I was very pleased to see recommendation 29 in the recent report of the House of Representatives Standing Committee on Health and Ageing entitled “The Blame Game: Report on the inquiry into health funding“:

The Australian Government supports the development of hospital and clinician-based performance information systems to better inform patients about the competence of health care providers and strengthen accountability of health professionals and health service providers. Reporting systems should allow, where appropriate, for performance information to be qualified to reflect differences in the type of patients being treated. (para 9.54)

There are various ways of doing this, but the most responsive and decentralised way to do it, the way which generally allows maximum scope for innovation, is what I’ve called the Gruen Tender. How does a Gruen Tender work, I hear you cry?

Well, I’m glad you asked. A service provider seeking to win your business predicts some quantitative outcome. You’d be familiar with real estate agents telling you how enthusiastic they are about your own house’s prospects on the market and plying you with optimistic estimates. So if you were holding a Gruen Tender to decide which real estate agent gets to auction your house, you ask all the agents who are after your business to predict what price they’ll achieve. Then – and this is the point – you adjust their estimate with knowledge of how optimistic or pessimistic their bids have been in the past. How do you know that? Well, part of the architecture of the scheme is that such bids and their outcomes are kept in “real time” on a website somewhere for just the purpose of generating general indications of service providers’ accuracy in making predictions.

The following diagram gives you the picture, I hope.

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How would this work for a clinical service provider? A hospital (say), would predict their chances of performing a procedure without an adverse event. Over time a system which could compare past predictions with past outcomes could also generate information from which one could adjust future predictions. The best provider would be one whose prediction (adjusted for their past optimism or pessimism) was the best. As illustrated in this table.

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I’ve written it up at greater length here.

So, as George Walker Bush might say – bring them on.

Edited from an article on Club Troppo.