Projects Delivered

Frailty describes people at high risk of developing disability or dying. Information held in GP records can help to screen older people for frailty to support targeting of earlier coordinated care. This has been automated using an electronic Frailty Index (eFI) score developed in NHS England. In the original English study, people with higher eFI scores were at higher risk of being admitted to hospital, a care home or dying. However, fewer than expected people with frailty are identified in Scotland using the eFI, probably due to differences in GP coding practices. As a result, Healthcare Improvement Scotland have created a ‘modified eFI’. In this study we will test its effectiveness, by calculating original and modified eFI scores for people aged over 65 years old in 2017. We will then compare how well each score related to outcomes like hospital admission and death over the last 5 years. This is important for GPs to have confidence in the use of this modified score, which has not been tested across a wide population before.

There are high pressures on NHS hospitals over winter periods particularly with the ongoing COVID-19 pandemic. In this project we will report how recent winter hospital admissions have varied across a whole population in Lothian by the patterns of peoples’ health conditions, frailty, socioeconomic deprivation and previous NHS contacts. We will see if simple tools already available to clinicians could predict which people are more likely to need hospital care over winter, including for COVID and other winter viruses like influenza. This could help to better target preventive care. The NHS is increasingly treating people at home using ‘virtual wards’, managed remotely by hospital teams. We will use Emergency Department data to identify how many recent winter attendances may have been suitable for care at home. Through this project, we aim to show how routinely collected health data could coordinate smarter responses to winter hospital pressures in the future.

Chest pain is one of the most common reasons for presentation to hospital worldwide, with more than one million attendances each year in the United Kingdom. Thankfully most people with chest pain are not experiencing a heart attack, but it often takes time and healthcare resources to rule-out this important diagnosis. Newer blood tests can now reassure patients and clinicians earlier after presentation to hospital in those who are not having a heart attack. However, there is variation in how these tests and clinical pathways are implemented across the UK. We will look at patient attendances with suspected heart attack and see how well patients are cared for, looking how care varies between men and women, different ages, and ethnic groups.

Diabetes is a common condition that affects more than one in 20 people in the general population and about one in 5 people who are in hospital. Early in the Covid pandemic diabetes was found to be an important risk factor for being infected, being admitted to hospital and having severe illness (admission to an intensive care unit or dying in hospital). This project will investigate whether the increased risk has changed over time, taking other important factors (such as age) into account. Another aspect of this project is to check how well a diagnosis of diabetes is recorded in different health records by comparing information collected by GPs and hospitals. If we find that recording is reliable this will be extremely helpful to support quality improvement programmes in the health service and achieve better outcomes for people with diabetes.

New medicines are in development that may improve survival after treatment for breast cancer. The UK regulators and NHS Scotland need strong evidence on the value of new treatments prior to approving medicines as standard care paid for by the NHS.

While clinical trials can provide much of this evidence, there is also a need to provide evidence on the current ‘real-world’ patient population, standard treatments and current outcomes.

The project will generate real world evidence on the current metastatic breast cancer patient pathway, on patients from NHS Cancer Centres in Leeds, London and Edinburgh. The anonymised data from Leeds and London will be sent to the NHS Lothian team. The analysis takes place within the NHS by NHS staff, with only a summary report that does not contain individual patient data being released for the purposes of NHS Health Technology Assessment.

The project output will enable NHS decision makers to make better informed decisions on future medicines to ensure the NHS is providing high value care to the patients it serves.

​​New medicines are in development that may reduce the risk of cancer recurrence and improve survival after treatment for early breast cancer. NHS cancer services in Leeds and Edinburgh need strong evidence on the of the current quality of care prior to adopting new treatments for their patients that may risk disrupting this quality.

​While clinical trials can provide much of this evidence, there is also a need to provide evidence on the current ‘real-world’ patient population, standard treatments and current outcomes that are considered quality metrics in the patient populations serviced by the current cancer service provided by the NHS in Edinburgh and Leeds.

​The project will generate a description of the current early breast cancer patient pathway in Leeds and Edinburgh. The anonymised Leeds data will be sent to Edinburgh for analysis. The analysis takes place within the NHS by NHS staff, with only a summary report that does not contain individual patient data being released for use by NHS cancer services. This project is an extension of a previous service evaluation undertaken in NHS Lothian only.

​The project output will enable the NHS to make better informed decisions on the place of future medicines to ensure the NHS is providing high quality care to the patients it serves.

Increasing numbers of people have multiple long-term conditions (MLTC). A real point of risk for people with MLTC is when admitted to hospital, where the risks of harm are higher for this group. In this project, we want to use advanced computing techniques to understand the journeys of patients through hospital, using the electronic records made when people are moved between wards or specialist areas.

This will highlight areas where delays are experienced, and also allow us to compare very clear pathways (e.g. a broken hip needing surgery) with more complex ones (e.g. a patient admitted with confusion). This mapping work will allow us to better understand key pressure points in the hospital system for people with MLTC.

Later in our research programme, we will use this work to develop new tools to predict the risk of harm when admitted to the hospital. These tools could be useful for clinicians to develop better services and care pathways for patients in the future.

An ageing population is a major success of modern healthcare, but this challenges the NHS to better support an increasingly frail hospital population. One third of older people acquire new disability by discharge, leaving hospital with less independence than before getting ill. Rehabilitation attempts to maximise recovery, but this is not well targeted to people at highest risk of disability, as the risk factors are not well understood. However, electronic health records now routinely hold information about rehabilitation progress. In this project, we will use methods like machine learning to find patterns from previous admissions of older patients. The aim is to build a tool that uses data to predict the rehabilitation needs of an older patient at the point of hospital admission, and display this in an understandable way for healthcare professionals and patients themselves. A future trial could then test this tool to help target hospital rehabilitation better.