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

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.

Socio-economic disadvantage and demographics as factors in stage of colorectal cancer presentation and survival.

BACKGROUND: Colorectal cancer (CRC) is common, and early diagnosis improves outcome. Overseas studies have suggested that low socio-economic status (SES) is related to advanced cancer stage at presentation and reduced survival. The situation in Australia is unclear. This study examines the effect of demographic and SES on CRC stage at presentation and survival in a single tertiary centre. METHODS: Patients undergoing surgical resection for CRC (1 January 2005 to 31 December 2010) were identified, and socio-demographic and histopathological information obtained. Four socio-economic indices using 2006 Australian Census data were assigned by residential postcode. Factors contributing to tumour (T) and American Joint Committee on Cancer (AJCC) stage at presentation and survival were assessed. RESULTS: Five hundred and fifty-seven patients were included. Results did not support a relationship between SES and either advanced stage at presentation or survival. Only one index (economic resources) was related to a more advanced T stage at presentation (P = 0.011); none were related to AJCC stage or survival. No significant relationship was found between an individual's country of birth, language spoken, private insurance or employment status and presenting with a later T or AJCC stage. Age, AJCC and T stage at diagnosis and emergency presentation significantly affected survival on multivariate analysis. CONCLUSION: SES and most demographic factors did not appear to significantly influence CRC stage at presentation and outcome. A focus on obtaining equivalent access to health care both nationally and internationally could prove beneficial in improving outcomes for CRC.

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.

The US Medicare policy of not reimbursing hospital-acquired conditions: what impact would such a policy have in Victorian hospitals?

OBJECTIVE: To model the effect of excluding payment for eight hospital-acquired conditions (HACs) on hospital payments in Victoria, Australia. DESIGN, SETTING AND PARTICIPANTS: Retrospective ecological study using the Victorian Admitted Episodes Dataset. The analysis involved all acute inpatient admissions to Victorian public and private hospitals between 1 July 2007 and 30 June 2008. INTERVENTIONS: Each admission record includes up to 40 diagnosis and procedure codes from which payments are calculated. The model deleted diagnosis codes for eight HACs from all records, then recalculated payments to estimate the impact of a policy of non-payment for HACs. MAIN OUTCOME MEASURE: The effect on hospital payments of excluding diagnosis codes for eight HACs. RESULTS: 2,047,133 cases with total estimated payments of $4902 million were identified; 994 cases (0.05%) had one or more diagnoses meeting the code definition for a definable HAC, representing total payments of $24.1 million. In-hospital falls and pressure ulcers were the most commonly coded HACs. Applying a model that excluded HAC diagnosis codes changed the diagnosis-related group for 134 cases (13.5%), thereby generating a $448,630 reduction in payments. CONCLUSIONS: Introducing a non-payment for HACs policy similar to that introduced by Medicare in the United States would have little direct financial impact in the Australian context, although additional savings would accrue if HAC rates were reduced. Such a policy could add further incentive to current initiatives aimed at reducing HACs.

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.

Describing Iranian hospital activity using Australian Refined DRGs: a case study of the Iranian Social Security Organisation.

OBJECTIVE: To describe Iran's hospital activity with Australian Refined Diagnosis Related Groups (AR-DRGs). METHOD: A total of 445,324 separations was grouped into discreet DRG classes using AR-DRGs. L(3)H(3); IQR and 10th-95th percentile were used to exclude outlier cases. Reduction in variance (R(2)) and coefficient of variation (CV) were applied to measure model fit and within group homogeneity. RESULTS: Total hospital acute inpatients were grouped into 579 DRG groups in which 'surgical' cases represented 63% of the total separations and 40% of total DRGs. Approximately 12.5% of the total separations fell into DRGs O60C (vaginal delivery) and 28% of the total separations classified into major diagnostic category (MDC) 14 (pregnancy and childbirth). Although reduction in variance (R(2)) for untrimmed data was low (R(2)=0.17) for LOS, trimming by L(3)H(3), IQR, and 10th-95th percentile methods improved the value of R(2) to 0.53, 0.48, and 0.51, respectively. Low value of R(2) for AR-DRGs within several MDCs were identified, and found to reflect high variability in one or two DRGs. High within-DRG variation was identified for 23% of DRGs using untrimmed data. CONCLUSION: Low quality and incomplete data undermines the accuracy of casemix information. This may require improvement in coding quality or further classification refinement in Iran. Further study is also required to compare AR-DRG performance with other versions of DRGs and to determine whether the low value of R(2) for several MDCs is due to the weakness of the AR-DRG algorithm or to Iranian specific factors.

Health technology assessment in Australia: challenges ahead.

Australia is well placed to again lead the world in health technology assessment.

Adverse events in Victorian admissions for elective surgery.

OBJECTIVES: To investigate a method to identify and understand patterns of adverse events by utilising secondary data analysis; to identify the types of complications associated with elective surgery; to identify any specific "adverse event-prone" elective procedures; and to consider the implications of these patterns for hospital patient safety programs. SETTING: Public hospitals in Victoria. DESIGN: Secondary analysis of data on acute hospital admissions for elective surgery in the period 1 July 2000 to 30 June 2001, for non-obstetric patients older than 15 years (n = 177 533). MAIN OUTCOME MEASURES: Estimated rates of adverse events for the most commonly performed elective surgery procedures; frequency of the most commonly recorded adverse event types. RESULTS: Of all admissions, 15.5% had at least one complication of care. The most frequent first-recorded single complication code, in 9.6% of cases with a complication, was "Haemorrhage and haematoma complicating a procedure". The most common adverse event categories were cardiac and circulatory complications (23%), symptomatic complications (18%), and surgical and drug-related complications (17%). The procedure blocks most frequently associated with an adverse event were coronary artery bypass surgery (67%), colectomy (52%), hip and knee arthroplasty (42% and 36%, respectively), and hysterectomy (20%). The types of complications associated with the four most adverse event-prone procedures were cardiac arrhythmias, surgical adverse events (haemorrhage or laceration), intestinal obstruction, anaemia, and symptomatic complications. CONCLUSION: Routinely collected data are valuable in obtaining information on complication types associated with elective surgery. International Classification of Diseases codes and surgical procedure "blocks" allow very sophisticated investigation of types of complications and differences in complication rates for different surgical approaches. The usefulness of such data relies on good documentation in the medical record, thorough coding and periodic data audit. The limitations of the method described here include the lack of follow-up after discharge, variable coding standards between institutions and over time (potentially distorting information on rates), lack of information on the causative factors for some adverse events, and a limited capacity to support investigation of particular cases. Hospitals should consider monitoring complication rates for individual elective procedures or blocks of similar procedures, and comparing adverse event rates over time and with peer hospitals as an integral part of their patient safety programs.

Patterns of first-coded complications in acute episodes of lung cancer care.

The objective of this research was to document the most common first-recorded adverse events of inpatient care for lung cancer in Victoria, Australia. The sample comprised record abstracts for 3642 admissions (overnight or longer) of adult patients with lung cancer, extracted from the Victorian Admitted Episodes Database for 2000-2001. The method involved analysis of diagnoses prefixed with "C" (an indicator for diagnoses which arose only after hospitalisation), calculation of complication rates by intervention type, and analysis of complication type by intervention. Overall, 23% of episodes recorded at least one in-hospital complication, with highest rates for radiotherapy and surgical interventions. The highest surgical complication rates were for pneumonectomies, lobectomies, and lung resections. Nausea and vomiting were the most common first-recorded complications for both chemotherapy and radiotherapy. It was concluded that complications through the use of morbidity data may offer a timely and economical method for health care organisations to screen large numbers of patient episodes.

Diagnosis of appendicitis in adults by ultrasonography or computed tomography: a systematic review and meta-analysis.

OBJECTIVES: The use of ultrasonography and computed tomography (CT) in the diagnosis of appendicitis in adult patients was compared. METHODS: Systematic review and meta-analysis of current evidence in two clinical situations: unselected nonpregnant, adult patients with symptoms of appendicitis, and more selective use in only those patients who still have an equivocal diagnosis subsequent to routine clinical investigations. RESULTS: Meta-analysis of eligible studies shows CT to have better sensitivity and specificity than ultrasound in both clinical situations. CONCLUSIONS: Application of these findings in clinical practice and/or policy would need to evaluate the better diagnostic performance of CT against its cost and availability. In addition, it is imperative that future studies be conducted in patient populations that are well-defined with respect to prior investigations. Sequelae of false-negative and false-positive diagnoses should also be evaluated.