Phd Studentship in Healthcare Improvement Studies
1 month ago
**Ref Number**
B02-04575
**Professional Expertise**
Academic
**Department**
School of Life & Medical Sciences (B02)
**Location**
London
**Working Pattern**
Full time
**Salary**
See advert text
**Contract Type**
Fixed-term
**Working Type**
Hybrid (20% - 80% on site)
**Available For Secondment**
No
**Closing Date**
08-Feb-2023
**About us**:
Background: Inefficiencies in the delivery of care currently have an enormous cost to the NHS. been translated into clinical tools [Ben-Israel 2020]. Recent high profile implementations of machine learning into health care (Google DeepMind's 'Streams' app for Acute Kidney Injury alerts) have been evaluated by our team, and demonstrate that translating the technically feasible improvement into practice is a complex and multifaceted problem.[Connell 2019] The reasons for this translational failure are, at least, two-fold:
1. Offline rather than online development: Algorithmic development away from the clinical environment on retrospective data that neither considers the wider clinical and cultural reasons and motivations for testing, nor the user's test ordering workflow. This non-contextual development risks seeing the tests as statistically but not biologically correlated, and misunderstanding the information value in the result. For example, most models define low-yield in terms of test stability with respect to the absolute measure. This ignores clinician's rationale in understanding disease trajectory and that the information value in a repeated test may be significant around inflection points even when the absolute value is stable.
2. Ignoring the importance of uncertainty in a prediction: Most predictive models are tuned to deliver the best prediction by measures of accuracy and its components (Precision/Recall etc). Information theory presents an important alternative approach that has demonstrated value in domains as diverse as molecular biology and medical imaging. The key insight is that the value of information is related to how it reduces randomness (entropy) or 'surprise' (i.e. a weather forecast in California that always predicts sunshine might be accurate has done nothing to reduce uncertainty or one’s surprise when it rains). In the context of blood testing, the ideal metric for prediction optimisation would be one that reduces entropy. In this way the clinical team would reorder a test when uncertainty was high, and avoid redundant information from tests that did little to reduce entropy.
Project Title: Personalised uncertainty estimates for intelligent blood test ordering
Hypothesis/Aims: This PhD is intended to template a pathway to using machine learning (ML) and digital innovations to improve care. We have identified a tractable problem (better blood test ordering) and then provided the student with a bedside prototyping platform, a clinical team, and methodological expertise to support the translation of ML into Healthcare Improvement.
**About the role**:
The student will join the prestigious UKRI UCL Centre for Doctoral Training in AI-enabled Healthcare Systems which offers a unique programme of a 1 Year MRes followed by a 3 year PhD embedded within an NHS setting. The MRes will cover the core competencies of AI with an emphasis on how healthcare organisations work, the ethics of medical AI, and a broad approach to research, innovation and entrepreneurship. They will be invited to join the Mini-MD programme where they undertake an immersive experience in an NHS setting, and attend special training programmes offered by the Alan Turing Institute. In Years 2-4, they will undertake their PhD at IHI supported by CORU and Department of Applied Health Research (DAHR). All departments have a thriving doctoral student programme with a high conversion rate (e.g. 94% in 2014 for CORU) to completed PhDs. Every student who is accepted onto the AI-enabled Healthcare Systems Centre for Doctoral Training (CDT) may take the MRes Artificial Intelligence Enabled Healthcare in their first year. This will be followed by a 3 year PhD. Throughout this period the CDT will continue to closely monitor the need for continuing training and support, tailored to each student, and provide ongoing training in research skills. As a cohort based PhD programme, students will also have the opportunity to participate in a range of seminars, training programmes, placements and other activities, including UCL's Doctoral Skills Development Programme.
**About you**:
**What we offer**:
This studentship covers the cost of tuition fees based on the UK (Home) rate. The student will receive a starting stipend of £24,093 per annum (including London weighting) as well as the cost of tuition fees based on UK fee status. Students will also be eligible for a research training support grant to support travel, subsistence and conference costs.
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