Clinical Research Department, International Centre for Eye Health, London School of Hygiene & Tropical Medicine, London, United Kingdom.
Kitale County and Referral Hospital, Kitale, Kenya.
JMIR Mhealth Uhealth. 2020 Jun 19;8(6):e16345. doi: 10.2196/16345.
The provision of eye care services is currently insufficient to meet the requirements of eye care. Many people remain unnecessarily visually impaired or at risk of becoming so because of treatable or preventable eye conditions. A lack of access and awareness of services is, in large part, a key barrier to handle this unmet need.
This study aimed to assess whether utilizing novel smartphone-based clinical algorithms can task-shift eye screening to community volunteers (CVs) to accurately identify and refer patients to primary eye care services. In particular, we developed the Peek Community Screening app and assessed its validity in making referral decisions for patients with eye problems.
We developed a smartphone-based clinical algorithm (the Peek Community Screening app) using age, distance vision, near vision, and pain as referral criteria. We then compared CVs' referral decisions using this app with those made by an experienced ophthalmic clinical officer (OCO), which was the reference standard. The same participants were assessed by a trained CV using the app and by an OCO using standard outreach equipment. The outcome was the proportion of all decisions that were correct when compared with that of the OCO.
The required sensitivity and specificity for the Peek Community Screening app were achieved after seven iterations. In the seventh iteration, the OCO identified referable eye problems in 65.9% (378/574) of the participants. CVs correctly identified 344 of 378 (sensitivity 91.0%; 95% CI 87.7%-93.7%) of the cases and correctly identified 153 of 196 (specificity 78.1%; 95% CI 71.6%-83.6%) cases as not having a referable eye problem. The positive predictive value was 88.9% (95% CI 85.3%-91.8%), and the negative predictive value was 81.8% (95% CI 75.5%-87.1%).
Development of such an algorithm is feasible; however, it requires considerable effort and resources. CVs can accurately use the Peek Community Screening app to identify and refer people with eye problems. An iterative design process is necessary to ensure validity in the local context.
目前，提供的眼科保健服务还不能满足眼科保健的需求。许多人因可治疗或可预防的眼病而视力受损或面临视力受损的风险，但他们得不到治疗。很大程度上，无法获得服务和对服务缺乏认识是造成这一未满足需求的主要障碍。
本研究旨在评估利用新型基于智能手机的临床算法是否可以将眼科筛查任务转移给社区志愿者（CVs），以便准确识别并将患者转介至初级眼科保健服务。具体而言，我们开发了 Peek 社区筛查应用程序，并评估了其在为有眼部问题的患者做出转诊决策方面的有效性。
我们使用年龄、远距视力、近距视力和疼痛作为转诊标准，开发了一种基于智能手机的临床算法（Peek 社区筛查应用程序）。然后，我们将 CVs 使用该应用程序做出的转诊决策与经验丰富的眼科临床医生（OCO）的参考标准进行了比较。同一批参与者由经过培训的 CV 使用该应用程序和 OCO 使用标准外展设备进行评估。结果是与 OCO 相比，所有决策中正确的比例。
在经过七次迭代后，Peek 社区筛查应用程序达到了所需的灵敏度和特异性。在第七次迭代中，OCO 识别出 574 名参与者中有 65.9%（378/574）有可转诊的眼部问题。CVs 正确识别出 378 例中的 344 例（敏感性 91.0%；95%CI 87.7%-93.7%），正确识别出 196 例中 153 例（特异性 78.1%；95%CI 71.6%-83.6%）为无可转诊的眼部问题。阳性预测值为 88.9%（95%CI 85.3%-91.8%），阴性预测值为 81.8%（95%CI 75.5%-87.1%）。
开发这样的算法是可行的，但需要大量的努力和资源。CVs 可以使用 Peek 社区筛查应用程序准确识别和转介有眼部问题的人。需要一个迭代设计过程来确保在当地环境中的有效性。
