Applying advanced data science for health and social care operational decision-support to reduces delays in care.

使用数据科学，可以从健康/社会护理数据中获得相当大的价值，以支持运营、战略和临床决策，从而在效率、患者结果、员工满意度和降低成本方面提高护理质量[1，2]。例如，离散事件模拟是一种开发过程或系统的计算机模型的方法，使该模型能够进行实验，而不是破坏真实的系统。它为医疗保健中的决策支持提供了很高的价值，例如通过识别改善治疗途径的关键杠杆[3，4]。由于在使用实时数据进行影响的应用环境中实施这些工具存在挑战，文献中几乎没有这样的例子。那些确实关注技术挑战而不是实施挑战的公司[5]。然而，随着技术的进步和公共部门对使用数据科学进行决策支持的兴趣与日俱增，预计在医疗保健研究中使用模拟/人工智能的实时操作决策支持工具的开发将会增加[6]。我的博士学位对这些应用程序采取了一种设计方法，强调早期关注使用迭代开发和评估周期实现的挑战。然而，安全、有效、有影响力的应用程序也需要高级编程技能，以便使用免费、开放源码的编程语言来模拟复杂的问题，以便共享学习。将正规培训与NHS问题的应用相结合，将为健康和社会护理合作伙伴增加价值，并将显著提高我在博士期间获得的编程技能。这项建议的一个关键方面是20%的研究部分。这将涉及与布里斯托尔的NHS组织(BNSSG CCG)和巴斯大学管理学院的研究人员合作，开发在综合医疗和社会保健领域使用数据科学的机会，并将其与未来的拨款提案相结合。参与PenPEG Exeter和Bristol患者安全倡议将确保计划中的研究与患者需求和当前的QI战略目标保持一致。离散事件模拟将用于医院和社会护理提供者之间的路径建模；随机森林方法将使模型输入能够预测住院时间。原型建模将总结这些方法的可行性，用于一个实质性的研究项目，重点是患者延迟出院，这可以归因于社区护理能力的缺乏[7]，并对患者的心理和身体健康产生不利影响[8]。已通过BNSSG CCG向NHS社区保健/社会护理服务和其他提供者(急性医院出院规划、地方当局社会护理服务)提供访问权限，以全面了解问题情况(如何优化急诊和社区服务的能力平衡以减少延迟的医院出院)，并访问匿名数据以进行初步建模。CCG的荣誉合同已经获得批准，这为我提供了一个与医疗保健和学术组织建立联系的宝贵机会，并提供了支持我未来职业目标的实质性研究基础。这些最终包括创新和有影响力的应用，例如在疾病的早期诊断、预防和治疗方面。这一奖学金机会还将支持我博士研究的出版物，将关于医疗保健实时决策支持的学术讨论转移到学术界的现实影响上。会议报告将传播新的研究，促进未来在一个研究不足的领域(社区护理)的合作，这可能会产生重大影响。缺乏社会护理服务是医院面临压力的公认因素；优化能力分配可以提高成本效益和患者结果。

Using data science, considerable value can be gained from health/social care data for supporting operational, strategic, and clinical decision-making to improve care quality in terms of efficiency, patient outcomes, staff satisfaction and reduced costs [1,2]. For example, discrete-event simulation is a method which develops a computer model of a process or system, enabling experimentation with the model, rather than disrupting the real system. It offers high value for decision-support in healthcare, for example by identifying key levers for improvement in treatment pathways [3,4]. Due to the challenges in implementing these tools in an applied setting using real-time data for impact, few examples exist in the literature. Those that do focus on technical, rather than implementation challenges [5]. However with advancing technology and increasing interest from the public sector in the use of data science for decision-support, there is expected to be an increase in the development of real-time operational decision-support tools using simulation/AI in healthcare research [6]. My PhD took a design approach to these applications, emphasising an early focus on the challenges of implementation using iterative cycles of development and evaluation. However safe, effective, impactful applications also require advanced programming skills for modelling complex problems using free, open-source programming languages for shared learning. Combining formal training with applicaton to NHS problems will add value for health and social care partners, and will significantly advance the programming skills I gained during my PhD.A key aspect of this proposal is a 20% research component. This will involve engaging with NHS organisations in Bristol (BNSSG CCG) and researchers at University of Bath School of Mangagement to develop opportunities for using data science in the integrated health and social care space, and leveraging this with a future grant proposal. Engaging with PenPEG Exeter and Bristol Patient Safety Initiative will ensure that planned research is aligned with patient needs and current QI strategic goals. Discrete-event simulation will be used for pathway modelling between hospitals and social care providers; Random Forest methods will enable prediction of hospital lengths-of-stay for model inputs. Prototype modelling will conclude the feasibility of these methods toward a substantive research project focusing on delayed hospital discharges for patients, which can be attributed to lack of capacity in community-based care [7], and which impacts adversely on patients' mental and physical health [8]. Access has been granted through BNSSG CCG to NHS community health/social care services and other providers (acute hospital discharge planning, Local Authority social care provision) to map the problem situation holistically (how to optimise the balance of capacity in acute and community services to reduce delayed hospital discharges), and to access anonymised data for preliminary modelling. A CCG honorary contract has been approved, providing an invaluable opportunity to develop connections with healthcare and academic organisations, and to deliver the basis of substantive research which will support my future career goals. These ultimately include innovative and impactful applications, for example in early diagnosis, prevention and treatment of disease. This fellowship opportunity would additionally support publications from my PhD research, shifting academic discussions on real-time decision-support in healthcare toward real-world impact in the academic community. Conference presentations will disseminate new research, facilitating future collaborations in an under-researched area (community care) which has the potential for significant impact. Lack of availability of social care services is a recognised contributor to the pressures faced by hospitals; optimising capacity allocation can improve cost-efficiency and patient outcomes.