Evaluation of prototype risk prediction tools for clinicians and people living with type 2 diabetes in North West London using the think aloud method.

The prevalence of type 2 diabetes in North West London (NWL) is relatively high compared to other parts of the United Kingdom with outcomes suboptimal. This presents a need for more effective strategies to identify people living with type 2 diabetes who need additional support. An emerging subset of web-based interventions for diabetes self-management and population management has used artificial intelligence and machine learning models to stratify the risk of complications from diabetes and identify patients in need of immediate support. In this study, two prototype risk prediction tools on the MyWay Diabetes and MyWay Clinical platforms were evaluated with six clinicians and six people living with type 2 diabetes in NWL using the think aloud method. The results of the sessions with people living with type 2 diabetes showed that the concept of the tool was intuitive, however, more instruction on how to correctly use the risk prediction tool would be valuable. The feedback from the sessions with clinicians was that the data presented in the tool aligned with the key diabetes targets in NWL, and that this would be useful for identifying and inviting patients to the practice who are overdue for tests and at risk of complications. The findings of the evaluation have been used to support the development of the prototype risk predictions tools. This study demonstrates the value of conducting usability testing on web-based interventions designed to support the targeted management of type 2 diabetes in local communities.

Evaluation of the North West London Diabetes Foot Care Transformation Project: A Mixed-Methods Evaluation.

Introduction: Diabetes foot ulceration (DFU) presents an enormous burden to those living with diabetes and to the local health systems and economies. There is an increasing interest in implementing integrated care models to enhance the quality of care for people living with diabetes and related complications and the value of co-production approaches to achieve sustainable change. This paper aims to describe the evaluation methodology for the North West London (NWL) Diabetes Foot Care Transformation project. Description: A mixed methods design including: i) a quasi-experimental quantitative analysis assessing the impact of the implementation of the local secondary care multi-disciplinary diabetes foot team clinics on service utilisation and clinical outcomes (amputations and number of healed patients); ii) a phenomenological, qualitative study to explore patient and staff experience; and iii) a within-trial cost-effectiveness analysis (pre and post 2017) to evaluate the programme cost-effectiveness. Discussion and Conclusion: Demonstrating the impact of multidisciplinary, integrated care models and the value of co-production approaches is important for health providers and commissioners trying to improve health outcome. Evaluation is also needed to identify strategies to overcome barriers which might have reduced the impact of the programme and key elements for improvement.

Using electronic health records to develop and validate a machine-learning tool to predict type 2 diabetes outcomes: a study protocol.

INTRODUCTION: Type 2 diabetes mellitus (T2DM) is a major cause of blindness, kidney failure, myocardial infarction, stroke and lower limb amputation. We are still unable, however, to accurately predict or identify which patients are at a higher risk of deterioration. Most risk stratification tools do not account for novel factors such as sociodemographic determinants, self-management ability or access to healthcare. Additionally, most tools are based in clinical trials, with limited external generalisability. OBJECTIVE: The aim of this work is to design and validate a machine learning-based tool to identify patients with T2DM at high risk of clinical deterioration, based on a comprehensive set of patient-level characteristics retrieved from a population health linked dataset. SAMPLE AND DESIGN: Retrospective cohort study of patients with diagnosis of T2DM on 1 January 2015, with a 5-year follow-up. Anonymised electronic healthcare records from the Whole System Integrated Care (WSIC) database will be used. PRELIMINARY OUTCOMES: Outcome variables of clinical deterioration will include retinopathy, chronic renal disease, myocardial infarction, stroke, peripheral arterial disease or death. Predictor variables will include sociodemographic and geographic data, patients' ability to self-manage disease, clinical and metabolic parameters and healthcare service usage. Prognostic models will be defined using multidependence Bayesian networks. The derivation cohort, comprising 80% of the patients, will be used to define the prognostic models. Model parameters will be internally validated by comparing the area under the receiver operating characteristic curve in the derivation cohort with those calculated from a leave-one-out and a 10 times twofold cross-validation. ETHICS AND DISSEMINATION: The study has received approvals from the Information Governance Committee at the WSIC. Results will be made available to people with T2DM, their caregivers, the funders, diabetes care societies and other researchers.