Chute C G, Yang Y, Buntrock J
Section of Medical Information Resources, Mayo Clinic/Foundation, Rochester, MN.
Proc Annu Symp Comput Appl Med Care. 1994:162-6.
The Mayo Clinic has a long tradition of indexing patient records in high resolution and volume. Several algorithms have been developed which promise to help human coders in the classification process. We evaluate variations on code browsers and free text indexing systems with respect to their speed and error rates in our production environment. The more sophisticated indexing systems save measurable time in the coding process, but suffer from incompleteness which requires a back-up system or human verification. Expert Network does the best job of rank ordering clinical text, potentially enabling the creation of thresholds for the pass through of computer coded data without human review.
梅奥诊所长期以来一直有对高分辨率和大容量患者记录进行索引的传统。已经开发了几种算法，有望在分类过程中帮助人工编码员。我们在生产环境中评估代码浏览器和自由文本索引系统在速度和错误率方面的差异。更复杂的索引系统在编码过程中节省了可测量的时间，但存在不完整性问题，这需要备份系统或人工验证。专家网络在对临床文本进行排名排序方面做得最好，有可能为无需人工审核的计算机编码数据通过创建阈值。
