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author:("western, G.P.")
1.  The association between chronic care management and the quality of thrombosis care 
Introduction
The oral anticoagulant therapy (OAT), used to prevent thrombosis, is associated with substantial avoidable hospitalization.
Aim
Identify the associations between chronic care management and the quality of OAT as suggested by the chronic care model (CCM) of Wagner.
Methods
Regression analysis with data of 61 thrombosis clinics and inductive analysis with 63 interviews with health care professionals of 23 thrombosis clinics.
Results
Results show substantial differences between regions in the quality of thrombosis care and the CCM activities. However, the variation in quality of care was not associated with the differences in CCM activities. The inductive analysis indicates that there are problems in the cooperation between caregivers. Several preferred CCM activities (e.g., multidisciplinary protocol) as well as the barriers to implement these activities (e.g., conflicting interests) were put forward by the health care professionals.
Conclusion
It can be concluded that there is variation in quality of thrombosis care between regions. This variation could not be explained by the observed differences in CCM activities. However, fragmentation is a major source of inefficiency according to health care professionals. The paper concludes with suggestions to improve chronic care management for thrombosis.
PMCID: PMC3031809
quality of care; thrombosis; chronic care management; disease management; effectiveness
2.  The hospital standardised mortality ratio: a powerful tool for Dutch hospitals to assess their quality of care? 
Aim of the study
To use the hospital standardised mortality ratio (HSMR), as a tool for Dutch hospitals to analyse their death rates by comparing their risk-adjusted mortality with the national average.
Method
The method uses routine administrative databases that are available nationally in The Netherlands—the National Medical Registration dataset for the years 2005–2007. Diagnostic groups that led to 80% of hospital deaths were included in the analysis. The method adjusts for a number of case-mix factors per diagnostic group determined through a logistic regression modelling process.
Results
In The Netherlands, the case-mix factors are primary diagnosis, age, sex, urgency of admission, length of stay, comorbidity (Charlson Index), social deprivation, source of referral and month of admission. The Dutch HSMR model performs well at predicting a patient's risk of death as measured by a c statistic of the receiver operating characteristic curve of 0.91. The ratio of the HSMR of the Dutch hospital with the highest value in 2005–2007 is 2.3 times the HSMR of the hospital with the lowest value.
Discussion
Overall hospital HSMRs and mortality at individual diagnostic group level can be monitored using statistical process control charts to give an early warning of possible problems with quality of care. The use of routine data in a standardised and robust model can be of value as a starting point for improvement of Dutch hospital outcomes. HSMRs have been calculated for several other countries.
doi:10.1136/qshc.2009.032953
PMCID: PMC2921266  PMID: 20172876
Healthcare quality improvement; quality of care; mortality; healthcare quality; control charts
3.  Measuring chronic care management experience of patients with diabetes: PACIC and PACIC+ validation 
Background
The patient assessment of chronic illness care (PACIC) is a promising instrument to evaluate the chronic care experiences of patients, yet additional validation is needed to improve its usefulness.
Methods
A total of 1941 patients with diabetes completed the questionnaire. Reliability coefficients and factor analyses were used to psychometrically test the PACIC and PACIC+ (i.e. PACIC extended with six additional multidisciplinary team functioning items to improve content validity). Intra-class correlations were computed to identify the extent to which variation in scores can be attributed to GP practices.
Results
The PACIC and PACIC+ showed a good psychometric quality (Cronbach’s alpha’s >0.9). Explorative factor analyses showed inconclusive results. Confirmative factor analysis showed that none of the factor structures had an acceptable fit (RMSEA>0.10). In addition, 5.1 to 5.4% of the total variation was identified at the GP practice level.
Conclusion
The PACIC and PACIC+ are reliable instruments to measure the chronic care management experiences of patients. The PACIC+ is preferred because it also includes multidisciplinary coordination and cooperation—one of the central pillars of chronic care management—with good psychometric quality. Previously identified subscales should be used with caution. Both PACIC instruments are useful in identifying GP practice variation.
PMCID: PMC3601510  PMID: 23593054
chronic care model; patient experience; chronic care management; integrated care; diabetes; PACIC

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