Combining free text and structured electronic medical record entries to detect acute respiratory infections.
Combining free text and structured electronic medical record entries to detect acute respiratory infections.
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DOI：
10.1371/journal.pone.0013377
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发表时间：
2010-10-14
期刊：
影响因子：
3.7
通讯作者：
Perl TM
中科院分区：
文献类型：
--
作者：
DeLisle S;South B;Anthony JA;Kalp E;Gundlapallli A;Curriero FC;Glass GE;Samore M;Perl TM
电子病历（EMR）包含丰富的信息来源，可用于流行病监测。我们询问结构化EMR数据是否可以与自由文本临床条目的计算机处理相结合，以提高急性呼吸道感染（ARI）的检测。对与15，377次门诊就诊相关的EMR记录进行人工审查，发现了280例ARI参考病例。我们使用逻辑回归和向后消除来确定候选结构化EMR参数（诊断代码，生命体征和测试，成像和药物的订单）中的哪些有助于检测这些参考病例。我们还开发了一种计算机化的自由文本搜索，以识别记录至少两个非否定的ARI症状的临床笔记。然后，我们使用语义学来构建案例检测算法，该算法将保留的结构化EMR参数与文本分析的结果最好地结合起来。调整后的诊断代码分组确定参考ARI患者的敏感性为79%，特异性为96%，阳性预测值（PPV）为32%。在考虑的21个额外的结构化临床参数中，有两个对ARI检测有显著贡献：咳嗽治疗的新处方和体温升高至至少38°C。与诊断代码一起，这些参数将检测灵敏度提高到87%，但特异性和PPV分别下降到95%和25%。增加文本分析将灵敏度提高到99%，但PPV进一步下降到14%。需要同时满足结构化EMR参数查询和文本分析的算法，其PPV为52-68%，敏感性为69- 73%。结构化的EMR参数和自由文本分析可以结合成算法，可以检测ARI病例，具有新的灵敏度或精度水平。这些结果突出了潜在的路径，通过这些路径，重新利用EMR信息可以在流行病造成大规模伤亡之前促进发现流行病。
The electronic medical record (EMR) contains a rich source of information that could be harnessed for epidemic surveillance. We asked if structured EMR data could be coupled with computerized processing of free-text clinical entries to enhance detection of acute respiratory infections (ARI). A manual review of EMR records related to 15,377 outpatient visits uncovered 280 reference cases of ARI. We used logistic regression with backward elimination to determine which among candidate structured EMR parameters (diagnostic codes, vital signs and orders for tests, imaging and medications) contributed to the detection of those reference cases. We also developed a computerized free-text search to identify clinical notes documenting at least two non-negated ARI symptoms. We then used heuristics to build case-detection algorithms that best combined the retained structured EMR parameters with the results of the text analysis. An adjusted grouping of diagnostic codes identified reference ARI patients with a sensitivity of 79%, a specificity of 96% and a positive predictive value (PPV) of 32%. Of the 21 additional structured clinical parameters considered, two contributed significantly to ARI detection: new prescriptions for cough remedies and elevations in body temperature to at least 38°C. Together with the diagnostic codes, these parameters increased detection sensitivity to 87%, but specificity and PPV declined to 95% and 25%, respectively. Adding text analysis increased sensitivity to 99%, but PPV dropped further to 14%. Algorithms that required satisfying both a query of structured EMR parameters as well as text analysis disclosed PPVs of 52–68% and retained sensitivities of 69–73%. Structured EMR parameters and free-text analyses can be combined into algorithms that can detect ARI cases with new levels of sensitivity or precision. These results highlight potential paths by which repurposed EMR information could facilitate the discovery of epidemics before they cause mass casualties.
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