The role of patients' meta-preferences in the design and evaluation of decision support systems.

The arrival of new analysis-based decision technologies will necessitate a profound rethinking both of the nature of the patient-doctor relationship and of the way aids and support systems designed to improve decision-making within that relationship are designed and evaluated. One-dimensional typologies of the traditional 'paternalist/shared/informed' sort do not provide the complexity called for by the heterogeneity of patient's 'meta-preferences' regarding their relationship with a doctor on the one hand and regarding the analytical level of judgement and decision-making on the other. A multidimensional matrix embodying this distinction is proposed as a framework of the minimal complexity required for the design and evaluation of the full range of decision aids and decision modes. Essentially aids should be conceived of and evaluated cell-specifically and the search for universally satisfactory decision support systems abandoned. 'shared' and'informed' are best interpreted as attributes which may or not be in line with a patient's meta-preferences. Future research should focus on the higher level goal of better decision-making, a goal that will need to respect and reflect these meta-preferences

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

shared decision making is a Preference-sensitive Formative Construct: the Implications

As with many constructs in healthcare (e.g., ‘evidence-based medicine’, ‘health-related quality of life’, ‘decision aid’) ‘shared decision-making’ is formative not reflective, that is, ‘it’ has no existence prior to its definition and measurement. Any particular formative construct is preference-sensitive, being based on the preferences of those who form it by their indicator selection and weighting. These preferences often reflect interests of various sorts, some material, many not (at least not directly), but often ones aligned with particular beliefs, ideologies or ideals. So cave litteras maiusculas - sdm not SDM. Since ‘shared’ is an adjectival qualifier of ‘decision-making’, fundamental preferences relevant to decision-making are relevant in any construction of sdm. We highlight two major preferences in relation to health decisions. One is for provider-controlled, direct-to-patient intermediation (inter) as contrasted with provider-independent, direct-to-person apomediation (apo). The second is for verbal deliberative reasoning (vdr) as contrasted with numerical analytical calculation (nac). From their cross-tabulation we can see that, within both practice and research - and in legal standards and ethical guidelines for both - sdm is currently being constructed exclusively within the intermediative verbal deliberative reasoning (‘inter-vdr’) frame. We compare and contrast inter-vdr with the three other possibilities - ‘inter-nac’, ‘apo-vdr’ and ‘apo-nac’. Dismissal or disregard of the latter, especially the last, on the grounds of credibility and trustworthiness, needs to be challenged by preference-based comparative evaluations, using unbiased measurement of costs and effectiveness, in order to optimise the development and delivery of personalised support for health and healthcare decisions

COVID-19, the Swedish 'Experiment', and Me.

In this personal vision paper the Swedish approach to COVID-19 prompts an exploration of how and why assuming individual rationality coupled with minimal social restriction may be as good a solution as any and better than most. A COVID sub-model is developed and populated with probabilities for four outcomes of infecting another person (asymptomatic, sick, hospitalized, dead), conditional on three observable characteristics (sex, age, and BMI), and (dis)utilities for three categories of person (nearest/dearest, friends/colleagues and unknown others) experiencing those outcomes. The implications for a liberal democracy are drawn, based on the assumptions that individual citizens will and should maximise their informed expected utility, exhibiting 'commons sense' as well as common sense

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

Separating Risk Assessment from Risk Management Poses Legal and Ethical Problems in Person-Centred Care.

Accelerating progress in screening technologies, e.g. genetic testing, means more individuals are facing the stressful decision of whether to request the test. Fully-informed and preference-based consent, as well as ethical practice, requires the full range of benefits and harms from any test or treatment to be identified and assessed from the individual's point of view. For both ethical and legal reasons, we see the decision on whether to undertake a genetic screening test being increasingly seen, in future, as calling for a personalised analysis of the full range of subsequent management options. The conventional dissociation of 'risk assessment' and 'risk management' phases is thereby ruled out. One way of addressing the resulting challenge is through personalised multi-criterial decision support tools. In this vision paper we provide conceptual proof of method of how such an interactive online tool could function. The polygenetic genetic screening decision is used, solely as illustration

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

Dual Purpose, Dual Audience: MCDA-Based Tools Can Simultaneously Support Personal Health Decisions and Educate Persons and Clinicians.

In this vision paper we envisage paradigmatic change transforming the health professional from the empowered agent of a dependent patient into the final decision supporter of an empowered person; a person who comes equipped with the preliminary opinion of an apomediative personalised decision support tool and the enhanced health decision literacy it produces. The anomalies in the current paradigm that will produce this change arise from unworkable attempts to combine multicriterial personal preferences and the best available evidence in a medical reasoning model. Multi-Criteria Decision Analysis (MCDA)-based decision support tools provide one mechanism for achieving this synthesis in a transparent way in a specific case whilst, simultaneously, increasing the generic health decision literacy and competencies of both person and clinician. The educational task for both is less than might be expected because of their familiarity, as consumers, with the many comparison websites and magazines for products and services using an informal version of MCDA. The educational task, particularly for the clinician, is primarily one of acknowledging that MCDA is a decision competence that has the potential, not only to enhance decision quality, but also facilitate communication between person and professional, who will now be talking the same decisional language. Experience from developing the MyBoneHealth tool confirms both the feasibility and challenges of delivering a MCDA-based decision support and educational tool