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Abstract Background The need to improve patient safety has been identified as a major priority for health reform in developed countries, including Australia. We investigated the implementation and appropriateness of Variable Life Adjusted Displays as a quality control procedure to monitor “in-control” versus “out-of-control” processes in Victorian public hospitals. Methods Victorian Admitted Episode Data from Department of Human Services, Victoria for 2004–7 were used. The VLAD is a plot of a cumulative sum of the difference in expected outcome (range 0–1) and observed outcome (0 or 1) for sequential separations. Three outcomes were assessed: in-hospital mortality for acute myocardial infarction, stroke and heart failure. Logistic regression was used to obtain a realistic measure of expected mortality over the period 2004–5, adjusting for covariates and comorbidities, to estimate expected mortality risk for the separations between 2005–7. VLAD were plotted for the years 2005–7, by the 11 hospitals with the highest frequency of separations. Signalling limits for 30%, 50% and 75% risk decrease and risk increase were determined and plotted for each VLAD utilizing risk-adjusted cumulative sum techniques. This is a likelihood-ratio test statistic for signalling. If the VLAD signalled by intersecting with a limit, the limit was reset. Results The three logit models displayed reasonable fit to the observed data. There were n = 2999 separations in the AMI model, n = 3598 in the HF model and n = 1922 in the stroke model. The number of separations plotted by VLAD ranged from n = 126 to n = 648. No signals were observed in 64%, 55% and 18% of VLAD for AMI, HF and stroke respectively. For AMI and HF 9% of hospitals signalled at least once for each of 30%, 50% and 75% risk increase, whereas this was 45% for stroke. Hospitals signalling at least once for risk decrease ranged from 18% to 36% across the levels of risk and outcomes. No VLAD signalled for both risk decrease and increase. Conclusions VLAD intersecting with limits to signal “out-of-control” states, may be an appropriate technique to help hospitals assess quality control. Preliminary work displays some between hospital differences. Relevant signals can be used to investigate why a system is potentially performing better than or worse than expected. Types and levels of investigation could depend on the type of signalling. Validation work, for example attempting to correlate signals with clinical notes, prior to VLAD distribution needs to be undertaken.

Abstract Background Frail older people admitted to acute care hospitals are at risk of a range of adverse outcomes, including geriatric syndromes, although targeted care strategies can improve health outcomes for these patients. It is therefore important to assess inter-hospital variation in performance in order to plan and resource improvement programs. Clinical quality outcome indicators provide a mechanism for identifying variation in performance over time and between hospitals, however to date there has been no routine use of such indicators in acute care settings. A barrier to using quality indicators is lack of access to routinely collected clinical data. The interRAI Acute Care (AC) assessment system supports comprehensive geriatric assessment of older people within routine daily practice in hospital and includes process and outcome data pertaining to geriatric syndromes. This paper reports the study protocol for the development of aged care quality indicators for acute care hospitals. Methods/Design The study will be conducted in three phases: 1. Development of a preliminary inclusive set of quality indicators set based on a literature review and expert panel consultation, 2. A prospective field study including recruitment of 480 patients aged 70 years or older across 9 Australian hospitals. Each patient will be assessed on admission and discharge using the interRAI AC, and will undergo daily monitoring to observe outcomes. Medical records will be independently audited, and 3. Analysis and compilation of a definitive quality indicator set, including two anonymous voting rounds for quality indicator inclusion by the expert panel. Discussion The approach to quality indicators proposed in this protocol has four distinct advantages over previous efforts: the quality indicators focus on outcomes; they can be collected as part of a routinely applied clinical information and decision support system; the clinical data will be robust and will contribute to better understanding variations in hospital care of older patients; The quality indicators will have international relevance as they will be built on the interRAI assessment instrument, an internationally recognised clinical system.

Abstract Background Policy makers, clinicians and researchers are demonstrating increasing interest in using data linked from multiple sources to support measurement of clinical performance and patient health outcomes. However, the utility of data linkage may be compromised by sub-optimal or incomplete linkage, leading to systematic bias. In this study, we synthesize the evidence identifying participant or population characteristics that can influence the validity and completeness of data linkage and may be associated with systematic bias in reported outcomes. Methods A narrative review, using structured search methods was undertaken. Key words "data linkage" and Mesh term "medical record linkage" were applied to Medline, EMBASE and CINAHL databases between 1991 and 2007. Abstract inclusion criteria were; the article attempted an empirical evaluation of methodological issues relating to data linkage and reported on patient characteristics, the study design included analysis of matched versus unmatched records, and the report was in English. Included articles were grouped thematically according to patient characteristics that were compared between matched and unmatched records. Results The search identified 1810 articles of which 33 (1.8%) met inclusion criteria. There was marked heterogeneity in study methods and factors investigated. Characteristics that were unevenly distributed among matched and unmatched records were; age (72% of studies), sex (50% of studies), race (64% of studies), geographical/hospital site (93% of studies), socio-economic status (82% of studies) and health status (72% of studies). Conclusion A number of relevant patient or population factors may be associated with incomplete data linkage resulting in systematic bias in reported clinical outcomes. Readers should consider these factors in interpreting the reported results of data linkage studies.

Background: The Safety Attitudes Questionnaire (SAQ) is commonly used to assess staff perception of safety climate within their clinical environment. The psychometric properties of the SAQ have previously been explored with confirmatory factor analysis and found to have some issues with construct validity. This study aimed to extend the psychometric evaluations of the SAQ by using Rasch analysis.Methods: Assessment of internal construct validity included overall fit to the Rasch model (unidimensionality), response formats, targeting, differential item functioning (DIF) and person-separation index (PSI).Results: A total of 420 nurses completed the SAQ (response rate 60 %). Data showed overall fit to a Rasch model of expected item functioning for interval scale measurement. The questionnaire demonstrated unidimensionality confirming the appropriateness of summing the items in each domain. Score reliabilities were appropriate (internal consistency PSI 0.6-0.8). However, participants were not using the response options on the SAQ in a consistent manner. All domains demonstrated suboptimal targeting and showed compromised score precision towards higher levels of safety climate (substantial ceiling effects).Conclusion: There was general support for the reliability of the SAQ as a measure of safety climate although it may not be able to detect small but clinically important changes in safety climate within an organisation. Further refinement of the SAQ is warranted. This may involve changing the response options and including new items to improve the overall targeting of the scale.Trial Registration: This study was registered with the Australian New Zealand Clinical Trials Registry, number ACTRN12611000332921 (21 March 2011). [ABSTRACT FROM AUTHOR]