An assessment of the potential for personalisation in patient decision aids

1. Malaga November 2011 An assessment of the potential for personalisation in patient decision aids Øystein Eiring, Psychiatrist, Editor NEHL Mental Health, National Knowledge Centre and the University of Oslo.

2. What is a decision aid?

3. Three patient roles Independent customer Shared decision-making Doctor knows best

4. Grey-zone decisions Minhas R. Clinical Evidence. BMJ Publishing Group, 2011

5. Some examples

6. Two very real problems Does the patient know enough? Does the physician know enough about the patients´ values?

7. Definition of patient decision aids Tools that support patients in making informed choices in accordance with their values…

8. Definition of patient decision aids Definition …when one particular treatment is not appropriate to all

9. The personalisation problem

10. Personalisation often referred to • Patient decision aids (DAs) differ from usual health education materials • because of their detailed, specific, and personalised focus on options and outcomes • for the purpose of preparing people for decision making» [1] • DAs are aids to make personalised choices O'Connor AM, Bennett CL, Stacey D, Barry M, Col NF, Eden KB, Entwistle VA, Fiset V, Holmes-Rovner M, Khangura S, Llewellyn-Thomas H, Rovner D. Decision aids for people facing health treatment or screening decision. Cochrane Database Syst Rev. 2009 (3):CD001431.

11. A broad survey does not exist • Little is known about • the current use of • and potential for web personalisation • …inherent in the tools

12. Explorative approach • The research field of web personalisation: • the employment of user features • in web systems • …that adapt their behavior to the user • Large inventory of techniques

13. Objective

14. To estimate the potential • Basic Requirement • Current use • for web personalization • in web-based decision aids

15. Simply: Is form and contenttailored to theindividual?

16. Methods

17. Method: coding scheme • Development of a simple codingscheme for web personalisation • userfeatures • adaptive systems behaviour • Basedon a researchanthology • Adjusted during thecodingprocess Brusilovsky P. Adaptive Navigation Support. In: Brusilovsky P, Kobsa A, Nejdl W. The Adaptive Web. Methods and Strategies of Web Personalization. Springer Verlag. Berlin, Heidelberg 2007

18. Method: identification of DAs • Developers represented in the Ottawa Inventory • Pdfs excluded • The functionally richest DA from each developer selected http://decisionaid.ohri.ca/AZinvent.php (Acessed July 20, 2011)

19. Mapping of attributes of DAs to coding scheme • System behaviour of DAs to fundamental system behaviour of adaptable systems • Specific user-adaptive behaviour present in DAs • User feature subgroups amenable to personalisation representation

20. Results

21. 10 decision aids selected • 10 producers of DAs met inclusion criteria • Producers responsible for 223 of the 259 DAs in the Ottawa Inventory • The functionally richest DA from each developer selected

22. 4 classes in the coding scheme • Media content • User features • User modelconstruction and representation • Adaptive system behaviour

23. Class 1: Content types • 8 of 10 DAs are hypermedia (2 or more media types and hyperlinks present in 8 of the 10 Das)

24. Class 2: User features • Knowledge level • Interests • Preferences • Goals/tasks • Background • Individual traits • Context

25. Class 4: Adaptive system behaviour • Navigation support • Selection • Organisation • Presentation of content • Search • Collaboration • Recommendations

26. Most frequent user subgroups • Coping styles • Emotional reactions • Cognitive skills • User beliefs • Experiences of users • Literacy level • Somatic parameters

27. Results: Somatic parameters • Risk factors • Eligibility for treatment • Incidences • Prevalences • Probabilities • Outcomes • Etiology • Lab results • Predictionofrecovery

28. Representation of subgroups 1 • Listing several subgroups and making specific statements true for each subgroup one by one • Making general statements that are irrelevant to at least one subgroup • Alluding to subgroups without specifying the attributes of the subgroups • Giving an average for all subgroups combined

29. Representation of subgroups 2 • Suggesting that a patient belongs to one, particular subgroup • Listing only some subgroups • Not acknowledging the existence of relevant subgroups • Asking user to determine the relevant subgroup her-/himself • Helping the patient determine the relevant subgroup e.g. through an interactive tool

30. Representation of subgroups 3 • Describinghowhealthpersonnelshoulddeterminethe relevant subgroup • Giving general informationbutacknowledgingthatsubgroups do exist

31. System behaviour and adaptation • Search field in 6 of 10 • 5 of 10 in tool only • Simple adaptive navigation in 2 of 10 • Selection, organisation and presentation present • 0 of 10 enabled user collaboration (forum in 1) • 1 of 10 included recommendations

32. Conclusions • Potentially adaptable system behaviour is present in quality-assessed, current decision aids • Adaptive behaviour as such is generally not present in current aids • User feature subgroups implicitly and explicitly represented • But generally not used for personalisation

33. Conclusions continued • Quality-assessed DAs personalised to a very limited degree • Subgroup strategies employed reflect a non-adaptive, paper-on-web approach • Potential for developing truly personalised DAs

34. Norwegian Electronic Health Library Discussion!