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1.  Hospital Standardized Mortality Ratios: Sensitivity Analyses on the Impact of Coding 
Health Services Research  2011;46(6 Pt 1):1741-1761.
Introduction
Hospital standardized mortality ratios (HSMRs) are derived from administrative databases and cover 80 percent of in-hospital deaths with adjustment for available case mix variables. They have been criticized for being sensitive to issues such as clinical coding but on the basis of limited quantitative evidence.
Methods
In a set of sensitivity analyses, we compared regular HSMRs with HSMRs resulting from a variety of changes, such as a patient-based measure, not adjusting for comorbidity, not adjusting for palliative care, excluding unplanned zero-day stays ending in live discharge, and using more or fewer diagnoses.
Results
Overall, regular and variant HSMRs were highly correlated (ρ > 0.8), but differences of up to 10 points were common. Two hospitals were particularly affected when palliative care was excluded from the risk models. Excluding unplanned stays ending in same-day live discharge had the least impact despite their high frequency. The largest impacts were seen when capturing postdischarge deaths and using just five high-mortality diagnosis groups.
Conclusions
HSMRs in most hospitals changed by only small amounts from the various adjustment methods tried here, though small-to-medium changes were not uncommon. However, the position relative to funnel plot control limits could move in a significant minority even with modest changes in the HSMR.
doi:10.1111/j.1475-6773.2011.01295.x
PMCID: PMC3393030  PMID: 21790587
Administrative data; mortality; hospitals; quality of care
2.  Current ICD10 codes are insufficient to clearly distinguish acute myocardial infarction type: a descriptive study 
Background
Acute myocardial infarction (AMI) type is an important distinction to be made in both clinical and health care research context, as it determines the treatment of the patient as well as affecting outcomes. The aim of the paper was to determine the feasibility of distinguishing AMI type, either ST elevation myocardial infarction (STEMI) or non-ST elevation myocardial infarction (NSTEMI), using ICD10 codes.
Methods
We carried out a retrospective descriptive analysis of hospital administrative data on AMI emergency patients in England, for financial years 2000/1 to 2009/10. We used the performance of an angioplasty procedure on the same day and on the same or next day of hospital admission as a proxy for STEMI.
Results
Among the ICD10 AMI subcategories, there were inconsistent trends, with some of the codes exhibiting a gradual decline (such as I21.0 Acute transmural myocardial infarction of anterior wall, I21.1 Acute transmural myocardial infarction of inferior wall, I22.0 Subsequent myocardial infarction of anterior wall and I22.1 Subsequent myocardial infarction of inferior wall) and other codes an increase (in particular I21.9 Acute myocardial infarction, unspecified and I22.9 Subsequent myocardial infarction of unspecified site). With the exception of the codes I21.4 Acute subendocardial myocardial infarction, I21.9 Acute myocardial infarction, unspecified, I22.8 Subsequent myocardial infarction of other sites and I22.9 Subsequent myocardial infarction of unspecified site, all the other AMI subcategories appear to have undergone a significant increase in the number of angioplasty procedures performed the same or the next day of hospital admission from around 2005/6. There appear to be difficulties in accurately identifying the proportion of STEMI/NSTEMI by sole reliance on ICD10 codes.
Conclusions
We suggest as the best sets of codes to select STEMI cases I21.0 to I21.3, I22.0, I22.1 and I22.8; however, without any further adaptations, ICD10 codes are insufficient to clearly distinguish acute myocardial infarction type.
doi:10.1186/1472-6963-13-468
PMCID: PMC4226256  PMID: 24195773
ST elevation myocardial infarction; Non-ST elevation myocardial infarction; ICD10
3.  Hospital episode statistics v central cardiac audit database 
BMJ : British Medical Journal  2007;335(7625):839.
doi:10.1136/bmj.39374.474965.BE
PMCID: PMC2043432  PMID: 17962247
4.  Measuring and explaining mortality in Dutch hospitals; The Hospital Standardized Mortality Rate between 2003 and 2005 
Background
Indicators of hospital quality, such as hospital standardized mortality ratios (HSMR), have been used increasingly to assess and improve hospital quality. Our aim has been to describe and explain variation in new HSMRs for the Netherlands.
Methods
HSMRs were estimated using data from the complete population of discharged patients during 2003 to 2005. We used binary logistic regression to indirectly standardize for differences in case-mix. Out of a total of 101 hospitals 89 hospitals remained in our explanatory analysis. In this analysis we explored the association between HSMRs and determinants that can and cannot be influenced by hospitals. For this analysis we used a two-level hierarchical linear regression model to explain variation in yearly HSMRs.
Results
The average HSMR decreased yearly with more than eight percent. The highest HSMR was about twice as high as the lowest HSMR in all years. More than 2/3 of the variation stemmed from between-hospital variation. Year (-), local number of general practitioners (-) and hospital type were significantly associated with the HSMR in all tested models.
Conclusion
HSMR scores vary substantially between hospitals, while rankings appear stable over time. We find no evidence that the HSMR cannot be used as an indicator to monitor and compare hospital quality. Because the standardization method is indirect, the comparisons are most relevant from a societal perspective but less so from an individual perspective. We find evidence of comparatively higher HSMRs in academic hospitals. This may result from (good quality) high-risk procedures, low quality of care or inadequate case-mix correction.
doi:10.1186/1472-6963-8-73
PMCID: PMC2362116  PMID: 18384695
5.  Privatising primary care 
PMCID: PMC1927100  PMID: 17132360
6.  Learning from death: a hospital mortality reduction programme 
Problem: There are wide variations in hospital mortality. Much of this variation remains unexplained and may reflect quality of care.
Setting: A large acute hospital in an urban district in the North of England.
Design: Before and after evaluation of a hospital mortality reduction programme.
Strategies for change: Audit of hospital deaths to inform an evidence-based approach to identify processes of care to target for the hospital strategy. Establishment of a hospital mortality reduction group with senior leadership and support to ensure the alignment of the hospital departments to achieve a common goal. Robust measurement and regular feedback of hospital deaths using statistical process control charts and summaries of death certificates and routine hospital data. Whole system working across a health community to provide appropriate end of life care. Training and awareness in processes of high quality care such as clinical observation, medication safety and infection control.
Effects: Hospital standardized mortality ratios fell significantly in the 3 years following the start of the programme from 94.6 (95% confidence interval 89.4, 99.9) in 2001 to 77.5 (95% CI 73.1, 82.1) in 2005. This translates as 905 fewer hospital deaths than expected during the period 2002-2005.
Lessons learnt: Improving the safety of hospital care and reducing hospital deaths provides a clear and well supported goal from clinicians, managers and patients. Good leadership, good information, a quality improvement strategy based on good local evidence and a community-wide approach may be effective in improving the quality of processes of care sufficiently to reduce hospital mortality.
PMCID: PMC1472716  PMID: 16738373
7.  Trends in day surgery rates 
BMJ : British Medical Journal  2005;331(7520):803.
PMCID: PMC1246075  PMID: 16210281
16.  Paediatric cardiac surgical mortality in England after Bristol: descriptive analysis of hospital episode statistics 1991-2002 
BMJ : British Medical Journal  2004;329(7470):825.
Objective To describe trends in mortality of open cardiac surgery in children in Bristol and England since 1991.
Design Retrospective analysis of hospital episode statistics data.
Setting All open cardiac surgery of children in England.
Population Patients younger than 16 undergoing open cardiac surgical procedures in England between April 1991 and March 2002. Three time periods were defined: epoch 3 (April 1991 to March 1995), epoch 5 (April 1996 to March 1999), epoch 6 (April 1999 to March 2002).
Main outcome measure Mortality in hospital within 30 days of a cardiac procedure.
Results We identified 5221 open operations between April 1996 and March 2002 in children under 1 year and 6385 in children aged 1-15 years. Mortality for all centres combined fell from 12% in epoch 3 to 4% in epoch 6. Mortality in children under 1 year at Bristol fell from 29% (95% confidence interval 21% to 37%) in epoch 3 to 3% (1% to 6%) in epoch 6, below the national average. The reduction in mortality did not seem to be due to fewer high risk procedures or an increase in the numbers of low risk cases. Oxford had a significantly higher mortality than the national average in all three epochs (11% (5% to 18%) in epoch 6), which was not affected by adjusting for procedure or the inclusion of cases with missing outcomes.
Conclusions At Bristol, mortality for open operations in children aged under 1 year has fallen markedly, to below the national average. Nationwide mortality has also fallen. Improved quality of care may account for the drop in mortality, through new technologies or improved perioperative and postoperative care, or both.
PMCID: PMC521569  PMID: 15472264

Results 1-25 (41)