Augusto Duenhas Accorsi Tarso, Tocci Moreira Flavio, Aires Eduardo Anderson, Albaladejo Morbeck Renata, Francine Köhler Karen, De Amicis Lima Karine, Henrique Sartorato Pedrotti Carlos
Telemedicine Department, Hospital Israelita Albert Einstein, São Paulo, Brazil.
Digital Platform, Hospital Israelita Albert Einstein, São Paulo, Brazil.
Telemed J E Health. 2024 Aug;30(8):2142-2147. doi: 10.1089/tmj.2024.0126. Epub 2024 May 28.
The quantification of self-triage effectiveness, guided by mobile applications, in urgent direct-to-consumer telemedicine (TM) encounters requires further investigation. The objective of this study was to evaluate the outcomes of referral guidance provided by a symptom-based self-management mobile application decision algorithm in the context of remote urgent care assessments. An observational retrospective single-center study was conducted from May 2022 to December 2023. The inclusion criteria encompassed individuals aged >18 years old, and those spontaneously seeking virtual emergency care through the EINSTEIN CONECTA application. Patients experiencing connectivity issues, preventing completion of the encounter, were excluded. The primary outcomes included the rate of patient concurrence with the algorithm's recommendation for seeking in-person emergency care and the referral rate to face-to-face assessment among cases evaluated through TM. The application's algorithm employs scientific evidence based on symptoms to recommend referrals to emergency departments (EDs). Out of 88,834 patients connected to the TM Center, self-triage obviated the need for virtual physician assessment in 53,302 (60%) encounters. A total of 35,532 patients were remotely evaluated by 316 on-duty physicians, resulting in 1,125 ICD-coded diagnoses. Among these, 21,722 (61.1%) were initially advised by self-triage to visit the ED, with subsequent medical assessment leading to in-person referrals in 6,354 (29.3%) of the evaluations. Of the 13,810 patients recommended to continue with virtual care post-self-triage, 157 (1.1%) were referred for in-person assessment. Self-triage effectively reduced the need for physician encounters in approximately three-fifths of TM consultations. Despite being based on scientific evidence, symptom-based referral algorithms demonstrated high sensitivity but poor correlation with physician decision-making.
在紧急的直接面向消费者的远程医疗(TM)就诊中,由移动应用程序指导的自我分诊有效性量化需要进一步研究。本研究的目的是评估基于症状的自我管理移动应用程序决策算法在远程紧急护理评估背景下提供的转诊指导结果。2022年5月至2023年12月进行了一项观察性回顾性单中心研究。纳入标准包括年龄大于18岁的个体,以及那些通过爱因斯坦CONNECTA应用程序自发寻求虚拟紧急护理的人。因连接问题而无法完成就诊的患者被排除。主要结果包括患者对算法建议寻求面对面紧急护理的认同率,以及通过TM评估的病例中面对面评估的转诊率。该应用程序的算法采用基于症状的科学证据来推荐转诊至急诊科(ED)。在连接到TM中心的88,834名患者中,自我分诊避免了53,302次(60%)就诊中对虚拟医生评估的需求。共有35,532名患者由316名值班医生进行了远程评估,得出1,125个国际疾病分类编码诊断。其中,21,722名(61.1%)最初被自我分诊建议前往急诊科就诊,随后的医学评估导致在6,354次(29.3%)评估中进行了面对面转诊。在自我分诊后被建议继续接受虚拟护理的13,810名患者中,有157名(1.1%)被转诊进行面对面评估。自我分诊有效地减少了大约五分之三的TM咨询中对医生就诊的需求。尽管基于科学证据,但基于症状的转诊算法显示出高敏感性,但与医生决策的相关性较差。