A perspective: use of machine learning models to predict the risk of multimorbidity

Machine Learning (ML) is a common Artificial Intelligence (AI) method. The use of ML offers the opportunity to develop better data mining techniques in order to analyse complex clinical interactions with a large number of variables. ML models should provide “real-time” clinical support reducing clinical risk to patients with model-agnostic interpretation to deduce a more specific clinical decision. Whilst ML algorithms have been used as the relatively “new kid on the block” in healthcare practice, they have shown promising results in predicting disease outcomes or risks in a variety of diseases such as depressive disorder, Type 2 diabetes mellitus, postoperative complications and cardiovascular diseases. However, patients suffering from a chronic condition are likely to have more than one condition requiring simultaneous attention and care. Therefore, a risk assessment model developed using ML methods, in theory, would be suitable to evaluate multimorbid populations. While there are many AI/ML algorithms and methods to build such a risk assessment tool, an optimal ‘fit-for-purpose’ model is chosen by comparing and contrasting across many possible alternatives. Furthermore, given the high-stake decisions associated with health, it is also important that the model is interpretable and explainable by the clinicians who are purported to use such a model as their decision support system. In this paper, we provide a perspective on the current landscape of multimorbidity treatment, potential benefit of employing AI/ML to enhance holistic care of multimorbid patients, and associated challenges, concerns that need to be addressed as we make progress in this direction

Digital Maturity Consulting and Strategizing to Optimize Services: Overview

The National Health Service (NHS), the health care system of the United Kingdom, is one of the largest health care entities in the world and has been successfully serving the UK population for decades. The NHS is also the fourth-largest employer globally. True to its reputation, some of the most modern and technically advanced medical services are available in the United Kingdom. However, between the acute, primary, secondary, and tertiary care providers of the NHS, there needs to be seamless integration and interoperability to provide timely holistic care to patients at a national level. Various efforts have been taken and programs launched since 2002 to achieve digital transformation in the NHS but with partial success rates. As it is important to understand a problem before trying to solve it, in this paper, we focus on tools used to assess the digital maturity of NHS trusts and organizations. Additionally, we aim to present the impact of ongoing transformation attempts on secondary services, particularly mental health. This paper considered the literature on digital maturity and performed a rapid review of currently available tools to measure digital maturity. We have performed a multivocal literature review that included white papers and web-based documents in addition to peer-reviewed literature. Further, the paper also provides a perspective of the ground reality from a mental health service provider’s point of view. Assessment tools adopted from the global market, later modified and tailor-made to suit local preferences, are currently being used. However, there is a need for a robust framework that assesses status, allows target setting, and tracks progress across diverse providers

Digital Maturity Consulting and Strategizing to Optimise Services: An Overview (Preprint)

UNSTRUCTURED The National Health Service (NHS), the healthcare system of the UK, is one of the largest healthcare entities in the world and has been successfully serving the UK population for decades. The NHS is also the 4th largest employer globally. True to its reputation, some of the most modern and technically advanced medical services are available in the UK. However, between the acute, primary, secondary, and tertiary care providers of NHS, there needs to be seamless integration and interoperability to provide timely, holistic care to the patients at a national level. Various efforts have been taken and programs launched since 2002 to achieve digital transformation in NHS, but with partial success rates. As it is important to completely understand a problem before trying to solve the problem, in this article, we focus on tools used to assess the digital maturity of NHS trusts and organizations. Assessment tools adopted from the global market, later modified and tailor-made to suit local preferences are currently being used. But there is a need for a robust framework that assesses status, allows target setting, and tracks progress across diverse providers. </sec

A methodological comparison study of the use of Natural Language Processing within Mental Health Services in the National Health Services in the United Kingdom (Preprint)

UNSTRUCTURED Background: Mental illness has a high disease burden within the UK, attributing to 22.8% in comparison to Cancer (15.9%) and Cardiovascular disease (16.2%). Costs of mental illness in England have been evaluated at £105.2 billion each year. This burden could be reduced by effective use of Electronic Health Records that could provide vital information around diagnosis, prevalence and incidence of mental illnesses to better understand the nuances of clinical and patient reported outcomes. To better evaluate some of the technical methods that could be better used, we explored Natural Language Processing. Objective: Our primary objective was to evaluate the use of Natural Language Processing methods and it’s association with unstructured EHR text data from U.K.-CRIS. Methods: We used a descriptive methodology to demonstrate the use of NLP and validated the method using Southern Health NHS Trust electronic health data. Conclusions: We can conclude that the method used is suitable for the mental health service. However, to generalize our findings, a wider validation study across mental health organisations in the UK would be required. </sec