Mobile Decision Support System for Nurse Management of Neuromodulation Therapy

 描述（由申请人提供）： 脑深部电刺激（DBS）在改善各种慢性疾病患者的生活方面具有巨大的潜力。DBS治疗帕金森病（PD）的良好结局与准确的电极放置和术后仔细选择刺激参数（电压、脉冲宽度、频率、有源电极触点等）密切相关。尽管DBS对多种疾病有益，但一个持续存在的问题是植入电极导线后编程时间长且成本高。这在很大程度上是因为在此过程中可用于辅助临床医生的工具非常少，因此DBS程控可能需要相当程度的经验和专业知识，以及临床医生搜索最佳器械设置的大量时间。在过去的几年中，已经开发了计算模型来预测和可视化基于个体患者的神经解剖学的DBS的效果。最近，这些模型已经显示出提高DBS编程效率的前景，并已被纳入临床决策支持系统。这项研究的长期目标是改善接受DBS治疗的神经系统疾病患者的生活。本申请旨在前瞻性测试DBS临床决策支持工具在术后临床护理中的使用。中心假设是，使用DBS临床决策支持系统进行个体患者管理将能够节省可观的时间，并减轻患者和护理人员的负担。这一假设是根据试点研究制定的，这些研究表明，与使用基于iPad的决策支持系统的临床医生的标准护理相比，DBS程控时间大幅减少（从4小时到2分钟节省了99%的时间）。拟议研究的基本原理是，计算模型、临床信息学和移动的计算设备可用于以前所未有的方式实现DBS管理。在强有力的初步数据的指导下，将在两个特定目标中对该假设进行检验：1）在已建立的PD诊所中测量DBS决策支持系统的有效性; 2）由家庭健康护士测量DBS锡永支持系统的有效性。在第一个目标下，我们将比较使用临床决策支持系统与标准护理管理的患者的编程时间和临床结果。根据第二个目标，我们将评估系统对患者和照顾者的压力时，家庭保健护士使用的效果。这种方法是创新的，因为它提供了基于iPad的临床决策支持应用程序（app），使护士和医生能够快速关注可能最有效的刺激设置。拟议的研究意义重大，因为它将为能够为DBS患者提供最大受益的医疗保健提供者提供强大的工具。所获得的知识可以使未来的DBS管理模式，其中护理提供在临床和家庭环境中由熟练的护士谁使用专家系统的指导。

 DESCRIPTION (provided by applicant): Deep brain stimulation (DBS) has tremendous potential to improve the lives of patients with a wide range of chronic illnesses. Good outcomes from DBS for Parkinson's disease (PD) are strongly correlated to accurate electrode placement and to careful post-operative selection of stimulation parameters (voltage, pulse width, frequency, active electrode contact(s), among others). Although DBS is beneficial for a variety of disorders, a persistent problem has been extensive, costly programming time after the electrode leads are implanted. This is largely because there are very few tools available to assist clinicians in this process, and as a result DBS programming can require a significant degree of experience and expertise, as well as a substantial amount of time for the clinician to search for optimal device settings. Over the last few years computational models have been developed to predict and visualize the effects of DBS based on the neuroanatomy of individual patients. Recently these models have shown promise for improving the efficiency of DBS programming, and have been incorporated into a clinical decision support system. The long-term goal of this research is to improve the lives of patients with neurological disease that are treated with DBS. The objective of this application is to prospec- tively test the use of DBS clinical decision support tool in post-operative clinical care. The central hypothesis is that the use of a DBS clinical decision support system for individual patient management will enable consider- able time savings and reduced burden on patients and caregivers. This hypothesis has been formulated from pilot studies that have shown dramatic decreases in DBS programming time compared to standard care for clinicians who used an iPad-based decision support system (99% time savings from over 4 hours to 2 minutes). The rationale for the proposed research is that computational models, clinical informatics, and mobile computing devices can be used to enable DBS management in a way that has never before been possible. Guided by strong preliminary data, this hypothesis will be tested in two specific aims: 1) Measure the effective- ness of DBS decision support system in an established PD clinic; 2) Measure the effectiveness of DBS deci- sion support system by home health nurses. Under the first aim we will compare programming time and clinical outcomes for patients managed using the clinical decision support system compared to standard care. Under the second aim we will assess the effects of the system on patient and caregiver strain when used by home health nurses. This approach is innovative because it provides an iPad-based clinical decision support applica- tion (app) to enable nurses and physicians to quickly focus on stimulation settings that are likely to be most effective. The proposed research is significant because it will provide powerful tools to the health care provid- ers who will be able to provide the greatest benefit for DBS patients. The knowledge gained could enable a future model for DBS management where care is provided in both clinical and home settings by skilled nurses who use expert systems for guidance.