A realistic evaluation of early warning systems and acute care training for early recognition and management of deteriorating ward-based patients.

    Prize: Fellowship awarded competitively

    Jennifer McGaughey (Recipient)

    Background: An increasing number of critically ill patients are suffering potentially preventable, serious complications that may result in Intensive Care Unit (ICU) admission or death because deterioration in their condition is often missed, misinterpreted or mismanaged on acute hospital wards [14]. Two clinical innovations, the Early Warning System (EWS) and the Acute Life-threatening Early Recognition and Treatment (ALERTTM) course are crucial to addressing these issues, but are dependent upon proper mechanisms being in place to enable ward staff to correctly identify, effectively assess and manage deterioration. Main research questions: (1) What are the underlying mechanisms influencing the implementation and sustainability of the two linked practice innovations of EWS and ALERTTM? (2) What are the key enabling/disabling characteristics of the organisational context for implementation and sustainability of EWS and ALERTTM? (3) How do the mechanisms of implementation and the characteristics of context combine to support or hinder the implementation of EWS and ALERTTM and the achievement of desired outcomes? Design: A multiple case study approach in six wards in three hospital Trusts in which EWS and ALERTTM have been implemented using a 4 stage Realistic Evaluation process. Methods: Individual and focus group interviews, field visits involving non-participant observation, reviews of relevant documentation and audit. Data analysis: A case study database will be created for each case and ‘cross-case synthesis’ will allow comparisons across cases. Interview data will be analysed using a suitable database (N6 (NUD*IST) software [38]) and audit data will be summarised using the SPSS software package.
    Awarded date2007
    Awarded at eventSandra Ryan PhD Fellowship, School of Nursing & Midwifery, Queen’s University of Belfast
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    ID: 126769580