Dataset Information

Examining quality improvement programs: the case of Minnesota hospitals.

ABSTRACT:

Objective

To determine if there is a hierarchy of improvement program adoption by hospitals and outline that hierarchy.

Data sources

Primary data were collected in the spring of 2007 via e-survey from 210 individuals representing 109 Minnesota hospitals. Secondary data from 2006 were assembled from the Leapfrog database.

Study design

As part of a larger survey, respondents were given a list of improvement programs and asked to identify those programs that are used in their hospital. DATA COLLECTION/DATA EXTRACTION: Rasch Model Analysis was used to assess whether a unidimensional construct exists that defines a hospital's ability to implement performance improvement programs. Linear regression analysis was used to assess the relationship of the Rasch ability scores with Leapfrog Safe Practices Scores to validate the research findings. Principal Findings. The results of the study show that hospitals have widely varying abilities in implementing improvement programs. In addition, improvement programs present differing levels of difficulty for hospitals trying to implement them. Our findings also indicate that the ability to adopt improvement programs is important to the overall performance of hospitals.

Conclusions

There is a hierarchy of improvement programs in the health care context. A hospital's ability to successfully adopt improvement programs is a function of its existing capabilities. As a hospital's capability increases, the ability to successfully implement higher level programs also increases.

SUBMITTER: Olson JR

PROVIDER: S-EPMC2654162 | biostudies-literature | 2008 Oct

REPOSITORIES: biostudies-literature

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Publications

Examining quality improvement programs: the case of Minnesota hospitals.

Olson John R JR Belohlav James A JA Cook Lori S LS Hays Julie M JM

Health services research 20080829 5 Pt 2

<h4>Objective</h4>To determine if there is a hierarchy of improvement program adoption by hospitals and outline that hierarchy.<h4>Data sources</h4>Primary data were collected in the spring of 2007 via e-survey from 210 individuals representing 109 Minnesota hospitals. Secondary data from 2006 were assembled from the Leapfrog database.<h4>Study design</h4>As part of a larger survey, respondents were given a list of improvement programs and asked to identify those programs that are used in their ...[more]

PMID: 18761677

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Project description:BackgroundPrevious research addressed the development of a classification scheme for quality improvement systems in European hospitals. In this study we explore associations between the 'maturity' of the hospitals' quality improvement system and clinical outcomes.MethodsThe maturity classification scheme was developed based on survey results from 389 hospitals in eight European countries. We matched the hospitals from the Spanish sample (113 hospitals) with those hospitals participating in a nation-wide, voluntary hospital performance initiative. We then compared sample distributions and explored associations between the 'maturity' of the hospitals' quality improvement system and a range of composite outcomes measures, such as adjusted hospital-wide mortality, -readmission, -complication and -length of stay indices. Statistical analysis includes bivariate correlations for parametrically and non-parametrically distributed data, multiple robust regression models and bootstrapping techniques to obtain confidence-intervals for the correlation and regression estimates.ResultsOverall, 43 hospitals were included. Compared to the original sample of 113, this sample was characterized by a higher representation of university hospitals. Maturity of the quality improvement system was similar, although the matched sample showed less variability. Analysis of associations between the quality improvement system and hospital-wide outcomes suggests significant correlations for the indicator adjusted hospital complications, borderline significance for adjusted hospital readmissions and non-significance for the adjusted hospital mortality and length of stay indicators. These results are confirmed by the bootstrap estimates of the robust regression model after adjusting for hospital characteristics.ConclusionsWe assessed associations between hospitals' quality improvement systems and clinical outcomes. From this data it seems that having a more developed quality improvement system is associated with lower rates of adjusted hospital complications. A number of methodological and logistic hurdles remain to link hospital quality improvement systems to outcomes. Further research should aim at identifying the latent dimensions of quality improvement systems that predict quality and safety outcomes. Such research would add pertinent knowledge regarding the implementation of organizational strategies related with quality of care outcomes.

Project description:BackgroundIn assessing quality of care in developing countries, retrospectively collected data are usually used given their availability. Retrospective data however suffer from such biases as recall bias and non-response bias. Comparing results obtained using prospectively and retrospectively collected data will help validate the use of the easily available retrospective data in assessing quality of care in past and future studies.MethodsProspective and retrospective datasets were obtained from a cluster randomized trial of a multifaceted intervention aimed at improving paediatric inpatient care conducted in eight rural Kenyan district hospitals by improving management of children admitted with pneumonia, malaria and diarrhea and/or dehydration. Four hospitals received a full intervention and four a partial intervention. Data were collected through 3 two weeks surveys conducted at baseline, after 6 and 18 months. Retrospective data was sampled from paediatric medical records of patients discharged in the preceding six months of the survey while prospective data was collected from patients discharged during the two week period of each survey. Risk Differences during post-intervention period of 16 quality of care indicators were analyzed separately for prospective and retrospective datasets and later plotted side by side for comparison.ResultsFor the prospective data there was strong evidence of an intervention effect for 8 of the indicators and weaker evidence of an effect for one indicator, with magnitude of effect sizes varying from 23% to 60% difference. For the retrospective data, 10 process (these include the 8 indicators found to be statistically significant in prospective data analysis) indicators had statistically significant differences with magnitude of effects varying from 10% to 42%. The bar-graph comparing results from the prospective and retrospective datasets showed similarity in terms of magnitude of effects and statistical significance for all except two indicators.ConclusionMultifaceted interventions can help improve adoption of clinical guidelines and hence improve the quality of care. The similar inference reached after analyses based on prospective assessment of case management is a useful finding as it supports the utility of work based on examination of retrospectively assembled case records allowing longer time periods to be studied while constraining costs.Trial registrationCurrent Controlled Trials ISRCTN42996612. Trial registration date: 20/11/2008.

Dataset Information

Examining quality improvement programs: the case of Minnesota hospitals.

Objective

Data sources

Study design

Conclusions

Publications

Examining quality improvement programs: the case of Minnesota hospitals.

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