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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. . What is a decision aid?. Three patient roles. Independent customer. Shared

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An assessment of the potential for personalisation in patient decision aids l.jpg

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.


What is a decision aid l.jpg

What is a decision aid?


Three patient roles l.jpg

Three patient roles

Independent

customer

Shared

decision-making

Doctor knows best


Grey zone decisions l.jpg

Grey-zone decisions

Minhas R. Clinical Evidence. BMJ Publishing Group, 2011


Some examples l.jpg

Some examples


Two very real problems l.jpg

Two very real problems

Does the patient know enough?

Does the physician know enough about the patients´ values?


Tools that support patients in making informed choices in accordance with their values l.jpg

Definition of patient decision aids

Tools that support patients in making informed choices in accordance with their values…


When one particular treatment is not appropriate to all l.jpg

Definition of patient decision aids

Definition

…when one particular treatment is not appropriate to all


The personalisation problem l.jpg

The personalisation problem


Personalisation often referred to l.jpg

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.


A broad survey does not exist l.jpg

A broad survey does not exist

  • Little is known about

    • the current use of

    • and potential for web personalisation

  • …inherent in the tools


Explorative approach l.jpg

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


Objective l.jpg

Objective


Slide14 l.jpg

  • To estimate the potential

    • Basic Requirement

    • Current use

  • for web personalization

  • in web-based decision aids


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Simply: Is form and contenttailored to theindividual?


Methods l.jpg

Methods


Method coding scheme l.jpg

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


Method identification of das l.jpg

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)


Mapping of attributes of das to coding scheme l.jpg

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


Results l.jpg

Results


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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


4 classes in the coding scheme l.jpg

4 classes in the coding scheme

  • Media content

  • User features

  • User modelconstruction and representation

  • Adaptive system behaviour


Class 1 content types l.jpg

Class 1: Content types

  • 8 of 10 DAs are hypermedia (2 or more media types and hyperlinks present in 8 of the 10 Das)


Class 2 user features l.jpg

Class 2: User features

  • Knowledge level

  • Interests

  • Preferences

  • Goals/tasks

  • Background

  • Individual traits

  • Context


Class 4 adaptive system behaviour l.jpg

Class 4: Adaptive system behaviour

  • Navigation support

  • Selection

  • Organisation

  • Presentation of content

  • Search

  • Collaboration

  • Recommendations


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Most frequent user subgroups

  • Coping styles

  • Emotional reactions

  • Cognitive skills

  • User beliefs

  • Experiences of users

  • Literacy level

  • Somatic parameters


Results somatic parameters l.jpg

Results: Somatic parameters

  • Risk factors

  • Eligibility for treatment

  • Incidences

  • Prevalences

  • Probabilities

  • Outcomes

  • Etiology

  • Lab results

  • Predictionofrecovery


Representation of subgroups 1 l.jpg

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


Representation of subgroups 2 l.jpg

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


Representation of subgroups 3 l.jpg

Representation of subgroups 3

  • Describinghowhealthpersonnelshoulddeterminethe relevant subgroup

  • Giving general informationbutacknowledgingthatsubgroups do exist


System behaviour and adaptation l.jpg

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


Conclusions l.jpg

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


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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


Discussion l.jpg

Norwegian Electronic Health Library

Discussion!


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