We found that patients with greater severity or comorbidity were more likely to be excluded from P4P programs no matter whether we used the logistic or mixed-effects models. In addition, we found that hospitals with a lower baseline score in the previous year (2006) were more likely to exclude patients in the current year (2007), perhaps because hospitals with lower baseline scores in the previous year may want to increase their benefits in the next year. This result is similar to the results of other related studies (Doran et al. 2008
). Another finding is that our study, like the other studies, demonstrated that larger hospitals may be more likely to exclude patients from P4P programs (Doran et al. 2008
). It is for this reason that 65 percent of the variance in patient participation is explained by hospital characteristics and only 35 percent by patient characteristics.
The primary policy implications of this study include a pronounced need to prevent adverse selections during P4P programs' patient selection process. Several approaches have been proposed for preventing adverse selection. The first is to set target thresholds below 100 percent. Physicians would earn the maximum financial reward without achieving the target for all patients (Fleetcroft et al. 2008
). For example, the United Kingdom sets thresholds of 40–50 percent for the measure A1C ≤7 (BMA 2009
). This approach is not suitable for Taiwan because it represents a design based in competition among hospitals (a dynamic threshold), not on a policy that would allow these hospitals to earn more money by exceeding the threshold (a fixed threshold).
A second approach would allow physicians to remove inappropriate patients from the calculation of quality achievement (exception reporting) (BMA 2009
). Several authors (Doran et al. 2008
; Fleetcroft et al. 2008
;) have observed that this approach offers three advantages: It is precise, can increase the acceptance rate of the P4P program because of its active exclusion design, and may also help to eliminate situations in which patients are refused care because of severe medical conditions. However, evidence continues to indicate that the benefits from this design may be hampered by abuses of the system (Doran et al. 2006
; Sigfrid et al. 2006
; Gravelle et al. 2010
The final approach is to make risk adjustment for outcome or process measures (Landon et al. 2003
; Asch et al. 2006
;). For payers, an appropriate risk adjustment framework is important because physicians' behavior can only be changed by incentives when they consider the data to be complete and accurate and the score calculation to be fair; otherwise there will be a backlash against the system or physicians may try to game the system (Bokhour et al. 2006
). As noted by Ryan and colleagues, “in the absence of complete risk adjustment, providers may engage in statistical discrimination: the application of perceived group characteristics to individuals” (Ryan 2009
). Statistical discrimination may make providers avoid patients on the basis of unmeasured severity.
America and the United Kingdom face problems of politics in the implementation of P4P programs (Gulland 2003a
; Tanenbaum 2009
). Taiwan's reforms face similar difficulties. In Taiwan, the design of the original 2001 P4P program had itself faced the problem of interest group politics (Chang 2004
). To resolve opposition to the P4P program and to encourage providers to participate, the original design was compromised in a manner that allowed voluntary provider participation and free patient program enrollment. In addition, there was no time schedule to complete the evolution of the P4P program. In May of 2009, the latest version of the DM-P4P program achieved some reforms by establishing the requirement that providers reach the new P4P patient enrollment rate of 30 percent and by requiring that providers attain a volume of P4P patients greater than the mandated threshold (50 or more).
Several other factors contribute to the problems of implementation. The insufficient funds for implementation of the P4P program represent yet another difficulty. The yearly DM-P4P cost is only about 3–5 percent of the total expenditure in DM care in Taiwan (Lai et al. 2009
). The limited investment by the NHI makes it difficult to learn more about implementing mandated participation from programs in the United Kingdom because it may not cover the additional expenses required for the implementation of the P4P program by hospitals (Epstein 2006
), which involves procedural changes such as the reporting of clinical data (Halladay et al. 2009
Universal electronic health record data made it possible for the United Kingdom to measure the process and outcome for all DM patients, and exclusions were selected only as an active process (Curcin et al. 2010
). Hence, while patient complexity may result in the exclusion, we cannot rule out the cost of reporting clinical data as an additional motivator for the decision to exclude patients. At the very least, this study proved that there is an association between patient complexity and exclusion from the P4P program.
There were some limitations in our study. First, our data are limited by having no recorded reasons for exclusions, so we cannot determine the specific reasons why patients were excluded from P4P programs. Second, because of considerations about patient privacy, we cannot link our claims data to the “cause of death” data file supported by the Department of Health. We can only calculate all-cause inpatient mortality, making it likely that the total number of deaths was underestimated in this study. However, Table SA2
in the Appendix shows that the exclusion of deaths affected the result only slightly using the logistic model. Third, while our interpretation and motivation are connected to physicians' individual practices of inclusion and exclusion, our analysis occurs at the hospital level rather than at the physician level.
There are several reasons that we perform our analysis at the hospital level. First, although in Taiwan incentive calculations were oriented toward physicians, the NHI's payment was actually given to the hospital, which then in theory delivered payments to its own physicians who were treating P4P patients. However, we do not know whether the administrators of hospitals actually passed this P4P benefit on to physicians. Every hospital has its own physician fee policy. Second, some U.K. studies related to P4P exclusion also performed analysis at the practice level (Doran et al. 2008
; Gravelle et al. 2010
;). Third, our physician-level data are incomplete, and we have records only of physician ids in our database. Because of these reasons, we decided to analyze exclusion behavior at the hospital level. In spite of this orientation, 65 percent of the variance in patient participation is explained by hospital characteristics, which indicates that these may factor significantly in patient enrollment in P4P.
Implications for Policy and Payment Reform
Based on our findings, we recommend that the government would benefit most from carrying out a deliberative and stepwise reform by first executing risk adjustment in the DM-P4P program and then gradually investing more money to cover the hospital costs of running the P4P program. Then, finally, the government will be in a well-grounded position, in terms of anticipating the reactions to the incentives and estimating its costs, to fully implement mandated participation in the P4P program.