Objective: To develop a tool to allow Australian hospitals to monitor the range of hospital‐acquired diagnoses coded in routine data in support of quality improvement efforts. Design and setting: Secondary analysis of abstracted inpatient records for all episodes in acute care hospitals in Victoria for the financial year 2005–06 (n = 2.032 million) to develop a classification system for hospital‐acquired diagnoses; each record contains up to 40 diagnosis fields coded with the ICD‐10‐AM (International Classification of Diseases, 10th revision, Australian modification). Main outcome measure: The Classification of Hospital Acquired Diagnoses (CHADx) was developed by: analysing codes with a “complications” flag to identify high‐volume code groups; assessing their salience through an iterative review by health information managers, patient safety researchers and clinicians; and developing principles to reduce double counting arising from coding standards. Results: The dataset included 126 940 inpatient episodes with any hospital‐acquired diagnosis (complication rate, 6.25%). Records had a mean of three flagged diagnoses; including unflagged obstetric and neonatal codes, 514 371 diagnoses were available for analysis. Of these, 2.9% (14 898) were removed as comorbidities rather than complications, and another 118 640 were removed as redundant codes, leaving 380 833 diagnoses for grouping into CHADx classes. We used 4345 unique codes to characterise hospital‐acquired conditions; in the final CHADx these were grouped into 144 detailed subclasses and 17 “roll‐up” groups. Conclusions: Monitoring quality improvement requires timely hospital‐onset data, regardless of causation or “preventability” of each complication. The CHADx uses routinely abstracted hospital diagnosis and condition‐onset information about in‐hospital complications. Use of this classification will allow hospitals to track monthly performance for any of the CHADx indicators, or to evaluate specific quality improvement projects.

This paper describes the limitations of using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM) to characterise patient harm in hospitals. Limitations were identified during a project to use diagnoses flagged by Victorian coders as hospital-acquired to devise a classification of 144 categories of hospital acquired diagnoses (the Classification of Hospital Acquired Diagnoses or CHADx). CHADx is a comprehensive data monitoring system designed to allow hospitals to monitor their complication rates month-to-month using a standard method. Difficulties in identifying a single event from linear sequences of codes due to the absence of code linkage were the major obstacles to developing the classification. Obstetric and perinatal episodes also presented challenges in distinguishing condition onset, that is, whether conditions were present on admission or arose after formal admission to hospital. Used in the appropriate way, the CHADx allows hospitals to identify areas for future patient safety and quality initiatives. The value of timing information and code linkage should be recognised in the planning stages of any future electronic systems.

BackgroundThe use of routine hospital data for understanding patterns of adverse outcomes has been limited in the past by the fact that pre-existing and post-admission conditions have been indistinguishable. The use of a 'Present on Admission' (or POA) indicator to distinguish pre-existing or co-morbid conditions from those arising during the episode of care has been advocated in the US for many years as a tool to support quality assurance activities and improve the accuracy of risk adjustment methodologies. The USA, Australia and Canada now all assign a flag to indicate the timing of onset of diagnoses. For quality improvement purposes, it is the 'not-POA' diagnoses (that is, those acquired in hospital) that are of interest.MethodsOur objective was to develop an algorithm for assessing the validity of assignment of 'not-POA' flags. We undertook expert review of the International Classification of Diseases, 10th Revision, Australian Modification (ICD-10-AM) to identify conditions that could not be plausibly hospital-acquired. The resulting computer algorithm was tested against all diagnoses flagged as complications in the Victorian (Australia) Admitted Episodes Dataset, 2005/06. Measures reported include rates of appropriate assignment of the new Australian 'Condition Onset' flag by ICD chapter, and patterns of invalid flagging.ResultsOf 18,418 diagnosis codes reviewed, 93.4% (n = 17,195) reflected agreement on status for flagging by at least 2 of 3 reviewers (including 64.4% unanimous agreement; Fleiss' Kappa: 0.61). In tests of the new algorithm, 96.14% of all hospital-acquired diagnosis codes flagged were found to be valid in the Victorian records analysed. A lower proportion of individual codes was judged to be acceptably flagged (76.2%), but this reflected a high proportion of codes used <5 times in the data set (789/1035 invalid codes).ConclusionAn indicator variable about the timing of occurrence of diagnoses can greatly expand the use of routinely coded data for hospital quality improvement programmes. The data-cleaning instrument developed and tested here can help guide coding practice in those health systems considering this change in hospital coding. The algorithm embodies principles for development of coding standards and coder education that would result in improved data validity for routine use of non-POA information.

Our findings demonstrate that the coding of complications is more comprehensive in Victoria than in Queensland. It is known that inconsistencies exist between states in routine hospital data quality. Comparative use of patient safety indicators should be viewed with caution until standards are improved across Australia. More exploration of data quality issues is needed to identify areas for improvement.

Collections of routine, or 'administrative', hospital data have many applications in health care and are now recognised as valuable sources of information. In recent decades, administrative data have been seen primarily as funding and billing tools to assist with the reimbursement of hospitals for services provided; this purpose remains the primary focus of the clinical coder workforce. More recently, hospital data have been recognised as valuable resources for a range of health system improvement processes beyond funding. The focus of this paper is to review and demonstrate the diverse uses of administrative data in health services research and quality improvement. By gaining an understanding of how the data are used, we can appreciate the importance of good quality data from the perspective of its multiple uses. This paper describes a sample of the studies conducted in Australia using administrative data in health care improvement.