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Abstract
The increasing demand for greater clinical accountability requires development of convenient tools to measure healthcare safety and quality, which are able to provide information contemporaneously. The purpose of this paper is to describe the development of the Hospital Mortality Project, a quality assurance initiative designed to encourage and facilitate clinical accountability for hospital mortality by all clinical departments and clinicians. T he project was carried out in two stages. Part 1: After registration of in-hospital patient deaths (1 May 2004 to 31 December 2007), the consultant in charge of patient care was notified and requested to assign the death to a predefined category. This categorisation leads to further investigation as appropriate. Part 2: Hospital administrative data from 1 April 1997 to 31 December 2007 were used to assess a defined index, the Hospital Mortality Index (HMI), which was the expressed in the form of an Attribute Control Chart (p-CHART ) and then used as a performance indicator for hospital departments and clinicians. Summary data are reported to the clinical departments and to the hospital executive via the Quality Improvement Committee on quarterly basis. The clinical review was complete for 2,990 of 3,132 (95%) inpatient deaths till 31 December 2007, while a further 142 (5%) deaths are still in the process of being reviewed as of 7 April 2008. The median age of all the cases was 78 years (IQR 67-86) of which 1,657 (53%) were male. The Poisson regression analysis showed that since 1997 departments with a minimum of 100 deaths in total showed no clinically significant change in HMI over time. The Hospital Mortality Project provides a simple and efficient tool to analyse data for clinical managers to facilitate accountability.

Abstract
The Performance Indicators for Coding Quality (PICQ) is a data quality assessment tool developed by Australia’s National Centre for Classification in Health (NCCH). PICQ consists of a number of indicators covering all ICD-10-AM disease chapters, some procedure chapters from the Australian Classification of Health Intervention (ACHI) and some Australian Coding Standards (ACS). The indicators can be used to assess the coding quality of hospital morbidity data by monitoring compliance of coding conventions and ACS; this enables the identification of particular records that may be incorrectly coded, thus providing a measure of data quality. There are 31 obstetric indicators available for the ICD-10-AM Fourth Edition. Twenty of these 31 indicators were classified as Fatal, nine as Warning and two Relative. These indicators were used to examine coding quality of obstetric records in the 2004-2005 financial year Australian national hospital morbidity dataset. Records with obstetric disease or procedure codes listed anywhere in the code string were extracted and exported from the SPSS source file. Data were then imported into a Microsoft Access database table as per PICQ instructions, and run against all Fatal and Warning and Relative (N=31) obstetric PICQ 2006 Fourth Edition Indicators v.5 for the ICD-10- AM Fourth Edition. There were 689,905 gynaecological and obstetric records in the 2004-2005 financial year, of which 1.14% were found to have triggered Fatal degree errors, 3.78% Warning degree errors and 8.35% Relative degree errors. The types of errors include completeness, redundancy, specificity and sequencing problems. It was found that PICQ is a useful initial screening tool for the assessment of ICD-10-AM/ACHI coding quality. The overall quality of codes assigned to obstetric records in the 2004- 2005 Australian national morbidity dataset is of fair quality.