A study of general practitioners' perspectives on electronic medical records systems in NHS Scotland

<b>Background</b> Primary care doctors in NHSScotland have been using electronic medical records within their practices routinely for many years. The Scottish Health Executive eHealth strategy (2008-2011) has recently brought radical changes to the primary care computing landscape in Scotland: an information system (GPASS) which was provided free-of-charge by NHSScotland to a majority of GP practices has now been replaced by systems provided by two approved commercial providers. The transition to new electronic medical records had to be completed nationally across all health-boards by March 2012. <p></p><b> Methods</b> We carried out 25 in-depth semi-structured interviews with primary care doctors to elucidate GPs' perspectives on their practice information systems and collect more general information on management processes in the patient surgical pathway in NHSScotland. We undertook a thematic analysis of interviewees' responses, using Normalisation Process Theory as the underpinning conceptual framework. <p></p> <b>Results</b> The majority of GPs' interviewed considered that electronic medical records are an integral and essential element of their work during the consultation, playing a key role in facilitating integrated and continuity of care for patients and making clinical information more accessible. However, GPs expressed a number of reservations about various system functionalities - for example: in relation to usability, system navigation and information visualisation. <b>Conclusion </b>Our study highlights that while electronic information systems are perceived as having important benefits, there remains substantial scope to improve GPs' interaction and overall satisfaction with these systems. Iterative user-centred improvements combined with additional training in the use of technology would promote an increased understanding, familiarity and command of the range of functionalities of electronic medical records among primary care doctors

Localization and Absorption of Light in 2D Composite Metal-Dielectric Films at the Percolation Threshold

We study in this paper the localization of light and the dielectric properties of thin metal-dielectric composites at the percolation threshold and around a resonant frequency where the conductivities of the two components are of the same order. In particular, the effect of the loss in metallic components are examined. To this end, such systems are modelized as random $L-C$ networks, and the local field distribution as well as the effective conductivity are determined by using two different methods for comparison: an exact resolution of Kirchoff equations, and a real space renormalization group method. The latter method is found to give the general behavior of the effective conductivity but fails to determine the local field distribution. It is also found that the localization still persists for vanishing losses. This result seems to be in agreement with the anomalous absorption observed experimentally for such systems.Comment: 14 page latex, 3 ps figures. submitte

Atomic layer deposition of a MgO barrier for a passivated black phosphorus spintronics platform

We demonstrate a stabilized black phosphorus (BP) 2D platform thanks to an ultrathin MgO barrier, as required for spintronic device integration. The in-situ MgO layer deposition is achieved by using a large-scale atomic layer deposition process with high nucleation density. Raman spectroscopy studies show that this layer protects the BP from degradation in ambient conditions, unlocking in particular the possibility to carry out usual lithographic fabrication steps. The resulting MgO/BP stack is then integrated in a device and probed electrically, confirming the tunnel properties of the ultrathin MgO contacts. We believe that this demonstration of a BP material platform passivated with a functional MgO tunnel barrier provides a promising perspective for BP spin transport devices

Evaluation of random forest and ensemble methods at predicting complications following cardiac surgery

Cardiac patients undergoing surgery face increased risk of postoperative complications, due to a combination of factors, including higher risk surgery, their age at time of surgery and the presence of co-morbid conditions. They will therefore require high levels of care and clinical resources throughout their perioperative journey (i.e. before, during and after surgery). Although surgical mortality rates in the UK have remained low, postoperative complications on the other hand are common and can have a significant impact on patients’ quality of life, increase hospital length of stay and healthcare costs. In this study we used and compared several machine learning methods – random forest, AdaBoost, gradient boosting model and stacking – to predict severe postoperative complications after cardiac surgery based on preoperative variables obtained from a surgical database of a large acute care hospital in Scotland. Our results show that AdaBoost has the best overall performance (AUC = 0.731), and also outperforms EuroSCORE and EuroSCORE II in other studies predicting postoperative complications. Random forest (Sensitivity = 0.852, negative predictive value = 0.923), however, and gradient boosting model (Sensitivity = 0.875 and negative predictive value = 0.920) have the best performance at predicting severe postoperative complications based on sensitivity and negative predictive value