Results:Although the transition from the ICD-9-CM to the ICD-10 version of MS-DRGs resulted in 1.68 percent of the patients being assigned to a different MS-DRG, payment increases and decreases due to the changes in MS-DRG assignment essentially netted out, resulting in a minimal impact on aggregate payments to hospitals (+0.05 percent) and on the distribution of payments across hospital types (-0.01 to +0.18 percent). Mapping ICD-10 data back to ICD-9-CM, and using the ICD-9-CM MS-DRGs, resulted in 3.66 percent of patients being assigned to a different MS-DRG, a modest decrease in aggregate payments to hospitals (-0.34 percent), and modest changes in the distribution of payments across hospital types (-0.14 to -0.46 percent).
Discussion:As demonstrated by MS-DRGs, a direct conversion of an application to ICD-10 can produce consistent results with the ICD-9-CM version of the application. However, the use of mappings between ICD-10 and ICD-9-CM will produce less consistent results, especially if the mapping is not tailored to the specific application.
A system has been developed to generate hospital budgets based on the types of patients served. Several hundred classes of patients are defined according to clinical attributes such as diagnoses and surgical procedures, and for each class a profile of resources consumed is determined. The class definitions are based both on homogeneity of patient care processes as well as resource consumption. These profiles are expressed as revenues generated by charging departments and as costs both direct and indirect for all services. A methodology has been developed to associate all indirect costs with their source for each service included in the profile. From a forecast of patient load by class, budgets can be computed from the cost profiles and revenues determined from the charging profiles. Further analysis thus can include the effect of changes in case mix as well as changes in patient care processes. The effect on revenues of different reimbursement mechanisms can also be projected as a function of the case mix. The system is currently being implemented for demonstration and evaluation of the Yale-New Haven Hospital.
The socioeconomic status (SES) component of the Social Vulnerability Index ranks US counties based on the SES of county residents and was used to evaluate the impact of SES on the performance of the health care delivery system. Using Medicare fee-for-service data, the performance of the health care delivery system was evaluated based on population measures such as per capita hospital admissions, quality of care measures such as surgical mortality, postacute care measures such as readmissions, and service volume measures such as posthospitalization nursing home and rehabilitation admissions. Substantial differences in delivery system performance across SES populations were observed.
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