Department of Systems and Information Engineering, University of Virginia, 151 Engineer's Way, Charlottesville, VA, 22904, USA.
Systems Engineering Technical Center, The Mitre Corporation, McClean, VA, USA.
J Med Syst. 2017 Oct 12;41(11):182. doi: 10.1007/s10916-017-0815-3.
The Veterans Health Administration (VHA) is plagued by abnormally high no-show and cancellation rates that reduce the productivity and efficiency of its medical outpatient clinics. We address this issue by developing a dynamic scheduling system that utilizes mobile computing via geo-location data to estimate the likelihood of a patient arriving on time for a scheduled appointment. These likelihoods are used to update the clinic's schedule in real time. When a patient's arrival probability falls below a given threshold, the patient's appointment is canceled. This appointment is immediately reassigned to another patient drawn from a pool of patients who are actively seeking an appointment. The replacement patients are prioritized using their arrival probability. Real-world data were not available for this study, so synthetic patient data were generated to test the feasibility of the design. The method for predicting the arrival probability was verified on a real set of taxicab data. This study demonstrates that dynamic scheduling using geo-location data can reduce the number of unused appointments with minimal risk of double booking resulting from incorrect predictions. We acknowledge that there could be privacy concerns with regards to government possession of one's location and offer strategies for alleviating these concerns in our conclusion.
退伍军人健康管理局（VHA）受到异常高的未到诊和取消率的困扰，这些问题降低了其门诊诊所的生产力和效率。我们通过开发一个利用移动计算和地理位置数据来估计患者按时到达预约的可能性的动态调度系统来解决这个问题。这些可能性用于实时更新诊所的日程安排。当患者的到达概率低于给定阈值时，患者的预约将被取消。该预约将立即重新分配给另一位积极寻求预约的患者。替换患者使用他们的到达概率进行优先级排序。本研究没有使用真实数据，因此生成了合成患者数据来测试设计的可行性。预测到达概率的方法在一组真实的出租车数据上进行了验证。这项研究表明，使用地理位置数据进行动态调度可以减少未使用预约的数量，同时最大限度地降低因预测错误导致的双重预订风险。我们承认政府拥有个人位置可能会引发隐私问题，并在结论中提出了缓解这些问题的策略。
