Zelingher J, Rind D M, Caraballo E, Tuttle M S, Olson N E, Safran C
Beth Israel Hospital, Harvard Medical School, Boston, MA, USA.
Proc Annu Symp Comput Appl Med Care. 1995:416-20.
Problem lists assist in organizing patient information in computer based medical records. However, in order to use problem lists for billing, research, decision support and standardization, a categorization of the problems entered is required. We describe the problem list component of our computerized patient record, the On-line Medical Record (OMR), which combines a free-text entry mechanism with a categorization scheme, using a dictionary containing 846 terms. All 118,040 problems entered during the system's six years of use have been analyzed, 477 clinicians have entered a mean +/- S.D. of 238 +/- 604 problems into 22,311 patient records. The average number of problems in each patient's file was 5.1 +/- 3.9. Comments were typed for 80,281 (68%) of the problems, ranging in length from 1 to 2456 characters, with a mean length of 98 +/- 110 characters. Half the problems were entered on the day of the encounter with the patient. Overall, 66% of all problems were categorized in relation to terms from the problem dictionary. Lexical analysis of all problem names showed that 80% could be mapped to Meta 1.4, Snomed 3.0 or a pre-release version of Read 3.0. We conclude that a problem list entry scheme combining free-text entry and optional categorization using a dictionary can result in a high proportion of problems being categorized as desired. Improvement of the system by elimination of unused dictionary terms and addition of 1000 terms identified by the lexical analysis is likely to result in even higher categorization rates.
问题列表有助于在基于计算机的医疗记录中整理患者信息。然而,为了将问题列表用于计费、研究、决策支持和标准化,需要对输入的问题进行分类。我们描述了我们的计算机化患者记录——在线医疗记录(OMR)中的问题列表组件,它将自由文本输入机制与分类方案相结合,使用了一个包含846个术语的词典。对系统使用六年期间输入的所有118,040个问题进行了分析,477名临床医生已将平均±标准差为238±604个问题输入到22,311份患者记录中。每个患者文件中的平均问题数为5.1±3.9。80,281个(68%)问题输入了注释,长度从1到2456个字符不等,平均长度为98±110个字符。一半的问题是在与患者会诊当天输入的。总体而言,所有问题中有66%根据问题词典中的术语进行了分类。对所有问题名称的词汇分析表明,80%可以映射到Meta 1.4、Snomed 3.0或Read 3.0的预发布版本。我们得出结论,一种将自由文本输入与使用词典进行可选分类相结合的问题列表输入方案可以使很大比例的问题按预期进行分类。通过消除未使用的词典术语并添加词汇分析确定的1000个术语来改进系统,可能会导致更高的分类率。