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

将先进的数据科学应用于健康和社会护理运营决策支持，以减少护理延误。

基本信息

批准号：
ES/W005875/1
负责人：
Alison Harper
金额：
$ 12.06万
依托单位：
UNIVERSITY OF EXETER
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=ES%2FW005875%2F1
关键词：
Applying advanced data science health

项目摘要

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.

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，患者结局，员工满意度和降低成本方面提高护理质量[1，2]。例如，离散事件仿真是一种开发过程或系统的计算机模型的方法，允许使用模型进行实验，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，例如通过确定改善治疗途径的关键杠杆[3，4]。由于在使用实时影响数据的应用环境中实施这些工具的挑战，文献中的例子很少。那些关注技术而不是实施挑战的人[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越来越大，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设计方法，强调早期关注使用开发和评估的迭代周期实现的挑战。然而，安全、有效、有影响力的应用程序还需要高级编程技能，以便使用免费的开源编程语言对复杂问题进行建模，以供共享学习。将正式培训与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，并将大大提高我在博士期间获得的编程技能。这将涉及与布里斯托的NHS组织（BNSSG CCG）和巴斯大学曼格登学院的研究人员合作，开发在综合健康和社会护理领域使用数据科学的机会，并利用未来的拨款提案。参与PenPEG埃克塞特和布里斯托患者安全倡议将确保计划的研究与患者需求和当前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;随机森林方法将能够预测模型输入的医院住院时间。原型建模将得出这些方法对于一个实质性研究项目的可行性，该项目的重点是患者延迟出院，这可能是由于缺乏社区护理能力[7]，并且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社区卫生/社会护理服务和其他提供者（急性出院规划，地方当局社会护理提供）提供访问权限，以全面了解问题情况（如何优化急性和社区服务的能力平衡，以减少延迟出院），并访问匿名数据进行初步建模。CCG荣誉合同已获得批准，提供了一个宝贵的机会，发展与医疗保健和学术组织的联系，并提供实质性研究的基础，这将支持我未来的职业目标。这些最终包括创新和有影响力的应用，例如疾病的早期诊断，预防和治疗。这个奖学金的机会将额外支持我的博士研究出版物，将医疗保健中实时决策支持的学术讨论转向学术界的现实影响。会议演讲将传播新的研究，促进未来在研究不足的领域（社区护理）的合作，这有可能产生重大影响。缺乏社会护理服务是医院面临压力的一个公认因素;优化能力分配可以提高成本效益和患者治疗效果。