Health Systems最新文献

This study identifies user-preferred features in apps for self-management of chronic headaches from the biopsychosocial perspective and examines the extent to which such features are represented in the current apps. We first conducted semi-structured interviews to identify user-preferred features that tap into the biopsychosocial domains of health. Then, we conducted a landscape analysis to review existing apps with respect to the identified features. Our findings revealed participants' preferences for features in apps to self-manage chronic headaches and were categorised based on the biopsychosocial model. Further, our landscape analysis showed that several features (e.g. physical symptom tracker) are highly present in existing apps, while most of the preferred features (e.g. journaling) are scarce. The identified features appear to align with theory-based behaviour change techniques and, therefore, have implications for health behaviour change. In addition, our findings demonstrate that most of the user-preferred biopsychosocial features are not widely present in the existing headache apps. Overall, our study highlights the importance of incorporating user-preferred features that align with the biopsychosocial needs of headache app users. By acknowledging and addressing these needs, we can broaden the existing perspectives concerning app design and evaluation and cater to the holistic health experiences of users.

This paper presents a two-stage approach for efficiently solving a weekly home healthcare scheduling and routing problem. Two new mixed-integer linear programming (MILP) models are proposed, where the first is used for making patient-therapist assignments over the week, and the second for deriving daily routes. In both MILPs, the objective function contains a hierarchically weighted set of goals. The major components of the full problem are continuity of care, downgrading, workload balance, time windows, overtime, and mileage costs. A new preprocessing procedure is developed to limit the service area of each therapist to a single group of overlapping patients. Once the groups are formed, weekly schedules are constructed with the MILPs. The overall objective is to minimize the number of unscheduled visits and total travel and service costs subject to the operational constraints mentioned above. Computational experiments are conducted with real data sets provided by a national home health agency. The results show that optimal solutions can be obtained quickly at both the assignment and routing stages and that they are comparable to the results obtained with a proposed integrated model. In either case, the corresponding schedules were better on all metrics when compared to the schedules used in practice.

Effective radiotherapy for cancer treatment requires precise and reproducible positioning of patients at linear accelerators. Assistance systems in digitally networked radiotherapy can help involved specialists perform these tasks more efficiently and accurately. This paper analyses patient positioning systems and develops new knowledge by applying the Design Science Research methodology. A systematic literature review ensures the rigour of the research. Furthermore, this article presents the results of an online survey on assistance systems for patient positioning, the derived design requirements and an artefact in the form of a conceptual model of a patient positioning system. Both the systematic literature review and the online survey serve as empirical evidence for the conceptual model. This paper thereby contributes to broadening the academic knowledge on patient positioning in radiotherapy and provides guidance to system designers.

Elderly falls are occurring at an alarming rate, with significant health risks for seniors. Current fall detection systems often lack accuracy, efficacy, and privacy considerations. This study examines three leading human pose estimation frameworks combined with transformer deep learning models to develop a lightweight, privacy-preserving fall detection system. Key features include: 1) It runs on low-power devices like Raspberry Pis; 2) It monitors seniors passively, without requiring active participation; 3) It can be deployed in any residential or senior care setting; 4) It does not rely on wearables; and 5) All processing occurs locally, ensuring privacy with only fall alerts transmitted to caregivers. In real-world tests, the model achieved 95.24% sensitivity, 89.80% specificity, 98.00% accuracy, a 90.91% F1 score, and 95.24% precision, highlighting its effectiveness in detecting falls among the elderly while maintaining privacy and security.

Pay-for-performance (P4P) reimbursement models were launched in 2013 to incentivise the value of healthcare delivered by including quality outcomes, such as mortality, readmission, and patient satisfaction, in hospital reimbursement in the U.S. Although a decade has passed, the efficacy of these P4P programs remains unclear. This research intends to evaluate their long-term performance implications along two critical dimensions - productivity and healthcare value. Drawing on a nationwide sample of U.S. hospitals collected from 2008 to 2019, we utilise data envelopment analysis to measure hospital performance and the Malmquist index to evaluate their longitudinal trends. Although average hospital productivity and value improved since the rollout of the P4P programs, we observe that a large proportion of laggard hospitals were unable to catch up with improvements to the performance frontier, raising concerns about disparities in the impact of future value-based programs. Our analyses also indicate that horizontal integration across hospitals is associated with greater productivity and value. While greater physician-hospital (vertical) integration is associated with higher hospital productivity, it does not have a positive impact on value. Our study provides new insights into the antecedents and performance consequences of implementing value-based healthcare initiatives and their implications for hospital managers and policymakers.

Outpatient chemotherapy scheduling has significant implications for both patients and health systems. Consideration of treatment location preference is important for patient satisfaction and outcomes, and it is a complex decision impacted by travel distance. In health systems with one treatment site that stands out from the rest as a destination medical center (the primary site), there are financial and resource utilization incentives to free up as much space as possible for appointments at that site. In this study, we demonstrate that leveraging the underutilized health system sites allows decompression of appointment volume at the primary site, and it takes full advantage of valuable resources such as oncology nurses and chair availability. A Mixed Integer Linear Programming approach was used to develop a model under four scenarios which reallocates appointments from the primary site to other health system sites based on patient travel distance to the sites. This approach was applied to data from the Mayo Clinic Health System Minnesota region, which demonstrated that the health system has the potential to move approximately 50% of eligible appointments out of the primary site, resulting in an overall volume change of approximately 30%. Implications for scheduling policies and infrastructure are discussed.

In the realm of public health management, ensuring a balanced assignment of patients to healthcare centres is a critical concern. This study introduces a novel approach for this purpose, utilizing dispatching rules. Highlighting the need for an easily applicable approach to regulating patient flow efficiently, the study shows the benefit of utilizing dispatching rules in healthcare management. Innovatively, this research departs from traditional approaches by introducing a multi-objective model grounded in the concept of sectorization. This model, unique in the public health literature, leverages dispatching rules to simplify complex, dynamic patient assignment scenarios. Incorporating various factors, the model is simulated, and the optimization of the dispatching rules is carried out. The study's findings demonstrate that the optimized dispatching rule significantly enhances the model's efficacy in balancing patient assignments across healthcare centres. This improvement is pivotal in addressing the uneven distribution of healthcare resources. This research makes a substantial contribution to the public health literature by offering a novel and practical solution for balancing patient load among healthcare centres. Its successful application in simulated environments suggests a promising pathway for real-world implementations, potentially leading to more efficient healthcare systems and improved patient care outcomes.

Neonatal interfacility transport ensures that critically ill neonatal patients can receive higher levels of care when needed. Delays in the transport process impact the quality of care and increase the risk of medical complications. The objective of this study is to investigate the operations-related factors that contribute to transport delays and explore the role of discrete-event simulation in improving the transport process. Semi-structured interviews were conducted with stakeholders to understand the neonatal interfacility transport process. Analysis of historical call logs and transport data was performed to identify inputs to the discrete-event simulation model. Statistical tests were used to identify the effect of various factors on wait time and transport time in the simulation model. High patient volume and limited bed capacity at the receiving hospitals are identified as bottlenecks that lead to extended wait time and transportation time. Additionally, having more geographically distributed ambulance resources does not significantly help with the time delays when the receiving hospital capacity stays unchanged. Discrete-event simulation models can be used to investigate the effects of operations-related factors in the interfacility transport of critically ill neonates to support future process improvement.

Hospitals play a critical role in ensuring continuous and effective healthcare delivery, especially during crises. However, the COVID-19 pandemic exposed vulnerabilities in hospital systems, prompting a need to enhance resilience-the ability to withstand, absorb, respond to, recover from, and learn from disasters. A systematic literature review, grounded in the resource-based view, identified organizational characteristics, in terms of resources and capabilities, and their synergistic effects that bolster hospital resilience. The results demonstrate that digital technologies impact on anticipation and adaptation abilities, organizational capabilities to reorganize roles, tasks, and spaces enhance adaptability, and Inter-organizational collaborations increase the responsiveness of the hospitals. The study provides substantial theoretical and practical contributions. It expands knowledge of hospital resilience in light of recent disruptive events and promotes integration capabilities as determinants for the majority of resilience dimensions. All organisational and inter-organisational collaboration, cooperation, and coordination are deemed crucial for hospital resilience.