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基于算法的在线软件,用于患者自我转诊至乳腺诊所,作为全科医生转诊途径的替代方案。

Algorithm-Based Online Software for Patients' Self-Referral to Breast Clinic as an Alternative to General Practitioner Referral Pathway.

作者信息

Rao Ahsan, Dhahri Adeel Abbas, Razzaq Humayun, Mokhtari Eshagh, Majeed Azeem, Patel Ashraf

机构信息

Breast Surgery, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, London, GBR.

General Surgery, Princess Alexandra Hospital NHS Trust, Harlow, GBR.

出版信息

Cureus. 2020 Nov 28;12(11):e11740. doi: 10.7759/cureus.11740.

DOI:10.7759/cureus.11740
PMID:33274168
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7707138/
Abstract

Introduction The study aimed to assess the accuracy of online software in the use of self-referral to breast surgery clinics for patients with new signs and symptoms. The study also evaluated the appropriateness of GP referrals to breast clinics and evaluated patients' perceptions of an online self-referral portal to the breast clinic for the assessment of breast signs and symptoms.  Design and methods The pilot study was divided into two phases. In the first phase, prospective questionnaire-based data was collected from patients who were referred by a GP and presented to the regional breast unit with new signs and symptoms for breast conditions, Princess Alexandra Hospital NHS Trust (May - October 2018). The questionnaire assessed the time at each stage required by the patient to have a visit at the breast unit. It also asked the patient's opinion about an online self-referral portal to the surgical clinic. They were given hypothetical scenarios to evaluate their understanding of breast conditions. In the second phase, the patients presenting to symptomatic breast clinics were provided with the iPad to fill in their medical information in the online software. The data was collected between July and October 2019. The software algorithm was based on the National Institute of Clinical Health and Excellence (NICE) guidelines for breast conditions (2015). Breast surgeons' recommendations acted as a standard to evaluate the accuracy of GPs' referrals and software outcome for each patient.  Results There were 80 patients (mean age 49.1 [SD: 17.7], all females) included in the first phase of the study. The most common clinical presentation was a breast lump (47.6%), followed by breast pain (26.9%) and nipple changes (7.9%). Breast surgeons considered appropriate 75.6% of the referrals made by the GP. Seventy-two percent of the patients got an urgent appointment to see their GP, and 94.8% of the patients were urgently referred by their GP to see the breast surgeon. Only 37.8% of the urgent referrals were correctly referred as urgent. Having a direct online referral system for breast conditions will be beneficial for patients was agreed by 78.4%. The majority (98.1%) of the participants answered correctly for the hypothetical questions requiring breast surgeon review. In the second phase, there were a total of 86 patients with a mean age of 43.9 (SD: 13.3). The most common presentation was breast lump (n=68, 79.1%) and other presentations included breast pain, nipple changes, and discharge. The GPs' accuracy of correct referral was 69.1%. One third (30.9%) of the referrals could have been managed in the community or as a routine review by the breast surgeon. In comparison, the online software's accuracy was 85.1% accurate (p=0.001). The accuracy for detecting patients who needed urgent breast clinic review was 100% for online software.  Conclusion A large proportion of referrals could have been dealt with in the community or referred routinely. Patients would prefer a direct online referral system to the breast clinic. They understand red flag signs and symptoms. Online software has the potential to streamline patients for symptomatic breast clinics. It can reduce the burden on the GPs who are constantly under pressure to diagnose patients accurately and refer to the correct specialty appropriately within a short time.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/920e/7707138/139743bca09d/cureus-0012-00000011740-i03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/920e/7707138/e8e425fbf8a8/cureus-0012-00000011740-i01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/920e/7707138/ce3c2c63b130/cureus-0012-00000011740-i02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/920e/7707138/139743bca09d/cureus-0012-00000011740-i03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/920e/7707138/e8e425fbf8a8/cureus-0012-00000011740-i01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/920e/7707138/ce3c2c63b130/cureus-0012-00000011740-i02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/920e/7707138/139743bca09d/cureus-0012-00000011740-i03.jpg
摘要

引言 本研究旨在评估在线软件用于有新体征和症状的患者自我转诊至乳腺外科诊所的准确性。该研究还评估了全科医生(GP)转诊至乳腺诊所的适宜性,并评估了患者对乳腺诊所在线自我转诊门户用于评估乳腺体征和症状的看法。

设计与方法 该试点研究分为两个阶段。在第一阶段,从由全科医生转诊并因乳腺疾病的新体征和症状前往亚历山德拉公主医院国民保健服务信托基金地区乳腺科就诊的患者(2018年5月至10月)中,收集基于问卷的前瞻性数据。问卷评估了患者在乳腺科就诊每个阶段所需的时间。它还询问了患者对手术诊所在线自我转诊门户的看法。给他们提供假设情景以评估他们对乳腺疾病的理解。在第二阶段,为前往有症状乳腺诊所就诊的患者提供iPad,以便他们在在线软件中填写医疗信息。数据收集于2019年7月至10月。软件算法基于国家临床优化研究所(NICE)关于乳腺疾病的指南(2015年)。乳腺外科医生的建议作为评估每位患者全科医生转诊准确性和软件结果的标准。

结果 研究的第一阶段纳入了80名患者(平均年龄49.1岁[标准差:17.7],均为女性)。最常见的临床表现是乳腺肿块(47.6%),其次是乳腺疼痛(26.9%)和乳头改变(7.9%)。乳腺外科医生认为全科医生做出的转诊中有75.6%是合适的。72%的患者获得了紧急预约看全科医生,94.8%的患者被全科医生紧急转诊去看乳腺外科医生。只有37.8%的紧急转诊被正确地作为紧急转诊。78.4%的人认为建立乳腺疾病直接在线转诊系统对患者有益。对于需要乳腺外科医生复查的假设问题,大多数参与者(98.1%)回答正确。在第二阶段,共有86名患者,平均年龄43.9岁(标准差:13.3)。最常见的表现是乳腺肿块(n = 68,79.1%),其他表现包括乳腺疼痛、乳头改变和溢液。全科医生正确转诊的准确率为69.1%。三分之一(30.9%)的转诊本可在社区处理或由乳腺外科医生进行常规复查。相比之下,在线软件的准确率为85.1%(p = 0.001)。在线软件检测需要紧急乳腺诊所复查患者的准确率为100%。

结论 很大一部分转诊本可在社区处理或常规转诊。患者更喜欢直接的乳腺诊所在线转诊系统。他们了解警示体征和症状。在线软件有潜力为有症状的乳腺诊所简化患者流程。它可以减轻全科医生的负担,因为他们一直面临着在短时间内准确诊断患者并适当地转诊到正确专科的压力。

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