Estimating the workload of a multi-disciplinary care team using patient-level encounter histories
Estimating the workload of a multi-disciplinary care team using patient-level encounter histories
复制标题
DOI：
10.1080/20476965.2023.2215848
复制
发表时间：
2023-06-14
期刊：
影响因子：
1.8
通讯作者：
Truchil，Aaron
中科院分区：
文献类型：
--
作者：
Koker，Ekin;Balasubramanian，Hari;Truchil，Aaron
美国的医疗保健支出集中在一小部分人身上，5%的人口占年度支出的50%。支出前 5% 的许多患者都有复杂的健康和社会需求。护理协调干预措施通常由护士、社区卫生工作者和社会工作者组成的多学科团队领导，是解决此类患者面临的挑战的一项策略。护理团队通过与客户建立牢固的关系、定期拜访他们、协调药物、安排初级和专科护理就诊以及解决住房不稳定、失业和保险等社会需求，努力改善健康结果。在本文中，我们提出了一种模拟算法，可以对患者层面的纵向接触历史进行采样，以估计多学科护理团队的人员需求。我们的数值结果说明了该算法在固定和非固定患者入组率下的人员配置的多种用途。
Healthcare spending in the United States is concentrated on a small percentage of individuals, with 5% of the population accounting for 50% of annual spending. Many patients among the top 5% of spenders have complex health and social needs. Care coordination interventions, often led by a multidisciplinary team consisting of nurses, community health workers and social workers, are one strategy for addressing the challenges facing such patients. Care teams strive to improve health outcomes by forging strong relationships with clients, visiting them on a regular basis, reconciling medications, arranging primary and speciality care visits, and addressing social needs such as housing instability, unemployment and insurance. In this paper, we propose a simulation algorithm that samples longitudinal patient-level encounter histories to estimate the staffing needs for a multidisciplinary care team. Our numerical results illustrate multiple uses of the algorithm for staffing under stationary and non-stationary patient enrollment rates.