Translation for Person-Centred Care: the Role of Web-Based Decision Support Using Multi-Criteria Decision Analysis
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Abstract
Background
Calls for greater patient involvement and respect for individual preferences, including those relating to non-clinical considerations, are coinciding with growing pressure for more effective ‘translation’ of research evidence into patient-centred practice. New online technologies, in conjunction with an appropriate analytical framework, have the potential to play a major part in reconciling these goals efficiently in the context of ever-increasing pressure on healthcare resources. The potential is very high in the management of chronic conditions which are complex, preference-sensitive, and have high public health impact because of rising incidence and costs. But it is also high in facilitating higher quality preference-sensitive screening decisions in cost-effective, possibly cost-saving, ways.
Objectives
To provide web-based, mobile-accessible decision support that can, through its use of the Multi-Criteria Decision Analysis framework, address the above challenges simultaneously and efficiently, by
• Permitting the real-time personalisation of the criteria (outcomes and process attributes) in the decision support, ensuring they are those important to the individual
• Permitting the individual to have the analysis reflect their personal importance weights over the selected criteria
• Determining precisely what needs to be extracted (‘translated’) from existing evidence and expertise to deliver person-centred decision making now, i.e. the performance ratings for each available option on each person-important criterion
• Establishing the limitations in the current evidence and expertise bases for these performance ratings, limitations which need to be translated backwards as research priorities for patient-centred care
• Empowering the user to access a tool to the extent they are willing and able, enabling them to go straight to the meat/filling of the sandwich or experience the multiple-course meal of decision aids that meet IPDASi standards
Methods
A flexible and generic online system for producing and delivering decision support tools has been developed in the weighted-sum implementation of MCDA called Annalisa©, which is embedded in the Elicia© survey program to enable both individual customisation and evidence personalisation. The single-screen graphic output from the tools developed in this system presents scores for all the available options. These can be seen as constituting an 'opinion' based on a transparent synthesis of the performance ratings for each option on each criterion and the criterion importance weights expressed by the user. This is not offered as a medical opinion per se but as one produced by the involved health professional team.
Results
As practitioners are aware, relatively few of cells in patient-centred decision matrices can be populated with personalised evidence that meets conventional robust standards. To produce the Best Estimates Available Now (the BEANs) we draw on realistic forms of Network Meta-Analysis and on expert judgments, as well as eliciting patient’s personal ratings for criteria where they are the expert. The use of these techniques is illustrated in tools for IBD and Bipolar Disorder.
Conclusion
The feasibility of producing and delivering personalised MCDA-based decision support tools has been confirmed at proof-of-method level in diverse contexts. Preference heterogeneity in respect of decision support makes the use of group averages problematic for evaluations within person-centred care.
Calls for greater patient involvement and respect for individual preferences, including those relating to non-clinical considerations, are coinciding with growing pressure for more effective ‘translation’ of research evidence into patient-centred practice. New online technologies, in conjunction with an appropriate analytical framework, have the potential to play a major part in reconciling these goals efficiently in the context of ever-increasing pressure on healthcare resources. The potential is very high in the management of chronic conditions which are complex, preference-sensitive, and have high public health impact because of rising incidence and costs. But it is also high in facilitating higher quality preference-sensitive screening decisions in cost-effective, possibly cost-saving, ways.
Objectives
To provide web-based, mobile-accessible decision support that can, through its use of the Multi-Criteria Decision Analysis framework, address the above challenges simultaneously and efficiently, by
• Permitting the real-time personalisation of the criteria (outcomes and process attributes) in the decision support, ensuring they are those important to the individual
• Permitting the individual to have the analysis reflect their personal importance weights over the selected criteria
• Determining precisely what needs to be extracted (‘translated’) from existing evidence and expertise to deliver person-centred decision making now, i.e. the performance ratings for each available option on each person-important criterion
• Establishing the limitations in the current evidence and expertise bases for these performance ratings, limitations which need to be translated backwards as research priorities for patient-centred care
• Empowering the user to access a tool to the extent they are willing and able, enabling them to go straight to the meat/filling of the sandwich or experience the multiple-course meal of decision aids that meet IPDASi standards
Methods
A flexible and generic online system for producing and delivering decision support tools has been developed in the weighted-sum implementation of MCDA called Annalisa©, which is embedded in the Elicia© survey program to enable both individual customisation and evidence personalisation. The single-screen graphic output from the tools developed in this system presents scores for all the available options. These can be seen as constituting an 'opinion' based on a transparent synthesis of the performance ratings for each option on each criterion and the criterion importance weights expressed by the user. This is not offered as a medical opinion per se but as one produced by the involved health professional team.
Results
As practitioners are aware, relatively few of cells in patient-centred decision matrices can be populated with personalised evidence that meets conventional robust standards. To produce the Best Estimates Available Now (the BEANs) we draw on realistic forms of Network Meta-Analysis and on expert judgments, as well as eliciting patient’s personal ratings for criteria where they are the expert. The use of these techniques is illustrated in tools for IBD and Bipolar Disorder.
Conclusion
The feasibility of producing and delivering personalised MCDA-based decision support tools has been confirmed at proof-of-method level in diverse contexts. Preference heterogeneity in respect of decision support makes the use of group averages problematic for evaluations within person-centred care.
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