Health Systems最新文献

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.

Increasing efficiency and reducing risk in radiotherapy cancer treatment is of high importance. User assistance systems within a digitally connected radiotherapy environment can support all involved professionals to perform their individual tasks faster and better. This paper presents a qualitative analysis of radiotherapy workflows and a corresponding process modelling in order to identify hypothetical user assistance systems for specific process activities. In addition, the results of an empirical study on the identified systems are presented together with derived requirements and design principles for these systems. A structured online survey with 50 medical physicists in Germany has been conducted. Among others the acceptance, the increase of perceived efficiency and the risk reduction while using the assistance systems are analysed and discussed. The results support the creation of value adding user assistance systems for radiotherapy that improve efficiency, reduce treatment risks and reach high user acceptance levels.

In hospitals, scheduled operations can often be cancelled in large numbers due to the unavailability of beds for post-operation recovery. Operating theatre scheduling is known to be an $NP$ -hard optimisation problem. Previous studies have shown that the correct scheduling of surgical procedures can have a positive impact on the availability of beds in hospital wards, thereby allowing a reduction in number of elective operation cancellations. This study proposes an exact technique based on the partitioned graph colouring problem for constructing optimal master surgery schedules, with the goal of minimising the number of cancellations. The resultant schedules are then simulated in order to measure how well they cope with the stochastic nature of patient arrivals. Our results show that the utilisation of post-operative beds can be increased, whilst the number of cancellations can be decreased, which may ultimately lead to greater patient throughput and reduced waiting times. A scenario-based model has also been employed to integrate the stochastic-nature associated with the bed requirements into the optimisation process. The results indicate that the proposed model can lead to more robust solutions.

A primary goal of emergency services is to minimise the response times to emergencies whilst managing operational costs. This paper is motivated by real data from the Welsh Ambulance Service which in recent years has been criticised for not meeting its eight-minute response target. In this study, four forecasting approaches (ARIMA, Holt Winters, Multiple Regression and Singular Spectrum Analysis (SSA)) are considered to investigate whether they can provide more accurate predictions to the call volume demand (total and by category) than the current approach on a selection of planning horizons (weekly, monthly and 3-monthly). Each method is applied to a training and test set and root mean square error (RMSE) and mean absolute percentage error (MAPE) error statistics are determined. Results showed that ARIMA is the best forecasting method for weekly and monthly prediction of demand and the long-term demand is best predicted using the SSA method.