Dhand Amar, Bucelli Robert, Varadhachary Arun, Tsiaklides Michael, de Bruin Gabriela, Dhaliwal Gurpreet
Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
Neurohospitalist. 2017 Jul;7(3):132-136. doi: 10.1177/1941874416677681. Epub 2016 Nov 16.
The Institute of Medicine report called for tools to monitor physicians' diagnostic process. We addressed this need by developing a tool for clinicians to record and analyze their diagnostic process. The tool was a secure web application in which clinicians used a structured grading system to assess the relative impact of clinical, laboratory, and neuroimaging data for every new diagnosis. Four neurohospitalists used the tool for 6.5 months on a general neurology ward service at a single tertiary-level teaching hospital. Process measures of tool use included number of diagnoses entered, time spent on each data entry, and concordance of diagnoses compared to the medical record. We also aggregated the data across clinicians to examine the average process scores across common inpatient disorders. The 4 clinicians entered 254 new diagnoses that took approximately 3 minutes per patient. In 50 randomly chosen cases, the neurohospitalists' diagnoses entered into the tool agreed with 92% of diagnoses in the medical record, which was better than the agreement between billing code and medical record diagnoses (74%). The diagnostic process varied across disease categories, showing a spectrum of clinical-dominant (eg, headache), laboratory-dominant (eg, encephalitis), and neuroimaging-dominant (eg, stroke) disorders. This study demonstrated the feasibility of a clinician-driven diagnostic process monitoring system, along with preliminary characterization of the process for common disorders. The tracking of diagnostic process has the potential to promote reflection on clinical practice, deconstruct neurologists' clinical decision-making, and improve health-care safety.
医学研究所的报告呼吁开发工具来监测医生的诊断过程。我们通过开发一种工具来满足这一需求，该工具可供临床医生记录和分析他们的诊断过程。该工具是一个安全的网络应用程序，临床医生在其中使用结构化评分系统来评估临床、实验室和神经影像数据对每个新诊断的相对影响。四名神经科住院医师在一家三级教学医院的普通神经科病房服务中使用该工具达6.5个月。工具使用的过程指标包括输入的诊断数量、每次数据输入花费的时间以及与病历相比诊断的一致性。我们还汇总了不同临床医生的数据，以检查常见住院疾病的平均过程得分。这4名临床医生输入了254个新诊断，每位患者大约花费3分钟。在随机选择的50个病例中，神经科住院医师输入到工具中的诊断与病历中的诊断有92%一致，这比计费代码与病历诊断之间的一致性（74%）要好。诊断过程因疾病类别而异，呈现出临床主导型（如头痛）、实验室主导型（如脑炎）和神经影像主导型（如中风）等一系列疾病。这项研究证明了临床医生驱动的诊断过程监测系统的可行性，以及对常见疾病诊断过程的初步特征描述。对诊断过程的跟踪有可能促进对临床实践的反思、解构神经科医生的临床决策，并提高医疗保健安全性。
