Content by Keyword: Medicare

1 Medicare Benefit Policy Manual, Chapter 3 Duration of Covered Inpatient Services. 2 Medicare Benefit Policy Manual, Chapter 7 Home Health Services. 3 An index hospitalization is an acute hospitalization with a prior 60-day clean period. In the prior 60 days, no acute or post-acute (IRF, LTCH, SNF, HHA, outpatient therapy) services were

Table B-1. Utilization 1 and Payment, by Claim Type, by DRG and APR-DRG Severity Index 2 , for Live Discharges, 2005 APR-DRG Severity of Illness Level DRG   N % with Claim Mean Use Mean Payment N

HHC Label HCC1 HIV/AIDS HCC2 Septicemia/Shock HCC5 Opportunistic Infections HCC7 Metastatic Cancer and Acute Leukemia HCC8 Lung, Upper Digestive Tract, and Other Severe Cancers

Berenson, R. 2004. The Medicare Chronic Improvement Program. Retrieved August 2008 from http://www.urban.org/publications/900714.html Buntin MB, AD Garten, S Paddock, D Saliba, M Totten, and JJ Escarce. 2005. How much is postacute care use affected by its availability? Health Serv. Res., 40(2): 413-34. CDC (Centers for Disease Control and P

The results of the current work will feed directly in to work we will be completing as part of a contract modification. The additional work will include use of the CCW data to identify the presence of chronic conditions in beneficiaries in our 2006 episode file. Similar to some of the analyses presented in this report, we will look at the patterns

These analyses provide important information for understanding who uses PAC services, how their likelihood of using them differs by certain characteristics, and which factors are most important in predicting hospital length of stay, probability of PAC use, relative probability of PAC site of care choices, hospital readmission rates, and average ep

In the regression predicting PAC episode payments, increasing severity is associated with increases in episode payments. For example, in the model using MS-DRG severity measures and HCCs (Set 3, Table 3-27 ), beneficiaries with MS-DRG w/MCC have $6,313 higher episode payments compared to beneficiaries in the lowest severity level.

As in the other models, severity is an important predictor of acute hospital readmission during PAC episodes. The odds of readmission increase with increasing severity. In the binomial logit model using both the MS-DRG severity measures and the HCCs (Set 3, Table 3-27 ), the odds of readmission for beneficiaries with MS-DRG w/MCC are 1.4 times t

The effect of severity on first site of PAC demonstrates that the odds of discharge to IRFs, LTCHs, and SNFs are much higher compared to the odds of discharge to hospital outpatient therapy or home health for beneficiaries of higher severity. For example, in the multinomial model run using the APR-DRG severity measures (Set 1, Table 3-24 ), the

As in the model predicting index acute admission length stay, severity measures were important factors in predicting the use of any PAC services. The odds of using any post-acute care services increased with increasing severity level across each set of severity measures. In the case of APR-DRGs (Set 1, Table 3-24 ), for beneficiaries in APR-DRG

The results of the models predicting index admission length of stay highlight the importance of severity measures ( Table 3-24 , Table 3-25 , and Table 3-26 ). The results using independent variable Set 1 with the APR-DRG severity measures ( Table 3-24 ) indicate that beneficiaries in APR-DRG severity level 4 (extreme severity) have a mean l

The regression models presented here provide more information on the factors that affect (1) 78 index admission length of stay; (2) use of any PAC services; (3) first site of PAC, (4) readmission to acute hospitals during PAC episodes; and (5) total episode payments. These five models were run using three sets of independent variables. The ke

The pie charts in the previous section showing the distribution of payments for service use within episodes by service type indicate the significant role of physician services within episodes of PAC. The proportion of total episode payments accounted for by physician services varied only slightly across the medical DRG (DRG 089: Simple Pneumonia &

One of the main goals of the analyses of post-acute care episodes using the 2006 Medicare claims was to incorporate Medicare carrier claims (including Part B physician services, independent clinical labs, ambulance providers, and freestanding ambulatory surgery centers), DME, and hospice claims in order to learn more about complete composition of

Patterns of care during PAC episodes vary significantly depending on many factors including diagnoses, patient severity, and supply of services. One of the primary goals of this work has been to understand utilization of services within episodes of PAC. While the factors explaining use are explored further in the multivariate analyses, Tables 3-

In Tables 3-13 through 3-15 , episode lengths of stay and Medicare payments during PAC episodes are presented for post-acute care users across all DRGs as well as for post-acute care users in DRG 089 and DRG 544. Within these tables, we show the number of beneficiaries with each claim type, the percent using each type of service, the mean leng

In this next section we present analyses of the patterns of PAC episode utilization and payment. We focus on the patterns of use for post-acute users overall, and on the patterns of use for one medical DRG, DRG 089 Simple Pneumonia & Pleurisy Age > 17 w CC; and for one rehabilitative DRG, DRG 544 Major Joint Replacement or Reattachment Proc

Table 3-12 shows the number and percent of beneficiaries in each DRG who have zero, one, two, three, four, or five or more HCCs, as well as the mean length of stay and Medicare payment for the overall episode and the index admission, by DRG and number of HCCs.

Table 3-11 shows the top 20 most frequently occurring HCCs among PAC users in our sample. This table also shows the rankings of HCCs within the five most common DRGs for post-acute users and the percent of beneficiaries in the DRG with each of the HCC indicators. To populate this table, we generated a list of the top 20 HCCs in PAC users and li