Optimizing Interhospital Transfers in Orthopedics and Trauma Surgery: Challenges, Insights, and Proposals for Standardized Care in Germany.

BACKGROUND: Germany's high density of under-equipped hospitals and anticipated surge in orthopedic and trauma surgery-related diseases by 2030, combined with personnel shortages, are expected to increase patient transfers between hospitals, an issue that urgently needs standardized protocols. Despite some existing cooperative agreements, such as between joint-replacement centers or within the Trauma Network DGU®, these measures do not adequately address the full range of patient-transfer cases, including those due to a lack of specialization or staff shortages, resulting in delayed treatment and potential health risks. This study aims to dissect the intricacies of interhospital transfers in orthopedics and trauma surgery across Germany, focusing on understanding the underlying reasons for transfers, comparing the operational structures of small and large hospitals, and laying the groundwork for future standardized protocols to enhance patient care. MATERIAL AND METHODS: A cross-sectional study was conducted in the form of an online survey via SoSci Survey, which was directed at orthopedic surgeons and trauma surgeons working in hospitals in Germany. The 22-question survey gathered information on participants' clinic roles, departmental details, transfer processes, frequent diagnoses, perceptions of transfer quality, and improvement areas. The survey was sent to orthopedic and trauma surgeons in Germany by the specialist society. The data were analyzed using descriptive and inferential statistics to ensure a comprehensive insight into interhospital transfer practices. RESULTS: The study involved 152 participants from various hospital ranks and located in different hospital sizes and types across rural and urban areas. A significant difference was observed between the care structures of basic/regular care and central/maximum care hospitals, especially regarding the available facilities and specialties. These findings suggest improvements such as better patient documentation, increased digital communication, optimized patient distribution, and standardization of transfer requests, among others. CONCLUSIONS: This study highlights the urgent need for improved protocols and resource allocation to eliminate inequalities in transfers between hospitals in orthopedics and trauma surgery in Germany.

It is (not) always on Friday: inter-hospital patient transfers in orthopedic and trauma surgery.

BACKGROUND: While inter-hospital transfers for patients who have suffered major trauma have been well investigated, patient flows for other injured patients, or cases with orthopedic complications, are rarely described. This study aims to analyze the affected collective and to show possible reasons, patterns, and pitfalls to optimize the process in future. MATERIALS AND METHODS: In a prospective cohort study, all consecutive transfers to a Level I trauma center in Germany were documented and assessed. Patients suffering a major trauma were excluded. Data on the primary treating hospital, patient characteristics, and differences between emergency and elective surgery were analyzed. RESULTS: A total of 227 patients were included; 162 were injured, while 65 had suffered a complication after elective orthopedic surgery or had a complex orthopedic pathology. The most common diagnoses leading to transfer were pathologies of the extremities (n = 62), pathologies of the spine (n = 50), and infections (n = 18). The main reasons stated by the transferring hospitals were a lack of expertise (137 cases) and a lack of capacity (43 cases). There was a significantly higher rate of transfers due to trauma (n = 162) than for orthopedic patients (n = 65), p < 0.0001. CONCLUSION: There is currently no structured procedure or algorithm for transferring patients in orthopedics and trauma surgery.

Forecasting patient flows with pandemic induced concept drift using explainable machine learning.

Accurately forecasting patient arrivals at Urgent Care Clinics (UCCs) and Emergency Departments (EDs) is important for effective resourcing and patient care. However, correctly estimating patient flows is not straightforward since it depends on many drivers. The predictability of patient arrivals has recently been further complicated by the COVID-19 pandemic conditions and the resulting lockdowns. This study investigates how a suite of novel quasi-real-time variables like Google search terms, pedestrian traffic, the prevailing incidence levels of influenza, as well as the COVID-19 Alert Level indicators can both generally improve the forecasting models of patient flows and effectively adapt the models to the unfolding disruptions of pandemic conditions. This research also uniquely contributes to the body of work in this domain by employing tools from the eXplainable AI field to investigate more deeply the internal mechanics of the models than has previously been done. The Voting ensemble-based method combining machine learning and statistical techniques was the most reliable in our experiments. Our study showed that the prevailing COVID-19 Alert Level feature together with Google search terms and pedestrian traffic were effective at producing generalisable forecasts. The implications of this study are that proxy variables can effectively augment standard autoregressive features to ensure accurate forecasting of patient flows. The experiments showed that the proposed features are potentially effective model inputs for preserving forecast accuracies in the event of future pandemic outbreaks.

Evaluating Different Strategies on the Blood Collection Counter Settings to Improve Patient Waiting Time in Outpatient Units.

Long patient waiting time is one of the major problems in the healthcare system and it would decrease patient satisfaction. Previous studies usually investigated how to improve the treatment flow in order to reduce patient waiting time or length of stay. The studies on blood collection counters have received less attention. Therefore, the objective of this study is to reduce the patient waiting time at outpatient clinics for metabolism and nephrology outpatients. A discrete-event simulation is used to analyze the four different strategies for blood collection counter resource allocation. Through analyzing four different strategic settings, the experimental results revealed that the maximum number of patients waiting before the outpatient clinics was reduced from 41 to 33 (20%); the maximum patient waiti-ng time at the outpatient clinics was decreased from 201.6 minutes to 83 minutes (59%). In this study, we found that adjusting the settings of blood collection counters would be beneficial. Assigning one exclusive blood collection counter from 8 to 10 am is the most suitable option with the least impact on the operational process for hospital staff. The results provide managerial insight regarding the cost-effective strategy selection for the hospital operational strategy.

Patient flow, health delivery processes, and areas for improvement in the UP Health Service (UPHS) during May to June 2020 of the COVID-19 Pandemic

@#Background. Workplace or employees’ clinics play a vital role in disease outbreaks as there could be an influx of sick personnel. Processes and patient flows during pandemics should be documented to identify good practices and sources of operational inefficiencies. Objective. To describe the patient flow, health delivery processes, and areas for improvement at the UPHS during the early phase of the COVID-19 pandemic from May to June 2020. Methods. This was a cross-sectional study involving patient flow analysis of processes at the employees’ clinic of the University of the Philippines-Philippine General Hospital. The study was divided into two major components: clinic process time measurement and process flow mapping. Data collection involved time elements and narrative descriptions of good practices and problems in the process flow. Results. The UPHS staff attended to 1,514 employees’ visits during the 15 working days from May to June 2020. The total UPHS service time from arrival to end of consultation of an employee with a COVID-19-related concern was an average of 1 hour 3 minutes (SD±39 minutes) with a mean total waiting time of 46 minutes (SD±37 minutes). Good practices identified were personnel flexibility in doing other tasks, good communication, and infection control measures. Areas for improvement included symptom screening, implementation of physical distancing, and disinfection practices. Conclusion. The process flows in the UPHS clinic consisted of COVID-19 related consultations, non-COVID-19 related concerns, and swabbing services. Good communication, staff flexibility, infection control measures, and leadership were identified as good practices. Occasional lapses in symptom screening at triage, physical distancing among employees in queuing lines, and inconsistent disinfection practices were the areas for improvement.

Forecasting emergency department overcrowding: A deep learning framework.

As the demand for medical cares has considerably expanded, the issue of managing patient flow in hospitals and especially in emergency departments (EDs) is certainly a key issue to be carefully mitigated. This can lead to overcrowding and the degradation of the quality of the provided medical services. Thus, the accurate modeling and forecasting of ED visits are critical for efficiently managing the overcrowding problems and enable appropriate optimization of the available resources. This paper proposed an effective method to forecast daily and hourly visits at an ED using Variational AutoEncoder (VAE) algorithm. Indeed, the VAE model as a deep learning-based model has gained special attention in features extraction and modeling due to its distribution-free assumptions and superior nonlinear approximation. Two types of forecasting were conducted: one- and multi-step-ahead forecasting. To the best of our knowledge, this is the first time that the VAE is investigated to improve forecasting of patient arrivals time-series data. Data sets from the pediatric emergency department at Lille regional hospital center, France, are employed to evaluate the forecasting performance of the introduced method. The VAE model was evaluated and compared with seven methods namely Recurrent Neural Network (RNN), Long short-term memory (LSTM), Bidirectional LSTM (BiLSTM), Convolutional LSTM Network (ConvLSTM), restricted Boltzmann machine (RBM), Gated recurrent units (GRUs), and convolutional neural network (CNN). The results clearly show the promising performance of these deep learning models in forecasting ED visits and emphasize the better performance of the VAE in comparison to the other models.

PaFloChar: An Innovating Approach to Characterise Patient Flows in Myocardial Infarction.

A better knowledge of patient flows would improve decision making in health planning. In this article, we propose a method to characterise patients flows and also to highlight profiles of care pathways considering times and costs. From medico-administrative data, we extracted spatio-temporal patterns. Then, we clustered time between hospitalisations and cost trajectories in order to identify profiles of change over time. This approach may support renewed management strategies.

Great expectations - introduction of flow managers in a university hospital.

A major reorganization of a university hospital included the clinical structure as well as the administrative structure of the hospital. The focus of the reorganization was to improve the coordination of patient flows through the hospital. An important part of the organizational change was the introduction of flow managers. The aim of the article is to describe and analyze the challenges of the flow managers in the implementation of the reorganization. The description is based on a number of individual and focus group interviews with professionals and managers on different organizational levels. The analysis is guided by a broad conceptual framework, focusing on the processes of change in a professional organization like a hospital. The results show that the flow managers started with a lot of uncertainty regarding their responsibilities. There was also a lot of resistance to the reorganization, which the flow managers came to personify. They proceeded by building relationships and shaping their role and tasks. They tried to balance proactive and reactive strategies of change. There were some positive results, but they felt that that the expectations placed on them had been unrealistic. The introduction of flow managers in a university hospital has touched upon many elements of the conceptual framework. There were a number of structural, cultural, financial and strategic barriers influencing the change process. The main conclusion of the analysis is that the flow managers need more power and legitimacy in the organization to deal with these barriers. Copyright © 2015 John Wiley & Sons, Ltd.

Organizing integrated care in a university hospital: application of a conceptual framework.

BACKGROUND AND AIM: As a result of New Public Management, a number of industrial models of quality management have been implemented in health care, mainly in hospitals. At the same time, the concept of integrated care has been developed within other parts of the health sector. The aim of the article is to discuss the relevance of integrated care for hospitals. THEORY AND METHODS: The discussion is based on application of a conceptual framework outlining a number of organizational models of integrated care. These models are illustrated in a case study of a Danish university hospital implementing a new organization for improving the patient flows of the hospital. The study of the reorganization is based mainly on qualitative data from individual and focus group interviews. RESULTS: The new organization of the university hospital can be regarded as a matrix structure combining a vertical integration of clinical departments with a horizontal integration of patient flows. This structure has elements of both interprofessional and interorganizational integration. A strong focus on teamwork, meetings and information exchange is combined with elements of case management and co-location. CONCLUSIONS: It seems that integrated care can be a relevant concept for a hospital. Although the organizational models may challenge established professional boundaries and financial control systems, this concept can be a more promising way to improve the quality of care than the industrial models that have been imported into health care. This application of the concept may also contribute to widen the field of integrated care.

A framework to analyze hospital-wide patient flow logistics: evidence from an Italian comparative study.

Through a comparative study of six Italian hospitals, the paper develops and tests a framework to analyze hospital-wide patient flow performance. The framework adopts a system-wide approach to patient flow management and is structured around three different levels: (1) the hospital, (2) the pipelines (possible patient journeys within the hospital) and (3) the production units (physical spaces, such as operating rooms, where service delivery takes places). The focus groups and the data analysis conducted within the study support that the model is a useful tool to investigate hospital-wide implications of patient flows. The paper provides also evidence about the causes of hospital patient flow problems. Particularly, while shortage of capacity does not seem to be a relevant driver, our data shows that patient flow variability caused by inadequate allocation of capacity does represent a key problem. Results also show that the lack of coordination between different pipelines and production units is critical. Finally, the problem of overlapping between elective and unscheduled cases can be solved by setting aside a certain level of capacity for unexpected peaks.