A few years back I came up with an idea that was sufficiently
simple that I thought someone else must have come up with it. It still surprises
me that I can’t find anyone else who’s suggested it but there you go, I
haven’t.

Like auctions for goods, Gruen tenders provide a means by which
someone responsible for allocating a job to a service provider can get
the service provider to produce an unbiased estimate of the prognosis
for the service provision. This offers a powerful tool for
administrators who must allocate jobs to service providers and, also
for consumers.

Step One: The service provider is required to
predict in advance the prognosis in terms of a particular quantitative
outcome and/or a statistical prediction of the likelihood of their
achieving certain desired benchmarks.

Step Two: These prognoses are logged into a system
and the service providers’ results are also logged when they become
known. The system then produces an ‘optimism factor’ indicating the
extent to which the service providers past predictions have tended to
be optimistic or pessimistic.

Step Three: Once the system has sufficient data to
give the ‘optimism factor’ some statistical robustness, ‘raw prognoses’
provided in Step One’ can be ‘moderated’ by reference to the ‘optimism
factor’ applying to the service provider. The moderated raw prognoses
then become unbiased predictions of actual results.

This is best explained with an example. This is easiest where the
service provider’s prognosis can be measured as a predicted result such
as the price a real estate agent indicates they will achieve upon
selling a person’s house.

Each real estate agent must enter their predicted price (as a single
point or an the average within a range) in a system and then return to
that system to log the actual result – each step of this process being
subject to occasional audit.

After an appropriate number of observations have been made, an
‘optimism factor’ will be generated. The agent must then provide both
their raw prognosis and their moderated prognosis to clients with the
data being input.

Assume there is a client seeking to engage a an agent to sell their
house. They receive a prognosis from three agents as indicated in the
attached table. The first agent does not offer the most attractive raw
prognosis, but when it is taken into account that it typically
underestimates the prices it will achieve by 5% whilst the other two
agents over-promise, its moderated prognosis is the most favourable.

In the case of clinical service providers the prognoses would be in
the form of some probabilistic standard of errors. Thus for instance on
setting a broken bone the prognosis would be in the form of a
probability that certain benchmarks would be met. Thus for instance the
prognosis might be that there is a 92 per cent chance of the fracture
being set without any adverse event as defined in some code such events
may include infection, the need to reset the bone and so on.

The service providers might provide prognoses as follows with the
indicated service provider being that with the best moderated prognosis.

The merits of such an approach are several-fold.

  • It produces simple numbers which generate important information about quality.
  • Those numbers can be used by medical administrators, and by
    patients to select the medical service best meeting their needs either
    on its own or in conjunction with information about the price service
    providers will charge.
  • It disciplines medical service providers to make predictions. In
    itself this process is likely to be beneficial in helping them to
    understand better their own competence and the factors influencing
    success.
  • Publishing the raw performance of service providers can not only
    provide a highly misleading picture of service quality but can also
    create invidious incentives, particularly in the case of clinical
    service providers, an incentive to turn away the worst risks.
  • Systems have tried to deal with this issue by ‘risk rating’ cases.
    But this is generally according to some mechanically followed ‘table of
    risks’ for different cases. Gruen tenders allow service providers to go
    by such a table of risks should they wish, but they can also ‘forward
    risk rate’ according to their own knowledge and experience.
  • There is never any incentive for medical service providers to turn
    someone away because they fear they will harm their rating. They simply
    make a prognosis that reflects their assessment of the relative risk of
    their patients.