Low attendance levels in medical appointments have been associated with poor health outcomes and efficiency problems for service providers. To address this problem, healthcare managers could aim at improving attendance levels or minimizing the operational impact of no-shows by adapting resource allocation policies. However, given the uncertainty of patient behaviour, generating relevant information regarding no-show probabilities could support the decision-making process for both approaches. In this context many researchers have used multiple regression models to identify patient and appointment characteristics than can be used as good predictors for no-show probabilities. This work develops a Decision Support System (DSS) to support the implementation of strategies to encourage attendance, for a preventive care program targeted at underserved communities in Bogotá, Colombia. Our contribution to literature is threefold. Firstly, we assess the effectiveness of different machine learning approaches to improve the accuracy of regression models. In particular, Random Forest and Neural Networks are used to model the problem accounting for non-linearity and variable interactions. Secondly, we propose a novel use of Layer-wise Relevance Propagation in order to improve the explainability of neural network predictions and obtain insights from the modelling step. Thirdly, we identify variables explaining no-show probabilities in a developing context and study its policy implications and potential for improving healthcare access. In addition to quantifying relationships reported in previous studies, we find that income and neighbourhood crime statistics affect no-show probabilities. Our results will support patient prioritization in a pilot behavioural intervention and will inform appointment planning decisions.

医疗预约的出勤率低与服务提供商的健康状况差和效率问题有关。为了解决这个问题，医疗保健管理者可以通过调整资源分配政策，以提高出勤率或最大程度地减少未上班的运营影响。但是，考虑到患者行为的不确定性，生成有关未出现机率的相关信息可以支持两种方法的决策过程。在这种情况下，许多研究人员已经使用多个回归模型来识别患者和约会特征，而这些模型可以用作未出现概率的良好预测指标。这项工作开发了决策支持系统（DSS），以支持鼓励出勤的策略的实施，针对哥伦比亚波哥大服务不足的社区的预防保健计划。我们对文学的贡献是三方面的。首先，我们评估不同机器学习方法的有效性，以提高回归模型的准确性。特别是，随机森林和神经网络用于对非线性和可变相互作用的问题进行建模。其次，为了提高神经网络预测的可解释性并从建模步骤中获得见识，我们提出了一种新的分层分层相关传播用法。第三，我们确定在发展中的情况下解释未出现机率的变量，并研究其政策含义以及改善医疗保健机会的潜力。除了量化先前研究中报告的关系外，我们发现收入和社区犯罪统计数据会影响未出现的概率。我们的结果将支持在先行行为干预中确定患者的优先级，并将为约会计划决策提供依据。