Dr Dionisio Acosta, University College London, on delivering a clinical decision support system for Cancer Multidisciplinary Meetings (MDMs).

1. Delivering Clinical Decision Support System
for Cancer Multidisciplinary Meetings: MATE
Dionisio Acosta, CHIME, UCL
Vivek Patkar, Cancer Institute, UCL
Mo Keshtgar, Department of Surgery, Royal Free Hospital
John Fox, Department of Engineering Science, University of Oxford

2. Outline
• Introduction
• Approach
• Results & Discussion
• Conclusion

3. Introduction
Cancer MDM
Cancer Multidisciplinary Meeting
(MDM) is a widely endorsed
mechanism for ensuring high
quality evidence-based cancer
treatment.
However, in a context of increased
demand and accountability there
are shortcomings in the current
conduct of MDMs that have made
them a priority of the National
Cancer Action Team (NCAT) and
the National Health Service (NHS).

4. Introduction
MDM Shortcomings
Inadequate documentation of both patient
data and MDM decisions.
Missed opportunities to screen patients
suitable for clinical trials.
Lack of appropriate tools for auditing and
monitoring the MDM performance.

5. Introduction
Challenges
Harmonize multiple clinical guidelines in one framework
Generate individual treatment recommendations
User-centred application design
Integrate prognostications tools

6. Approach
Clinical Knowledge PROforma Graphical Electronic
Base (17 CPGs) Decision Engine User Interface Patient Record
MATE Middle-ware
Audit & Accurate Clinical Prognostication
Performance MDM Documentation Trial Screening Tools

7. Results
Multidisciplinary Assistant & Treatment sElector (MATE)

24. Results
Successfully piloted at the Royal Free
Hospital Breast MDM in over 1000 patients.
Concordance with clinical guidelines in 97%
of cases.
Identified 60% more eligible patients for
clinical trials.
Currently evaluated in a randomised control
trial at the same institution.

25. Discussion
Potential Impact
• The methodology, know-how and the underlying
technology can be directly applied to other cancer
MDMs.
• Reducing unwarranted variations in cancer care
– Promoting adherence to best practices.
– Minimizing unjustifiable deviations.
• Supporting the life-cycle of clinical practice guidelines
– Documenting deviations and their corresponding justifications.
– Dissemination and implementation.

26. Discussion
Limitations
• Personalised patient recommendations:
– Does not account for patient preferences.
– Does not seamlessly integrate with prognostic
calculators.
– Does not capture (local) treatment outcomes.
• Communicating Risk
– Does not depicts patient pathways.
– Does not harmonize recommendations with prognostic
calculators.
– Not designed for patients.

27. Discussion
Risks for Technology Uptake
• Integration with EHR
– Clinical document standard architectures (HL7 CDA,
EN13606).
– Controlled terminologies.
– Governance.
• Knowledge base updates
– Centralised vs. distributed approach.
– Governance: Who?, when?, where?, how?
• Understanding and communicating economic
impact.