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2 results

  • PP102 Perceived Quality By Patients Hospitalized At Home Undergoing Domicillary Radiography

  • Ottavio Davini, Marika Giacometti, Matteo Scardino, Stefano Cerutti, Antonio Guida, Stefano Tibaldi, Valeria Romano, Chiara Rivoiro, Marco Grosso
  • Journal:
    International Journal of Technology Assessment in Health Care / Volume 33 / Issue S1 / 2017
    Published online by Cambridge University Press:
    12 January 2018, pp. 119-120
    • Article
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  • INTRODUCTION:

    Europe currently has the highest proportion of older people in the world and is expected to maintain this leading position for the next 50 years. A teleradiology service for frail patients living at home or in nursing-homes (R@dhome), funded by the Italian Ministry of Health, has been operating from June 2013 to May 2015 (1). The goal was to offer, within the path of home care patients, radiologic care for fragile elderly patients (2). In this report of the study a secondary outcome was evaluated; the perceived quality for patients and caregivers undergoing home radiology.

    METHODS:

    The study was a randomized controlled trial (RCT) with 136 patients; 71 cases and 65 controls. The perceived quality assessment was only performed among home inpatient group cases, at home or in nursing homes and not in the control group patients (who had treatment in a hospital). The assessment instrument was a semi-structured interview based on the benchmark system SERQUAL®. The identified dimensions of quality were: tangible aspects, reliability, responsiveness and empathy in a subpopulation of non-dementia patients. Eligible subjects were interviewed between May 2014 and May 2015.

    RESULTS:

    The percentage of satisfied patients, evaluated on the dimensions of perceived quality, was 97.7 percent. The main reasons given were: short waiting time, best comfort and safety, efficiency, operator's kindness and less need to travel for the treatment.

    CONCLUSIONS:

    R@dhome benefits are not limited to clinical or financial aspects but ethical, social and relational advantages have also been shown in this study (3). Given the fragility of the patients, positive results were mostly achieved by reducing the risk of trauma in transport between their home and the hospital.

  • Chapter 18 - Limited-area ensemble forecasting: the COSMO-LEPS system

  • Edited by Tim Palmer, Renate Hagedorn
  • Book:
    Predictability of Weather and Climate
    Published online:
    03 December 2009
    Print publication:
    27 July 2006, pp 489-513

  • Summary

    Introduction

    The improvement of quantitative precipitation forecasting (QPF) is still one of the major challenges in numerical weather prediction (NWP). Despite the constant increase of computer power resources, which has allowed the development of more and more sophisticated and resolved NWP models, accurate forecasts of extreme weather conditions, especially when related to intense and localised precipitation structures, are still difficult beyond day 2 (Mullen and Buizza, 2001) and, in rare and selected cases, even at 24 hours. This limitation is due, among other reasons, to the inherently low degree of predictability typical of the relevant physical phenomena. The probabilistic approach has been recently increasingly explored to try to come to terms with the chaotic behaviour of the atmosphere and to help forecasting phenomena with low deterministic predictability.

    In addition to this, almost twenty years ago Henk Tennekes, at the time member of the ECMWF (European Centre for Medium-Range Weather Forecasts) Scientific Advisory Committee, raised the question of the opportunity of producing a-priori estimates of forecast skill stating that ‘no forecast is complete without a forecast of the forecast skill’. It is not an overstatement to say that his bold assertion contributed greatly to the development, at least at ECMWF, of forecast skill studies, estimates and prediction techniques (e.g. Palmer and Tibaldi, 1988) and to the related development of statistical-dynamical prediction methods like ensemble forecasting.