Grade A Surgeon/Surgical Method? If We Don’t Measure Our Success Against a Standard Will Anybody Notice?

ABSTRACT

Codman introduced the concept of outcomes analysis in 1900, calling it “the end-result idea.” How far have we come since Codman’s time?

The discipline of outcomes science has gained some ground in the past 20 years. However, several attempts to begin regular data collection by surgical societies have failed. Collecting meaningful data has been impractical for all but a dedicated, well-financed few. This may be about to change. Factors are now in play, which can be viewed as a perfect opportunity or perfect storm—government, insurers, and technology.

Medical errors as a source of cost and disability or death have reached lay and now legislative awareness. In June 2003, Minnesota passed a bill mandating reporting of all adverse events beginning in 2005. Insurance premiums are sky-rocketing and insurers and employers are searching for ways to identify and reward quality. As a result, many groups are searching for valid results related to care for specific diagnoses. Finally, Moore’s law (the cost of technology halves while speed and capacity doubles every 18 months) has helped create an Internet that has become a powerful, secure means of exchanging, storing, and analyzing data on a real-time basis.

During the past 3 years, mEdisonOnline has worked to create a secure online method of measured data capture that is being used to document care and create an outcomes comparison across multiple hospital/physician systems. The key principles of this system are integration with existing information systems, web-browser interface (minimal training), no redundant data entry, use of an existing Joint Commission on Hospital Accreditation requirement to generate revenue (thus no new expense), creation of a new product (comparative outcomes) from existing system dollars, and energy (physician and nurse/therapist time).

This method is successful; however, questions remain. During our presentation, four areas will be highlighted: what is a measurement, how easy does the interface have to be, why open source data, and who wins and who loses if this becomes the standard data collection method.