Dionisio Acosta: Clinical decision support systems


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

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Dionisio Acosta: Clinical decision support systems

  1. 1. Delivering Clinical Decision Support Systemfor Cancer Multidisciplinary Meetings: MATEDionisio Acosta, CHIME, UCLVivek Patkar, Cancer Institute, UCLMo Keshtgar, Department of Surgery, Royal Free HospitalJohn Fox, Department of Engineering Science, University of Oxford
  2. 2. Outline• Introduction• Approach• Results & Discussion• Conclusion
  3. 3. IntroductionCancer MDMCancer Multidisciplinary Meeting(MDM) is a widely endorsedmechanism for ensuring highquality evidence-based cancertreatment.However, in a context of increaseddemand and accountability thereare shortcomings in the currentconduct of MDMs that have madethem a priority of the NationalCancer Action Team (NCAT) andthe National Health Service (NHS).
  4. 4. IntroductionMDM 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. 5. IntroductionChallenges Harmonize multiple clinical guidelines in one framework Generate individual treatment recommendations User-centred application design Integrate prognostications tools
  6. 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. 7. ResultsMultidisciplinary Assistant & Treatment sElector (MATE)
  8. 8. 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.
  9. 9. DiscussionPotential 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.
  10. 10. DiscussionLimitations• 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.
  11. 11. DiscussionRisks 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.
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