Organizational Climate as Performance Driver: Health Care Workers’ Perception in a Large Hospital

Recently health care (HC) organizations have increasingly embarked on organizational climate (OC) assessment with the intent to improve their efficiency and the quality of the delivered services. This is important; however, it is even if more crucial to ensure that workers engaged in the evaluation process are aware of the importance of their fruitful engagement in this investigation as well as of its potential benefits. From the management viewpoint, this is crucial to plan and implement management initiatives able to create a great place to work. The purpose of this paper is to shed empirical light on how, in effect, HC workers perceive OC for itself and as a performance driver to assess and manage. The study was carried out through an action research (AR) project, which included the use of both qualitative and quantitative approaches. Key phases of the AR project were some focus groups and a survey. During the focus groups, several methods and approaches were adopted for getting opinions from people and animating discussion. About the survey, a total sample of 560 HC workers was investigated. The AR project has shown that even if HC workers intuitively conceive OC as an important performance driver, the meaning of the construct is not completely clear. Moreover, a good level of awareness among HC workers about how and why OC can improve individual and organizational performance represents a key issue to address in evaluating and managing OC

Meeting the four-hour deadline in an A&E department

This is the print version of the Article. The official published version can be obtained from the link below - Copyright @ 2011 EmeraldPurpose – Accident and emergency (A&E) departments experience a secondary peak in patient length of stay (LoS) at around four hours, caused by the coping strategies used to meet the operational standards imposed by government. The aim of this paper is to build a discrete-event simulation model that captures the coping strategies and more accurately reflects the processes that occur within an A&E department. Design/methodology/approach – A discrete-event simulation (DES) model was used to capture the A&E process at a UK hospital and record the LoS for each patient. Input data on 4,150 arrivals over three one-week periods and staffing levels was obtained from hospital records, while output data were compared with the corresponding records. Expert opinion was used to generate the pathways and model the decision-making processes. Findings – The authors were able to replicate accurately the LoS distribution for the hospital. The model was then applied to a second configuration that had been trialled there; again, the results also reflected the experiences of the hospital. Practical implications – This demonstrates that the coping strategies, such as re-prioritising patients based on current length of time in the department, employed in A&E departments have an impact on LoS of patients and therefore need to be considered when building predictive models if confidence in the results is to be justified. Originality/value – As far as the authors are aware this is the first time that these coping strategies have been included within a simulation model, and therefore the first time that the peak around the four hours has been analysed so accurately using a model

Automatic covariate selection in logistic models for chest pain diagnosis: A new approach

A newly established method for optimizing logistic models via a minorization-majorization procedure is applied to the problem of diagnosing acute coronary syndromes (ACS). The method provides a principled approach to the selection of covariates which would otherwise require the use of a suboptimal method owing to the size of the covariate set. A strategy for building models is proposed and two models optimized for performance and for simplicity are derived via ten-fold cross-validation. These models confirm that a relatively small set of covariates including clinical and electrocardiographic features can be used successfully in this task. The performance of the models is comparable with previously published models using less principled selection methods. The models prove to be portable when tested on data gathered from three other sites. Whilst diagnostic accuracy and calibration diminishes slightly for these new settings, it remains satisfactory overall. The prospect of building predictive models that are as simple as possible for a required level of performance is valuable if data-driven decision aids are to gain wide acceptance in the clinical situation owing to the need to minimize the time taken to gather and enter data at the bedside