Australian Refined Diagnosis Related Groups Research Articles

Reinvigorating the Quality Improvement Incentives of Hospital Prospective Payment

Being hospitalized is traumatic enough. No one wants anything more to go wrong certainly least of all patients. Clinicians, other hospital staff, and management also share powerful incentives to strive for the best possible outcomes of care. For health care professionals, many of whom likely entered their fields motivated by humanistic goals of reducing suffering and doing good, moral imperatives add to very practical inducements: happy patients return, recommend them to family and friends, and do not file lawsuits. Hospital managers, also wary of legal actions and regulatory investigations, wish to avoid embarrassing headlines in local newspapers and to fulfill their mission of community service. Nonetheless, these compelling and multifaceted motivations have not yet sufficed to eliminate or significantly reduce iatrogenic inpatient complications. Despite landmark studies that raised public consciousness about unacceptable levels of adverse events in the United States,1 hospital patients continue to confront threats from poor quality services. Maybe financial forces could finally induce hospitals to address this persistent risk by conducting aggressive quality improvement programs to reduce complications of inpatient care. As described in this issue of Medical Care, the underlying premise of the proposals of McNair et al involves aligning incentives structuring an inpatient hospital prospective payment system (PPS) that will financially reward hospitals and physicians for reducing in-hospital complications, thereby improving patient outcomes. Using administrative data files from the State of Victoria in Australia, McNair et al have deftly manipulated their analytic database containing more than 1 .2 million hospital discharges to produce a new scheme for assigning per case payment weights. Victoria currently sets hospital PPS payment levels using Australian-refined diagnosis related groups (AR-DRGs). After stripping off all diagnoses flagged as occurring after hospital admission and thus considered complications of care along with other diagnoses viewed as prima facie indicators of in-hospital complications (including prolonged ventilator use without a tetraplegia diagnosis), the researchers reran the AR-DRG grouper algorithm. With the newly assigned groupings, now unaffected by putative inpatient complications, they recomputed PPS payment weights using total cost figures from the 132 Victoria public acute care hospitals. These budget neutral cost weights, they assert, could support a PPS that would still compensate the total Victoria public hospital system for the costs of inpatient complications but would financially benefit individual hospitals that manage (such as through quality improvement interventions) to reduce their complication rates. Despite the theoretical attractions of this approach and the clever manipulations of the administrative database, something about the proposal leaves me uneasy. Yes, I know I have slipped into the first person, singular pronoun not the omniscient voice of the all-knowing, all-wise editorial writer. But as a serial manipulator of administrative data myself, I recognize others who indulge in this pursuit. Although McNair et al offer intriguing ideas and hopeful speculation, they provide no evidence that their approach will achieve its ultimate aim of reducing inpatient complications. Retaining the first person conceit, I therefore respond in kind, offering primarily my opinion organized around 6 brief arguments to explain my unease.

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

To describe Iran's hospital activity with Australian Refined Diagnosis Related Groups (AR-DRGs). 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. 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. 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.

New approaches to reimbursement schemes based on patient classification systems and their comparison

We propose reimbursement schemes based on patient classification systems (PCSs) that include adjustments for length of stay (LOS) and exceptional costs and are designed to minimize undesirable effects of economic incentives. In addition, a statistical approach to compare the schemes and the underlying PCSs is proposed, where costs and LOSs for two successive years are used. The first year data provides estimates of the class cost means and the next year's reimbursements which are compared with the second year's costs. This method focuses on the predictive power of a PCS and differs from the usual retrospective analyses based on the proportion of explained variance for single year data. The approach is applied to discharge data of Swiss hospitals where stays are grouped according to five PCSs: All Patient Diagnosis-Related Groups (AP-DRGs), All Patient Refined Diagnosis-Related Groups (APR-DRGs), International Refined Diagnosis-Related Groups (IR-DRGs), Australian Refined Diagnosis-Related Groups (AR-DRGs), and SQLape. When adjusting for LOS and outliers, these systems do not differ substantially in their ability to predict cost of stay. Therefore, increasing the number of classes does not necessarily improve cost predictions. However, the payment of a fixed amount per diem (not exceeding the marginal cost) and correcting the reimbursements for exceptional costs substantially reduces the average discrepancy between costs and reimbursements.

Describing Chinese hospital activity with diagnosis related groups (DRGs): A case study in Chengdu

Objective: To examine the applicability of an Australian casemix classification system to the description of Chinese hospital activity. Design: A total of 161,478 inpatient episodes from three Chengdu hospitals with demographic, diagnosis, procedure and billing data for the year 1998/1999, 1999/2000 and 2000/2001 were grouped using the Australian refined-diagnosis related groups (AR-DRGs) (version 4.0) grouper. Main outcome measures: Reduction in variance ( R 2) and coefficient of variation (CV). Results: Untrimmed reduction in variance ( R 2) was 0.12 and 0.17 for length of stay (LOS) and cost respectively. After trimming, R 2 values were 0.45 and 0.59 for length of stay and cost respectively. Conclusions: The Australian refined DRGs provide a good basis for developing a Chinese grouper.

German diagnosis-related groups. Problems in the field of mental and behavioural disorders shown in the example of hospital treatment of alcoholics

Hospital treatment in Germany will clearly change: instead of paying by the number of days, blanket prices including all necessary benefits will become the norm. The nearly innumerable treatments possible were collected into diagnosis-related groups (DRGs) requiring similar clinicomedical benefits and consuming similar financial resources. The DRG classification in Germany (G-DRG) will be oriented to the Australian refined diagnosis-related group (AR-DRG) system, which has existed since 1998. German psychiatric hospitals have by policy not yet been involved in the new system. However, a big sector of psychiatric activity--as far as number of patients is concerned--has already been incorporated in this new system: detoxification from legal and illegal drugs is involved in G-DRG inasmuch as it does not take place in psychiatric hospitals. If the DRG system becomes established in German psychiatric hospitals, grave difficulties will occur. These problems are described together with the expected results in the example of hospital treatment of alcoholics.

Measuring case severity with a DRG-based reimbursement system

The Australian Refined Diagnosis-Related Groups (AR-DRGs) will be the model for the German DRGs (G-DRGs). Their system to measure severity of illness will be a major point of interest. The most common systems for measuring severity of illness are presented and compared with the AR-DRGs based on criteria regarding applicability. None of the systems for measuring severity of illness fits all the criteria. They can be used for reimbursement of inpatient care or for quality assurance, but not for both at the same time. The designated areas for the use of the systems should not be exceeded. AR-DRGs are very complex in measuring the costs per case (severity of illness in terms of efficiency). They are not able to support quality assessment by risk adjustment (severity of illness in terms of medical complexity). A less complex system would have been easier to transfer to Germany with the same incentives for providing effective care.