Stephen Darlington
choices that effectively control the portability of their medical records. If participation and satisfaction with the system is high then it could be seen as successful.
What is the ideal power‐relationship ratio in an optimal e‐health system? Where stakeholder competence and capability is high and enhanced by system ableness then optimal e‐health system utility and stakeholder satisfaction may be best served by adopting an expansive opt‐in system that has opt‐out and mandatory components that best serve. Here, power‐relationship ratios favour the citizen and support key values the European Council identified as central to health policy that sustains the“ growing expectations and empowerment of patients”( Huijboom 2009: 26). Those key values are“ universality, access to good‐quality care, equity and solidarity”( Huijboom 2009: 26). This approach has the most potential to transform service delivery but requires significant stakeholder participation, consultation and input. McGee( 2011) has argued that the US has made enormous efforts to obtain input from relevant e‐health stakeholders before creating the e‐health institutional rules, procedures and financial incentives, effectively creating a hybrid top‐down and bottom‐up system that is far more likely to be successful than the failed British top‐down“ one size fits all … you’ re using this and will like it” model.
Where stakeholder competence and capability is low and unsupported by system ableness then it may be more prudent to focus first on utility by establishing opt‐out and mandatory systems that are designed to transform into the opt‐in system described above as system ableness increases stakeholder competence and capability. Here, power‐relationship ratios favour the state. If the state’ s initial goal in establishing an e‐health system is to enhance stakeholder competence and capability power‐relationship ratios should eventually shift towards the citizen while still maintaining system utility.
7. Theoretical implications
This analysis is a work of practical political philosophy in that it provides bases for public justification and political agreement about e‐health systems by creatively constructing a valuation frame of reference whose rules and procedures are both normative and evidential( see Lehning 2009: 12,100). Normative in the sense that they‘ should’ be adopted.‘ Should’ is justified by reason. Evidential in that the‘ should be’ is in some way better than the‘ is now’. Reason presupposes an outcome‘ better than is now’ that must in some way be measurable or otherwise the premise does not lead logically to the conclusion ‐ the outcome would be meaningless and‘ should’ an emotion without substance to shift the reality of‘ is now’. Therefore, the scientific method can support or deny normative claims.
Lehning( 2009: 12) notes that since the 1950s, and particularly the 1960s,“ making the step from observable facts to norms is [ when applying the scientific method ] in principle impossible”. I would argue it is both possible and natural. Observable facts establish what‘ is’, which naturally leads to what‘ ought to be’. Something‘ ought to be’ because reason X and its predicted outcome Z 1 is better than reason Y and its current‘ is’ outcome Z 2 or competing predicted outcomes Z 3, Z 4, Z 5. In an institutional setting such as e‐health the path dependencies of X and Y could be used to measure predicted outcomes of‘ is’ and‘ ought’. When applied to Rawls’ concepts of justice as fairness and the difference principle, a measured value can be given to normative values.
Why? Because outcomes have stakeholder values. While these values can be normatively expressed they can also be measured. For example, satisfaction is a normative term that can be measured as more or less satisfied on a scale. This scale can be numbered giving more precise measurements of 1, very unsatisfied, through to say 5, very satisfied. Measurement, and for that matter subject response, may be subjective and therefore value relative but this is a notion of change over time. For example, when suffering from influenza in 1920 citizen A may well be satisfied with a hospital bed and comforting care but no cure. However, were they living in 2013 they may well be unsatisfied with that medical response and be expecting a cure that enables them to leave hospital in the near future healthy and full of vigor. The outcome in 1920 may well have been death whereas the outcome in 2013 may well be life – a significant difference. Therefore, some aspects of particular values may be objectively measured if linked to outcomes.
8. Conclusion
I have argued that in choosing a balance of rights versus utility, e‐health systems determine powerrelationship ratios. The balance chosen could explain e‐health system success or failure. Competent and
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