Preferred provider organization
A preferred provider organization is a subscription-based medical care arrangement. A membership allows a substantial discount below the regularly charged rates of the designated professionals partnered with the organization. Preferred provider organizations themselves earn money by charging an access fee to the insurance company for the use of their network (unlike the usual insurance with premiums and corresponding payments paid either in full or partially by the insurance provider to the medical doctor). They negotiate with providers to set fee schedules, and handle disputes between insurers and providers. PPOs can also contract with one another to strengthen their position in certain geographic areas without forming new relationships directly with providers. This will be mutually beneficial in theory, as be billed at a reduced rate when its insureds utilize the services of the "preferred" provider and the provider will see an increase in its business as almost all insureds in the organization will use only providers who are members. PPOs have gained popularity because, although they tend to have slightly higher premiums than HMOs and other more restrictive plans, they offer patients more flexibility overall.
Predictive probability of success
Traditional pilot trial design is typically done by controlling type I error rate and power for detecting a specific parameter value. The goal of a pilot trials such as a phase II trial is usually not to support registration. Therefore, it doesn't make sense to control type I error rate especially a big type I error as typically done in a phase II trial. A pilot trail usually provides evidence to support a Go/No Go decision for a confirmatory trial. Therefore, it makes more sense to design a trial based on PPOS. 
To support a No/Go decision, traditional methods require the PPOS to be small. However the PPOS can be small just due to chance. To solve this issue, we can require the PPOS credible interval to be tight such that the PPOS calculation is supported by sufficient information and hence PPOS is not small just due to chance. 
Finding an optimal design is equivalent to find the solution to the following 2 equations.
where PPOS1 and PPOS2 are some user-defined cutoff values. The first equation ensures that the PPOS is small such that not too many trials will be prevented entering next stage to guard against false negative. The first equation also ensures that the PPOS is not too small such that not too many trials will enter the next stage to guard against false positive. The second equation ensures that the PPOS credible interval is tight such that the PPOS calculation is supported by sufficient information. The second equation also ensures that the PPOS credible interval is not too tight such that it won't demand too much resource.