Health Systems最新文献

We provide an open-source model to estimate the number of secondary Covid-19 infections caused by potentially infectious students returning from university to private homes with other occupants. Using a Monte-Carlo method and data derived from UK sources, we predict that an infectious student would, on average, infect 0.94 other household members. Or, as a rule of thumb, each infected student would generate (just less than) one secondary within-household infection. The total number of secondary cases for all returning students is dependent on the virus prevalence within each student population at the time of their departure from campus back home. Although the proposed estimation method is general and robust, the results are sensitive to the input data. We provide Matlab code and a helpful online app (http://bit.ly/Secondary_infections_app) that can be used to estimate numbers of secondary infections based on local parameter values. This can be used worldwide to support policy making.

By using nationally representative consumption expenditure surveys (CES) conducted by the National Sample Survey Organisation (NSSO) in 1999-2000, 2004-05 and 2011-12, this paper has analysed the socioeconomic differentials in the burden of paying for healthcare in India. The study found that in all waves of data, the concentration of population reporting OOP health expenditure has shown a shift towards poor population, while the concentration of overshoot expenditure is still constant among the rich which is more pronounced in the rural areas of the country. Furthermore, Muslims and Sikhs among different religions, Scheduled Casts among social categories, self-employed and casual/agricultural labour among household types and rural areas among sectors are more likely to incur OOP health expenditure as compared to their counterparts. This study argues for the universal health insurance coverage to protect households from the significant burden of expenditure on critical healthcare.

Despite an increasing number of papers reporting applications of operational research (OR) to problems in healthcare, there remains little empirical evidence of OR improving healthcare delivery in practice. Without such evidence it is harder both to justify the usefulness of OR to a healthcare audience and to learn and continuously improve our approaches. To progress, we need to build the evidence-base on whether and how OR improves healthcare delivery through careful empirical evaluation. This position paper reviews evaluation standards in healthcare improvement research and dispels some common myths about evaluation. It highlights the current lack of robust evaluation of healthcare OR and makes the case for addressing this. It then proposes possible ways for building better empirical evaluations of OR interventions in healthcare.

We present a discrete-event simulation model of the kidney transplantation system in an Indian state, Rajasthan. Organs are generated across the state based on the organ donation rate among the general population, and are allocated to patients on the kidney transplantation waitlist. The organ allocation algorithm is developed using official guidelines published for kidney transplantation, and model parameters were estimated using publicly available data to the extent possible. Transplantation outcomes generated by the model include: (a) the probabilities of a patient receiving an organ within one to 5 years of registration and (b) the average number of deaths per year due to lack of donated organs. Simulation experiments involving observing the effect of increasing the organ arrival rate and establishing additional transplantation centres on transplantation outcomes are also conducted. We also demonstrate the use of such a model to optimally locate additional transplantation centres using simulation optimisation methods.

The Thoraxcenter of Erasmus MC started an improvement project in 2015 in order to increase the number of open-heart surgeries by 150 for three consecutive years (450 in total, +46%), and to decrease the access time from 12-14 to 2-3 weeks by the end of 2016. This was required to attain economy of scale in a highly competitive market. In this paper we describe the first year of the project, focusing on its structure and interventions taken, resulting in 165 additional open-heart surgeries carried out in 2016 and a significantly shorter access time of 2-3 weeks.

Patient-held Health Information Technologies (HIT) can reduce medical error by improving communication between patients and the healthcare team. Despite the proposed benefits, the roll-out of patient-held HIT solutions remains nascent, leaving considerable gaps in our understanding of the adoption challenges inherent. This paper adopts Normalisation Process Theory to study the factors which support or impede the adoption and "normalisation" of patient-held HIT, particularly across the primary-secondary care interface. The authors conducted an in-depth case study of HIT adoption across four GP practices, and the wards of a 350 bed hospital. 35 semi-structured interviews were completed. Findings point towards both user-specific and network-specific factors as significant challenges to normalisation across primary-secondary care. This includes factors related to interactional workability, skill set workability, relational integration, and contextual integration. We also discuss challenges specific to patient-held HIT adoption e.g., understanding the patient/clinician experience, supporting informal clinician networks, and spanning across IT boundaries.