| UCDa
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A large research community to draw upon with broad use in human-computer interaction and related fields. User-validated process directly addresses uptake concerns in DHIs. Broad approach is adaptable to all categories of DHIs.
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Defining the end-user in health care interventions is difficult because of the complex collaboration of stakeholders in DHIb facilitation (ie, clinicians, caregivers, and patients) who may all be end-users of the DHI. Aligning preferences to patient end-users may conflict with health policy based on expert-led evidence-based practices. Largely qualitative feedback often represents a small sample size that opposes the rigors of traditional longitudinal health metrics.
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| PBDc
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Psychoanalytical approach that contextualizes improved well-being as a design outcome is well suited to behavior changing DHIs. Empathetically guided “sensitive design” process broadens stakeholder focus beyond active users to also include passive users and collaborators in the DHI as a whole person network approach.
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Behavior change metrics from PBDs may not be transferable to other types of DHI designs. Psychoanalytic “sensitive design” may create an expert-led barrier to entry for other collaborators in the DHI (developers, designers, etc) and add scope.
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| HCDd
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Highly adopted and International Organization for Standardization–recognized approach for system design, already has health care provider backing (Mayo Clinic, Kaiser Permanente). Combining approaches of user-centered design, human-computer research, anthropology, and sociology under the banner of “social innovation” has broad appeal to unite a wide swathe of DHI collaborators.
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An “umbrella term” that approaches design ethos and policy framework from many fields of research, there is a lack of unified guidelines, thus there is a need for a demonstrable lightweight framework for DHI design and facilitation. Project scope is challenged by the breadth of collaborators (patients, clinicians, designers, developers, and academics) which may expand timelines in a fast-paced design environment.
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| PCDe
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Pivoting focus from user to patient (commercial to health care) creates better alignment with health care infrastructure policy, allowing for better buy-in from health care stakeholders. Empowering patients to take leadership of their health care management is a leading metric in DHI retention and advocacy (particularly in wearables and sensor-based DHIs).
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Positioning patients as primary stakeholders (or as experts) oversimplifies the complexity of health safety and clinical efficacy guidelines and may lead to undesired patient outcomes. Crowdsourcing DHI preferences may lead to misdiagnosis by popular convention, democratized data sets will still need to be weighed against medical best practices.
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| PLDf
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Self-tracking, self-analysis PLD approach is positioned well for today’s emerging personalized health care marketplace. Machine learning–backed “citizen science” approach offers large quantitative data sets for better triangulation of patient preferences.
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Patient-led approach may lack the holisticity of HCD or PBD and the safety and efficacy of traditional health care methods, this may limit the focus to preferences rather than clinical health outcomes Scalability is questionable because of limited stakeholder base (lack of consensus) and self-experimentation approach (lack of clinical validation).
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