Wonderful people can be terrible patients. Some of us demand drugs we don鈥檛 need, and fail to take the ones we do. Others worry about minor ailments, but avoid checkups that would prevent more serious conditions. Still more of us suffer from uncontrolled, chronic diseases we ignore for far too long, requiring costly emergency interventions.
Managing the needs of high-cost patients can result in expensive and unnecessary procedures and prescriptions. Diagnosis-related groups (DRGs), such as the IR-DRG (International Refined DRG) used most commonly outside the U.S., were designed as one possible solution.
Developed in the 1970s by Yale University, the DRG patient classification system seeks to encourage value-based care by standardizing payments to doctors and hospitals based on the disease being treated. The goal is to reward providers for positive patient outcomes, rather than for the volume of services provided as is typical under a traditional fee for service (FFS) system.
Providers collect discharge claims and classify them into homogeneous, clinically meaningful groups. The DRG system then requires insurers to make reimbursements based on diagnosis code, irrespective of the underlying services or length of stay. So, for example, a typical DRG payment covers all charges associated with an inpatient hospital stay from the time of admission to discharge (though in some countries, the ambulatory family of benefits are also priced under the DRG system). Long-term care benefits, inpatient dental treatments, and certain consumable items would normally fall outside the scope of a DRG.
When the system works well, it can improve patient outcomes while removing the incentive to pursue unneeded procedures. The system is also designed to promote the use of technological innovation to minimize a patent鈥檚 length of stay. While evidence suggests that the use of DRG systems often coincides with reductions in average patient lengths of stay, it can be difficult to ascertain whether this is due to medical intervention or the DRG system itself.
Use of DRG has grown throughout the U.S. starting in 1983 and spread to Australia in 1988 and throughout Europe in the 1990s. The DRG system was introduced in the Middle East by the Department of Health - Abu Dhabi (HAAD) in late 2010 in Abu Dhabi, and observers expect the system to reach Dubai in first quarter of 2020.Given its growing international popularity and cost containment potential, it is increasingly important for claims analysts and underwriters to understand the DRG structure, risk measurement methodology, ratemaking process, and grouper algorithms.
At a very basic level, DRG refers to a code, yet one must go much deeper to master this patient classification system. Although DRG system designs may vary, most reflect the following seven concepts:
To understand how the DRG system classifies risk and assigns cost, consider the following typical patients (using United Arab Emirates dirham as currency):
(a) Patient 1 has a DRG with weight 1.5, incurred hospital cost => 145,000 AED
(b) Patient 2 has a DRG with weight 0.80, incurred hospital cost => 16,000 AED
As these examples illustrate, the principal focus of DRG ratemaking is to determine a base factor and/or means to 鈥渨eight鈥 the underlying value of a set of medical services. For instance, under the HAAD model the base factor is set to 8,500 AED and a grouper algorithm informs the calculation of a weight to arrive at a total payment.
The benefit of this calculation lies in its ability to deliver fair payment for a medical service while also achieving the lowest cost. Borrowing from classical risk theory, an efficient risk measure adheres to the following principles:
These same principles can be applied to the DRG system.
DRG weights are derived actuarially, either from base statistics or via customized grouper algorithms. After collecting cost information from hospital databases or a hospital information system, claims can be classified by DRG code and Major Disease Classification (MDC). Typically, the following key fields are completed:
(a) Total number/volume of activities
(b) Total cost incurred for the entire treatment
(c) Severity level
After a claim is classified, it can be evaluated for inappropriate codes, errors, or outliers or include vital services which have not been captured under the hospital cost information system. Typically, the outlier threshold would be set to around 95% Value at Risk (VaR). Next, weights can be derived through the DRG grouper algorithm, with the 鈥渨eights鈥 being equivalent to a 鈥減arameter鈥 estimation under a regression model. Testing follows to ensure lower co-morbidities are assigned lower weights (monotonicity of reimbursement). Upon review (one- to three-year basis), all weights should be updated to maximize the correlation between costs and weights and, at the same time, adhere to the following constraints:
(a) Adjust for difference in credibility of DRG weights
(b) Keep severity levels in line with underlying risks
(c) Ensure that the overall case mix is constant 鈥 before and after DRG implementation
A DRG system should be updated periodically based on hospital costs submitted by providers. The above risk pricing process is based on weights inferred from nationwide hospital statistics. Very often, however, the DRG system is 鈥榠mported鈥 from another country and can be subject to a cost differential adjustment; testing and review are paramount in these cases.
When deriving DRG weights using available hospital cost data, commercially available grouper models are always preferred initially. Doing so enables an organization to:
Typically, a statistical model such as the logistic regression or a generalized linear model is fitted to underlying data to produce the parameters that link every risk factor. The following factors are typically employed:
The DRG system is far from alone in its reliance on grouper algorithms to assign value to risk. In fact, the advent of DRG has unleashed a wave of innovations. Multiple alternatives have emerged to model inpatient medical events, including:
If there is a single commonality behind these models, it is an attempt to deliver greater efficiency by the more accurate calculation of risk and a stronger connection between payment and treatment.
Indeed, DRG coding itself succeeds when it closely links hospital reimbursement payments to underlying services or provider activities and attains efficiency in the provision of hospital services, without compromising on the level of quality of care. But success is not assured.
In the second installment of this two-part series, RGA will explore governance and risk mitigation approaches and what can go wrong in DRG implementations.
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