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.

Malaga November 2011

What is a decision aid?

Three patient roles

Doctor knows best

Independent customer

Shared decision-making

Grey-zone decisions

Minhas R. Clinical Evidence. BMJ Publishing Group, 2011

Condition DilemmaEarly prostate cancer

Surgery, radiotherapy or wait?

Early breast cancer

Breast-conservation or full removal?

Elevated cholesterol

Start taking statins?

Atrial fibrillation Begin warfarin to prevent stroke?

Depression Start with an antidepressive?Multiple sclerosis Medication or not?

Some examples

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…

Definition of patient decision aids

…when one particular treatment is not appropriate to all

DefinitionDefinition of patient decision aids

The personalisation problem

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

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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

Little is known about – the current use of – and potential for web personalisation

…inherent in the tools

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

To estimate the potential – Basic Requirement– Current use

for web personalization in web-based decision aids

Simply: Is form and content tailored to the individual?

Methods

Development of a simple coding scheme for web personalisation– user features– adaptive systems behaviour

Based on a research anthology Adjusted during the coding process

Method: coding scheme

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

Developers represented in the Ottawa Inventory

Pdfs excluded

The functionally richest DA from each developer selected

Method: identification of DAs

http://decisionaid.ohri.ca/AZinvent.php (Acessed July 20, 2011)

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

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

10 decision aids selected

1. Media content

2. User features

3. User model construction and representation

4. Adaptive system behaviour

4 classes in the coding scheme

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

Class 1: Content types

1. Knowledge level2. Interests3. Preferences4. Goals/tasks5. Background6. Individual traits7. Context

Class 2: User features

1. Navigation support2. Selection3. Organisation 4. Presentation of content5. Search6. Collaboration7. Recommendations

Class 4: Adaptive system behaviour

Coping styles Emotional reactions Cognitive skills User beliefs Experiences of users Literacy level Somatic parameters

Most frequent user subgroups

Risk factors Eligibility for treatment Incidences Prevalences Probabilities Outcomes Etiology Lab results Prediction of recovery

Results: Somatic parameters

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

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

Describing how health personnel should determine the relevant subgroup

Giving general information but acknowledging that subgroups do exist

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

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

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

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Discussion!

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