right level of immunization coverage.

An Integrated Approach of Machine Learning and Systems Thinking for Waiting Time Prediction in an Emergency Department.

OBJECTIVE: The objective of this study is to apply machine learning algorithms for real-time and personalized waiting time prediction in emergency departments. We also aim to introduce the concept of systems thinking to enhance the performance of the prediction models. METHODS: Four popular algorithms were applied: (i) stepwise multiple linear regression; (ii) artificial neural networks; (iii) support vector machines; and (iv) gradient boosting machines. A linear regression model served as a baseline model for comparison. We conducted computational experiments based on a dataset collected from an emergency department in Hong Kong. Model diagnostics were performed, and the results were cross-validated. RESULTS: All the four machine learning algorithms with the use of systems knowledge outperformed the baseline model. The stepwise multiple linear regression reduced the mean-square error by almost 15%. The other three algorithms had similar performances, reducing the mean-square error by approximately 20%. Reductions of 17 - 22% in mean-square error due to the utilization of systems knowledge were observed. DISCUSSION: The multi-dimensional stochasticity arising from the ED environment imposes a great challenge on waiting time prediction. The introduction of the concept of systems thinking led to significant enhancements of the models, suggesting that interdisciplinary efforts could potentially improve prediction performance. CONCLUSION: Machine learning algorithms with the utilization of the systems knowledge could significantly improve the performance of waiting time prediction. Waiting time prediction for less urgent patients is more challenging.

Tracking Nosocomial Diseases at Individual Level with a Real-Time Indoor Positioning System.

Our research is motivated by the rapidly-evolving outbreaks of rare and fatal infectious diseases, for example, the severe acute respiratory syndrome (SARS) and the Middle East respiratory syndrome. In many of these outbreaks, main transmission routes were healthcare facility-associated and through person-to-person contact. While a majority of existing work on modelling of the spread of infectious diseases focuses on transmission processes at a community level, we propose a new methodology to model the outbreaks of healthcare-associated infections (HAIs), which must be considered at an individual level. Our work also contributes to a novel aspect of integrating real-time positioning technologies into the tracking and modelling framework for effective HAI outbreak control and prompt responses. Our proposed solution methodology is developed based on three key components - time-varying contact network construction, individual-level transmission tracking and HAI parameter estimation - and aims to identify the hidden health state of each patient and worker within the healthcare facility. We conduct experiments with a four-month human tracking data set collected in a hospital, which bore a big nosocomial outbreak of the 2003 SARS in Hong Kong. The evaluation results demonstrate that our framework outperforms existing epidemic models for characterizing macro-level phenomena such as the number of infected people and epidemic threshold.

RFID analytics for hospital ward management.

In this paper, we present an RFID-enabled platform for hospital ward management. Active RFID tags are attached to individuals and assets in the wards. Active RFID readers communicate with the tags continuously and automatically to keep track of the real-time information about the locations of the tagged objects. The data regarding the locations and other transmitted information are stored in the ward management system. This platform enables capabilities of real-time monitoring and tracking of individuals and assets, reporting of ward statistics, and providing intelligence and analytics for hospital ward management. All of these capabilities benefit hospital ward management by enhanced patient safety, increased operational efficiency and throughput, and mitigation of risk of infectious disease widespread. A prototype developed based on our proposed architecture of the platform was tested in a pilot study, which was conducted in two medical wards of the intensive care unit of one of the largest public general hospitals in Hong Kong. This pilot study demonstrates the feasibility of the implementation of this RFID-enabled platform for practical use in hospital wards. Furthermore, the data collected from the pilot study are used to provide data analytics for hospital ward management.