Implementing paediatric early warning scores systems in the Netherlands: future implications.

BACKGROUND: Paediatric Early Warning Scores (PEWS) are increasingly being used for early identification and management of clinical deterioration in paediatric patients. A PEWS system includes scores, cut-off points and appropriate early intervention. In 2011, The Dutch Ministry of Health advised hospitals to implement a PEWS system in order to improve patient safety in paediatric wards. The objective of this study was to examine the results of implementation of PEWS systems and to gain insight into the attitudes of professionals towards using a PEWS system in Dutch non-university hospitals. METHODS: Quantitative data were gathered at start, midway and at the end of the implementation period through retrospective patient record review (n = 554). Semi-structured interviews with professionals (n = 8) were used to gain insight in the implementation process and experiences. The interviews were transcribed and analysed using an inductive approach. RESULTS: Looking at PEWS systems of the five participating hospitals, different parameters and policies were found. While all hospitals included heart rate and respiratory rate, other variables differed among hospitals. At baseline, none of the hospitals used a PEWS system. After 1 year, PEWS were recorded in 69.2% of the patient records and elevated PEWS resulted in appropriate action in 49.1%. Three themes emerged from the interviews: 1) while the importance of using a PEWS system was acknowledged, professionals voiced some doubts about the effectiveness and validity of their PEWS system 2) registering PEWS required little extra effort and was facilitated by PEWS being integrated into the electronic patient record 3) Without a national PEWS system or guidelines, hospitals found it difficult to identify a suitable PEWS system for their setting. Existing systems were not always considered applicable in a non-university setting. CONCLUSIONS: After 1 year, hospitals showed improvements in the use of their PEWS system, although some were decidedly more successful than others. Doubts among staff about validity, effectiveness and communication with other hospitals during transfer to higher level care hospital might hinder sustainable implementation. For these purposes the development of a national PEWS system is recommended, consisting of a "core set" of PEWS, cut-off points and associated early intervention.

Can preventable adverse events be predicted among hospitalized older patients? The development and validation of a predictive model.

OBJECTIVE: To develop and validate a predictive model for preventable adverse events (AEs) in hospitalized older patients, using clinically important risk factors that are readily available on admission. DESIGN: Data from two retrospective patient record review studies on AEs were used. Risk factors included patient characteristics as well as admission and organizational characteristics. Multilevel logistical regression analysis was used to develop the model. Backward elimination was applied to identify the most parsimonious model. SETTING: Twenty-one Dutch hospitals were included in the 2004 sample and 20 Dutch hospitals in the 2008 sample. PARTICIPANTS: A total of 3977 patients aged 70 years or over who were admitted to a Dutch hospital in 2004 and 2119 patients aged 70 years or over admitted in 2008. MAIN OUTCOME MEASURES: Identified predictors of preventable AEs in older patients. RESULTS: In 2004 predictors of preventable AEs in patients aged 70 years or over were increased age (OR 1.04, confidence interval (CI) 1.01-1.06); elective admission (OR 1.65, CI 1.14-2.40) and admission to a surgical department (OR 1.53, CI 1.08-2.16). The area under the receiver operating characteristic curve for the 2004 sample was 0.60 and for 2008, 0.59. CONCLUSIONS: This study showed that several expected risk factors for preventable AEs in older patients, including comorbidity, could not predict these events. It was not possible, using in-patient data available on admission and collected during the course of two patient record review studies, to develop a satisfactory predictive model for preventable AEs in older patients.