Masters dissertation project
I am finishing an exciting dissertation for my Masters in Data Science, Technology and Innovation.
I am hoping to look at understanding clinical pathways and identifying variations and predictors of clinical outcomes. Rather than focusing on diagnoses or on a specific clinical pathway, I will focus on symptoms (for example, breathlessness). I am also exploring opportunities to analyse and link up datasets across different care settings and organisations.
Taking a symptom based approach and using process mining could help improve the effectiveness of clinical pathways – i.e. by reducing delays in diagnosis, helping explain differences in response to treatments and helping guide clinicians on the best time to organise tests such as specialist scans.
Our literature review, using the common symptom of breathlessness, only found 13 relevant studies and of those, only two focussed on the symptom. Most were small-scale and single centre. Only one study used true process mining (in the ED).
There is a lack of symptom focused clinical pathway analysis (compared to disease focused approaches) and unmet need for larger, prospective multi-centre studies in this area.
More details about the research
Key questions I will be exploring:
1. What are the different pathways for the patients presenting with the specific symptoms and which are the common ones?
2. What are predictors of entering each of these pathways – including demographics, prescribed medicines, co-morbidities, and prior healthcare utilisation?
3. How do outcomes from these pathways vary in terms of healthcare investigations, utilisation (e.g. outpatient activity, readmissions) and harms (including mortality)?
4. How has the COVID-19 pandemic affected these pathways including treatment activities and patient outcomes?
Please get in touch if you would like a chat …