Meystre Stéphane M, Haug Peter J
Department of Biomedical Informatics, University of Utah, School of Medicine, Salt Lake City, UT 84112-5750, USA.
Int J Med Inform. 2008 Sep;77(9):602-12. doi: 10.1016/j.ijmedinf.2007.12.001. Epub 2008 Feb 15.
To improve the completeness and timeliness of an electronic problem list, we have developed a system using Natural Language Processing (NLP) to automatically extract potential medical problems from clinical, free-text documents; these problems are then proposed for inclusion in an electronic problem list management application.
A prospective randomized controlled evaluation of the Automatic Problem List (APL) system in an intensive care unit and in a cardiovascular surgery unit is reported here. A total of 247 patients were enrolled: 76 in an initial control phase and 171 in the randomized controlled trial that followed. During this latter phase, patients were randomly assigned to a control or an intervention group. All patients had their documents analyzed by the system, but the medical problems discovered were only proposed in the problem list for intervention patients. We measured the sensitivity, specificity, positive and negative predictive values, likelihood ratios and the timeliness of the problem lists.
Our system significantly increased the sensitivity of the problem lists in the intensive care unit, from about 9% to 41%, and even 77% if problems automatically proposed but not acknowledged by users were also considered. Timeliness of addition of problems to the list was greatly improved, with a time between a problem's first mention in a clinical document and its addition to the problem list reduced from about 6 days to less than 2 days. No significant effect was observed in the cardiovascular surgery unit.
为提高电子问题清单的完整性和及时性，我们开发了一个利用自然语言处理（NLP）技术的系统，用于从临床自由文本文件中自动提取潜在的医疗问题；然后将这些问题推荐纳入电子问题清单管理应用程序。
本文报告了对重症监护病房和心血管外科病房的自动问题清单（APL）系统进行的一项前瞻性随机对照评估。总共招募了247名患者：76名处于初始对照阶段，171名参与随后的随机对照试验。在随机对照试验阶段，患者被随机分配到对照组或干预组。所有患者的文件均由该系统进行分析，但仅将为干预组患者发现的医疗问题推荐至问题清单中。我们测量了问题清单的敏感性、特异性、阳性和阴性预测值、似然比以及及时性。
我们的系统显著提高了重症监护病房问题清单的敏感性，从约9%提高到41%，如果将自动推荐但未被用户认可的问题也计算在内，甚至可达77%。问题添加到清单的及时性得到了极大改善，从临床文件首次提及问题到将其添加到问题清单的时间从约6天缩短至不到2天。在心血管外科病房未观察到显著效果。
