Health Systems最新文献

We examine the information acquisition process regarding a patient's status under emergency department (ED) congestion conditions. We focus on two key information channels: 1) Electronic Health Record (EHR) that provide the patient's medical history and 2) Medical tests conducted in real-time. Whereas the EHR provides the physician with easily accessible information with little delay, real-time medical tests can provide more current information, but are time-consuming. We examine physicians' decisions in cases of ED congestion, using a dataset that includes more than 1.4 million visits. When congestion is low, the information channels are complementary - acquiring information from the EHR is positively correlated with information acquisition from the medical tests channel, representing an incentive for the physician to acquire all possible information before providing diagnosis. However, as the congestion increases, there is less reliance on medical tests; this effect is amplified when EHR information is used. To avoid excessive congestion, physicians apparently refrain from sending patients for medical tests, and compensate for loss of information using EHR information. The impact of high system workload on the quality of medical service is an essential concern for managers; we show the indirect benefit of investment in EHRs through reduced blood-tests without increasing revisit rates.

Preeclampsia, a life-threatening condition in late pregnancy, has unclear causes and risk factors. Machine learning (ML) offers a promising approach for early prediction. This systematic review analyzes state-of-the-art studies on preeclampsia prediction using ML approaches. We reviewed articles published between January 1 2013 and December 31 2023, from Google Scholar and PubMed. Of 183 identified studies, 35 were selected based on inclusion and exclusion criteria. Our findings reveal that key predictive features commonly used in machine learning models include age, number of pregnancies, body mass index, diabetes, hypertension, and blood pressure. In contrast, factors such as medications, genetic data, and clinical imaging were considered less frequently. Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and Naïve Bayes were the most commonly used algorithms. Most studies were conducted in China and the USA, indicating geographic concentration. The field has seen a notable rise in research, especially in the past two years, though many studies rely on small datasets from single hospitals. This review highlights the need for more diverse and comprehensive research to enhance early detection and management of preeclampsia.

"Hurt people hurt people" is a phrase which summarises the cyclical nature of painful experiences and harmful actions. Arguably, this cycle of hurt and harm applies to the UK's National Health Service (NHS), where employees are reporting record low levels of physical and mental wellbeing, while experiencing a climate of hostility, bullying and harassment, and pressures to meet targets. Such working environments carry several risks, not only for the employees themselves but also in terms of clinical outcomes for patients. As a result, a range of systemic and targeted infrastructure interventions have been trialled in several NHS hospitals (managed in the UK by independent Trusts), seeking to promote a culture of compassion, and improve the psychological safety of workers. However, the effectiveness of such measures in achieving ethical working environments and preventing unethical behaviours, has been questioned. We join the ongoing debate by proposing the need to go beyond ethical infrastructures and instead consider ethical environments in their systemic complexity. We conclude, by putting forward a model of a complex and ethical health system, which incorporates workplace networks, policy frameworks, and accounts for regional demographics.

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.