The Future of Health Is Self-Production and Co-Creation Based on Apomediative Decision Support.

Cultural changes are needed in medicine if the benefits of technological advances are to benefit healthcare users. The Digital Health Manifesto of 'medical futurist' doctor Bertalan Meskó and 'e-patient' Dave deBronkart, The Patient Will See You Now by Eric Topol and The Patient as CEO by Robin Farmanfarmaian, are among the proliferating warnings of the approaching paradigm shift in medicine, resulting, above all, from technological advances that gives users independent access to exponentially increasing amounts of information about themselves. We question their messages only in suggesting they do not sufficiently shift the focus from 'patient' to 'person' and consequently fail to recognise the need for the credible, efficient, ethical and independent decision support that can ensure the 'democratisation of knowledge' is person empowering, not overpowering. Such decision support can ensure the 'democratisation of decision,' leading to higher quality decisions and fully-informed and preference-based consent to health provider actions. The coming paradigm will therefore be characterised by apomediative ('direct-to-consumer') decision support tools, engaged with by the person in the community to help them make health production decisions for themselves (including whether to consult a healthcare professional or provider), as well as intermediative ('direct-from-clinician') tools, delivered by a health professional in a 'shared decision making' or 'co-creation of health' process. This vision paper elaborates on the implementation of these preference-sensitive decision support tools through the technique of Multi-Criteria Decision Analysis

Decision Quality Is a Preference-Sensitive Formative Concept: How Do Some Existing Measures Compare?

The primary output of a decision making process is a decision and a key outcome measure is therefore decision quality. However, being a formative construct, 'decision quality' is both preference- and context-sensitive and legitimate alternative measures accordingly exist. A decision maker wishing to measure decision quality in the evaluation of a decision or decision making process needs to be aware of the attributes of the measures on offer. This paper establishes some of the key conceptual differences by examining two measures: Decision Quality Instruments and MyDecisionQuality. Four of their main conceptual differences relate to: the timing of the measurement (at the point of decision or at follow-up when the 'downstream' outcome is known); (whether or not an objective assessment of the information state of the individual is included (as opposed to self-reported state); whether the instrument itself is preference-sensitive; and whether the measure is to be used in the context of individualised clinical practice at the point of care or only in research to produce group level feedback. Establishing agreed measures of decision quality is necessary and useful, so long as it is accepted that it is a preference- and context-sensitive construct, in the way that is widely acknowledged in relation to, for example, Health-Related Quality of Life, with its many measures

Strong Recommendations Are Inappropriate in Person-Centred Care: The Case of Anti-Platelet Therapy

A ‘Rapid Recommendation’ has been produced by the GRADE group, in collaboration with MAGIC and BMJ, in response to an RCT showing Dual Anti-Platelet Therapy (DAPT) is superior to Aspirin alone for patients who had suffered acute high risk transient ischaemic attack or minor ischaemic stroke. The interactive MAGIC decision aid that accompanies each Rapid Recommendation is the main route to their clinical implementation. It can facilitate preference-sensitive person-centred care, but only if a Multi-Criteria Decision Analysis-based decision support tool is added. A demonstration version of such an add-on to the MAGIC aid, divested of recommendations, is available online. Exploring the results of different preference inputs into the tool raises questions about the strong recommendation for DAPT

PROMs Need PRIMs: Standardised Outcome Measures Lack the Preference-Sensitivity Needed in Person-Centred Care

A growing number of condition-specific standard outcome sets have been developed by the International Consortium for Health Outcomes Measurement in pursuit of ‘value-based care’. These sets embrace many Patient-Reported Outcome Measures (PROMs), reflecting a simultaneous commitment to ‘patient-centred care’. However, none of these sets embody recognition of the preference-sensitive nature of the decisions that eventually generate the outcome database. ‘Patient-Reported Importance Measures’ (PRIMs) are the valid source of the required preferences. The ICHOM Stroke standard set is input into a hypothetical Multi-Criteria Decision Analysis-based decision support tool to provide simple confirmation that PROMs should be preference-mix adjusted as well as case-mix adjusted. PROMs need PRIMs if value-based care is to be personalised values-based care

Risk Thresholds and Risk Classifications Pose Problems for Person-Centred Care.

Classification of a continuous risk score into risk levels is common. However, while the absolute risk score is essential, it is arguably unethical to label anyone at 'high, moderate or low risk' of a serious event, simply because management based on a single criterion (e.g. avoiding the target condition) has been determined to be effective or cost-effective at a population level. Legally, mono-criterial risk labeling can inhibit the obtaining of a fully-informed, preference-based consent, since multiple considerations (various benefits and harms) matter to most individuals, not only the single criterion that is the basis of the provided risk category. These ethical and legal challenges can be met by preference-sensitive multi-criteria decision support tools. In this future vision paper, we demonstrate, at a conceptual proof-of-method level, how such decision support can and should be developed without reference to risk-level classifications. The statin decision is used as illustration, without any empirical claims

Uncertainty-Adjusted Translation for Preference-Sensitive Decision Support.

In Multi-Criteria Decision Analysis-based decision support for person-centred care, the person's quantitative preferences (as criterion weightings) are combined with quantified evidence and expert assessments (as option performance ratings on all criteria) to produce a personalised quantified opinion (as a set of expected value option scores). In our current decision support tools, we use the best available (central point) estimates for option performance ratings. The uncertainty surrounding the performance rating estimates, routinely reported by researchers as intervals around the means, are ignored. While defensible, this paper responds to questioning of this disregard. Apart from the inappropriate 'inverse variance' method, we find no attempt to integrate parameter uncertainty into decision analyses, simply an emphasis on reporting it fully, leaving decision makers unsupported in the burden of dealing with the separated outputs - e.g. Means and Credible Intervals. The paper suggests that uncertainty can be brought within Multi-Criteria Decision Analysis-based decision support by treating the means and uncertainties of all outcomes and process considerations as separate criteria, having them traded-off in an individually preference-sensitive manner at the point of decision. An empirical proof of method via an online example on bone health medications is provided, involving six options, two considerations and four criteria

Preference-Sensitive Apomediative Decision Support Is Key to Facilitating Self-Produced Health.

In the health capital model, the main function of health services is not to produce health, but to support the person in their self-production investments. In the health context there are three types of decision support tools, depending on the role of the provider (e.g. clinician) and person. Non-mediative tools are designed to help the clinician decide what is best for the patient. Intermediative Patient Decision Aids are designed to help the clinician and patient decide together, in an encounter, what is best for the patient. Apomediative Personalised Decision Support Tools are designed to help the person decide what is best for themselves, including whether to seek a professional consultation and/or to prepare for, and engage in, an intermediative consultation. Only preference-sensitive apomediative support tools ensure that the key requirements of self-produced health are met, along with legally informed and preference-based consent to any subsequent provider action. The desirable form of apomediative support is a publicly accessible, direct-to-citizen, provider-independent, multi-criteria analysis-based decision support of the sort available in many other areas of self-production. Which (UK), Tænk (Denmark), Choice (Australia) and numerous other comparison magazines and websites provide independent multi-criterial support for decisions on, for example, which food and transport to buy to self-produce nutrition and movement. A personalised decision support tool for the statin decision is provided as illustration: Should I go to my general practitioner and ask for a statin prescription or go to discuss taking statins, in the light of the preliminary opinion of the tool

The Evaluation of Decision Support Tools Needs to Be Preference Context-Sensitive.

Individuals have different preferences in how they wish to relate to healthcare professionals such as doctors. Given choice, they also have preferences in relation to the type and location of support they want for their health and healthcare decisions. We argue that preference-based clusters within this heterogeneity constitute different contexts and that evaluations of decision aids should be context-sensitive in this respect. We draw attention to two distinct preference-based clusters: individuals with a preference for 'intermediative' decision support as a patient, implemented in a largely qualitative deliberative model, on the one hand, and for 'apomediative' decision support as a person, implemented in a largely quantitative multi-criteria decision analytic model, on the other. For convenience, we refer to the latter as Person Decision Support Tools (PDSTs), leaving Patient Decision Aids (PDAs) for its former, conventional use. Seeking to establish proof of method, we present an online PDST that can help individuals establish which of these two types of decision support they would find optimal. It is based on nine key attributes on which PDAs and PDSTs can be contrasted. Within population heterogeneity, preference clusters should be identified, and acknowledged and respected as contexts relevant to the evaluation of decision support tools

Why a Global PROMIS® Can't Be Kept

Composite multi-dimensional constructs, such as ‘global mental health’ and ‘global physical health’, in PROMIS® instruments and ICHOM standard outcome sets, are formative, not reflective. Their preference-insensitivity means they are potentially misleading in both clinical and policy decision making practice. Their frequent validation by reflective psychometric tests is also improper methodologically. The spread of these instruments is occurring without sufficient awareness on the part of patients, clinicians, researchers and policy makers that the need for group-specific preference bases (‘tariffs’) for such measures rules out any possibility of ‘international gold standard metrics’