We show a strong and consistent effect of CAH status on PSIs. In the sample of Iowa rural hospitals, the cross-sectional comparisons showed that CAHs had better performance than rural PPS hospitals. The pre- and postconversion comparisons showed that postconversion hospitals had better performance in PSIs than preconversion hospitals. The results are robust to a number of appropriate estimation strategies, and hold up to rigorous sensitivity analyses. To address the concern that difference in PSIs might reflect primarily differences in patient mix, time trends, and differences in markets and environment, we added two additional analyses. First, we used risk-adjusted PSIs4
as the measures for the performance of hospital patient safety. Risk-adjusted rates reflect the performance of providers on the PSIs if those providers had an “average case mix” (AHRQ 2006a
, AHRQ 2006c
). Thus, it greatly reduces the effects of patient case mix on patient safety measures. Second, we used GEE logit models, random-effects Tobit models and other sensitivity analyses to control for the impact of patient case mix, market variables, and time trend. Owing to multicollinearity, hospital characteristics were not included in the original models.
A possible explanation for these findings could be that hospitals might change their coding behaviors in response to the change in Medicare reimbursement scheme; that is, observed lower rates of adverse events may be due to a change in hospital coding behavior when they converted from PPS to cost-based reimbursement. In general, under PPS, hospitals have more incentive to code severe cases, which are associated with higher reimbursement rates. If there is no change in patient case mix after conversion, a significant change in coding behavior after converting from PPS to cost-based would result in a less severe observed case mix in rural hospitals. To examine this possibility, we conducted separate analyses to examine the relationship between CAH conversion and hospital-level average number of patient diagnoses, average number of Elixhauser comorbidities (Elixhauser et al. 1998
), and mean score of the Charlson comorbidity index. We found that there was no significant association between CAH conversion and these measures.
Another alternative explanation for the positive effects of CAH conversion on patient safety could be that it reflects a trend toward improvement in patient safety for all hospitals. However, according to AHRQ, PSI-2, PSI-3, PSI-5, PSI-6, PSI-7, and PSI-15 in the Nationwide Inpatient Sample (NIS) did not exhibit an overall trend toward improvement over the 1997–2003 time period (AHRQ 2006b
). Likewise, our computations of the Iowa SIDs from 1997 to 2004 using AHRQ PSI software indicated that there was no significant overall improvement trend for PSI-2, PSI-5, PSI-6, and PSI-15 in Iowa's urban and rural referral hospitals. In addition, we controlled for time effects by including year dummy variables in the GEE and tobit models. The 8-year panel data design helps adjust for selection-maturation threats (Shadish, Cook, and Campbell 2002
), regression to the mean biases5
(Antel, Ohsfeldt, and Becker 1995
), and delay causation (Shadish, Cook, and Campbell 2002
). These analyses consistently showed that CAH conversion led to improved performance of PSI-6, PSI-7, and PSI-15. In the sensitivity analyses, GEE logit models and Tobit models also showed that moving-average estimates of the CAH effect had a larger scale than the dichotomous CAH measure, which indicates that CAH conversion may lead to larger improvement in patient safety in the long run.
CAH conversion has been shown to significantly improve rural hospitals' financial condition. Previous studies have shown that preconversion rural hospitals faced serious financial pressure (Cameron, Zelman, and Stewart 2001
; Stensland, Davidson, and Moscovice 2004
) and that over half of the hospitals that converted to CAH in FY1999 or FY2000 were losing money before conversion (Stensland, Davidson, and Moscovice 2004
). Stensland, Davidson, and Moscovice (2004)
reported that hospitals that converted to CAH in FY1999 experienced an average increase in Medicare payment of 36 percent, around $500,000 in FY2000 inflation adjusted dollars after conversion, and that CAH conversion increased hospital profit margins by 2–4 percentage points.
Improved financial conditions and lower risk sharing associated with CAH conversion are likely to contribute to improvements in patient safety. After conversion, under cost-based reimbursement, risk sharing decreased substantially. Under PPS, the marginal costs associated with QI are not reimbursed, and the hospital has to bear all the cost incurred by the increased intensity and quality (Cutler 1995
). Under cost-plus reimbursement, marginal costs associated with increased quality are fully reimbursed. Hospitals tend to have higher intensity and produce a higher level of quality under lower risk sharing (Hodgkin and McGuire 1994
; Cutler 1995
; Chalkley and Malcomson 2000
To the extent that more resources are needed to bring about meaningful improvements in quality of care, patient safety is inextricably linked to the financial condition of hospitals (Encinosa and Bernard 2005
). It is harder for hospitals with financial problems to make investments in patient safety improvements (e.g. error-reducing information technology system), or to attract or retain high-cost specialists. Likewise, hospitals with financial problems might cut nurse staffing which may adversely affect patient safety (Encinosa and Bernard 2005
). Cutler (1995)
found that the fiscal pressures from the PPS in Medicare in the 1980s were associated with higher mortality. Shen (2003)
also has shown that financial pressure was associated with increased mortality rates after treatment of acute myocardial infarction. Encinosa and Bernard (2005)
found that a within-hospital erosion of hospital operating margins was associated with an increased rate of adverse patient safety events.
The Rural Hospital Flexibility Program Tracking Team found that QI- or QA-related activities were widely undertaken in CAHs and have been reinforced over time since CAH conversion (Moscovice and Gregg 2001
; Moscovice, Gregg, and Klingner 2002
). Staffing improvement was one of the most significant factors contributing to progress in quality of care and increased reimbursement was cited as the reason for improved staffing (Moscovice, Gregg, and Klingner 2002
). Consistent with these findings, and expanding them to outcomes, we found that CAHs strengthened their scores on PSIs after conversion, at the time that they would have been experiencing higher reimbursement.
Apart from the change in reimbursement method and the resulting financial relief, other factors may also contribute to better quality of care in CAHs. These factors may include the establishment of a network relationship with affiliated hospitals, improvement in case management and discharge planning, expansion in qualified QA and QI staff, and enhancement in equipment (Moscovice and Gregg 2001
; Moscovice, Gregg, and Klingner 2002
There are several limitations in our study. First, there are limitations inherent to administrative databases including the possibility of missing codes, coding errors, and variation in coding practices across hospitals (Simborg 1981
; Hsia et al. 1988
; Iezzoni 1997
; Zhan and Miller 2003
). Although we did not find a significant association between CAH conversion and various measures of patient severity, it is possible the lower rates of adverse events in CAH hospitals are simply due to a reduced incentive to code certain diagnoses. In this study, we are not able to completely rule out this possibility.
A second issue of concern is endogeneity and omitted variable bias. The CAH variable is likely to be endogenous given that hospitals choose to convert to CAH status. However, endogeneity is to a large extent mitigated through the fixed-effects panel models with year dummy variables, which capture the effects of unmeasured hospital-specific and time-specific factors (Woolridge 2002
). Furthermore, the results of the sensitivity analysis using a sample of 81 Iowa rural hospitals (all of which were in rural PPS status in 1997 and in CAH status at the end of 2005) suggests that endogeneity is not an exceptionally large concern. The consistency between the sensitivity analyses and our main results indicate that our findings are robust.
A third limitation involves using AHRQ software to calculate PSIs and evaluate hospital patient safety performance. The AHRQ software is not able to fully identify all the preventable adverse events due to the limited clinical information available in administrative data (AHRQ 2006a
). In addition, only six of the 29 PSIs had adequate cases to measure patient safety in our sample of rural hospitals. The six PSIs are less comprehensive than the complete package of PSIs created by AHRQ PSI software. Furthermore, the assumption for the Tobit models is that the distribution is a censored normal distribution. However, we were not able to test this assumption given that the censored data are unobservable (Duan 1983
). We only include the results of Tobit models in the sensitivity analyses. The findings of Tobit models were consistent with GEE logit models. Finally, our sample only includes Iowa rural hospitals, thus, our results may not generalize to other states.