Aims
To learn more about the best care pathways for Acute Myeloid Leukaemia (AML) patients, by analysing data using artificial intelligence.
Background
Acute Myeloid Leukaemia is a rare cancer. The best treatment for patients with AML remains uncertain. This is due to small patient numbers and delays in diagnoses. There are also differences in care pathways between NHS treatment centres, and uncertainty around side-effects of drugs used to treat the condition.
The reasons that diagnosis is delayed is complex. Many patients are diagnosed after a visit to emergency services with weakness, breathlessness or infections.
The project aims to improve understanding of how diagnoses are made in acute healthcare services and the impact of different care approaches on patient care. It will also look at the side effects of treatments which could help to improve outcomes and quality of life for patients.
Research
This study will analyse data from patients with CD33+ AML, a particular type of AML defined by features of the cancer cells. The analysis of this data will use Artificial Intelligence/Machine Learning technology. The project hopes to improve patient outcomes and quality of life by understanding the impact of delayed diagnosis and treatment options on real patients.
Patient wellbeing and privacy are very important. All data analysed is de-identified to protect patient confidentiality. The results of this analysis will be published in a scientific journal. The results will help doctors and researchers in the AML community choose the best care for their patients and will also help with the development of new treatments.
Patient involvement
The researchers have talked to patients about the project to find out how they feel about the research and what is important to patients. Patients will be involved during the project.
Approval
This project was supported unanimously by the PIONEER Data Trust Committee.
Principal investigators
The Draper and Dash Group (Real World Health)
