Almario Christopher V, Chey William D, Iriana Sentia, Dailey Francis, Robbins Karen, Patel Anish V, Reid Mark, Whitman Cynthia, Fuller Garth, Bolus Roger, Dennis Buddy, Encarnacion Rey, Martinez Bibiana, Soares Jennifer, Modi Rushaba, Agarwal Nikhil, Lee Aaron, Kubomoto Scott, Sharma Gobind, Bolus Sally, Chang Lin, Spiegel Brennan M R
Division of Gastroenterology, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Division of Gastroenterology, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA; Division of Digestive Diseases, UCLA, Los Angeles, CA, USA; Cedars-Sinai Center for Outcomes Research and Education (CS-CORE), Los Angeles, CA, USA.
Division of Gastroenterology, University of Michigan, Ann Arbor, MI, USA.
Int J Med Inform. 2015 Dec;84(12):1111-7. doi: 10.1016/j.ijmedinf.2015.07.006. Epub 2015 Jul 26.
It is important for clinicians to inquire about "alarm features" as it may identify those at risk for organic disease and who require additional diagnostic workup. We developed a computer algorithm called Automated Evaluation of Gastrointestinal Symptoms (AEGIS) that systematically collects patient gastrointestinal (GI) symptoms and alarm features, and then "translates" the information into a history of present illness (HPI). Our study's objective was to compare the number of alarms documented by physicians during usual care vs. that collected by AEGIS.
We performed a cross-sectional study with a paired sample design among patients visiting adult GI clinics. Participants first received usual care by their physicians and then completed AEGIS. Each individual thus contributed both a physician-documented and computer-generated HPI. Blinded physician reviewers enumerated the positive alarm features (hematochezia, melena, hematemesis, unintentional weight loss, decreased appetite, and fevers) mentioned in each HPI. We compared the number of documented alarms within patient using the Wilcoxon signed-rank test.
Seventy-five patients had both physician and AEGIS HPIs. AEGIS identified more patients with positive alarm features compared to physicians (53% vs. 27%; p<.001). AEGIS also documented more positive alarms (median 1, interquartile range [IQR] 0-2) vs. physicians (median 0, IQR 0-1; p<.001). Moreover, clinicians documented only 30% of the positive alarms self-reported by patients through AEGIS.
Physicians documented less than one-third of red flags reported by patients through a computer algorithm. These data indicate that physicians may under report alarm features and that computerized "checklists" could complement standard HPIs to bolster clinical care.
临床医生询问“警示特征”很重要,因为这可能识别出存在器质性疾病风险且需要进一步诊断检查的患者。我们开发了一种名为胃肠道症状自动评估(AEGIS)的计算机算法,该算法系统地收集患者的胃肠道(GI)症状和警示特征,然后将这些信息“转化”为现病史(HPI)。我们研究的目的是比较医生在常规诊疗过程中记录的警示特征数量与AEGIS收集的数量。
我们在就诊于成人胃肠病诊所的患者中采用配对样本设计进行了一项横断面研究。参与者首先接受医生的常规诊疗,然后完成AEGIS评估。因此,每个个体都提供了一份医生记录的和计算机生成的现病史。不知情的医生审阅者列举了每份现病史中提到的阳性警示特征(便血、黑便、呕血、非故意体重减轻、食欲减退和发热)。我们使用Wilcoxon符号秩检验比较患者记录的警示特征数量。
75名患者同时拥有医生记录的和AEGIS生成的现病史。与医生相比,AEGIS识别出更多具有阳性警示特征的患者(53%对27%;p<0.001)。AEGIS记录的阳性警示特征也更多(中位数为1,四分位间距[IQR]为0 - 2),而医生记录的中位数为0,IQR为0 - 1;p<0.001)。此外,临床医生记录的通过AEGIS患者自我报告的阳性警示特征仅占30%。
医生记录的通过计算机算法报告的警示信号不到三分之一。这些数据表明医生可能对警示特征报告不足,并且计算机化的“检查表”可以补充标准现病史以加强临床护理。