Verma Neha, Buch Bimal, Pandya R S, Taralekar Radha, Masand Ishita, Rangparia Hardik, Katira J M, Acharya Soumyadipta
Division of Biomedical Informatics & Data Science, Johns Hopkins University School of Medicine, 2024 East Monument St. S 1-200, Baltimore, MD 21205, USA.
Intelehealth, 14A Shreeji Arcade, Panchpakhadi, Thane 400602, Maharashtra, India.
Oxf Open Digit Health. 2024 Feb 2;2:oqae008. doi: 10.1093/oodh/oqae008. eCollection 2024.
Assisted history-taking systems can be used in provider-to-provider teleconsultations to task-shift the collection of evidence-based medical history and physical exam information to a frontline health worker. We developed such a task-shifting digital assistant, called 'Ayu', for nurses in rural India to collect clinical information from a patient and share it with a remote doctor to arrive at an accurate diagnosis and triage decision.
MATERIALS & METHODS: We evaluated the ability of the task-shifting digital assistant to collect a comprehensive patient history by using 190 standardized patient case studies and evaluating the information recall of the assistant by a skilled clinician. Following this, we tested the ability of nurses to use the system by training and evaluating the system's accuracy when used by 19 nurses in rural Gujarat, India. We also measured the diagnostic and triage accuracy based on the generated history note. Finally, we evaluated the system's acceptability by using the Technology Acceptance Model framework.
Ayu could capture 65% of patient history information and 42% of physical exam information from patient case studies. When used by nurses, the mean accuracy of the generated clinical note was 7.71 ± 2.42. Using the information collected by a nurse using Ayu, a primary care physician could arrive at the correct diagnosis in 74% of cases, and correct triage decision in 88% of cases. Overall, we saw a high acceptability from nurses to use the system.
Ayu can capture an acceptable proportion of clinical information and can aid in collecting an evidence-based medical history by task-shifting some of the early investigational steps. Further development of Ayu to increase its information retrieval ability and ease of use by health workers is needed.
辅助病史采集系统可用于医疗服务提供者之间的远程会诊,将循证病史和体格检查信息的收集工作任务转移给一线医护人员。我们为印度农村地区的护士开发了这样一个名为“Ayu”的任务转移数字助手,用于从患者那里收集临床信息,并与远程医生共享,以做出准确的诊断和分诊决策。
我们通过使用190个标准化患者案例研究来评估任务转移数字助手收集全面患者病史的能力,并由一名经验丰富的临床医生评估该助手的信息召回情况。在此之后,我们通过培训并评估印度古吉拉特邦农村地区的19名护士使用该系统时的准确性,来测试护士使用该系统的能力。我们还根据生成的病史记录来衡量诊断和分诊的准确性。最后,我们使用技术接受模型框架来评估该系统的可接受性。
Ayu能够从患者案例研究中获取65%的患者病史信息和42%的体格检查信息。当由护士使用时,生成的临床记录的平均准确率为7.71±2.42。使用护士通过Ayu收集的信息,初级保健医生在74%的病例中能够做出正确诊断,在88%的病例中能够做出正确的分诊决策。总体而言,我们发现护士对使用该系统的接受度很高。
Ayu能够获取可接受比例的临床信息,并且通过将一些早期调查步骤的任务转移,有助于收集循证病史。需要进一步开发Ayu,以提高其信息检索能力以及医护人员使用的便捷性。