Are diagnosis specific outcome indicators based on administrative data useful in assessing quality of hospital care?

Background: Hospital performance reports based on administrative data should distinguish differences in quality of care between hospitals from case mix related variation and random error effects. A study was undertaken to determine which of 12 diagnosis-outcome indicators measured across all hospitals in one state had significant risk adjusted systematic ( or special cause) variation (SV) suggesting differences in quality of care. For those that did, we determined whether SV persists within hospital peer groups, whether indicator results correlate at the individual hospital level, and how many adverse outcomes would be avoided if all hospitals achieved indicator values equal to the best performing 20% of hospitals. Methods: All patients admitted during a 12 month period to 180 acute care hospitals in Queensland, Australia with heart failure (n = 5745), acute myocardial infarction ( AMI) ( n = 3427), or stroke ( n = 2955) were entered into the study. Outcomes comprised in-hospital deaths, long hospital stays, and 30 day readmissions. Regression models produced standardised, risk adjusted diagnosis specific outcome event ratios for each hospital. Systematic and random variation in ratio distributions for each indicator were then apportioned using hierarchical statistical models. Results: Only five of 12 (42%) diagnosis-outcome indicators showed significant SV across all hospitals ( long stays and same diagnosis readmissions for heart failure; in-hospital deaths and same diagnosis readmissions for AMI; and in-hospital deaths for stroke). Significant SV was only seen for two indicators within hospital peer groups ( same diagnosis readmissions for heart failure in tertiary hospitals and inhospital mortality for AMI in community hospitals). Only two pairs of indicators showed significant correlation. If all hospitals emulated the best performers, at least 20% of AMI and stroke deaths, heart failure long stays, and heart failure and AMI readmissions could be avoided. Conclusions: Diagnosis-outcome indicators based on administrative data require validation as markers of significant risk adjusted SV. Validated indicators allow quantification of realisable outcome benefits if all hospitals achieved best performer levels. The overall level of quality of care within single institutions cannot be inferred from the results of one or a few indicators.

Reduced between-hospital variation in short term survival after acute myocardial infarction: the result of improved cardiac care?

Objectives: To re-examine interhospital variation in 30 day survival after acute myocardial infarction ( AMI) 10 years on to see whether the appointment of new cardiologists and their involvement in emergency care has improved outcome after AMI. Design: Retrospective cohort study. Setting: Acute hospitals in Scotland. Participants: 61 484 patients with a first AMI over two time periods: 1988 - 1991; and 1998 - 2001. Main outcome measures: 30 day survival. Results: Between 1988 and 1991, median 30 day survival was 79.2% ( interhospital range 72.1 - 85.1%). The difference between highest and lowest was 13.0 percentage points ( age and sex adjusted, 12.1 percentage points). Between 1998 and 2001, median survival rose to 81.6% ( and range decreased to 78.0 - 85.6%) with a difference of 7.6 ( adjusted 8.8) percentage points. Admission hospital was an independent predictor of outcome at 30 days during the two time periods ( p< 0.001). Over the period 1988 - 1991, the odds ratio for death ranged, between hospitals, from 0.71 ( 95% confidence interval ( CI) 0.58 to 0.88) to 1.50 ( 95% CI 1.19 to 1.89) and for the period 1998 - 2001 from 0.82 ( 95% CI 0.60 to 1.13) to 1.46 ( 95% CI 1.07 to 1.99). The adjusted risk of death was significantly higher than average in nine of 26 hospitals between 1988 and 1991 but in only two hospitals between 1998 and 2001. Conclusions: The average 30 day case fatality rate after admission with an AMI has fallen substantially over the past 10 years in Scotland. Between-hospital variation is also considerably less notable because of better survival in the previously poorly performing hospitals. This suggests that the greater involvement of cardiologists in the management of AMI has paid dividends.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD

Caries experience in preschool children referred for specialist dental care in hospital

Background : Increasing numbers of preschool children are being referred for specialist dental management in a paediatric hospital. Most cases have severe early childhood caries and require comprehensive management under general anaesthesia. The present study investigated risk factors for disease presence at initial consultation. Methods : A convenience sample of 125 children under four years of age from the north Brisbane region were examined and caries experience recorded using dmft and dmfs indices. A self-administered questionnaire obtained information regarding social, demographic, birth, neonatal, infant feeding and dental health behaviour variables. The data were analysed using the chi-square and one-way analysis of variance procedures. Results : Ninety-four per cent of referred children had severe ECC with mean dmft of 10.5 ± 3.8 and mean dmfs of 27.1 ± 15.1. Prevalence of severe ECC was significantly higher in children allowed a sweetened liquid in the infant feeding bottle (99 per cent) and allowed to sip from an infant feeding bottle during the day (100 per cent). Mean dmfs was significantly higher in children allowed to sleep with a bottle (28.7) and sip from a bottle during the day (29.9), children from a non-Caucasian background (31.8), those children that commenced regular toothbrushing between 6 to 12 months of age (28.1), had no current parental supervision of daily tooth-brushing (34.2) and had not taken daily fluoride supplements (27.8), vitamin supplements (27.8) or prescription medicine previously (27.6). Conclusions : The behavioural determinants for severe early childhood caries presence in hospital-referred children were similar to those identified in the regional preschool population.

Placard on truck during Labour Day procession Brisbane 1966

Placard "Brisbane 1966 not Germany 1936" on truck during Labour Day procession Brisbane 1966. Image shows arrest of student during anti war demonstration in Brisbane, Australia, 1966. City Hall tower can be seen in background.

Conscription banner on truck during Labour Day procession 1966 Brisbane

Conscription headstone banner on truck during Labour Day procession 1966 Brisbane, Australia.

Education not war May Day 1968 Brisbane

Children on float during May Day march in Brisbane 1968. Banners say Education not war and Overseas for Peace Trade not war.

Moscow Circus performers during May Day march in Brisbane 1968

Performers from the Moscow Circus on float during May Day parade, Brisbane, Australia, 1968

Inala Darra Peace Committee during Hiroshima Day 1964, Brisbane

Members of the Inala Darra Peace Committee during Hiroshima Day 1964, Brisbane.

Graceville and District Peace Committee members with banner during Hiroshima Day 1964, Brisbane

Graceville and District Peace Committee members with banner during Hiroshima Day 1964, Brisbane, Australia.

Dockers utility in Labour Day procession 1965 Brisbane Australia

Holden utility carrying members of the Federated Ship Painters and Dockers Union during the Labour Day march in 1965, Brisbane, Australia. Anti conscription banners can be seen in the background, and the facade of the Pearl Assurance Building.

Young Labor demands better deal for Aborigines - Labour Day 1965 Brisbane Australia

Young Labor Association float during the Labour Day procession in Brisbane, Australia 1965. Placards call for voting rights for Aborigines, more education facilities and award wages.

Boys with placard during the Labour Day procession in Brisbane, 1965

Boys with placard during the Labour Day procession in Brisbane, 1965.

APSR Market Day

Notes for presentation

Family orientated delivery of routine nursing care in hospital

Hospital nursing may be better deployed to acute clinical patient care. The recruitment of family assistance will facilitate this process in patients in hospital awaiting placement and without acute care issues.