Table 2 Sources of heterogeneity in decision-making tools and patient preference integration

Type of Decision Support

Model, Decision Aid, DSS

Different types of decision support vary in complexity and transparency. Models often use statistical or algorithmic approaches, while Decision Aids and DSS are more likely to involve interactive tools or systems integrated with clinical information [14, 15].

Use Cases

Neoplasms, Circulatory Diseases, Nervous System, Others

Focus varies by disease, with most applications in complex, high-incidence diseases (e.g., cancers, cardiovascular). Disease type often influences the depth of patient preference integration, as more complex diseases benefit more from patient input [9].

Method of Incorporating Preferences

Statistical Models, MCDA, Decision Trees

Statistical models provide probabilistic outcomes, MCDA balances multiple criteria, and Decision Trees offer visual decision pathways. MCDA is commonly part of DSS, while Decision Trees can function as both models and aids, adding methodological diversity [43, 54].

Elicitation of Patient Preferences

Standard Gamble, Time Trade-Off, DCE, Conjoint Analysis

Different elicitation methods impact how preferences are quantified; for example, DCE and Conjoint Analysis emphasize attribute importance, while Standard Gamble and Time Trade-Off measure risk tolerance and preferences under uncertainty [55].

Involvement of End Users

Patient, Clinician, Both

Tools designed for patient-only use emphasize empowerment, while those for both patients and clinicians aim to facilitate shared decision-making. Clinician-targeted tools often incorporate organizational priorities and resource management considerations [35, 37].