Tag: edinburgh napier university

Launch of Digital Health for Heart Health Survey 2

The Digital Health for Heart Health – Priority Setting Partnership is identifying key questions about digital health in heart care. Join Survey 2 to help us prioritise them!

What is heart disease?

Heart disease is a collection of various conditions that impact heart function, such as coronary heart disease, irregular heart rhythms, heart failure, and valve problems. Digital health, which utilises technology to enhance healthcare, has the potential to transform heart disease care. However, there is still much to learn about its effectiveness in improving heart disease management.

What is the survey about?

To address this, patients, carers, family members, and healthcare professionals are collaborating to identify and prioritise crucial questions about digital health in heart care. This effort, supported by the James Lind Alliance, involves a UK-wide team working on the Digital Health for Heart Health Priority Setting Partnership (PSP) to determine the top 10 research priorities, ensuring that future advancements meet the needs of those most affected by heart disease.

In Survey 1, stakeholders shared what issues and questions were most important to them. We narrowed down these responses to create a list of priority research questions and are now asking you to rank your ‘Top 10.’ From here, we will seek funding to carry out research that will hopefully answer these questions.

If you are: a patient with a heart condition or heart disease, you care for someone who has a heart condition or heart disease, or a healthcare professional whose work involves those with heart conditions or heart disease,  we would like to hear what current and future digital technology research priorities are most important to you. This is an opportunity to:

  • Influence heart research priorities
  • Have your voice heard, or be the voice of your patients

The survey is open until October 1st 2024. You can access here. Please take some time to fill it out and share it with those who may be interested. If you would like a paper copy, please contact the team at DigitalHeart@napier.ac.uk

Accessing National Safe Haven (NSH) Data as a PhD student

This blog post was written by PhD student Nicola Rea. Nicola’s PhD ‘Improving care transitions for critically ill patients’ requires access and use of National Safe Haven Data for data analysis. Nicola writes about her experience below.

Over the last two decades, National Safe Havens (NSH) have become ubiquitous in healthcare data research. The term “Safe Haven” covers two fundamental functions – a physical environment containing linked datasets and governance procedures to ensure sensitive data is managed in a secure manner. This study, carried out as part of my PhD sought the use of routinely collected data, which is pseudonymised at the point whereby researchers access the data and permitted the analyses of a national cohort of patients in Scotland.

National Safe Haven approval process

This study aimed to understand the long-term outcomes for critical care survivors with pre-existing cardiovascular disease, an under-researched area whereby the answers remain unknown. The specific research questions are as follows:

 

  1. Does pre-existing cardiovascular disease influence hospital readmission (within one year of index hospital discharge) rates?
  2. What is the prevalence of pre-existing co-morbid disease in the Scottish ICU population?
  3. What is the prevalence of pre-existing CVD in the Scottish ICU population?
  4. Does pre-existing CVD influence whether patients are discharged home following an ICU admission?
  5. Does pre-existing co-morbid disease influence whether patients are discharged home follow an ICU admission?

 

This study was carried out as part of a wider National Health Service (NHS) project which had previously sought and been granted both Public Benefit and Privacy Policy (PBPP) approval and generic NHS ethical approval. For me to gain access to the linked data held in the Safe Haven as part of this wider project, completion of Medical Research Council (MRC) Research, Confidentiality and GDPR training was required. On completion I was added as a “User”. Approvals were also sought and granted from Edinburgh Napier University (ENU) School of Health and Social Care (SHSC) Research Integrity Committee.

As a PhD student, I carried out extensive training in both statistical analysis and RStudio data analysis software. The analysis presents both descriptive and inferential statistics to answer the research questions outlined above. More specifically, binary logistic regression models have been developed to determine if CVD is a predictor of hospital readmission (within one year of index hospital discharge) or to determine discharge destination for patients with CVD or non-CVD co-morbidities. The findings from this study are required to undergo review by the electronic Data Research Innovation Service (eDRIS) for statistical disclosure prior to removal from the NSH. Until then, we are unable to share the results but hope to do so in the coming weeks.

If you would like to know more about this study, please contact myself, Nicola Rea (Nicola.Rea@napier.ac.uk).

The NSH accessed for use in this study is managed and maintained by eDRIS (part of Public Health Scotland).