Developing a summary hospital mortality index: retrospective analysis in English hospitals over five years

Abstract

Objectives To develop a transparent and reproducible measure for hospitals that can indicate when deaths in hospital or within 30 days of discharge are high relative to other hospitals, given the characteristics of the patients in that hospital, and to investigate those factors that have the greatest effect in changing the rank of a hospital, whether interactions exist between those factors, and the stability of the measure over time.Design Retrospective cross sectional study of admissions to English hospitals. Setting Hospital episode statistics for England from 1 April 2005 to 30September 2010, with linked mortality data from the Office for National Statistics.Participants 36.5 million completed hospital admissions in 146 general and 72 specialist trusts.Main outcome measures Deaths within hospital or within 30 days of discharge from hospital. ResultsThe predictors that were used in the final model comprised admission diagnosis, age, sex, type of admission, and comorbidity. The percentage of people admitted who died in hospital or within 30 days of discharge was 4.2% for males and 4.5% for females. Emergency admissions comprised 75% of all admissions and 5.5% died, in contrast to 0.8% who died after an elective admission. The percentage who died with a Charlson comorbidity score of 0 was 2% in contrast with 15% who died with a score greater than 5. Given these variables, the relative standardised mortality rates of the hospitals were not noticeably changed by adjusting for the area level deprivation and number of previous emergency visits to hospital. There was little evidence that including interaction terms changed the relative values by any great amount. Using these predictors the summary hospital mortality index (SHMI) was derived. For 2007/8 the model had a C statistic of 0.911 and accounted for 81% of the variability of between hospital mortality. A random effects funnel plot was used to identify outlying hospitals. The outliers from the SHMI over the period 2005-10 have previously been identified using other mortality indicators. ConclusionThe SHMI is a relatively simple tool that can be used in conjunction with other information to identify hospitals that may need further investigation. IntroductionAbout 60% of deaths occur in hospital.1 Although a large proportion of these are inevitable, avoidance of unnecessary death is an important objective for health services. Several methods are used within the United Kingdom's health service to identify trusts with high in-hospital mortality, the most widely publicised being the standardised mortality ratio (a ratio of observed to expected deaths), which is calculated from a statistical model. The hospital standardised mortality ratio (HSMR)2 produced by Dr Foster, a provider of healthcare information based at Imperial College, London has been used by the Department of Health for several years to identify failing hospitals. 3 Concerns and criticism over the methodology and interpretation of standardised mortality ratios have, however, been raised both i...

Variação do desempenho hospitalar segundo fontes de pagamento nos estados de São Paulo e Rio Grande do Sul, Brasil

“…Quanto ao ajuste de risco realizado, as variá-veis utilizadas neste estudo guardam similaridades com outros trabalhos 4,14,26,27,31 . Nos estudos nacionais, a idade, o sexo, a utilização de UTI, o tipo de admissão e o diagnóstico principal têm sido as principais variáveis usadas para a padronização de taxas e modelos de ajuste de risco, pois se encontram disponíveis nas bases brasileiras 28,29,33,34 .…”

“…Além disso, testou-se a inclusão da variá-vel para indicar se o diagnóstico principal estava entre as causas responsáveis por 50% das mortes, mantendo coerência com a proposta de Jarman et al 21 . As variáveis aqui testadas são semelhantes às incorporadas no ajuste de risco em vários estudos, especialmente naqueles baseados em dados administrativos 4,13,26,27 . Daquelas amplamente empregadas, apenas o tipo de admissão (emergência/eletiva) não pôde ser utilizado, por indisponibilidade no CIHA.…”

“…Por fim, há a limitação quanto ao recorte do estudo, visto que aspectos organizacionais e financeiros dos hospitais e do contexto que poderiam contribuir para melhor compreensão dos resultados não fizeram parte do escopo, destacando-se: a disponibilidade de profissionais de saúde e equipamentos por paciente; a rotatividade de leitos; os incentivos ou pressões financeiras; as características de mercado; o investimento em atividades voltadas à qualidade; o contexto e a cultura institucional; as diferenças regionais; entre outros 4,24,25 .…”

“…As medidas de qualidade da atenção hospitalar com foco no resultado do cuidado, especialmente a mortalidade hospitalar, destacamse quando são empregados apenas poucos indicadores, devido à sua capacidade de expressar o produto de todo o processo de cuidado 4,5,6 . Entre as vantagens do uso da mortalidade hospitalar como medida de resultado do cuidado está o fato de a morte ser um evento definitivo e único, que, por lei, deve ser registrado e, portanto, espera-se melhor completude e acurácia no seu registro.…”

Learning from practice variation to improve the quality of care

“…The latter are also more susceptible to case-mix variation, care processes outside the direct control of the QI team and to variation in how the case mix is coded. 10,[13][14][15][16] Practice variation can only be identified by collecting data on several providers or facilities and comparing the results. Therefore, measurement usually occurs at the regional or national level.…”