Decision Support Systems

The module addresses how decisions are made in healthcare, why decision-making goes wrong and how technology can help improve the process in various measures. The forms of healthcare knowledge – know-how, skills and data – are defined and types and sources of knowledge explained.

Clinical knowledge needs are discussed, including the role of knowledge in support decision-making and empirical studies of knowledge needs and knowledge use. Issues in evidence based healthcare and shared patient-doctor decision-making are explored. Other inputs to the decision-making process are covered, including values and resources. Specific knowledge management techniques are introduced and compared.

The module also explores the different paradigms of computer-based decision support systems, discussing their advantages and pitfalls. Best practices for design and implementation of these systems and presented together with methods for assessing their impact in clinical practice. A wide range of computer methods and tools for clinical knowledge representation are described and their appropriate application in a range of clinical contexts is demonstrated.

The module then looks at clinical decision support systems in a wider perspective, studying the methodological and technical issues that arise when attempting to integrate decision support systems with electronic health records systems. The module surveys existing approaches and international standards and describes their application.

The module concludes by presenting a series of case studies on clinical decision support systems currently in use at institutions in the UK and abroad.

Module code

CHME0008

UCL credits

15

Course Length

9 Weeks

SYNCH Days

Wk 7: 21-23 April 2021

Assessment Dates

04 May 2021

Module organiser

Dr Dionisio Acosta Please direct queries to courses-IHI@ucl.ac.uk

Who can study this course?

Anyone with a direct or supportive role in healthcare (clinicians, nurses, etc), healthcare managers, health informaticians, IT staff.

Admission requirements

A minimum of an upper second-class Bachelor's degree in a relevant discipline from a UK university or an overseas qualification of an equivalent standard. Students who do not meet these requirements but have appropriate professional experience will also be considered. Students who have previously undertaken CPD may apply for accreditation of prior learning.

Content

• Clinical Knowledge: What is Clinical Knowledge? Clinical Knowledge Management, Sources of Clinical Knowledge, Knowledge Management Systems.
• Clinical Decision Making: Statistical Decision Theory, Psychological Decision Theories, Patient-Doctor Shared Decision-Making.
• Paradigms of Clinical Decision Support: Prompts, Alerts, Computerised Clinical Guidelines, Computer Assisted Drug Prescription, Computer Assisted Imaging Diagnosis.
• Lectures and Workshops on Design and Implementation of Decision Support Systems, Invited Presentations of several Decision Support Systems, Preparation for the Assignment.
• Clinical Knowledge Modelling Tools: Regression Models, Statistical Classification Methods, Graphical Methods, Ontological Modelling and Argumentation.
• Evaluation of Clinical Decision Support Systems: Success Factors, Appraisal Methods, and Human Factors.
• Clinical Decision Support and Interoperability Standards: Clinical Terminologies and Decision Support, Integrating Clinical Decision Support and EHR Systems.
• Case Studies: Anticoagulation Service, Breast Cancer Multidisciplinary Meeting, Brain Tumour Diagnosis, Internal Medicine.

Teaching and learning methods

Blended learning: web-based distance learning in the UCL Virtual Learning Environment plus a 3-day face-to-face teaching session, webinars, self-study, tutorials, seminars and workshops including substantial use of examples of real clinical systems.

Assessment

Summative assessment: Written report worth 100% of the overall module mark.

Selected reading list

Afzal, M., Hussain, M., Ali Khan, W., Ali, T., Lee, S., Huh, E.-N., Farooq Ahmad, H., Jamshed, A., Iqbal, H., Irfan, M., Abbas Hydari, M. (2017) Comprehensible knowledge model creation for cancer treatment decision making. Comput. Biol. Med. 82, 119–129. doi:10.1016/j.compbiomed.2017.01.010

Hannon, T.S., Dugan, T.M., Saha, C.K., McKee, S.J., Downs, S.M., Carroll, A.E. (2017) Effectiveness of Computer Automation for the Diagnosis and Management of Childhood Type 2 Diabetes: A Randomized Clinical Trial. JAMA Pediatr. doi:10.1001/jamapediatrics.2016.4207

Novo, J., Hermida, A., Ortega, M., Barreira, N., Penedo, M.G., López, J.E., Calvo, C., (2017) Hydra: A web-based system for cardiovascular analysis, diagnosis and treatment. Comput Methods Programs Biomed 139, 61–81. doi:10.1016/j.cmpb.2016.10.019

Lyman, J.A. et al., (2010) Clinical decision support: progress and opportunities. Journal of the American Medical Informatics Association, 17(5), pp.487 -492.

Karsh, B. et al., (2010) Health information technology: fallacies and sober realities. Journal of the American Medical Informatics Association, 17(6), pp.617 -623.

Phansalkar, S. et al. (2010). A review of human factors principles for the design and implementation of medication safety alerts in clinical information systems. Journal of the American Medical Informatics Association: JAMIA, 17(5), pp.493-501.

Wyatt J. (2000) Decision support systems, J R Soc Med 2000;93:629–633

Taylor P. (2006) From Patient Data to Medical Knowledge: The Principles and Practice of Health Informatics, Wiley-Blackwell

Coiera E. (2003) A Guide to Health Informatics, Hodder Arnold

Berner ES Ed., (2010) Clinical Decision Support Systems: Theory and Practice, Springer

Sullivan F, Wyatt J (2006) ABC of Health Informatics, BMJ Books Wiley