Objectives: To determine the incidence of adverse events in patients admitted in the year 2003–04 to selected Victorian hospitals; to identify the main hospital‐acquired diagnoses; and to estimate the cost of these complications to the Victorian and Australian health system. Design: The patient‐level costing dataset for major Victorian public hospitals, 1 July 2003 – 30 June 2004, was analysed for adverse events by identifying C‐prefixed diagnosis codes denoting complications, preventable or otherwise, arising during the course of hospital treatment. The in‐hospital cost of adverse events was estimated using linear regression modelling, adjusting for age and comorbidity. Main outcome measures: Cost of each patient admission (“admitted episode”), length of stay and mortality. Results: During the designated timeframe, 979 834 admitted episodes were in the sample, of which 67 435 (6.88%) had at least one adverse event. Patients with adverse events stayed about 10 days longer and had over seven times the risk of in‐hospital death than those without complications. After adjusting for age and comorbidity, the presence of an adverse event adds $6826 to the cost of each admitted episode. The total cost of adverse events in this dataset in 2003–04 was $460.311 million, representing 15.7% of the total expenditure on direct hospital costs, or an additional 18.6% of the total inpatient hospital budget. Conclusion: Adverse events are associated with significant costs. Administrative datasets are a cost‐effective source of information that can be used for a range of clinical governance activities to prevent adverse events.

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.

An "incidence flag" is essential to identify those adverse events for which a hospital has unambiguous responsibility. Using such a flag, secondary analysis of administrative data can provide hospital quality assurance programmes with a comprehensive view of all adverse events (not just "sentinel" events) at a reasonable cost and with more timely results than more intensive methods can achieve. Although the method is likely to underestimate the true rate of adverse events (in particular, by not capturing adverse events which only manifest after discharge), in this study of Australian hospitals, rates of adverse events were found to be similar to those derived from studies using manual review of patient records.

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Private health insurance subsidy is now estimated to cost $2.19 billion; government support for private health care includes a further $1.2 billion of Medicare benefits expenditure in hospitals. The subsidy cannot be justified on efficiency grounds, as, on the basis of available evidence and taking casemix into account, public hospitals are more efficient than private hospitals. The original stated objective of the subsidy was to “take pressure off public hospitals”. If the insurance subsidy and the Medicare Benefit Schedule rebate expenditure were applied to purchasing public hospital treatment at full average cost, 58% of current private sector demand could be accommodated. If 10% of the demand were met at marginal cost, this would increase to 65%. The objective of “taking pressure off public hospitals” could be more efficiently achieved by direct funding of public hospitals rather than through subsidies for private health insurance.