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Diagnosis-Related Grouping

Diagnosis-related grouping (DRG) refers to a patient classification system intended to standardize future payments to healthcare facilities and encourage initiatives on cost containment. Overall, DRG payments are expected to cover all charges related to inpatient stays beginning from the admission point to the event of a discharge. The history of DRGs can be traced to the late 1980s, when the system was first implemented in the country by the Health Care Financing Administration as a measure of controlling costs for inpatient services that are being billed to Medicare (Davis, 2020). In 1989, DRGs started to be used comprehensively as the Preferred Provider Plan was initiated and later introduced within other plans of the Administration (Davis, 2020). If used correctly, DRGs can be highly beneficial for cost management and quality improvement within healthcare settings.

While DRGs have been historically used for payments for care at inpatient settings, the 21st Century Cures Act that was introduced at the end of 2016, mandated the Centers for Medicare and Medicaid Services to “develop some DRGs that apply to outpatient surgeries” (Davis, 2020, para. 5). The new payments were expected to be as identical as possible to the DRGs applying to the same surgeries implemented on an inpatient basis. In addition, private insurers and Medicare programs have also been introducing new systems of payment that are similar to DRGs, although with some differences, including the method combining outpatient and inpatient services into one bundle of payment.

DRG payments have both advantages and disadvantages in their implementation. The benefit of the payment system is associated with enhanced transparency and efficiency as well as the reduced average length of stay (Mihailovic, Kocic, & Jakovljevic, 2016). However, the limitation of DRGs is concerned with creating financial incentives toward earlier discharges from hospitals. Besides, the policies can often not be aligned with the priorities of clinical benefits (Mihailovic et al., 2016). Therefore, while DRGs emphasize the importance of provider efficiency, this may result in providers encouraging discharges among patients, which can take a toll on quality.

Considering the limitations of DRG use at hospitals, nurse managers are challenged with having to manage costs and maximizing the reimbursement of their healthcare facilitates. To ensure the proper functioning of their units and to ensure high-quality care, nurse managers will need to collect data on factors influenced by DRGs that influence nurses and the subsequent nursing-sensitive patient outcomes (Spirig et al., 2014). Patient length of stay (LOS) is a crucial factor to monitor because it impacts the complexity of nursing care and professionals’ workloads, which influences performance. It can also impact nursing staffing as well as interpersonal collaboration and leadership within facilities. Finding the right balance between patient LOS, nursing workloads, care complexity, leadership, and interprofessional collaboration is imperative. Because of this, it is important to control interprofessional collaboration between nurses and physicians, unify staff around shared care goals, address the complex demands of professional nurses in healthcare settings, as well as adjust LOS to specific patients to reduce readmissions and prevent adverse events.

The implementation of DRGs at healthcare facilities requires healthcare facilities to allocate funds to pay for training and adapting to new care trends. Specifically, considerations regarding IT use are important because they have shown to improve the effectiveness of hospital operation and provide the necessary data on patient admissions and discharges (Szynkiewicz et al., 2015). Using electronic medical records (EMR), nurse managers will have access to abundant patient information that can form the basis of cost calculation at the lowest possible level (Kruse et al., 2018). Besides, grouping depersonalized data from EMRs combined with information on costs and reimbursements can help develop calculations on important dimensions of care. Specifically, a nurse-manager can make calculations for such dimensions as DRG units, organizational units, medical processes and procedures, cost/profit centers, hospital wards, as well as medical teams (Szynkiewicz et al., 2015). The analysis of the data for the relevant dimensions can enable hospital managers to establish appropriate procedures for DRG implementation. For example, it is possible to identify the services and procedures that should remain in the care process and those that should be abandoned in case if costs must be decreased.

For nurse managers, monitoring and adjusting costs based on the needs of the facility is becoming more and more complex and multi-dimensional. As a result, it leads to the increased demand for multi-dimensional DRG databases, which are time-relevant and accurate. This means that managers will have to connect the data available through EMRs with the data concerning DRGs as well as performance management tools. Thus, in order to monitor the impact of DRGs on the cost-effectiveness without compromising on quality, hospital managers can use the following sources:

  • The analysis of usage of health care services;
  • The analysis of time allocated for patient wait lists;
  • Costs going toward the surplus provision of services within hospitals;
  • Expenses for financing hospitalizations of critically ill and elderly patients (Szynkiewicz et al., 2015).

To use relevant DRG data effectively and in favor of improved hospital performance, it is necessary to collect information on planned contracts, such as the volume and value of services. Comprehensive data from the previous years is necessary alongside with the actual costs of services compared to the DRG-recommended prices. However, in order for all information to serve in favor of nursing managers’ efforts to maximize costs and maximize reimbursements, it is important that the necessary IT are available and convenient to use. In the current complex healthcare climate, it is becoming essential to step away from the mere collection of medical data on patients to using them actively within the decision-making processes. Thus, DRG can become a valuable decision supporting tool in the process of hospital management.

To conclude, DRG systems have been developed as a means to ensure that healthcare reimbursements are adequately reflective of the types of patients being treated. They consider the extent of medical issues, which determine the costs of care as well as the resources used to address patient needs. The key design characteristic of the DRG-related payment system is the classification of patient cases and their related payment formulation. This means that cases within the same DRG code group develop under similar clinical circumstances, thus incurring pre-defined expenses for diagnostics and treatment. While the predictability that comes with DRGs is beneficial for increasing transparency of the reimbursement process, they incentivize reducing the average length of stay at facilities, which may result in readmissions and missed care. Therefore, nurse managers who use DRGs should ensure that the model aligns with the clinical benefits of their facilities and that all relevant patient data is available for appropriate cost management and performance improvement.

References

Davis, E. (2020). How a DRG determines how much a hospital gets paid. Web.

Kruse, C. S., Stein, A., Thomas, H., & Kaur, H. (2018). The use of electronic health records to support population health: A systematic review of the literature. Journal of Medical Systems, 42(11), 214. Web.

Mihailovic, N., Kocic, S., & Jakovljevic, M. (2016). Review of diagnosis-related group-based financing of hospital care. Health Services Research and Managerial Epidemiology, 3, 2333392816647892. Web.

Spirig, R., Spichiger, E., Martin, J. S., Frei, I. A., Müller, M., & Kleinknecht, M. (2014). Monitoring the impact of the DRG payment system on nursing service context factors in Swiss acute care hospitals: Study protocol. German Medical Science: GMS E-journal, 12, Doc07. Web.

Szynkiewicz, P., Iltchev, P., Piechota, A., Sierocka, A., & Marczak, M. (2015). Diagnosis-related groups (DRG) and hospital business performance management. Studies in Logic, 39(52), 143-153.

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StudyKraken. "Diagnosis-Related Grouping." October 25, 2022. https://studykraken.com/diagnosis-related-grouping/.

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StudyKraken. 2022. "Diagnosis-Related Grouping." October 25, 2022. https://studykraken.com/diagnosis-related-grouping/.

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StudyKraken. (2022) 'Diagnosis-Related Grouping'. 25 October.

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