Analysing and recommending personalised care pathways for multimorbid patients with the use of artificial intelligence (AI) techniques

Background
Healthcare systems are unable to cope with the complex need of managing multiple health conditions of multimorbid patients. Their conditions are treated individually by specialised healthcare professionals, without a coordination of goals and treatment plans, which often leads to duplications of care and iatrogenicity. Digital tools are being recognised as the foundation towards person-centered, proactive and integrated care, however most implementations have been studied in isolation with a lack of workflow optimisation.
At the same time, there is a growing interest in the use of process mining (PM), a type of process analysis, in healthcare. This is because little is known about how care is being delivered, especially to patients with coexisting conditions whose care is often fragmented. In fact, there is a growing body of literature supporting the use of PM in improving patient outcomes and reducing cost and waiting times guidelines.
In order to deal with the problem of care fragmentation and conflicting guidelines in multimorbidity researchers developed approaches to identify and address adverse interactions, such as constraint logic programming and analysis of computer interpretable clinical guidelines. These, however, can tell little about how care is being delivered and cannot identify new, emergent patterns of best practice. On the other hand, despite PM becoming a common approach to study existing care pathways, only 13% of papers propose a novel solution and, to the best of our knowledge, there has been no research published on PM in multimorbidity.
Aims
The primary aim of the project is to contribute to the digitally supported, person centered and integrated model of care using data science and AI techniques to analyse and recommend personalised care pathways for multimorbid patients. A decision support tool will be used to disseminate the findings.
The project is broken down into three main segments with increasingly broader area of focus: diabetes as single disease, diabetes and its related complications, such as renal disease, and finally diabetes accompanied by unrelated comorbidities, such as cancer.
In each scope segment we aim to discover and analyse care pathways, which will provide insight into how care is delivered and into the conformance with published guidelines. Additionally, it will allow us to suggest novel care pathways with the focus on patient progression, disease outcome and care efficacy. The recommendations will be disseminated as a decision support tool that suggest care pathways based on current medical guidelines or, when these are not available due to conflicting targets of coexisting diseases, based on pathways with best outcomes.
Methodology
Datasets will be explored in order to produce an overview of the population and the data. This would include time to event analysis as well as predictive modelling to estimate relevant clinical outcomes.
Subsequently, healthcare processes will be discovered and analysed in terms of conformance with published guidelines and performance, by identification of bottlenecks, while the correctness of the model will be formally verified. AI tools will be used to cluster and classify patients, enabling the identification and analysis of pathway variants.
In order to create recommendations for personalised care planning, we will use AI approaches to group patients to identify subpopulations with similar characteristics. Care pathways with best outcomes within each subset, as well as clinical guidelines, will be used to recommend novel care pathways, tailored to the individual, and these will then be formally verified.
Finally, the findings will be disseminated as a decision support tool in the form of an interactive application. After inputting patient characteristics, it will present the user with a visual representation of predicted risk of adverse events in an intuitive format.