A Johari-Inspired Framework for Population Gap Analysis: Linking Underdiagnosis, Overdiagnosis, Diagnostic Competition, and Prevention

Linear care cascades (the Rule of Halves and awareness-treatment-control frameworks) quantify attrition among system-recognized cases but cannot represent true disease that remains undiagnosed, system-labeled disease that is absent or non-actionable, or symptomatic individuals whose diagnostic encounters are consumed by competing labels. A structural solution to these simultaneous limitations does not exist within current population health methodology. We conducted a narrative review of prior healthcare uses of the Johari Window, empirical applications and limitations of linear gap frameworks, evidence on the burden and consequences of underdiagnosis and inappropriate labeling, and literature on diagnostic competition and competing-diagnosis bias. We searched PubMed/Medical Literature Analysis and Retrieval System Online (MEDLINE) and Google Scholar (2000 to January 2026) using terms spanning Johari Window applications in health, population care cascades, underdiagnosis, overdiagnosis, and competing diagnoses. We adapted the Johari Window by jointly classifying populations along two operational axes, true disease state and health-system labeling state, and applied a priority-ordered classification rule to yield four mutually exclusive, jointly exhaustive quadrants. The adapted framework generates four quadrants: Open (disease present, target-disease label present); Hidden (disease present, no label); Facade (label present without actionable target diagnosis, with two mechanistically distinct subtypes: type I diagnostic misclassification and type II diagnostic competition); and Unknown (disease absent, no label, stratified into at-risk and not at-risk). Four system-level diagnostic performance metrics (system sensitivity, system specificity, positive predictive value (PPV), and negative predictive value (NPV)) are derived directly from quadrant counts, supplemented by the Hidden Burden Index, Facade Burden Index, and Facade-to-Hidden Ratio. The framework is illustrated with global pools of undiagnosed diabetes (~252 million) and hypertension (~580 million), substantial proportions of clinically labeled chronic obstructive pulmonary disease lacking spirometric confirmation (30%-60%), and the diagnostic shadow cast by depression, stroke, and other competing diagnoses. The adapted Johari Window framework provides a structurally complete, operationalizable, and metric-yielding solution to limitations inherent in linear cascade models, enabling simultaneous assessment of underdiagnosis, overdiagnosis, diagnostic competition, and system-level diagnostic performance at the population scale.