Clinical trial · Interventional
PROSAIC-DS Study (PROState AI in Cancer - Decision Support)
PROSAIC-DS (PROState AI in Cancer - Decision Support): Evaluation of the Deontics AI Platform for Evidence-based Treatment Planning in Multidisciplinary Cancer Care: Increasing Compliance and Streamlining MDTs in Prostate Cancer
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Prostate Cancer | Malignant Prostate Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Clinical decision support tool recommended outcome | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- type
- OTHER
- label
- Arm A: Visible to MDTM
- description
- Patients going through this arm have the decision support tool outcome visible to the MDTM
- interventionNames
- Other: Clinical decision support tool recommended outcome
- type
- OTHER
- label
- Arm B: Not-visible to MDTM
- description
- Patients going through this arm will not have the decision support tool outcome visible to the MDTM
- interventionNames
- Other: Clinical decision support tool recommended outcome
Primary outcomes (2)
- measure
- PROSAIC-DS as a triage tool
- timeFrame
Eligibility
Eligibility (as posted)
- Sex
- Male
- Minimum age
- 35 Years
Show eligibility criteria text
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.
References
Publications (12)
- BACKGROUNDKnight SR, Cao KN, South M, Hayward N, Hunter JP, Fox J. Development of a Clinical Decision Support System for Living Kidney Donor Assessment Based on National Guidelines. Transplantation. 2018 Oct;102(10):e447-e453. doi: 10.1097/TP.0000000000002374. PMID 30028418
- BACKGROUNDTaylor C, Atkins L, Richardson A, Tarrant R, Ramirez AJ. Measuring the quality of MDT working: an observational approach. BMC Cancer. 2012 May 29;12:202. doi: 10.1186/1471-2407-12-202. PMID 22642614
- BACKGROUNDMunro AJ. Multidisciplinary Team Meetings in Cancer Care: An Idea Whose Time has Gone? Clin Oncol (R Coll Radiol). 2015 Dec;27(12):728-31. doi: 10.1016/j.clon.2015.08.008. Epub 2015 Sep 11. No abstract available. PMID 26365047
- BACKGROUNDPatkar V, Acosta D, Davidson T, Jones A, Fox J, Keshtgar M. Cancer multidisciplinary team meetings: evidence, challenges, and the role of clinical decision support technology. Int J Breast Cancer. 2011;2011:831605. doi: 10.4061/2011/831605. Epub 2011 Jul 17. PMID 22295234
- BACKGROUNDMiles A, Chronakis I, Fox J, Mayer A. Use of a computerised decision aid (DA) to inform the decision process on adjuvant chemotherapy in patients with stage II colorectal cancer: development and preliminary evaluation. BMJ Open. 2017 Mar 24;7(3):e012935. doi: 10.1136/bmjopen-2016-012935. PMID 28341685
- BACKGROUNDPatkar V, Acosta D, Davidson T, Jones A, Fox J, Keshtgar M. Using computerised decision support to improve compliance of cancer multidisciplinary meetings with evidence-based guidance. BMJ Open. 2012 Jun 25;2(3):e000439. doi: 10.1136/bmjopen-2011-000439. Print 2012. PMID 22734113
- BACKGROUNDPatkar V, Hurt C, Steele R, Love S, Purushotham A, Williams M, Thomson R, Fox J. Evidence-based guidelines and decision support services: A discussion and evaluation in triple assessment of suspected breast cancer. Br J Cancer. 2006 Dec 4;95(11):1490-6. doi: 10.1038/sj.bjc.6603470. Epub 2006 Nov 21.