Mapping the Queensland Health iPharmacy Medication File to the Australian Medicines Terminology Using Snapper.

An exploratory exercise in mapping approximately 8000 medication terms from the Queensland Health iPharmacy Medication File to the Australian Medicines Terminology (AMT) was carried out to determine coverage, build specialist knowledge, and inform future clinical terminology strategies. Snapper was the mapping tool selected for this exercise. The Automap function of the tool mapped 39.2% of the items that were successfully mapped, and the remainder were manually mapped. A total of 51.8% of the sample items were mapped to a semantically equivalent AMT concept with 50.0% of terms being mapped to a satisfactory fully specified term, and 1.8% of terms being mapped to a fully specified term that was considered unsuitable for QH clinical purposes. Rules and guidelines on how to deal with the emerging differences between the two terminologies were developed during the course of the project. Snapper was found to be an appropriate tool for this exercise; its functionality is being constantly refined to assist users. As a result, this exercise will provide NEHTA with input for the national scope and content for AMT, and QH will endeavour to prepare the iPharmacy medication file for future interfaces with other terminologies.

Comparing the coding of complications in Queensland and Victorian admitted patient data.

OBJECTIVE: To examine differences between Queensland and Victorian coding of hospital-acquired conditions and suggest ways to improve the usefulness of these data in the monitoring of patient safety events. DESIGN: Secondary analysis of admitted patient episode data collected in Queensland and Victoria. METHODS: Comparison of depth of coding, and patterns in the coding of ten commonly coded complications of five elective procedures. RESULTS: Comparison of the mean complication codes assigned per episode revealed Victoria assigns more valid codes than Queensland for all procedures, with the difference between the states being significantly different in all cases. The proportion of the codes flagged as complications was consistently lower for Queensland when comparing 10 common complications for each of the five selected elective procedures. The estimated complication rates for the five procedures showed Victoria to have an apparently higher complication rate than Queensland for 35 of the 50 complications examined. CONCLUSION: 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.

Development of a validation algorithm for 'present on admission' flagging.

BACKGROUND: The 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. METHODS: Our 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. RESULTS: Of 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). CONCLUSION: An 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.

A classification of hospital-acquired diagnoses for use with routine hospital data.

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.

Using ICD-10-AM codes to characterise hospital-acquired complications.

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.

Australian Hospital Data: Not Just for Funding.

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.