Leiva-Araos Andrés, Contreras Cristián, Kaushal Hemani, Prodanoff Zornitza
Department of Computing, University of North Florida, 1 UNF Dr., Jacksonville, 32246, FL, USA.
Instituto Data Science, Universidad del Desarrollo, Av. La Plaza 680, 7610658, RM, Las Condes, Chile.
J Med Syst. 2025 Jan 14;49(1):7. doi: 10.1007/s10916-025-02143-w.
The "no-show" problem in healthcare refers to the prevalent phenomenon where patients schedule appointments with healthcare providers but fail to attend them without prior cancellation or rescheduling. In addressing this issue, our study delves into a multivariate analysis over a five-year period involving 21,969 patients. Our study introduces a predictive model framework that offers a holistic approach to managing the no-show problem in healthcare, incorporating elements into the objective function that address not only the accurate prediction of no-shows but also the management of service capacity, overbooking, and idle resource allocation resulting from mispredictions. Our approach simplifies preprocessing and eliminates the need for expert judgment in variable selection, thereby enhancing the model's usability in routine healthcare operations. Our research revealed that key predictors of no-shows are consistent across various studies. We employed semi-automatic feature selection techniques, achieving results comparable to state-of-the-art approaches but with significantly reduced complexity in their selection. This method not only streamlines the feature selection process but also enhances the overall efficiency and scalability of our predictive models, making them more adaptable to diverse healthcare settings. This comprehensive strategy enables healthcare providers to optimize resource allocation and improve service delivery, making our findings relevant for healthcare systems globally facing similar challenges. Future work aims to expand the analysis by incorporating additional third-party data sources, such as weather and commuting activities, to explore the broader impacts of external factors on patient no-show behavior. To the best of our knowledge, this innovative approach is expected to provide deeper insights and further enhance the predictability and effectiveness of no-show mitigation strategies in healthcare systems.
医疗保健中的“爽约”问题是指一种普遍现象，即患者与医疗服务提供者预约了就诊时间，但未事先取消或重新安排就未能赴约。在解决这个问题时，我们的研究对21969名患者进行了为期五年的多变量分析。我们的研究引入了一个预测模型框架，该框架提供了一种全面的方法来管理医疗保健中的爽约问题，在目标函数中纳入了一些要素，这些要素不仅涉及对爽约情况的准确预测，还包括对服务能力的管理、超额预订以及因预测错误导致的闲置资源分配。我们的方法简化了预处理过程，消除了变量选择中对专家判断的需求，从而提高了该模型在日常医疗运营中的可用性。我们的研究表明，不同研究中爽约的关键预测因素是一致的。我们采用了半自动特征选择技术，取得了与最先进方法相当的结果，但在选择过程中的复杂性显著降低。这种方法不仅简化了特征选择过程，还提高了我们预测模型的整体效率和可扩展性，使其更能适应不同的医疗环境。这种全面的策略使医疗服务提供者能够优化资源分配并改善服务提供，使我们的研究结果与全球面临类似挑战的医疗系统相关。未来的工作旨在通过纳入更多第三方数据源（如天气和通勤活动）来扩大分析范围，以探索外部因素对患者爽约行为的更广泛影响。据我们所知，这种创新方法有望提供更深入的见解，并进一步提高医疗系统中减轻爽约策略的可预测性和有效性。
