背景:国家卫生服务(NHS)谈话疗法计划根据“阶梯式护理”在英格兰治疗患有常见心理健康问题的人,“首先提供较低强度的干预措施,临床上适当的。有限的资源和达到服务标准的压力意味着计划提供商正在探索所有机会来评估和改善患者通过其服务的流动。现有的研究已经发现了不同的临床表现和跨站点的逐步护理实施,并且已经确定了服务提供和患者结果之间的关联。流程挖掘提供了一种数据驱动的方法来分析和评估医疗保健流程和系统,能够比较服务交付的假定模式及其在实践中的实际执行情况。尚未研究将过程挖掘应用于NHSTalkingTherapies数据以分析护理途径的价值和实用性。
目标:更好地了解服务交付系统将支持改进和计划中的计划扩展。因此,本研究旨在证明使用电子健康记录将过程挖掘应用于NHSTalkingTherapies护理路径的价值和实用性。
方法:常规收集关于活动和患者结果的各种数据是TalkingTherapies计划的基础。在我们的研究中,通过绘制护理路径图并确定共同路径路径,使用过程挖掘对来自2个站点的匿名患者转诊记录进行分析,以可视化护理路径过程.
结果:过程挖掘能够直接从常规收集的数据中识别和可视化患者流。这些可视化说明了等待期和确定的潜在瓶颈,例如在1号站点等待更高强度的认知行为治疗(CBT)。此外,我们观察到,与开始治疗的患者相比,从治疗等待名单中出院的患者等待时间似乎更长.工艺开采允许分析处理途径,表明患者通常经历的治疗途径涉及低强度或高强度干预。在最常见的路线中,>5倍的患者经历了直接获得高强度治疗而不是阶梯式护理。总的来说,所有患者中有3.32%(站点1:1507/45,401)和4.19%(站点2:527/12,590)经历了逐步护理。
结论:我们的研究结果证明了如何将过程挖掘应用于TalkingTherapies护理路径以评估路径性能,探索绩效问题之间的关系,突出系统性问题,例如分级护理在分级护理系统中相对不常见。将流程挖掘能力整合到常规监控中,将使NHSTalkingTherapies服务利益相关者能够从流程角度探索此类问题。这些见解将通过确定服务改进的领域来为服务提供价值,为容量规划决策提供证据,并促进更好的质量分析,以了解卫生系统如何影响患者的预后。
BACKGROUND: The National Health Service (NHS) Talking Therapies program treats people with common mental health problems in England according to \"stepped care,\" in which lower-intensity interventions are offered in the first instance, where clinically appropriate. Limited resources and pressure to achieve service standards mean that program providers are exploring all opportunities to evaluate and improve the flow of patients through their service. Existing research has found variation in clinical performance and stepped care implementation across sites and has identified associations between service delivery and patient outcomes. Process mining offers a data-driven approach to analyzing and evaluating health care processes and systems, enabling comparison of presumed models of service delivery and their actual implementation in practice. The value and utility of applying process mining to NHS Talking Therapies data for the analysis of care pathways have not been studied.
OBJECTIVE: A better understanding of systems of service delivery will support improvements and planned program expansion. Therefore, this study aims to demonstrate the value and utility of applying process mining to NHS Talking Therapies care pathways using electronic health records.
METHODS: Routine collection of a wide variety of data regarding activity and patient outcomes underpins the Talking Therapies program. In our study, anonymized individual patient referral records from two sites over a 2-year period were analyzed using process mining to visualize the care pathway process by mapping the care pathway and identifying common pathway routes.
RESULTS: Process mining enabled the identification and visualization of patient flows directly from routinely collected data. These visualizations illustrated waiting periods and identified potential bottlenecks, such as the wait for higher-intensity cognitive behavioral therapy (CBT) at site 1. Furthermore, we observed that patients discharged from treatment waiting lists appeared to experience longer wait durations than those who started treatment. Process mining allowed analysis of treatment pathways, showing that patients commonly experienced treatment routes that involved either low- or high-intensity interventions alone. Of the most common routes, >5 times as many patients experienced direct access to high-intensity treatment rather than stepped care. Overall, 3.32% (site 1: 1507/45,401) and 4.19% (site 2: 527/12,590) of all patients experienced stepped care.
CONCLUSIONS: Our findings demonstrate how process mining can be applied to Talking Therapies care pathways to evaluate pathway performance, explore relationships among performance issues, and highlight systemic issues, such as stepped care being relatively uncommon within a stepped care system. Integration of process mining capability into routine monitoring will enable NHS Talking Therapies service stakeholders to explore such issues from a process perspective. These insights will provide value to services by identifying areas for service improvement, providing evidence for capacity planning decisions, and facilitating better quality analysis into how health systems can affect patient outcomes.