Reconciling hospital-acquired complications and CHADx+ in Victorian coded hospital data

Abstract

Background: The Council of Australian Governments has focused the attention of health service managers and state health departments on a list of hospital-acquired complications (HACs) proposed as the basis of funding adjustments for poor quality of hospital inpatient care. These were devised for the Australian Commission on Safety and Quality in Health Care as a subset of their earlier classification of hospital-acquired complications (CHADx) and designed to be used by health services to monitor safety performance for their admitted patients.

Objective: To improve uptake of both classification systems by clarifying their purposes and by reconciling the ICD-10-AM code sets used in HACs and the Victorian revisions to the CHADx system (CHADx+).

Method: Frequency analysis of individual clinical codes with condition onset flag (COF 1) included in both classification systems using the Victorian Admitted Episodes Dataset for 2014/2015 ( n = 2,623,275 separations). Narrative description of the resulting differences in definition of "adverse events" embodied in the two systems.

Results: As expected, a high proportion of ICD-10-AM codes used in the HACs also appear in CHADx+, and given the wider scope of CHADx+, it uses a higher proportion of all COF 1 diagnoses than HACs (82% vs. 10%). This leads to differing estimates of rates of adverse events: 2.12% of cases for HACs and 11.13% for CHADx+. Most CHADx classes (70%) are not covered by the HAC system; discrepancies result from the exclusion from HACs of several major CHADx+ groups and from a narrower definition of detailed HAC classes compared with CHADx+. Case exclusion criteria in HACs (primarily mental health admissions) resulted in a very small proportion of discrepancies (0.13%) between systems.

Discussion: Issues of purpose and focus of these two Australian systems, HACs for clinical governance and CHADx+ for local quality improvement, explain many of the differences between them, and their approach to preventability, and risk stratification.

Conclusion: A clearer delineation between these two systems using routinely coded hospital data will assist funders, clinicians, quality improvement professionals and health information managers to understand discrepancies in case identification between them and support their different information needs.

Keywords: International Classification of Diseases; activity-based funding; classification; clinical coding; financial management; health classification; health information management; health services administration; hospital; hospital-acquired conditions; medical classification; patient safety; quality and safety.