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Many countries are seeking ways to measure the safety and performance of their health systems. The ability to track improvement and monitor safety event rates at a population level is provided by routinely collected administrative data in conjunction with a set of well-developed indicators such as the patient safety indicators from the Agency for Healthcare Research and Quality (AHRQ) in the United States of America. These indicators are currently in the International Classification of Diseases Ninth Revision, Clinical Modification (ICD-9-CM) whereas Australia has coded its data in ICD-10-Australian Modification (ICD-10-AM) since 1998. We describe the process recently undertaken to translate and revise the patient safety indicators (PSIs) so they can be of use with ICD-10-AM. The initial translation (electronic mapping, review and revision by expert coder, programming of codes and testing on data from 1996-1998 [ICD 9-CM] to 1998-2006 [ICD-10-AM, through 4 editions]) found that differences between ICD-9-CM and ICD-10-AM datasets presented some challenges. After this phase, which was faithful to AHRQ's case definitions, the indicators were refined for use with the condition onset flag, resulting in the AusPSIs. [ABSTRACT FROM AUTHOR]

OBJECTIVES:: The International Classification of Disease, 10th Revision (ICD-10) was introduced worldwide beginning in the late 1990s. Because there have been no published data on the quality of coding using ICD-10, the aim of our analysis is to assess the quality of ICD-10 coding in routinely collected hospital discharge data from Australia, which began using ICD-10 in 1998. METHODS:: Audit data from the years 1998-1999 (n = 7004) and 2000-2001 (n = 7631), excluding same-day chemotherapy and dialysis cases, were used in data analysis. Quality measures included prevalence comparisons, sensitivity, positive predictive value (PPV), and the kappa statistic. RESULTS:: Comparison of the audit sample to public hospital discharges showed little difference in age and gender, with audited cases more likely to be overnight stays. There was no difference in the median number of hospital assigned diagnosis and procedure codes per discharge. Agreement of the principal diagnosis code was 85% at the 3-digit level and 79% at the 4-digit level in 1998-1999; this rate had improved to 87% and 81% in 2000-2001. Principal procedure code agreement was 85% in 1998-1999 and 83% in 2000-2001 at the 5-digit level, and 81% and 80% at the 7-digit level, respectively. Specific major diagnoses, comorbid diagnoses, major procedures, and minor procedures showed good-to-excellent coding quality. CONCLUSIONS:: The transition to ICD-10 has occurred with no loss of data quality, with data showing a high level of reliability and adherence to coding standards. When consideration is given to the nature of the analysis, administrative data can provide highly reliable population-based estimates of hospitalization rates. [ABSTRACT FROM AUTHOR]

Objective: To determine the clinical characteristics, outcomes and longitudinal trends of sepsis occurring in cancer patients., Method: Retrospective study using statewide Victorian Cancer Registry data linked to various administrative datasets., Results: Among 215,763 incident cancer patients, incidence of sepsis within one year of cancer diagnosis was estimated at 6.4%. The incidence of sepsis was higher in men, younger patients, patients diagnosed with haematological malignancies and those with de novo metastatic disease. Of the 13,316 patients with a first admission with sepsis, 55% had one or more organ failures, 29% required care within an intensive care unit and 13% required mechanical ventilation. Treatments associated with the highest sepsis incidence were stem cell/bone marrow transplant (33%), major surgery (4.4%), chemotherapy (1.1%) and radical radiotherapy (0.6%). The incidence of sepsis with organ failure increased between 2008 and 2015, while 90-day mortality decreased., Conclusions: Sepsis in patients with cancer has high mortality and occurs most frequently in the first year after cancer diagnosis. Implications for public health: The number of cancer patients diagnosed with sepsis is expected to increase, causing a substantial burden on patients and the healthcare system., (© 2019 The Authors.)

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.

Philip Hoyle presents a compelling argument for the significant and highly valued role that the management of health information plays in the Australian healthcare system and the delivery of health services in this country. However, he also brings to our attention the ill-defined nature of the ethical oversight of this very information. Hoyle uses words such as "honesty," "commitment to beneficence," "commitment to equity" and "respect for variation" when describing the characteristics of ethical leadership. He singles out health information management professionals - Health Information Managers (HIMs) and Clinical Coders (CCs) - as the key professional group who need to step up and seize the initiative, get conversations going, form partnerships, do research and publish findings, so the knowledge and insights that the health information management profession has the potential to offer are not only more widely known and understood but also more useful to others working in the healthcare arena. Hoyle calls on health information management professionals to step out from behind the scenes and take responsibility for the ethical use of the information they help produce. Hoyle's words resonated powerfully with me, particularly with respect to the clinical coding workforce in Australia, which is made up of trained CCs and qualified HIMs. In a truly ethical environment, HIMs and CCs would not be asked to meet performance indicators for increased funding metrics or to change codes to avoid triggering certain indicators; they would simply be asked to ensure complete, accurate coding for every episode of care. This is what ethical leadership would look like. I am concerned about our clinical coding workforce. I am now asking, are our CCs and HIMs up to the task of taking back absolute and unchallenged ownership of their particular skill set, which makes them the keepers of the clinical coding standards and the experts in accurate and complete code assignment?

Clinical documentation improvement (CDI) roles are being increasingly created in Australian hospitals. It is important to understand what good clinical documentation is and who is responsible for it as well as what these roles potentially offer our health system. This article explores the role of a CDI specialist, the benefits and pitfalls of clinical documentation improvement programs, and mounts an argument that health information managers and clinical coders are uniquely placed to fill these roles in Australian hospitals.

Background: In the United States, the Agency for Healthcare Research and Quality (AHRQ) has developed 20 Patient Safety Indicators (PSIs) to measure the occurrence of hospital adverse events from medico-administrative data coded according to the ninth revision of the international classification of disease (ICD-9-CM). The adaptation of these PSIs to the WHO version of ICD-10 was carried out by an international consortium., Methods: Two independent teams transcoded ICD-9-CM diagnosis codes proposed by the AHRQ into ICD-10-WHO. Using a Delphi process, experts from six countries evaluated each code independently, stating whether it was "included", "excluded" or "uncertain". During a two-day meeting, the experts then discussed the codes that had not obtained a consensus, and the additional codes proposed., Results: Fifteen PSIs were adapted. Among the 2569 proposed diagnosis codes, 1775 were unanimously adopted straightaway. The 794 remaining codes and 2541 additional codes were discussed. Three documents were prepared: (1) a list of ICD-10-WHO codes for the 15 adapted PSIs; (2) recommendations to the AHRQ for the improvement of the nosological frame and the coding of PSI with ICD-9-CM; (3) recommendations to the WHO to improve ICD-10., Conclusions: This work allows international comparisons of PSIs among the countries using ICD-10. Nevertheless, these PSIs must still be evaluated further before being broadly used., (Copyright © 2011 Elsevier Masson SAS. All rights reserved.)

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.

Objective: To compare two methods for identifying adverse events using routinely recorded hospital abstract data in all public and private hospitals in Victoria, Australia., Methods: Secondary analysis of data on all admissions in the period 1 July 2000-30 June 2001 (n = 1,645,992) to estimate the rates of adverse events using International Classification of Diseases 10th Revision Australian Modification codes alone and in combination with an "incidence" data flag indicating complicating diagnoses which arise after hospitalization; rates of incidence and pre-existing adverse events, and rates for same-day and multi-day admissions., Results: In total, 8% of all admissions were recorded with an adverse event. Use of ICD codes alone identified only 59% of the events identified using the combined method, giving a prevalence rate of only 5%. Incident cases, that is, those occurring in the index admission, represented 68% of identified adverse events. The adverse events incidence rate for multi-day admissions was significantly higher at 12%, compared with the same day rate of 0.4%., Conclusion: 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.

This article reports on the assignment of ICD-10-AM and EAN codes to 2500 topics in Therapeutic Guidelines (TG). The analysis of the assignment of ICD-10-AM codes in this project has revealed that ICD-10-AM is not capable of describing the complete clinical information in the guidelines series. It is not likely that any existing single classification scheme will be capable of this and that a combination of schemes will be necessary. The TG data model was integrated with the prototype MCCA data model for drug products. This integration indicates that the representation of drugs, while not ideal, is an appropriate means of linking clinical drug reference information to drug product information.

The key drivers of change to clinical coding practice are identified and examined, and a major shift is predicted. The traditional purposes of the coding function have been the provision of data for research and epidemiology, in morbidity data reporting and, latterly, for casemix-based funding. It is contended that, as the development of electronic health records progresses, the need for an embedded nomenclature will force major change in clinical coding practice. Clinical coders must become expert in information technology and analysis, change their work practices, and become an integral part of the clinical team.