Prostate Cancer
Conditions
Brief summary
Around 375,000 cancers are diagnosed in the UK annually, with this figure expected to reach 500,000 by 2035. As the number of different cancer treatment options and our scientific understanding continue to grow rapidly, it can be difficult for clinicians to keep up-to-date with best practice, causing unjustified variations in the quality of care and clinical outcomes for patients. Currently, when a patient has been referred to and seen by a clinician, their treatment is then discussed in a Multi-Disciplinary Team Meeting (MDTM). MDTM is a meeting of medical experts, including Surgeons, Oncologists, Nurses, and specialists in cancer, imaging and diagnosis. This is the case even if a treatment decision is straightforward. A nationwide review published by CRUK in 2017 highlighted the demands on cancer teams and the MDTM process: * Increased caseloads are causing dramatic increases in the time spent by clinicians in MDTMs, leading to an unsustainable rise in costs: the cost in England has increased from £88m to £159m in 4 years; * There is not enough time in the MDTM to discuss complex cases; * There is a failure to involve patients in the decision-making process: around 75% of patients feel their views are unrepresented in MDTMs; In our study we are looking at the potential of technology - particularly Clinical Decision Support Systems (CDSS) - to improve MDTM decision making. Deontics has a CE marked AI-based CDSS that integrates individual patient data and preferences with evidence-based clinical guidelines. This dynamically and transparently generates best-practice, individualised treatment recommendations which can help determine treatment. Deontics' AI tool has already been shown to provide personalised recommendations concordant with UK best practice while incorporating patient values, and can be used to safely triage less complex patients straight to treatment with minimal clinical oversight. Our project partners with Deontics to develop PROSAIC-DS - A CDSS for prostate cancer.
Detailed description
I
Interventions
OTHERClinical decision support tool recommended outcome
The PROSAIC-DS tool will take the variables and produce a suggested outcome. It will supply supporting evidence and best practice for its recommendations
Sponsors
King's College Hospital NHS Trust
King's College London
Deontics Limited
Somerset NHS Foundation Trust
Prostate Cancer UK
Guy's and St Thomas' NHS Foundation Trust
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
OTHER
Masking
DOUBLE (Subject, Outcomes Assessor)
Eligibility
Sex/Gender
MALE
Age
35 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* All patients referred to the GSTT and KCH Prostate MDT meetings where sufficient information is available for the MDT to make a treatment decision (approximately 40-50 per week) will be eligible for the study.
Exclusion criteria
* If data available for patients is not adequate to make any treatment decisions they will be excluded. Non-consenting patients will be excluded.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| PROSAIC-DS as a triage tool | 6-9 months | The % of patients the PROSAIC-DS tool can appropriately triage away non-complex Prostate Cancer cases from the MDTM with appropriate treatment plans as directed by approved guidelines (EAU, BAUS, NICE, AUA). |
| PROSAIC-DS influence on MDTM concordance with approved guidelines | 6-9 months | Evaluation of PROSAIC-DS as a member of the MDTM via the impact of live PROSAIC-DS recommendations on MDTM decision concordance with approved guidelines (EAU, BAUS, NICE, AUA) on randomised patients discussed in the MDTM. This is measured through the difference in level of concordance between the MDTM with PROSAIC-DS switched off and the MDTM with PROSAIC-DS on when less complex cases (ones triaged away) are excluded. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Cost effectiveness of PROSAIC-DS | 6-9 months - duration of data collection | Estimate the financial savings that PROSAIC-DS brings by triaging non-complex cases away from the MDTM. |
| Qualitative Analysis: Patient acceptability | 12 months | We will use qualitative methods, and data capture methods including self-administered questionnaires, focus groups, semi-structured interviews and video analysis, to explore patient and staff views about, including concerns and satisfaction with, the use of AI technology to support clinical decisions in prostate cancer, both prior to their engagement with PROSAIC-DS, during and after deployment of the tool of the technology to patients. |
Countries
United Kingdom
Contacts
Primary ContactDanny Ruta, MBBS MSc
Backup ContactKate Dodgson, LLB LLM
Outcome results
None listed