In addition, because the harm-based weights were developed based on the potential harm associated with each PSI (e.g., mortality and readmission) at the patient level, we hypothesized that the harm-based PSI-90 would have a stronger correlation with hospital mortality and readmission rates than the volume-based PSI-90.
In the VA, the Inpatient Evaluation Center (IPEC) applies the PSI software (currently version 5.0) to administrative data collected from all hospitals to generate individual risk- and reliability-adjusted PSI rates and PSI-90 and reports them back to VA hospitals.
Specifically, based on literature review and input from a clinical expert panel, each PSI was linked to potential downstream harms (e.g., mortality, readmission, dialysis, being on a ventilator, living in a skilled nursing facility) over a one-period year after the occurrence of a PSI event (i.e., postPSI harm state).
where Q the total number of component quality indicators, q, in PSI-90; H is the total number of outcome types (harms), h, related to each component indicator; volume is the number of total events within the component indicator in the reference population; harm is the excess risk of each type of outcome (i.e., harm) within each component indicator estimated from a regression model comparing people with and without PSI events in an "at risk" cohort; and disutility is the complement of a utility weight (l-utility_wt) assigned to each excess occurrence of each type of outcome within each component indicator.
Because the PSIs vary so widely in the annual numbers of adverse events underlying them, we sought to assess their stability as health care quality measures in systems of different size, i.e., the VA as a whole; the health care networks, VISNs, into which the VA has been organized; and the individual hospitals.
This study is limited, in part, because AHRQ PSIs are calculated from administrative data.
Most importantly, AHRQ PSIs have not yet been strongly validated within the VA or other health care systems.
"Applying Patient Safety Indicators (PSIs) across Healthcare Systems: Achieving Data Comparability." In Advances in Patient Safety: From Research to Implementation, edited by K.
The GEE logit models showed that CAH conversion yielded significant improvement in hospital performance (or lower odds of poor performance) in PSI-6, PSI-7, PSI-15, and composite score of the four PSIs (Table 4).
In our sensitivity analyses, we found that CAH conversion was associated with lower odds of poor performance in denominator-weighted composite score of four PSIs. There is no significant impact on denominator-weighted composite score of six PSIs.
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.
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.