Mapping patient pathways to health outcomes

Patient pathways describe the steps to be taken for a patient to be diagnosed or treated for a disease. NICE outlines best practice patient pathways for both diagnosis and treatment of hundreds of issues, all which can be visualized on the NICE website (NICE Pathways). Most of these pathways start at the primary care level, with a patient speaking to their GP about a symptom. Currently, there is no standardized system for monitoring if changes in patient pathways lead to changes in patient outcomes. There is also minimal literature on developing models of patient pathways to allow for rapid testing of different changes in treatment delivery, with most evaluations being conducted manually and retrospectively (Adams et al 2014, Demir et al 2018, Aspland et al 2021, Chapman et al 2020, Street 2013, Konrad 2013, Funkner et al. 2017). As the NHS moves towards the creation of Integrated Care Systems, the need to develop automated, data-driven, holistic tools to monitor quality of care and patient outcomes is critical. This project aims to fill this gap by establishing a method to investigate how patient pathways relate to outcomes. This will occur by utilizing know methods for mapping multi-state systems to study the patient pathway, and then building analysis methods on top of the map to monitor how each stage affects patient outcomes. This will allow for easy detection of areas for improvement in patient pathways, as well as answer long held questions by clinicians about the effectiveness of specific stages. The project will focus on cancer patients, with data collected both at a hospital and primary care setting, accessed via the OUH Clinical Data Warehouse. The first aim is to develop a model, or mapping, of various patient cancer pathways, for example the OUH's new SCAN pathway developed in 2017, using established methods outlined in Aspland et al. (2019) such as discrete event simulation or Bayesian belief networks. This model development will allow us to understand the pathway as a system, compare a patient's treatment to the guidelines, and compare pathways to each other to see variation in their outcomes. The second aim is to use the modelled pathway to determine its role in health outcomes. This will focus on answering clinician driven questions. In the context of SCAN, such a question may be how effective is the pathway at improving early detection of cancer in patients with "low risk but not no-risk symptoms" (Nicholson et al, 2018). The third aim is to establish quality control algorithms that can determine discrepancies in outcomes between care providers, by pathway, and to determine where in the patient pathway there is a variance in patient management and if this impacts clinical outcomes.
This project will support the NHS Long Term Plan by harnessing data to monitor patient pathways and reduce health inequalities by ensuring best practice is being used by creating a framework for automated quality control systems and providing a way to monitor prospective trials in real time, supporting future changes to NHS care guidelines. This project falls within the UKRI ESPRC themes of Artificial Intelligence and Robotics and Healthcare Technologies by creating and implementing novel AI methodologies to generate impactful healthcare solutions

In the same way that bioinformatics has transformed genomic research and clinical practice, health data science will have a dramatic and lasting impact upon the broader fields of medical research, population health, and healthcare delivery. The beneficiaries of the proposed training programme, and of the research that it delivers and enables, will include academia, industry, healthcare, and the broader UK economy.

Academia: Graduates of the training programme will be well placed to start their post-doctoral careers in leading academic institutions, engaging in high-impact multi-disciplinary research, helping to build training and research capacity, sharing their experience within the wider academic community.

Industry: Partner organisations will benefit from close collaboration with leading researchers, from the joint exploration of research priorities, and from the commercialisation of arising intellectual property. Other organisations will benefit from the availability of highly-qualified graduates with skills in big health data analytics.

Healthcare: Healthcare organisations and patients will benefit from the results of enabled and accelerated health research, leading to new treatments and technologies, and an improved ability to identify and evaluate potential improvements in practice through the analysis of real-world health data.

Economy: The life sciences sector is a key component of the UK economy. The programme will provide partner companies with direct access to leading-edge research. Graduates of the programme will be well-qualified to contribute to economic growth - supporting health research and the development of new products and services - and will be able to inform policy and decision making at organisational, regional, and national levels.