Suspended Recognition: Institutional Thresholds in Rare Disease Diagnostic Systems

Suspended Recognition: Institutional Thresholds in Rare Disease Diagnostic Systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Suspended Recognition: Institutional Thresholds in Rare Disease Diagnostic Systems Gregory Fagan¹ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9451240/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Rare diseases are frequently associated with prolonged and uncertain diagnostic journeys, commonly described as the diagnostic odyssey. Existing research has documented substantial diagnostic delays and their social and psychological consequences for patients and families. However, most explanations focus on clinician awareness, technological limitations, or patient experiences, while relatively little attention has been devoted to examining how diagnostic recognition systems themselves are institutionally structured. This study investigates how institutional rules and infrastructures regulate access to rare disease diagnosis. Using a qualitative institutional analysis, the study examines a corpus of policy documents, diagnostic programs, clinical pathways, and registry systems describing the organization of rare disease diagnosis across multiple healthcare contexts. Documents were coded to identify institutional recognition thresholds—rules, requirements, or infrastructural conditions that must be satisfied for patients to progress through diagnostic systems. The analysis identified 81 recognition thresholds distributed across referral systems, diagnostic testing infrastructures, program eligibility structures, classification frameworks, and registry inclusion processes. These thresholds form sequential recognition chains governed by multiple institutional gatekeepers, including specialist centers, diagnostic laboratories, diagnostic programs, and classification authorities. Thresholds accumulate across the diagnostic pathway through sequential, layered, and recursive processes, creating bottlenecks that regulate progression toward diagnosis. The findings demonstrate that rare disease diagnosis is structured by institutional threshold architectures rather than solely by clinical decision-making. When patients cannot traverse these thresholds, they remain engaged with healthcare systems without achieving formal diagnostic classification. This study conceptualizes this condition as suspended recognition, highlighting the role of institutional system design in shaping diagnostic outcomes and contributing a new perspective on persistent diagnostic delay in rare disease care. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Rare diseases collectively affect an estimated 300 million people worldwide, representing a substantial global health burden despite the low prevalence of individual conditions. Patients with rare diseases frequently experience prolonged and uncertain diagnostic journeys. Extended periods between symptom onset and diagnostic confirmation—widely described as the diagnostic odyssey —are a defining feature of many rare disease experiences. Numerous studies across rare disease contexts document substantial delays in diagnostic recognition. Godet et al. (2001) reported an average delay of two years and eight months between symptom onset and diagnosis, with extreme variation ranging from immediate diagnosis to delays of up to forty years. More recent studies suggest that such delays remain widespread. Molster et al. ( 2016 ) found that 30% of patients waited five years or longer for diagnosis, while Ferrara et al. (2017) documented a mean delay of 7.3 years from symptom onset to diagnostic confirmation. Similar variability has been reported across disease groups, with diagnostic timelines ranging from months to more than two decades depending on clinical context (Phillips et al., 2024 ). The diagnostic process often involves repeated interactions with healthcare providers across multiple specialties. In many health systems, primary care providers serve as the first point of contact for patients with undiagnosed conditions and play a critical role in identifying potential rare diseases and initiating referral pathways to specialist services (Baynam et al., 2024 ). However, navigating these referral pathways can be complex and time-consuming. Benito-Lozano et al. ( 2022 ) found that patients who consulted more than ten specialists had significantly increased odds of diagnostic delay (OR 2.6), while waiting more than six months for specialist referral accounted for 30.2% of total diagnostic delays. These findings illustrate the fragmented structure of rare disease diagnostic pathways, in which patients often move through multiple layers of healthcare services before reaching clinicians with appropriate expertise. Beyond delays in diagnostic recognition, the literature has also documented the social and psychological consequences of prolonged diagnostic uncertainty. Patients and families frequently experience significant emotional stress, uncertainty, and social disruption during extended diagnostic journeys. Blöß et al. ( 2017 ) describe how patients may be stigmatized through psychosomatic attribution when symptoms remain medically unexplained. Similarly, Llubes-Arrià et al. (2021) identify both internal emotional burdens and external structural constraints shaping patient experiences during the diagnostic process. Parents and caregivers often assume roles as care coordinators and advocates while navigating fragmented healthcare systems lacking specialized rare disease infrastructure (Baumbusch et al., 2018). Social and cultural factors, including stigma associated with genetic testing, have also been identified as barriers to diagnostic engagement and care seeking (Baynam et al., 2024 ). Together, these studies highlight the substantial human impact of delayed or unresolved diagnosis. While these studies highlight important psychosocial and experiential barriers, relatively little attention has been devoted to examining how diagnostic recognition systems themselves are institutionally structured. Existing explanations for diagnostic delay typically emphasize a range of systemic and clinical factors, including limited awareness of rare diseases among healthcare professionals, fragmented coordination across specialties, difficulties accessing specialized diagnostic testing, and broader gaps in rare disease infrastructure. For example, Akesson et al. ( 2025 ) describe barriers such as limited provider awareness and restricted access to diagnostic testing, while Benito-Lozano et al. ( 2022 ) highlight referral barriers and geographic access constraints. In addition, diagnostic delays may be reinforced by limitations in disease classification and coding systems, which often fail to systematically identify or track individuals with suspected or undiagnosed rare diseases within health system data infrastructures (Baxter et al., 2023 ). Taken together, these studies demonstrate that delays in rare disease diagnosis arise from a complex interaction of clinical, organizational, and infrastructural factors. At the same time, some studies suggest that structured institutional models can significantly improve diagnostic outcomes. Choukair et al. ( 2021 ) demonstrated that multidisciplinary diagnostic pathways supported by structured case conferences can produce more efficient diagnostic processes, reporting a median time to diagnosis of 109 days across 587 evaluated cases with a diagnostic success rate of 62.8%. Similarly, Hay et al. ( 2020 ) describe how sociotechnical redesign of diagnostic workflows within the Western Australian Undiagnosed Diseases Program improved coordination across specialties and facilitated diagnostic progression for complex cases. Other institutional initiatives include the designation of Centers of Expertise (Taruscio et al., 2014 ), the development of European Reference Networks enabling cross-border consultation through the Clinical Patient Management System (Reinhard et al., 2021 ), and integrated service coordination models using single entry points and case management systems (Hébert et al., 2016). These institutional initiatives increasingly intersect with broader health-system approaches that integrate genomics, epidemiology, and policy frameworks to improve rare disease diagnosis at the population level. This perspective is often described as precision public health (Baynam et al., 2017 ). Within this framework, diagnostic programs such as the Western Australian Undiagnosed Diseases Program illustrate how coordinated genomic diagnostics and multidisciplinary expertise can be embedded within public health systems to strengthen diagnostic capacity. More recent models extend these approaches further by emphasizing cross-sector coordination, linking healthcare with education, disability services, and social support systems to address the broader institutional needs of patients with rare and undiagnosed conditions (Mertens et al., 2024 ). Despite these examples of institutional innovation, implementation remains uneven across healthcare systems. More than two decades ago, Godet et al. (2001) observed that diagnosis management was often “determined by personnel initiatives or exceptional opportunities rather than by a real organizational strategy of the healthcare system.” This observation remains relevant today. Although a substantial body of research documents diagnostic delays and the patient experiences associated with the diagnostic odyssey, far less attention has been devoted to examining how diagnostic recognition systems themselves are institutionally structured. In particular, the literature rarely addresses how institutional rules, infrastructures, and governance mechanisms regulate access to diagnostic recognition. Referral requirements, eligibility criteria for diagnostic testing, program admission rules, and disease classification frameworks all shape whether patients can progress through diagnostic systems, yet these structural features of diagnostic governance have seldom been examined systematically. This gap raises an important question: how are institutional rules and infrastructures within healthcare systems structured to regulate access to rare disease diagnosis? Understanding these institutional mechanisms is critical for explaining why diagnostic delays persist even in healthcare systems with advanced diagnostic technologies and specialized expertise. Rather than focusing solely on clinical awareness or technological capacity, it is necessary to examine how diagnostic pathways are organized and governed. This study addresses this gap through a qualitative institutional analysis of diagnostic systems, examining rare disease policy frameworks, clinical pathways, diagnostic programs, and registry infrastructures. The analysis identifies recognition thresholds embedded within these institutional arrangements and examines how they shape patient progression from symptom presentation to formal diagnosis. The study proposes that diagnostic delay emerges not simply from isolated barriers but from the accumulation of institutional recognition thresholds within the diagnostic pathway. When patients cannot successfully traverse these thresholds, they remain engaged within healthcare systems without achieving formal diagnostic classification. This condition is conceptualized here as suspended recognition—a state in which patients continue to interact with healthcare providers while institutional recognition of disease remains unresolved. While often described in clinical literature as being undiagnosed , the concept of suspended recognition emphasizes the institutional processes through which diagnostic recognition becomes delayed, deferred, or structurally constrained. By examining the institutional architecture of diagnostic recognition, this study offers a new perspective on rare disease diagnostic delay and develops a framework for understanding how healthcare systems structure access to diagnosis through institutional recognition thresholds. Methods Study Design This study uses a qualitative institutional analysis to examine how diagnostic recognition is structured within rare disease health systems. The analysis focuses on the institutional rules, infrastructures, and governance structures that regulate access to diagnosis. Institutional analysis was chosen because rare disease diagnosis occurs within complex organizational systems involving multiple actors, including clinicians, specialist centers, laboratories, diagnostic programs, classification authorities, and registries. Rather than focusing solely on clinician decision-making or patient experiences, this study examines how institutional rules and infrastructures shape diagnostic recognition pathways. The objective of the analysis was to identify the recognition thresholds embedded within diagnostic systems and to examine how these thresholds interact to shape access to diagnosis. The analysis was interpretive and conceptual in orientation, aiming to identify structural patterns in diagnostic governance rather than to quantify clinical outcomes. Data Sources and Document Selection The study analyzed a corpus of institutional and policy-oriented documents describing the organization of rare disease diagnosis. These included: national rare disease policy analyses clinical diagnostic guidelines genomic testing governance frameworks undiagnosed disease program descriptions registry and data infrastructure studies health system analyses of diagnostic pathways Documents were identified through targeted searches of biomedical and social science databases, including Scopus and PubMed, using search terms related to rare diseases, diagnostic systems, referral pathways, genomic testing governance, diagnostic programs, and rare disease registries. Figure 1 illustrates the literature identification and selection process used to construct the analytical corpus. Documents describing the organization of rare disease diagnostic systems were identified through targeted searches of biomedical and social science databases. Titles and abstracts were screened for institutional relevance to diagnostic governance, referral pathways, diagnostic programs, genomic testing systems, and registry infrastructures. Following eligibility assessment, a corpus of 27 institutional documents was selected for qualitative institutional analysis. The search strategy aimed to capture literature describing institutional structures governing diagnosis, rather than studies focused exclusively on clinical outcomes or epidemiology. After screening titles and abstracts for relevance to diagnostic systems or governance structures, a final corpus of 27 institutional documents was selected for analysis. Table 1 summarizes the documents included in the analytical corpus. Table 1 Institutional Documents Included in the Analytical Corpus (n = 27) Author (Year) Country / Region Document Type System Domain Institutional Focus Expected Threshold Domain Dharssi et al. ( 2017 ) Multi-country Policy review National rare disease policy Comparison of 11 national strategies Policy eligibility Li et al. (2023) China Policy analysis National rare disease system Rare disease policy development Policy recognition Mayrides et al. (2020) Latin America Policy review Regional policy Rare disease strategy across countries Policy eligibility Gentilini et al. (2025) High-income countries Policy review Policy ecosystem Governance of rare disease programs System eligibility Williams (2011) United States Policy analysis Legislative framework Disparities in rare disease policy Coverage eligibility Syed et al. (2015) Europe Policy recommendations Rare disease centers Centres of expertise framework Referral eligibility Schmidtke et al. (2026) Germany Health system analysis Rare disease services Structure of RD care delivery Referral thresholds Evans et al. (2025) United Kingdom Clinical pathway study Care pathways Development of diagnostic pathways Referral pathways Friedrich et al. (2023) United Kingdom Implementation study Genomic medicine service NHS genomic diagnostic system Testing eligibility Ellard et al. (2024) UK / Ireland Consensus statement Genomic testing Access framework for genetic testing Testing authorization López-Martín et al. (2018) Spain Program description Undiagnosed disease program SpainUDP diagnostic model Program eligibility Gahl et al. (2016) United States Program description NIH UDP Diagnostic program architecture Program admission Brownstein et al. (2015) United States Program analysis Undiagnosed disease network Data sharing and governance Program eligibility Hay et al. ( 2020 ) United States Organizational study UDP Work design and system redesign Program thresholds Poli et al. (2024) Chile Program description Diagnostic initiative DECIPHERD diagnostic program Testing access Cloney et al. (2022) Australia Program description Undiagnosed disease program Victoria UDP model Referral eligibility Tang et al. (2026) China Observational study Referral pathways Urban–rural referral disparities Referral thresholds Félix et al. (2022) Brazil Network analysis National RD network RARAS rare disease centers Referral eligibility Alves et al. (2021) Brazil Protocol paper Diagnostic network RARASnet diagnostic infrastructure Network admission Feng et al. (2018) China Registry description Rare disease registry National registry governance Registry inclusion Choquet et al. (2015) France Data framework Rare disease centers Minimum dataset standardization Classification rules Barcella et al. (2009) Italy Registry study Regional RD registry Lombardy rare disease registry Registry eligibility de Oliveira et al. (2023) Brazil Health system study Diagnostic testing Availability of diagnostic tests Testing infrastructure Verloes et al. (2012) Europe Diagnostic guideline Clinical diagnostic criteria Developmental disability diagnosis Evidence thresholds Yang-Li et al. (2022) China Diagnostic guideline Disease classification Prader–Willi diagnostic criteria Diagnostic criteria Cottin et al. (2023) France Clinical guideline Rare lung disease LAM diagnosis Diagnostic criteria Dhombres et al. (2025) Europe Classification study Diagnostic classification Spina bifida classification Classification thresholds These documents spanned multiple geographic contexts and institutional domains, including national policy frameworks, genomic medicine services, diagnostic programs, and rare disease registries. Analytical Framework The analysis focused on identifying institutional recognition thresholds within rare disease diagnostic systems. A recognition threshold was defined as: an institutional rule, requirement, or infrastructural condition that must be satisfied before patients can progress to the next stage of diagnostic recognition. Examples include referral requirements for specialist evaluation, eligibility criteria for genomic testing, program admission rules for undiagnosed disease programs, and classification criteria for diagnostic confirmation. The analytical framework was informed by institutional theory and health systems analysis, which conceptualize healthcare processes as structured by organizational rules, professional authority, and governance systems. Coding Procedure Documents were coded using a structured Recognition Threshold Coding Framework developed for this study. Table 2 summarizes the threshold categories used in the analysis. Table 2 Recognition Threshold Coding Framework Threshold Type Definition Diagnostic Stage Example of Institutional Rule Interpretive thresholds Conditions governing the recognition of symptoms as requiring rare disease investigation Clinical suspicion Symptoms must be recognized as potential indicators of rare disease before referral Referral thresholds Institutional requirements regulating access to specialist care Referral stage Referral to a rare disease center required before diagnostic escalation Testing thresholds Eligibility requirements for access to diagnostic testing technologies Diagnostic testing Approval required for genomic sequencing or specialized laboratory testing Program eligibility thresholds Admission criteria governing entry into specialized diagnostic programs Diagnostic program stage Case must meet criteria to enter an undiagnosed disease program Classification thresholds Formal diagnostic criteria defining recognized disease categories Diagnostic confirmation Patient must meet defined diagnostic criteria for classification Registry thresholds Requirements for inclusion within epidemiological or clinical registries Registry inclusion Confirmed diagnosis required for entry into rare disease registry Infrastructure thresholds Availability of diagnostic resources enabling diagnostic investigation Testing infrastructure Genomic laboratory capacity required for sequencing Each document was examined to identify institutional rules governing diagnostic escalation, testing access, classification, or system recognition. For each threshold identified, the following information was recorded: document identifier threshold domain threshold type rule description stage of the diagnostic pathway effect on diagnostic recognition Thresholds were then categorized into standardized types, including: interpretive thresholds (clinical suspicion requirements) referral thresholds (access to specialist care) testing thresholds (eligibility for diagnostic testing) program eligibility thresholds (admission to diagnostic programs) classification thresholds (diagnostic criteria and coding systems) registry thresholds (inclusion in epidemiological registries) infrastructure thresholds (availability of diagnostic resources) Across the corpus, 81 institutional thresholds were identified and coded. Analytical Process Analysis proceeded through a multi-stage qualitative synthesis designed to identify patterns in how diagnostic recognition is structured across institutional systems. Stage 1: Threshold Distribution Analysis The first stage examined the distribution of thresholds across diagnostic domains in order to identify where recognition rules are concentrated within the diagnostic pathway. Stage 2: Recognition Chain Identification Thresholds were then analyzed to identify recurring sequences of institutional rules, referred to as recognition chains, through which patients progress toward diagnosis. Stage 3: Threshold Dependency Analysis The analysis examined whether certain thresholds functioned as prerequisites for others, producing sequential diagnostic dependencies. Stage 4: Gatekeeping Node Identification Thresholds were mapped to institutional actors in order to identify which organizations exercise control over diagnostic progression, such as specialist centers, genomic laboratories, diagnostic programs, and classification authorities. Stage 5: Threshold Accumulation Analysis The analysis examined how multiple thresholds combine across the diagnostic pathway, producing cumulative barriers to recognition. Three forms of threshold accumulation were identified: sequential accumulation (ordered thresholds) layered accumulation (multiple thresholds within the same stage) recursive accumulation (earlier delays intensifying later barriers) Stage 6: Typology of Recognition Systems Finally, the documents were grouped into institutional system types based on how thresholds were organized within the diagnostic pathway. This analysis produced a typology of recognition systems, including referral-gated systems, genomic diagnostic systems, program-gated systems, and classification-mediated recognition systems. Mechanism Development The final stage of analysis synthesized findings across all analytical stages in order to identify the mechanism through which institutional diagnostic systems produce delayed recognition. The analysis revealed that diagnostic progression is governed by a layered architecture of institutional thresholds regulating access to referral systems, diagnostic testing infrastructure, diagnostic programs, and disease classification authorities. Across these institutional layers, thresholds accumulate sequentially. Patients must satisfy multiple recognition requirements before progressing to subsequent diagnostic stages, producing a system of interdependent diagnostic gatekeeping. These dependencies create conditions in which diagnostic progression may stall or fail entirely when thresholds cannot be satisfied. This synthesis identified a mechanism in which institutional threshold architectures generate accumulated barriers across the diagnostic pathway, producing a condition of delayed or incomplete diagnostic recognition. This condition is conceptualized in this study as suspended recognition. Figure 2 illustrates the institutional threshold constellation identified in the analysis and the dependency chains linking thresholds across the patient diagnostic pathway. The shaded region highlights areas where sequential threshold accumulation generates delayed or incomplete diagnostic recognition, conceptualized in this study as suspended recognition. Each point represents an individual diagnostic threshold identified in the document corpus (n = 81). Clusters correspond to institutional domains regulating diagnostic progression, including specialist referral systems, genomic diagnostic laboratories, diagnostic programs, and classification authorities. Arrows represent recognition chains linking thresholds sequentially across the diagnostic pathway. Shaded regions indicate areas where multiple thresholds accumulate, producing a zone of delayed or incomplete diagnostic recognition conceptualized as suspended recognition. Reflexivity and Interpretive Approach Consistent with qualitative institutional analysis, the study adopts an interpretive approach to examining how institutional rules structure diagnostic processes. The analysis focuses on identifying patterns and mechanisms across institutional contexts rather than quantifying clinical outcomes. The objective is to develop a conceptual model explaining how institutional diagnostic systems shape access to rare disease recognition. Results Institutional Threshold Landscape Analysis of the institutional corpus identified 81 recognition thresholds embedded across rare disease diagnostic systems. These thresholds represent institutional rules, infrastructural requirements, and governance mechanisms that regulate access to different stages of diagnostic recognition. Thresholds were distributed across several stages of the diagnostic pathway, including clinical interpretation, referral systems, diagnostic testing, program admission, classification frameworks, and registry inclusion, as illustrated in Fig. 3 . Among these, the largest concentration of thresholds occurred in domains associated with diagnostic testing infrastructure, evidence requirements, and classification systems. Distribution of institutional recognition thresholds identified in the corpus (n = 81) across stages of the rare disease diagnostic pathway. Threshold density clusters at the diagnostic testing stage, indicating that institutional constraints most strongly regulate access to diagnostic confirmation rather than initial clinical suspicion. Infrastructure and capacity-related thresholds were the most frequent, followed by evidence thresholds governing diagnostic investigations and classification thresholds defining diagnostic categories. Referral thresholds and program eligibility thresholds also appeared frequently across the corpus. By contrast, relatively few thresholds were associated with the initial interpretive stage of clinical suspicion. These findings indicate that the majority of institutional constraints affecting diagnosis occur after rare disease suspicion emerges, within the institutional systems responsible for diagnostic confirmation and recognition. Recognition Bottlenecks Across the Diagnostic Pathway Mapping thresholds across the diagnostic pathway revealed a pronounced clustering of institutional constraints at the diagnostic testing stage. Of the thresholds identified, approximately one-third occurred at the stage where patients seek access to diagnostic investigations such as genomic testing, advanced imaging, or specialized laboratory testing. These testing-stage thresholds included requirements for specialist referral, eligibility criteria for genomic sequencing, laboratory capacity limitations, workforce constraints affecting interpretation of results, and administrative rules governing test ordering or reporting. Together, these requirements create a dense bottleneck through which patients must pass before diagnostic confirmation can occur. Additional concentrations of thresholds were observed at the stages of advanced diagnostic program admission and formal diagnostic classification. Diagnostic programs frequently required extensive prior clinical evaluation before accepting cases, while classification systems required patients to meet defined diagnostic criteria before formal disease recognition could occur. Taken together, these findings suggest that diagnostic recognition in rare disease systems is heavily regulated at the stages where diagnostic evidence is produced and validated, rather than at the stage where symptoms are first interpreted. Recognition Chains in Diagnostic Systems Analysis of threshold sequences across documents revealed several recurring recognition chains through which rare disease diagnosis is institutionally produced. These chains represent ordered sequences of thresholds that patients must traverse to achieve formal diagnostic recognition. The most common chain resembled a clinical escalation pathway, beginning with clinical suspicion and progressing through specialist referral, diagnostic testing, evidence accumulation, formal classification, and registry inclusion. A second chain centered on genomic diagnostic pathways, in which referral to genetic services enabled access to genomic testing, followed by variant interpretation and diagnostic confirmation. A third chain involved diagnostic escalation through undiagnosed disease programs, where patients entered multidisciplinary evaluation pathways only after extensive prior testing failed to yield a diagnosis. Finally, a fourth chain was associated with classification and registry systems, where diagnostic recognition occurred through coding assignment and inclusion within epidemiological databases. These findings suggest that rare disease diagnosis does not occur through a single institutional pathway. Rather, diagnostic recognition emerges through the interaction of multiple institutional systems that structure diagnostic escalation in different ways, highlighting the importance of coordinated multi-institutional and cross-sector responses in supporting rare disease diagnosis. Threshold Dependencies and Sequential Diagnostic Architecture Many recognition thresholds were found to be structurally dependent on earlier thresholds within the diagnostic pathway. For example, access to genomic testing frequently depended on prior referral to specialist services, while admission to diagnostic programs required evidence of extensive prior clinical workup. Similarly, registry inclusion depended on formal diagnostic classification, which itself depended on the production of diagnostic evidence and the assignment of standardized rare disease codes such as ORPHAcodes that enable disease recognition within clinical and epidemiological data systems. These dependencies create a sequential diagnostic architecture in which patients must traverse earlier institutional thresholds before subsequent stages of recognition can occur. Failure to cross an earlier threshold prevents access to downstream stages of diagnostic escalation. This sequential structure helps explain why patients may remain within healthcare systems for extended periods without receiving a diagnosis, even when clinical suspicion exists. Institutional Gatekeeping Mapping thresholds to institutional actors revealed that diagnostic recognition is governed by a network of institutional gatekeepers rather than a single clinical authority. Diagnostic laboratories and genomic services controlled the largest number of thresholds, primarily through rules governing access to diagnostic testing, interpretation of results, and reporting standards. Specialist centers and rare disease centers also functioned as major gatekeepers through their control over referral pathways and diagnostic escalation. Diagnostic programs such as undiagnosed disease networks introduced additional gatekeeping through case selection and program admission rules. Classification authorities and expert committees controlled thresholds related to diagnostic criteria and disease coding, while registries governed the inclusion of recognized cases within epidemiological systems. By contrast, primary clinicians controlled relatively few thresholds beyond the initial interpretive stage of clinical suspicion. These findings suggest that diagnostic authority in rare disease systems is distributed across multiple specialized institutional actors, with particular concentration in diagnostic infrastructures and classification systems. Threshold Accumulation Beyond individual thresholds, the analysis identified patterns of threshold accumulation across the diagnostic pathway. Thresholds accumulated in three principal forms. First, thresholds accumulated sequentially, requiring patients to traverse ordered stages of diagnostic escalation. Second, thresholds accumulated within stages, particularly at the diagnostic testing stage where multiple eligibility requirements operated simultaneously. Third, thresholds accumulated recursively, as earlier delays produced additional evidentiary challenges and fragmented clinical histories that complicated later stages of diagnosis. Together, these forms of accumulation create cumulative barriers that slow or interrupt progression toward diagnostic recognition. Typology of Recognition Systems Comparison across documents revealed that rare disease diagnostic systems organize recognition thresholds in different ways. Four dominant recognition system types were identified, as illustrated in Fig. 4 . Comparative representation of four dominant institutional recognition systems identified in the corpus. Referral-gated systems regulate diagnostic escalation through specialist referral pathways. Genomic diagnostic systems center recognition around access to sequencing technologies and variant interpretation. Program-gated systems organize diagnostic evaluation through specialized multidisciplinary programs for unresolved cases. Classification-mediated systems rely on formal diagnostic criteria and coding frameworks to define recognized diseases and cases. In practice, these systems frequently operate simultaneously, and patients may move between them during their diagnostic journeys. Referral-gated systems rely primarily on specialist referral pathways to regulate diagnostic escalation. Genomic diagnostic systems center recognition around access to sequencing technologies and variant interpretation. Program-gated systems organize recognition through specialized diagnostic programs that evaluate unresolved cases. Classification-mediated systems rely on formal diagnostic criteria and coding systems to define recognized diseases and cases. These systems often operate simultaneously, meaning that patients may move between several institutional recognition systems during their diagnostic journeys. Mechanism of Suspended Recognition Synthesizing the findings across analytical stages reveals a mechanism through which institutional diagnostic systems produce prolonged diagnostic uncertainty. Rare disease diagnostic systems are structured through layered institutional threshold architectures. These thresholds operate sequentially, are governed by multiple institutional gatekeepers, and accumulate across the diagnostic pathway. When patients are unable to traverse the full sequence of thresholds required for diagnostic confirmation, they remain within healthcare systems without achieving formal recognition. This condition is conceptualized here as suspended recognition, a state in which patients experience persistent symptoms and ongoing clinical engagement but remain without formal diagnostic classification. Suspended recognition emerges not from a single barrier but from the cumulative interaction of institutional thresholds embedded within diagnostic systems. Discussion Summary of Findings This study examined how institutional rules and infrastructures regulate access to rare disease diagnosis by analyzing policy documents, diagnostic programs, clinical pathways, and registry systems. Across the corpus, 81 institutional recognition thresholds were identified that structure progression through diagnostic systems. These thresholds occur at multiple stages of the diagnostic pathway, including referral access, diagnostic testing, program admission, classification frameworks, and registry inclusion. The analysis revealed several key patterns. First, institutional thresholds cluster most heavily at the diagnostic testing stage, creating a major bottleneck where patients must navigate eligibility criteria, laboratory capacity limitations, and interpretation infrastructures before diagnostic confirmation can occur. Second, thresholds frequently form recognition chains, in which progression toward diagnosis requires sequential traversal of institutional decision points. Third, many thresholds are structurally dependent, meaning that later stages of diagnostic recognition cannot occur unless earlier stages are successfully completed. The study also found that diagnostic recognition is governed by a distributed network of institutional gatekeepers, including specialist centers, diagnostic laboratories, diagnostic programs, classification authorities, and registries. These actors collectively regulate progression through the diagnostic pathway. Finally, the analysis demonstrates that thresholds accumulate across the diagnostic system through sequential, layered, and recursive processes. This accumulation produces a condition in which patients remain engaged with healthcare systems yet unable to achieve formal diagnostic recognition. This condition is conceptualized here as suspended recognition. Reframing Diagnostic Delay Existing literature has extensively documented diagnostic delays in rare disease care and their consequences for patients and families. Studies have highlighted long diagnostic timelines (Godet et al., 2001; Ferrara et al., 2017), repeated specialist consultations (Benito-Lozano et al., 2022 ), and the emotional and practical burdens associated with prolonged diagnostic uncertainty (Blöß et al., 2017 ; Baumbusch et al., 2018; Llubes-Arrià et al., 2021). These studies have made important contributions to understanding the patient experience of the diagnostic odyssey. However, explanations for diagnostic delay have often focused on factors such as clinician awareness, access to testing technologies, or fragmented care coordination. While these explanations capture important aspects of the problem, they do not fully explain why diagnostic delays persist even in healthcare systems with advanced diagnostic capabilities. The findings of this study suggest that diagnostic delay is not simply the result of isolated barriers or knowledge gaps. Instead, diagnostic recognition is governed by institutional architectures composed of multiple recognition thresholds embedded within healthcare systems. These thresholds regulate progression through referral systems, diagnostic testing infrastructures, program eligibility structures, and classification frameworks. Rather than a single barrier, diagnostic delay emerges from the interaction and accumulation of these institutional thresholds. This perspective shifts the focus from individual-level explanations toward the organizational design of diagnostic systems. Institutional Architecture of Diagnostic Recognition The analysis demonstrates that rare disease diagnosis occurs within a layered institutional architecture involving multiple specialized actors. Primary clinicians typically initiate the diagnostic process through clinical suspicion, but subsequent stages of recognition are governed by specialized institutions. Specialist centers regulate referral pathways, diagnostic laboratories control access to testing and interpretation, diagnostic programs evaluate unresolved cases, classification authorities define diagnostic categories, and registries determine epidemiological recognition. This distributed governance structure means that diagnostic recognition depends on coordination across multiple institutional domains. Each domain introduces additional thresholds that patients must traverse in order to progress toward diagnosis. The result is a sequential architecture in which diagnostic progression is contingent upon satisfying multiple institutional criteria. Importantly, the study finds that the greatest concentration of thresholds occurs not at the stage of clinical suspicion but at the stage of diagnostic testing and evidentiary confirmation. This suggests that institutional infrastructures governing diagnostic technologies play a particularly significant role in shaping access to recognition. Emerging evidence from health data analytics also indicates that diagnostic signals may be detectable earlier within primary care data systems than within formal diagnostic pathways. For example, algorithmic analysis of general practice electronic health records has been shown to identify certain rare diseases months before specialist referral occurs. Such findings further highlight how institutional diagnostic infrastructures, rather than the initial emergence of clinical suspicion, often determine when formal recognition of disease occurs. Suspended Recognition as a Systemic Outcome The concept of suspended recognition provides a framework for understanding how institutional diagnostic systems can produce prolonged states of unresolved diagnosis. Suspended recognition occurs when patients successfully enter diagnostic systems but cannot traverse the full sequence of thresholds required for formal diagnostic classification. In this state, patients may experience persistent symptoms and repeated clinical encounters while remaining without formal diagnostic recognition. They are neither fully excluded from healthcare systems nor fully recognized within them. Instead, they occupy an intermediate institutional position characterized by ongoing investigation without definitive classification. This concept helps explain why diagnostic journeys can persist for years even when patients are actively engaged with healthcare providers. Diagnostic uncertainty is therefore not simply the absence of recognition but a systemically produced condition resulting from accumulated institutional thresholds. At the same time, improving health-system responses to this population requires clearer operational definitions of the undiagnosed state. Recent calls in the rare disease literature emphasize the need for standardized terminology and coding frameworks to enable visibility of undiagnosed cohorts within health systems and support coordinated institutional responses (Baynam et al., 2024 ). Concepts such as suspended recognition may contribute to these efforts by helping explain how undiagnosed states are produced and sustained within institutional diagnostic systems. Implications for Diagnostic System Design The findings also have implications for the design of rare disease diagnostic systems. The literature already demonstrates that structured diagnostic programs and coordinated care models can significantly improve diagnostic outcomes. For example, Choukair et al. ( 2021 ) showed that multidisciplinary diagnostic pathways can substantially reduce time to diagnosis, while Hay et al. ( 2020 ) demonstrated how sociotechnical redesign of diagnostic workflows can improve cross-specialty coordination. The present analysis suggests that improving diagnostic systems requires attention not only to clinical expertise or diagnostic technologies but also to the institutional architecture governing diagnostic progression. Reducing diagnostic delay may involve redesigning referral pathways, simplifying eligibility criteria for diagnostic testing, strengthening coordination across diagnostic infrastructures, and improving integration between classification systems and clinical practice. Efforts to expand genomic testing or specialized diagnostic programs are likely to be most effective when accompanied by reforms that address the broader institutional thresholds regulating diagnostic progression. Emerging policy initiatives illustrate how such system-level responses can be implemented in practice, including national rare disease quality standards developed by NICE (NICE, 2024), national health system recommendations such as the National Recommendations for Rare Disease Health Care (RArEST Project, 2024), and research priority-setting initiatives designed to accelerate diagnostic discovery for undiagnosed populations (Zurynski et al., 2022). Policy and System Implications The findings of this study also have implications for rare disease policy and health system design. If diagnostic delay is partly produced by institutional threshold architectures, then addressing the diagnostic odyssey requires system-level responses that extend beyond individual clinical interventions. Policy initiatives aimed at improving rare disease diagnosis increasingly emphasize coordinated diagnostic infrastructures, standardized disease classification systems, and integrated data frameworks capable of identifying both diagnosed and undiagnosed populations. Efforts such as the implementation of ORPHAcodes, national rare disease quality standards, and coordinated genomic diagnostic programs illustrate attempts to reduce fragmentation across diagnostic systems. Strengthening these infrastructures may improve the visibility of undiagnosed populations, facilitate earlier referral and diagnostic investigation, and support more coordinated multi-institutional responses to rare disease diagnosis. Understanding diagnostic delay as an institutional phenomenon therefore highlights the importance of policy frameworks that align clinical practice, diagnostic technologies, data infrastructures, and classification systems within a coherent diagnostic ecosystem. Limitations This study has several limitations. First, the analysis is based on institutional and policy-oriented documents rather than direct observation of clinical practice. As a result, the study focuses on formal structures governing diagnostic systems rather than variations in how these structures are implemented in practice. Second, the corpus includes documents from multiple healthcare systems with different institutional arrangements. While this diversity allows identification of cross-system patterns, it also means that the findings should be interpreted as conceptual patterns rather than precise descriptions of any single healthcare system. Third, the analysis focuses on institutional structures regulating diagnostic recognition and does not directly examine patient-level diagnostic trajectories. Future research could combine institutional analysis with patient pathway studies to further examine how recognition thresholds operate in practice. Future Research Future research could extend this analysis in several directions. First, empirical studies examining patient diagnostic pathways could provide additional insight into how institutional thresholds operate in real-world clinical settings. Second, comparative analyses across healthcare systems could examine how different institutional designs influence diagnostic timelines and outcomes. Third, research examining interactions between diagnostic infrastructures and emerging genomic technologies could help identify strategies for reducing recognition bottlenecks within diagnostic systems. More broadly, examining diagnostic systems through an institutional lens may provide new insights into how healthcare systems structure access to recognition across other complex or poorly understood conditions. Conclusion This study demonstrates that rare disease diagnosis is not solely a clinical process but also an institutional one. Progression from symptom presentation to formal diagnostic recognition is structured by a sequence of institutional thresholds embedded within referral systems, diagnostic infrastructures, program eligibility rules, classification frameworks, and registry systems. These thresholds regulate how patients move through diagnostic systems and determine whether diagnostic recognition can occur. When thresholds accumulate across the diagnostic pathway, patients may remain within healthcare systems without achieving formal diagnostic classification. Conceptualizing this condition as suspended recognition highlights how diagnostic uncertainty may be produced by the institutional design of diagnostic systems rather than solely by gaps in medical knowledge or clinical awareness. By examining the institutional architecture governing diagnostic recognition, this study shifts attention from individual barriers to the systemic structures that organize diagnostic progression. Understanding how these structures operate may be essential for designing diagnostic systems capable of reducing prolonged diagnostic uncertainty in rare disease care. Declarations Ethics approval and consent to participate This study analyzed publicly available institutional and policy documents describing rare disease diagnostic systems. No human participants or identifiable personal data were involved. Ethical approval and participant consent were therefore not required. Consent for publication Not applicable. Competing interests The author declares that there are no competing interests. Funding No specific funding was received for this study. Author Contribution GF conceived the study, conducted the analysis, and wrote the manuscript. Acknowledgement The author thanks Dr Gareth Baynam for valuable discussions related to rare disease diagnostic pathways and recognition systems. The author also acknowledges the broader dialogue occurring within international rare disease research communities, including working groups associated with the International Rare Diseases Research Consortium (IRDiRC), which continue to advance discussions on diagnostic equity and access. Data Availability All documents analyzed in this study are publicly available sources. The corpus of institutional documents used for analysis is listed in Table 1 of the manuscript. Additional details regarding the analytical framework and coding approach are described in the Methods section. All documents analyzed are cited in the manuscript and listed in Table 1. References Akesson LS, Parekh S, Alderdice A et al. 2025. Developing Best Practice Rare Disease Diagnostic Care Models in a Real-World Rural/Regional Setting. Preprint, Genetic and Genomic Medicine, July 24. https://doi.org/10.1101/2025.07.24.25332141 Baumbusch J, Mayer S, and Isabel Sloan-Yip. Alone in a Crowd? Parents of Children with Rare Diseases’ Experiences of Navigating the Healthcare System. J Genet Couns. 2019;28(1):80–90. https://doi.org/10.1007/s10897-018-0294-9 . Baxter MF, Hansen M, Gration D, Groza T, and Gareth Baynam. Surfacing Undiagnosed Disease: Consideration, Counting and Coding. Front Pead. 2023;11:1283880. https://doi.org/10.3389/fped.2023.1283880 . Baynam G, Bowman F, Lister K et al. 2017. Improved Diagnosis and Care for Rare Diseases through Implementation of Precision Public Health Framework. In Rare Diseases Epidemiology: Update and Overview , edited by Manuel Posada De La Paz, Domenica Taruscio, and Stephen C. Groft, vol. 1031. Advances in Experimental Medicine and Biology. Springer International Publishing. https://doi.org/10.1007/978-3-319-67144-4_4 Baynam G, Boycott KM, Johnson K, Mckay L. Language in Rare Disease: A Call for Systemic and Empathetic Action. Lancet. 2026;S0140673626003594. https://doi.org/10.1016/S0140-6736(26)00359-4 . February. Baynam G, Hartman AL, Letinturier MCV, et al. Global Health for Rare Diseases through Primary Care. Lancet Global Health. 2024;12(7):e1192–99. https://doi.org/10.1016/S2214-109X(24)00134-7 . Benito-Lozano J, Arias-Merino G, Gómez-Martínez M, et al. Diagnostic Process in Rare Diseases: Determinants Associated with Diagnostic Delay. Int J Environ Res Public Health. 2022;19(11):6456. https://doi.org/10.3390/ijerph19116456 . Blöß S, Klemann C, Rother A-K, et al. Diagnostic Needs for Rare Diseases and Shared Prediagnostic Phenomena: Results of a German-Wide Expert Delphi Survey. PLoS ONE. 2017;12(2):e0172532. https://doi.org/10.1371/journal.pone.0172532 . Choukair D, Hauck F, Bettendorf M, et al. An Integrated Clinical Pathway for Diagnosis, Treatment and Care of Rare Diseases: Model, Operating Procedures, and Results of the Project TRANSLATE-NAMSE Funded by the German Federal Joint Committee. Orphanet J Rare Dis. 2021;16(1):474. https://doi.org/10.1186/s13023-021-02092-w . Dharssi S, Wong-Rieger D, Harold M, and Sharon Terry. Review of 11 National Policies for Rare Diseases in the Context of Key Patient Needs. Orphanet J Rare Dis. 2017;12(1):63. https://doi.org/10.1186/s13023-017-0618-0 . Ferrara L, Morando V, and Valeria Tozzi. The Transition of Patients with Rare Diseases between Providers: The Patient Journey from the Patient Perspective. Int J Integr Care. 2017;17(5):157. https://doi.org/10.5334/ijic.3465 . Hay GJ, Florian E, Klonek CS, Thomas A, Bauskis G, Baynam, Parker SK. SMART Work Design: Accelerating the Diagnosis of Rare Diseases in the Western Australian Undiagnosed Diseases Program. Front Pead. 2020;8(September):582. https://doi.org/10.3389/fped.2020.00582 . Hébert Réjean. Applying Integrated Care Systems to Rare Diseases. Expert Opin Orphan Drugs. 2016;4(11):1095–97. https://doi.org/10.1080/21678707.2016.1232649 . Llubes-Arrià L, Sanromà‐Ortíz M, Torné‐Ruiz A, Carillo‐Álvarez E, García‐Expósito J, and Judith Roca. Emotional Experience of the Diagnostic Process of a Rare Disease and the Perception of Support Systems: A Scoping Review. J Clin Nurs. 2022;31(1–2):20–31. https://doi.org/10.1111/jocn.15922 . Mertens A, Kho M, Parker SK, Baynam G, Baker S, and Kaila Stevens. The Cross-Sector Model of Care: A Work Design Perspective. Rare. 2024;2:100049. https://doi.org/10.1016/j.rare.2024.100049 . Molster C, Urwin D, Di Pietro L, et al. Survey of Healthcare Experiences of Australian Adults Living with Rare Diseases. Orphanet J Rare Dis. 2016;11(1):30. https://doi.org/10.1186/s13023-016-0409-z . National Institute for Health and Care Excellence (NICE). Rare diseases: supporting people to live well. Quality standard QS204. London: NICE; 2024. https://www.nice.org.uk/guidance/qs204 . Pavisich K, Hannah Jones, and Gareth Baynam. The Diagnostic Odyssey for Children Living with a Rare Disease – Caregiver and Patient Perspectives: A Narrative Review with Recommendations. Rare. 2024;2:100022. https://doi.org/10.1016/j.rare.2024.100022 . Phillips C, Parkinson A, Namsrai T, et al. Time to Diagnosis for a Rare Disease: Managing Medical Uncertainty. A Qualitative Study. Orphanet J Rare Dis. 2024;19(1):297. https://doi.org/10.1186/s13023-024-03319-2 . Rare Disease Awareness, Education, Support and Training (RArEST) Project. 2024. National Recommendations for Rare Disease Health Care . Policy Recommendations. Rare Voices Australia. https://www.rarevoices.org.au/national-recommendations Reinhard C, Bachoud-Lévi A-C, Bäumer T, et al. The European Reference Network for Rare Neurological Diseases. Front Neurol. 2021;11:616569. https://doi.org/10.3389/fneur.2020.616569 . Schiff GD. Minimizing Diagnostic Error: The Importance of Follow-up and Feedback. Am J Med. 2008;121(5):S38–42. https://doi.org/10.1016/j.amjmed.2008.02.004 . Sivaramakrishnan H, Reilly M, Jaffe A, et al. Australia’s Top 10 Rare Disease Research Priorities: A Priority Setting Partnership. J Rare Dis. 2026;5(1):5. https://doi.org/10.1007/s44162-026-00146-w . Taruscio D, Gentile AE, Evangelista T, Frazzica RG, Bushby K, and Antoni Moliner Montserrat. Centres of Expertise and European Reference Networks: Key Issues in the Field of Rare Diseases. The EUCERD Recommendations. Blood Transfus = Trasfusione Del Sangue. 2014;12(3):s621–625. https://doi.org/10.2450/2014.0026-14s . Tumiene B, and Holm Graessner. Rare Disease Care Pathways in the EU: From Odysseys and Labyrinths towards Highways. J Community Genet. 2021;12(2):231–39. https://doi.org/10.1007/s12687-021-00520-9 . Tumiene B, Kristoffersson U, Hedley V, Kääriäinen H. Rare Diseases: Past Achievements and Future Prospects. J Community Genet. 2021;12(2):205–6. https://doi.org/10.1007/s12687-021-00529-0 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 17 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 03 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviewers invited by journal 01 May, 2026 Editor invited by journal 30 Apr, 2026 Editor assigned by journal 18 Apr, 2026 Submission checks completed at journal 18 Apr, 2026 First submitted to journal 17 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9451240","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":636687062,"identity":"b9382665-60b5-4686-80b0-7141a5f97f31","order_by":0,"name":"Gregory Fagan¹","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYDAD9gYQWQEiGB8cIEoLD1jZGRDBbECCFsY2iBa8KuWjDx9+8XHPYQYe9uMXPxfOq5M3Z29mPMBQYxONS4vhubQ0yxnPgFp4coqlZ247bLiz5zDDAYZjabkNuLT08JgZ8xw4zGDPkJMgzbvtAOOGG/kHDjA2HMajhf8bWAsP/5vk37xz6uw33EhmwKtFnoeH+TFYi0T6MWneBuZEgloMeNjMGGccSOfhkXjDZs1z7HDyhjNAvyTg8Yt8D/PjDx8OWMvx8Kc/vs1TU2e74Xgz84cPNTa4bTnAwCbBwNDMA4wZpOhIwKEcbEsDA/MHBoY6IJP9AR51o2AUjIJRMJIBAMT+XkuvIb7FAAAAAElFTkSuQmCC","orcid":"","institution":"¹Independent Global Health Researcher; Foundation S","correspondingAuthor":true,"prefix":"","firstName":"Gregory","middleName":"","lastName":"Fagan¹","suffix":""}],"badges":[],"createdAt":"2026-04-17 16:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9451240/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9451240/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109076567,"identity":"33ff6fe0-ec67-4855-aafc-ad375e9c78fc","added_by":"auto","created_at":"2026-05-12 11:03:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63835,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInstitutional document identification and selection process.\u003c/strong\u003e\u003cbr\u003e\nDocuments describing the organization of rare disease diagnostic systems were identified through targeted searches of biomedical and social science databases. Titles and abstracts were screened for institutional relevance to diagnostic governance, referral pathways, diagnostic programs, genomic testing systems, and registry infrastructures. Following eligibility assessment, a corpus of 27 institutional documents was selected for qualitative institutional analysis.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9451240/v1/b9904e03a7cf8c44591795d0.png"},{"id":109076279,"identity":"621b10bd-8594-4ac4-9756-11055a7bf72e","added_by":"auto","created_at":"2026-05-12 11:01:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":187642,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInstitutional threshold constellation and dependency chains in rare disease diagnostic systems.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEach point represents an individual diagnostic threshold identified in the document corpus (n = 81). Clusters correspond to institutional domains regulating diagnostic progression, including specialist referral systems, genomic diagnostic laboratories, diagnostic programs, and classification authorities. Arrows represent recognition chains linking thresholds sequentially across the diagnostic pathway. Shaded regions indicate areas where multiple thresholds accumulate, producing a zone of delayed or incomplete diagnostic recognition conceptualized as suspended recognition.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9451240/v1/21cbafb8a502919a362b40e3.png"},{"id":109076285,"identity":"474e6d17-ebde-4c13-8fd7-700ebf030d44","added_by":"auto","created_at":"2026-05-12 11:01:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":45252,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInstitutional Threshold Landscape Across the Rare Disease Diagnostic Pathway.\u003c/strong\u003e\u003cbr\u003e\nDistribution of institutional recognition thresholds identified in the corpus \u003cem\u003e(n = 81)\u003c/em\u003e across stages of the rare disease diagnostic pathway. Threshold density clusters at the diagnostic testing stage, indicating that institutional constraints most strongly regulate access to diagnostic confirmation rather than initial clinical suspicion.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9451240/v1/023f2c1f6901a8fdf839a04c.png"},{"id":109076284,"identity":"6dd55f97-35a5-4a7c-9466-eff76c9017d5","added_by":"auto","created_at":"2026-05-12 11:01:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":87744,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTypology of Institutional Recognition Systems in Rare Disease Diagnosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComparative representation of four dominant institutional recognition systems identified in the corpus. Referral-gated systems regulate diagnostic escalation through specialist referral pathways. Genomic diagnostic systems center recognition around access to sequencing technologies and variant interpretation. Program-gated systems organize diagnostic evaluation through specialized multidisciplinary programs for unresolved cases. Classification-mediated systems rely on formal diagnostic criteria and coding frameworks to define recognized diseases and cases. In practice, these systems frequently operate simultaneously, and patients may move between them during their diagnostic journeys.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9451240/v1/3f0b34c8064c204ea8ff13f5.png"},{"id":109078523,"identity":"138cb373-fe47-4cf4-8913-6dadad3ebe3e","added_by":"auto","created_at":"2026-05-12 11:14:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":662908,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9451240/v1/164b637b-300d-4e25-b305-4e99ae8bca61.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Suspended Recognition: Institutional Thresholds in Rare Disease Diagnostic Systems","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRare diseases collectively affect an estimated 300\u0026nbsp;million people worldwide, representing a substantial global health burden despite the low prevalence of individual conditions. Patients with rare diseases frequently experience prolonged and uncertain diagnostic journeys. Extended periods between symptom onset and diagnostic confirmation\u0026mdash;widely described as the \u003cem\u003ediagnostic odyssey\u003c/em\u003e\u0026mdash;are a defining feature of many rare disease experiences. Numerous studies across rare disease contexts document substantial delays in diagnostic recognition. Godet et al. (2001) reported an average delay of two years and eight months between symptom onset and diagnosis, with extreme variation ranging from immediate diagnosis to delays of up to forty years. More recent studies suggest that such delays remain widespread. Molster et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found that 30% of patients waited five years or longer for diagnosis, while Ferrara et al. (2017) documented a mean delay of 7.3 years from symptom onset to diagnostic confirmation. Similar variability has been reported across disease groups, with diagnostic timelines ranging from months to more than two decades depending on clinical context (Phillips et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe diagnostic process often involves repeated interactions with healthcare providers across multiple specialties. In many health systems, primary care providers serve as the first point of contact for patients with undiagnosed conditions and play a critical role in identifying potential rare diseases and initiating referral pathways to specialist services (Baynam et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, navigating these referral pathways can be complex and time-consuming. Benito-Lozano et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that patients who consulted more than ten specialists had significantly increased odds of diagnostic delay (OR 2.6), while waiting more than six months for specialist referral accounted for 30.2% of total diagnostic delays. These findings illustrate the fragmented structure of rare disease diagnostic pathways, in which patients often move through multiple layers of healthcare services before reaching clinicians with appropriate expertise.\u003c/p\u003e \u003cp\u003eBeyond delays in diagnostic recognition, the literature has also documented the social and psychological consequences of prolonged diagnostic uncertainty. Patients and families frequently experience significant emotional stress, uncertainty, and social disruption during extended diagnostic journeys. Bl\u0026ouml;\u0026szlig; et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) describe how patients may be stigmatized through psychosomatic attribution when symptoms remain medically unexplained. Similarly, Llubes-Arri\u0026agrave; et al. (2021) identify both internal emotional burdens and external structural constraints shaping patient experiences during the diagnostic process. Parents and caregivers often assume roles as care coordinators and advocates while navigating fragmented healthcare systems lacking specialized rare disease infrastructure (Baumbusch et al., 2018). Social and cultural factors, including stigma associated with genetic testing, have also been identified as barriers to diagnostic engagement and care seeking (Baynam et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Together, these studies highlight the substantial human impact of delayed or unresolved diagnosis.\u003c/p\u003e \u003cp\u003eWhile these studies highlight important psychosocial and experiential barriers, relatively little attention has been devoted to examining how diagnostic recognition systems themselves are institutionally structured. Existing explanations for diagnostic delay typically emphasize a range of systemic and clinical factors, including limited awareness of rare diseases among healthcare professionals, fragmented coordination across specialties, difficulties accessing specialized diagnostic testing, and broader gaps in rare disease infrastructure. For example, Akesson et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) describe barriers such as limited provider awareness and restricted access to diagnostic testing, while Benito-Lozano et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) highlight referral barriers and geographic access constraints. In addition, diagnostic delays may be reinforced by limitations in disease classification and coding systems, which often fail to systematically identify or track individuals with suspected or undiagnosed rare diseases within health system data infrastructures (Baxter et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Taken together, these studies demonstrate that delays in rare disease diagnosis arise from a complex interaction of clinical, organizational, and infrastructural factors.\u003c/p\u003e \u003cp\u003eAt the same time, some studies suggest that structured institutional models can significantly improve diagnostic outcomes. Choukair et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) demonstrated that multidisciplinary diagnostic pathways supported by structured case conferences can produce more efficient diagnostic processes, reporting a median time to diagnosis of 109 days across 587 evaluated cases with a diagnostic success rate of 62.8%. Similarly, Hay et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) describe how sociotechnical redesign of diagnostic workflows within the Western Australian Undiagnosed Diseases Program improved coordination across specialties and facilitated diagnostic progression for complex cases. Other institutional initiatives include the designation of Centers of Expertise (Taruscio et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), the development of European Reference Networks enabling cross-border consultation through the Clinical Patient Management System (Reinhard et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and integrated service coordination models using single entry points and case management systems (H\u0026eacute;bert et al., 2016).\u003c/p\u003e \u003cp\u003eThese institutional initiatives increasingly intersect with broader health-system approaches that integrate genomics, epidemiology, and policy frameworks to improve rare disease diagnosis at the population level. This perspective is often described as precision public health (Baynam et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Within this framework, diagnostic programs such as the Western Australian Undiagnosed Diseases Program illustrate how coordinated genomic diagnostics and multidisciplinary expertise can be embedded within public health systems to strengthen diagnostic capacity. More recent models extend these approaches further by emphasizing cross-sector coordination, linking healthcare with education, disability services, and social support systems to address the broader institutional needs of patients with rare and undiagnosed conditions (Mertens et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these examples of institutional innovation, implementation remains uneven across healthcare systems. More than two decades ago, Godet et al. (2001) observed that diagnosis management was often \u0026ldquo;determined by personnel initiatives or exceptional opportunities rather than by a real organizational strategy of the healthcare system.\u0026rdquo; This observation remains relevant today.\u003c/p\u003e \u003cp\u003eAlthough a substantial body of research documents diagnostic delays and the patient experiences associated with the diagnostic odyssey, far less attention has been devoted to examining how diagnostic recognition systems themselves are institutionally structured. In particular, the literature rarely addresses how institutional rules, infrastructures, and governance mechanisms regulate access to diagnostic recognition. Referral requirements, eligibility criteria for diagnostic testing, program admission rules, and disease classification frameworks all shape whether patients can progress through diagnostic systems, yet these structural features of diagnostic governance have seldom been examined systematically.\u003c/p\u003e \u003cp\u003eThis gap raises an important question: how are institutional rules and infrastructures within healthcare systems structured to regulate access to rare disease diagnosis? Understanding these institutional mechanisms is critical for explaining why diagnostic delays persist even in healthcare systems with advanced diagnostic technologies and specialized expertise. Rather than focusing solely on clinical awareness or technological capacity, it is necessary to examine how diagnostic pathways are organized and governed.\u003c/p\u003e \u003cp\u003eThis study addresses this gap through a qualitative institutional analysis of diagnostic systems, examining rare disease policy frameworks, clinical pathways, diagnostic programs, and registry infrastructures. The analysis identifies recognition thresholds embedded within these institutional arrangements and examines how they shape patient progression from symptom presentation to formal diagnosis.\u003c/p\u003e \u003cp\u003eThe study proposes that diagnostic delay emerges not simply from isolated barriers but from the accumulation of institutional recognition thresholds within the diagnostic pathway. When patients cannot successfully traverse these thresholds, they remain engaged within healthcare systems without achieving formal diagnostic classification. This condition is conceptualized here as suspended recognition\u0026mdash;a state in which patients continue to interact with healthcare providers while institutional recognition of disease remains unresolved. While often described in clinical literature as being \u003cem\u003eundiagnosed\u003c/em\u003e, the concept of suspended recognition emphasizes the institutional processes through which diagnostic recognition becomes delayed, deferred, or structurally constrained.\u003c/p\u003e \u003cp\u003eBy examining the institutional architecture of diagnostic recognition, this study offers a new perspective on rare disease diagnostic delay and develops a framework for understanding how healthcare systems structure access to diagnosis through institutional recognition thresholds.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis study uses a qualitative institutional analysis to examine how diagnostic recognition is structured within rare disease health systems. The analysis focuses on the institutional rules, infrastructures, and governance structures that regulate access to diagnosis.\u003c/p\u003e \u003cp\u003eInstitutional analysis was chosen because rare disease diagnosis occurs within complex organizational systems involving multiple actors, including clinicians, specialist centers, laboratories, diagnostic programs, classification authorities, and registries. Rather than focusing solely on clinician decision-making or patient experiences, this study examines how institutional rules and infrastructures shape diagnostic recognition pathways.\u003c/p\u003e \u003cp\u003eThe objective of the analysis was to identify the recognition thresholds embedded within diagnostic systems and to examine how these thresholds interact to shape access to diagnosis. The analysis was interpretive and conceptual in orientation, aiming to identify structural patterns in diagnostic governance rather than to quantify clinical outcomes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Sources and Document Selection\u003c/h3\u003e\n\u003cp\u003eThe study analyzed a corpus of institutional and policy-oriented documents describing the organization of rare disease diagnosis. These included:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003enational rare disease policy analyses\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eclinical diagnostic guidelines\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003egenomic testing governance frameworks\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eundiagnosed disease program descriptions\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eregistry and data infrastructure studies\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ehealth system analyses of diagnostic pathways\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eDocuments were identified through targeted searches of biomedical and social science databases, including Scopus and PubMed, using search terms related to rare diseases, diagnostic systems, referral pathways, genomic testing governance, diagnostic programs, and rare disease registries. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the literature identification and selection process used to construct the analytical corpus.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDocuments describing the organization of rare disease diagnostic systems were identified through targeted searches of biomedical and social science databases. Titles and abstracts were screened for institutional relevance to diagnostic governance, referral pathways, diagnostic programs, genomic testing systems, and registry infrastructures. Following eligibility assessment, a corpus of 27 institutional documents was selected for qualitative institutional analysis.\u003c/p\u003e \u003cp\u003eThe search strategy aimed to capture literature describing institutional structures governing diagnosis, rather than studies focused exclusively on clinical outcomes or epidemiology.\u003c/p\u003e \u003cp\u003eAfter screening titles and abstracts for relevance to diagnostic systems or governance structures, a final corpus of 27 institutional documents was selected for analysis. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the documents included in the analytical corpus.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInstitutional Documents Included in the Analytical Corpus (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor (Year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountry / Region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDocument Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSystem Domain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInstitutional Focus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExpected Threshold Domain\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDharssi et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulti-country\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePolicy review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNational rare disease policy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eComparison of 11 national strategies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePolicy eligibility\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi et al. (2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePolicy analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNational rare disease system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRare disease policy development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePolicy recognition\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMayrides et al. (2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePolicy review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRegional policy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRare disease strategy across countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePolicy eligibility\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGentilini et al. (2025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh-income countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePolicy review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolicy ecosystem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGovernance of rare disease programs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSystem eligibility\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWilliams (2011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePolicy analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLegislative framework\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDisparities in rare disease policy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoverage eligibility\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSyed et al. (2015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePolicy recommendations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRare disease centers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCentres of expertise framework\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReferral eligibility\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchmidtke et al. (2026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealth system analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRare disease services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStructure of RD care delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReferral thresholds\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvans et al. 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(2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImplementation study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGenomic medicine service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNHS genomic diagnostic system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTesting eligibility\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEllard et al. 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(2018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegistry description\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRare disease registry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNational registry governance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRegistry inclusion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChoquet et al. (2015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData framework\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRare disease centers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMinimum dataset standardization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eClassification rules\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarcella et al. (2009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegistry study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRegional RD registry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLombardy rare disease registry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRegistry eligibility\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ede Oliveira et al. (2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrazil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealth system study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiagnostic testing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAvailability of diagnostic tests\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTesting infrastructure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVerloes et al. (2012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiagnostic guideline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClinical diagnostic criteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDevelopmental disability diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEvidence thresholds\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYang-Li et al. (2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiagnostic guideline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDisease classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePrader\u0026ndash;Willi diagnostic criteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDiagnostic criteria\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCottin et al. (2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClinical guideline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRare lung disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLAM diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDiagnostic criteria\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDhombres et al. (2025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClassification study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiagnostic classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpina bifida classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eClassification thresholds\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThese documents spanned multiple geographic contexts and institutional domains, including national policy frameworks, genomic medicine services, diagnostic programs, and rare disease registries.\u003c/p\u003e\n\u003ch3\u003eAnalytical Framework\u003c/h3\u003e\n\u003cp\u003eThe analysis focused on identifying institutional recognition thresholds within rare disease diagnostic systems.\u003c/p\u003e \u003cp\u003eA recognition threshold was defined as:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ean institutional rule, requirement, or infrastructural condition that must be satisfied before patients can progress to the next stage of diagnostic recognition.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExamples include referral requirements for specialist evaluation, eligibility criteria for genomic testing, program admission rules for undiagnosed disease programs, and classification criteria for diagnostic confirmation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe analytical framework was informed by institutional theory and health systems analysis, which conceptualize healthcare processes as structured by organizational rules, professional authority, and governance systems.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eCoding Procedure\u003c/h3\u003e\n\u003cp\u003eDocuments were coded using a structured Recognition Threshold Coding Framework developed for this study. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the threshold categories used in the analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRecognition Threshold Coding Framework\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThreshold Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiagnostic Stage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExample of Institutional Rule\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterpretive thresholds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConditions governing the recognition of symptoms as requiring rare disease investigation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClinical suspicion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSymptoms must be recognized as potential indicators of rare disease before referral\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReferral thresholds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstitutional requirements regulating access to specialist care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReferral stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReferral to a rare disease center required before diagnostic escalation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTesting thresholds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEligibility requirements for access to diagnostic testing technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiagnostic testing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApproval required for genomic sequencing or specialized laboratory testing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProgram eligibility thresholds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdmission criteria governing entry into specialized diagnostic programs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiagnostic program stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCase must meet criteria to enter an undiagnosed disease program\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClassification thresholds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormal diagnostic criteria defining recognized disease categories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiagnostic confirmation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePatient must meet defined diagnostic criteria for classification\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegistry thresholds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRequirements for inclusion within epidemiological or clinical registries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegistry inclusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConfirmed diagnosis required for entry into rare disease registry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfrastructure thresholds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAvailability of diagnostic resources enabling diagnostic investigation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTesting infrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGenomic laboratory capacity required for sequencing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEach document was examined to identify institutional rules governing diagnostic escalation, testing access, classification, or system recognition.\u003c/p\u003e \u003cp\u003eFor each threshold identified, the following information was recorded:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003edocument identifier\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ethreshold domain\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ethreshold type\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003erule description\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003estage of the diagnostic pathway\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eeffect on diagnostic recognition\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThresholds were then categorized into standardized types, including:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003einterpretive thresholds (clinical suspicion requirements)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ereferral thresholds (access to specialist care)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003etesting thresholds (eligibility for diagnostic testing)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eprogram eligibility thresholds (admission to diagnostic programs)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eclassification thresholds (diagnostic criteria and coding systems)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eregistry thresholds (inclusion in epidemiological registries)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003einfrastructure thresholds (availability of diagnostic resources)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAcross the corpus, 81 institutional thresholds were identified and coded.\u003c/p\u003e\n\u003ch3\u003eAnalytical Process\u003c/h3\u003e\n\u003cp\u003eAnalysis proceeded through a multi-stage qualitative synthesis designed to identify patterns in how diagnostic recognition is structured across institutional systems.\u003c/p\u003e \u003cp\u003eStage 1: Threshold Distribution Analysis\u003c/p\u003e \u003cp\u003eThe first stage examined the distribution of thresholds across diagnostic domains in order to identify where recognition rules are concentrated within the diagnostic pathway.\u003c/p\u003e \u003cp\u003eStage 2: Recognition Chain Identification\u003c/p\u003e \u003cp\u003eThresholds were then analyzed to identify recurring sequences of institutional rules, referred to as recognition chains, through which patients progress toward diagnosis.\u003c/p\u003e \u003cp\u003eStage 3: Threshold Dependency Analysis\u003c/p\u003e \u003cp\u003eThe analysis examined whether certain thresholds functioned as prerequisites for others, producing sequential diagnostic dependencies.\u003c/p\u003e \u003cp\u003eStage 4: Gatekeeping Node Identification\u003c/p\u003e \u003cp\u003eThresholds were mapped to institutional actors in order to identify which organizations exercise control over diagnostic progression, such as specialist centers, genomic laboratories, diagnostic programs, and classification authorities.\u003c/p\u003e \u003cp\u003eStage 5: Threshold Accumulation Analysis\u003c/p\u003e \u003cp\u003eThe analysis examined how multiple thresholds combine across the diagnostic pathway, producing cumulative barriers to recognition.\u003c/p\u003e \u003cp\u003eThree forms of threshold accumulation were identified:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003esequential accumulation (ordered thresholds)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003elayered accumulation (multiple thresholds within the same stage)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003erecursive accumulation (earlier delays intensifying later barriers)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eStage 6: Typology of Recognition Systems\u003c/p\u003e \u003cp\u003eFinally, the documents were grouped into institutional system types based on how thresholds were organized within the diagnostic pathway.\u003c/p\u003e \u003cp\u003eThis analysis produced a typology of recognition systems, including referral-gated systems, genomic diagnostic systems, program-gated systems, and classification-mediated recognition systems.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMechanism Development\u003c/h2\u003e \u003cp\u003eThe final stage of analysis synthesized findings across all analytical stages in order to identify the mechanism through which institutional diagnostic systems produce delayed recognition. The analysis revealed that diagnostic progression is governed by a layered architecture of institutional thresholds regulating access to referral systems, diagnostic testing infrastructure, diagnostic programs, and disease classification authorities.\u003c/p\u003e \u003cp\u003eAcross these institutional layers, thresholds accumulate sequentially. Patients must satisfy multiple recognition requirements before progressing to subsequent diagnostic stages, producing a system of interdependent diagnostic gatekeeping. These dependencies create conditions in which diagnostic progression may stall or fail entirely when thresholds cannot be satisfied.\u003c/p\u003e \u003cp\u003eThis synthesis identified a mechanism in which institutional threshold architectures generate accumulated barriers across the diagnostic pathway, producing a condition of delayed or incomplete diagnostic recognition. This condition is conceptualized in this study as suspended recognition.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the institutional threshold constellation identified in the analysis and the dependency chains linking thresholds across the patient diagnostic pathway. The shaded region highlights areas where sequential threshold accumulation generates delayed or incomplete diagnostic recognition, conceptualized in this study as suspended recognition.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eEach point represents an individual diagnostic threshold identified in the document corpus (n\u0026thinsp;=\u0026thinsp;81). Clusters correspond to institutional domains regulating diagnostic progression, including specialist referral systems, genomic diagnostic laboratories, diagnostic programs, and classification authorities. Arrows represent recognition chains linking thresholds sequentially across the diagnostic pathway. Shaded regions indicate areas where multiple thresholds accumulate, producing a zone of delayed or incomplete diagnostic recognition conceptualized as suspended recognition.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eReflexivity and Interpretive Approach\u003c/h3\u003e\n\u003cp\u003eConsistent with qualitative institutional analysis, the study adopts an interpretive approach to examining how institutional rules structure diagnostic processes. The analysis focuses on identifying patterns and mechanisms across institutional contexts rather than quantifying clinical outcomes.\u003c/p\u003e \u003cp\u003eThe objective is to develop a conceptual model explaining how institutional diagnostic systems shape access to rare disease recognition.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eInstitutional Threshold Landscape\u003c/h2\u003e \u003cp\u003eAnalysis of the institutional corpus identified 81 recognition thresholds embedded across rare disease diagnostic systems. These thresholds represent institutional rules, infrastructural requirements, and governance mechanisms that regulate access to different stages of diagnostic recognition.\u003c/p\u003e \u003cp\u003eThresholds were distributed across several stages of the diagnostic pathway, including clinical interpretation, referral systems, diagnostic testing, program admission, classification frameworks, and registry inclusion, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Among these, the largest concentration of thresholds occurred in domains associated with diagnostic testing infrastructure, evidence requirements, and classification systems.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDistribution of institutional recognition thresholds identified in the corpus \u003cem\u003e(n\u0026thinsp;=\u0026thinsp;81)\u003c/em\u003e across stages of the rare disease diagnostic pathway. Threshold density clusters at the diagnostic testing stage, indicating that institutional constraints most strongly regulate access to diagnostic confirmation rather than initial clinical suspicion.\u003c/p\u003e \u003cp\u003eInfrastructure and capacity-related thresholds were the most frequent, followed by evidence thresholds governing diagnostic investigations and classification thresholds defining diagnostic categories. Referral thresholds and program eligibility thresholds also appeared frequently across the corpus. By contrast, relatively few thresholds were associated with the initial interpretive stage of clinical suspicion.\u003c/p\u003e \u003cp\u003eThese findings indicate that the majority of institutional constraints affecting diagnosis occur after rare disease suspicion emerges, within the institutional systems responsible for diagnostic confirmation and recognition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRecognition Bottlenecks Across the Diagnostic Pathway\u003c/h2\u003e \u003cp\u003eMapping thresholds across the diagnostic pathway revealed a pronounced clustering of institutional constraints at the diagnostic testing stage. Of the thresholds identified, approximately one-third occurred at the stage where patients seek access to diagnostic investigations such as genomic testing, advanced imaging, or specialized laboratory testing.\u003c/p\u003e \u003cp\u003eThese testing-stage thresholds included requirements for specialist referral, eligibility criteria for genomic sequencing, laboratory capacity limitations, workforce constraints affecting interpretation of results, and administrative rules governing test ordering or reporting. Together, these requirements create a dense bottleneck through which patients must pass before diagnostic confirmation can occur.\u003c/p\u003e \u003cp\u003eAdditional concentrations of thresholds were observed at the stages of advanced diagnostic program admission and formal diagnostic classification. Diagnostic programs frequently required extensive prior clinical evaluation before accepting cases, while classification systems required patients to meet defined diagnostic criteria before formal disease recognition could occur.\u003c/p\u003e \u003cp\u003eTaken together, these findings suggest that diagnostic recognition in rare disease systems is heavily regulated at the stages where diagnostic evidence is produced and validated, rather than at the stage where symptoms are first interpreted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRecognition Chains in Diagnostic Systems\u003c/h2\u003e \u003cp\u003eAnalysis of threshold sequences across documents revealed several recurring recognition chains through which rare disease diagnosis is institutionally produced. These chains represent ordered sequences of thresholds that patients must traverse to achieve formal diagnostic recognition.\u003c/p\u003e \u003cp\u003eThe most common chain resembled a clinical escalation pathway, beginning with clinical suspicion and progressing through specialist referral, diagnostic testing, evidence accumulation, formal classification, and registry inclusion.\u003c/p\u003e \u003cp\u003eA second chain centered on genomic diagnostic pathways, in which referral to genetic services enabled access to genomic testing, followed by variant interpretation and diagnostic confirmation.\u003c/p\u003e \u003cp\u003eA third chain involved diagnostic escalation through undiagnosed disease programs, where patients entered multidisciplinary evaluation pathways only after extensive prior testing failed to yield a diagnosis.\u003c/p\u003e \u003cp\u003eFinally, a fourth chain was associated with classification and registry systems, where diagnostic recognition occurred through coding assignment and inclusion within epidemiological databases.\u003c/p\u003e \u003cp\u003eThese findings suggest that rare disease diagnosis does not occur through a single institutional pathway. Rather, diagnostic recognition emerges through the interaction of multiple institutional systems that structure diagnostic escalation in different ways, highlighting the importance of coordinated multi-institutional and cross-sector responses in supporting rare disease diagnosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eThreshold Dependencies and Sequential Diagnostic Architecture\u003c/h2\u003e \u003cp\u003eMany recognition thresholds were found to be structurally dependent on earlier thresholds within the diagnostic pathway. For example, access to genomic testing frequently depended on prior referral to specialist services, while admission to diagnostic programs required evidence of extensive prior clinical workup. Similarly, registry inclusion depended on formal diagnostic classification, which itself depended on the production of diagnostic evidence and the assignment of standardized rare disease codes such as ORPHAcodes that enable disease recognition within clinical and epidemiological data systems.\u003c/p\u003e \u003cp\u003eThese dependencies create a sequential diagnostic architecture in which patients must traverse earlier institutional thresholds before subsequent stages of recognition can occur. Failure to cross an earlier threshold prevents access to downstream stages of diagnostic escalation.\u003c/p\u003e \u003cp\u003eThis sequential structure helps explain why patients may remain within healthcare systems for extended periods without receiving a diagnosis, even when clinical suspicion exists.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eInstitutional Gatekeeping\u003c/h2\u003e \u003cp\u003eMapping thresholds to institutional actors revealed that diagnostic recognition is governed by a network of institutional gatekeepers rather than a single clinical authority.\u003c/p\u003e \u003cp\u003eDiagnostic laboratories and genomic services controlled the largest number of thresholds, primarily through rules governing access to diagnostic testing, interpretation of results, and reporting standards. Specialist centers and rare disease centers also functioned as major gatekeepers through their control over referral pathways and diagnostic escalation.\u003c/p\u003e \u003cp\u003eDiagnostic programs such as undiagnosed disease networks introduced additional gatekeeping through case selection and program admission rules. Classification authorities and expert committees controlled thresholds related to diagnostic criteria and disease coding, while registries governed the inclusion of recognized cases within epidemiological systems.\u003c/p\u003e \u003cp\u003eBy contrast, primary clinicians controlled relatively few thresholds beyond the initial interpretive stage of clinical suspicion.\u003c/p\u003e \u003cp\u003eThese findings suggest that diagnostic authority in rare disease systems is distributed across multiple specialized institutional actors, with particular concentration in diagnostic infrastructures and classification systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eThreshold Accumulation\u003c/h2\u003e \u003cp\u003eBeyond individual thresholds, the analysis identified patterns of threshold accumulation across the diagnostic pathway. Thresholds accumulated in three principal forms.\u003c/p\u003e \u003cp\u003eFirst, thresholds accumulated sequentially, requiring patients to traverse ordered stages of diagnostic escalation. Second, thresholds accumulated within stages, particularly at the diagnostic testing stage where multiple eligibility requirements operated simultaneously. Third, thresholds accumulated recursively, as earlier delays produced additional evidentiary challenges and fragmented clinical histories that complicated later stages of diagnosis.\u003c/p\u003e \u003cp\u003eTogether, these forms of accumulation create cumulative barriers that slow or interrupt progression toward diagnostic recognition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTypology of Recognition Systems\u003c/h2\u003e \u003cp\u003eComparison across documents revealed that rare disease diagnostic systems organize recognition thresholds in different ways. Four dominant recognition system types were identified, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eComparative representation of four dominant institutional recognition systems identified in the corpus. Referral-gated systems regulate diagnostic escalation through specialist referral pathways. Genomic diagnostic systems center recognition around access to sequencing technologies and variant interpretation. Program-gated systems organize diagnostic evaluation through specialized multidisciplinary programs for unresolved cases. Classification-mediated systems rely on formal diagnostic criteria and coding frameworks to define recognized diseases and cases. In practice, these systems frequently operate simultaneously, and patients may move between them during their diagnostic journeys.\u003c/p\u003e \u003cp\u003eReferral-gated systems rely primarily on specialist referral pathways to regulate diagnostic escalation. Genomic diagnostic systems center recognition around access to sequencing technologies and variant interpretation. Program-gated systems organize recognition through specialized diagnostic programs that evaluate unresolved cases. Classification-mediated systems rely on formal diagnostic criteria and coding systems to define recognized diseases and cases.\u003c/p\u003e \u003cp\u003eThese systems often operate simultaneously, meaning that patients may move between several institutional recognition systems during their diagnostic journeys.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMechanism of Suspended Recognition\u003c/h2\u003e \u003cp\u003eSynthesizing the findings across analytical stages reveals a mechanism through which institutional diagnostic systems produce prolonged diagnostic uncertainty.\u003c/p\u003e \u003cp\u003eRare disease diagnostic systems are structured through layered institutional threshold architectures. These thresholds operate sequentially, are governed by multiple institutional gatekeepers, and accumulate across the diagnostic pathway. When patients are unable to traverse the full sequence of thresholds required for diagnostic confirmation, they remain within healthcare systems without achieving formal recognition.\u003c/p\u003e \u003cp\u003eThis condition is conceptualized here as suspended recognition, a state in which patients experience persistent symptoms and ongoing clinical engagement but remain without formal diagnostic classification.\u003c/p\u003e \u003cp\u003eSuspended recognition emerges not from a single barrier but from the cumulative interaction of institutional thresholds embedded within diagnostic systems.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSummary of Findings\u003c/h2\u003e \u003cp\u003eThis study examined how institutional rules and infrastructures regulate access to rare disease diagnosis by analyzing policy documents, diagnostic programs, clinical pathways, and registry systems. Across the corpus, 81 institutional recognition thresholds were identified that structure progression through diagnostic systems. These thresholds occur at multiple stages of the diagnostic pathway, including referral access, diagnostic testing, program admission, classification frameworks, and registry inclusion.\u003c/p\u003e \u003cp\u003eThe analysis revealed several key patterns. First, institutional thresholds cluster most heavily at the diagnostic testing stage, creating a major bottleneck where patients must navigate eligibility criteria, laboratory capacity limitations, and interpretation infrastructures before diagnostic confirmation can occur. Second, thresholds frequently form recognition chains, in which progression toward diagnosis requires sequential traversal of institutional decision points. Third, many thresholds are structurally dependent, meaning that later stages of diagnostic recognition cannot occur unless earlier stages are successfully completed.\u003c/p\u003e \u003cp\u003eThe study also found that diagnostic recognition is governed by a distributed network of institutional gatekeepers, including specialist centers, diagnostic laboratories, diagnostic programs, classification authorities, and registries. These actors collectively regulate progression through the diagnostic pathway. Finally, the analysis demonstrates that thresholds accumulate across the diagnostic system through sequential, layered, and recursive processes. This accumulation produces a condition in which patients remain engaged with healthcare systems yet unable to achieve formal diagnostic recognition. This condition is conceptualized here as suspended recognition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eReframing Diagnostic Delay\u003c/h2\u003e \u003cp\u003eExisting literature has extensively documented diagnostic delays in rare disease care and their consequences for patients and families. Studies have highlighted long diagnostic timelines (Godet et al., 2001; Ferrara et al., 2017), repeated specialist consultations (Benito-Lozano et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and the emotional and practical burdens associated with prolonged diagnostic uncertainty (Bl\u0026ouml;\u0026szlig; et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Baumbusch et al., 2018; Llubes-Arri\u0026agrave; et al., 2021). These studies have made important contributions to understanding the patient experience of the diagnostic odyssey.\u003c/p\u003e \u003cp\u003eHowever, explanations for diagnostic delay have often focused on factors such as clinician awareness, access to testing technologies, or fragmented care coordination. While these explanations capture important aspects of the problem, they do not fully explain why diagnostic delays persist even in healthcare systems with advanced diagnostic capabilities.\u003c/p\u003e \u003cp\u003eThe findings of this study suggest that diagnostic delay is not simply the result of isolated barriers or knowledge gaps. Instead, diagnostic recognition is governed by institutional architectures composed of multiple recognition thresholds embedded within healthcare systems. These thresholds regulate progression through referral systems, diagnostic testing infrastructures, program eligibility structures, and classification frameworks. Rather than a single barrier, diagnostic delay emerges from the interaction and accumulation of these institutional thresholds.\u003c/p\u003e \u003cp\u003eThis perspective shifts the focus from individual-level explanations toward the organizational design of diagnostic systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eInstitutional Architecture of Diagnostic Recognition\u003c/h2\u003e \u003cp\u003eThe analysis demonstrates that rare disease diagnosis occurs within a layered institutional architecture involving multiple specialized actors. Primary clinicians typically initiate the diagnostic process through clinical suspicion, but subsequent stages of recognition are governed by specialized institutions. Specialist centers regulate referral pathways, diagnostic laboratories control access to testing and interpretation, diagnostic programs evaluate unresolved cases, classification authorities define diagnostic categories, and registries determine epidemiological recognition.\u003c/p\u003e \u003cp\u003eThis distributed governance structure means that diagnostic recognition depends on coordination across multiple institutional domains. Each domain introduces additional thresholds that patients must traverse in order to progress toward diagnosis. The result is a sequential architecture in which diagnostic progression is contingent upon satisfying multiple institutional criteria.\u003c/p\u003e \u003cp\u003eImportantly, the study finds that the greatest concentration of thresholds occurs not at the stage of clinical suspicion but at the stage of diagnostic testing and evidentiary confirmation. This suggests that institutional infrastructures governing diagnostic technologies play a particularly significant role in shaping access to recognition. Emerging evidence from health data analytics also indicates that diagnostic signals may be detectable earlier within primary care data systems than within formal diagnostic pathways. For example, algorithmic analysis of general practice electronic health records has been shown to identify certain rare diseases months before specialist referral occurs. Such findings further highlight how institutional diagnostic infrastructures, rather than the initial emergence of clinical suspicion, often determine when formal recognition of disease occurs.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eSuspended Recognition as a Systemic Outcome\u003c/h2\u003e \u003cp\u003eThe concept of suspended recognition provides a framework for understanding how institutional diagnostic systems can produce prolonged states of unresolved diagnosis. Suspended recognition occurs when patients successfully enter diagnostic systems but cannot traverse the full sequence of thresholds required for formal diagnostic classification.\u003c/p\u003e \u003cp\u003eIn this state, patients may experience persistent symptoms and repeated clinical encounters while remaining without formal diagnostic recognition. They are neither fully excluded from healthcare systems nor fully recognized within them. Instead, they occupy an intermediate institutional position characterized by ongoing investigation without definitive classification.\u003c/p\u003e \u003cp\u003eThis concept helps explain why diagnostic journeys can persist for years even when patients are actively engaged with healthcare providers. Diagnostic uncertainty is therefore not simply the absence of recognition but a systemically produced condition resulting from accumulated institutional thresholds. At the same time, improving health-system responses to this population requires clearer operational definitions of the undiagnosed state. Recent calls in the rare disease literature emphasize the need for standardized terminology and coding frameworks to enable visibility of undiagnosed cohorts within health systems and support coordinated institutional responses (Baynam et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Concepts such as suspended recognition may contribute to these efforts by helping explain how undiagnosed states are produced and sustained within institutional diagnostic systems.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eImplications for Diagnostic System Design\u003c/h2\u003e \u003cp\u003eThe findings also have implications for the design of rare disease diagnostic systems. The literature already demonstrates that structured diagnostic programs and coordinated care models can significantly improve diagnostic outcomes. For example, Choukair et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) showed that multidisciplinary diagnostic pathways can substantially reduce time to diagnosis, while Hay et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) demonstrated how sociotechnical redesign of diagnostic workflows can improve cross-specialty coordination.\u003c/p\u003e \u003cp\u003eThe present analysis suggests that improving diagnostic systems requires attention not only to clinical expertise or diagnostic technologies but also to the institutional architecture governing diagnostic progression. Reducing diagnostic delay may involve redesigning referral pathways, simplifying eligibility criteria for diagnostic testing, strengthening coordination across diagnostic infrastructures, and improving integration between classification systems and clinical practice.\u003c/p\u003e \u003cp\u003eEfforts to expand genomic testing or specialized diagnostic programs are likely to be most effective when accompanied by reforms that address the broader institutional thresholds regulating diagnostic progression. Emerging policy initiatives illustrate how such system-level responses can be implemented in practice, including national rare disease quality standards developed by NICE (NICE, 2024), national health system recommendations such as the National Recommendations for Rare Disease Health Care (RArEST Project, 2024), and research priority-setting initiatives designed to accelerate diagnostic discovery for undiagnosed populations (Zurynski et al., 2022).\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003ePolicy and System Implications\u003c/h2\u003e \u003cp\u003eThe findings of this study also have implications for rare disease policy and health system design. If diagnostic delay is partly produced by institutional threshold architectures, then addressing the diagnostic odyssey requires system-level responses that extend beyond individual clinical interventions. Policy initiatives aimed at improving rare disease diagnosis increasingly emphasize coordinated diagnostic infrastructures, standardized disease classification systems, and integrated data frameworks capable of identifying both diagnosed and undiagnosed populations. Efforts such as the implementation of ORPHAcodes, national rare disease quality standards, and coordinated genomic diagnostic programs illustrate attempts to reduce fragmentation across diagnostic systems. Strengthening these infrastructures may improve the visibility of undiagnosed populations, facilitate earlier referral and diagnostic investigation, and support more coordinated multi-institutional responses to rare disease diagnosis. Understanding diagnostic delay as an institutional phenomenon therefore highlights the importance of policy frameworks that align clinical practice, diagnostic technologies, data infrastructures, and classification systems within a coherent diagnostic ecosystem.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, the analysis is based on institutional and policy-oriented documents rather than direct observation of clinical practice. As a result, the study focuses on formal structures governing diagnostic systems rather than variations in how these structures are implemented in practice.\u003c/p\u003e \u003cp\u003eSecond, the corpus includes documents from multiple healthcare systems with different institutional arrangements. While this diversity allows identification of cross-system patterns, it also means that the findings should be interpreted as conceptual patterns rather than precise descriptions of any single healthcare system.\u003c/p\u003e \u003cp\u003eThird, the analysis focuses on institutional structures regulating diagnostic recognition and does not directly examine patient-level diagnostic trajectories. Future research could combine institutional analysis with patient pathway studies to further examine how recognition thresholds operate in practice.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eFuture Research\u003c/h2\u003e \u003cp\u003eFuture research could extend this analysis in several directions. First, empirical studies examining patient diagnostic pathways could provide additional insight into how institutional thresholds operate in real-world clinical settings. Second, comparative analyses across healthcare systems could examine how different institutional designs influence diagnostic timelines and outcomes. Third, research examining interactions between diagnostic infrastructures and emerging genomic technologies could help identify strategies for reducing recognition bottlenecks within diagnostic systems.\u003c/p\u003e \u003cp\u003eMore broadly, examining diagnostic systems through an institutional lens may provide new insights into how healthcare systems structure access to recognition across other complex or poorly understood conditions.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that rare disease diagnosis is not solely a clinical process but also an institutional one. Progression from symptom presentation to formal diagnostic recognition is structured by a sequence of institutional thresholds embedded within referral systems, diagnostic infrastructures, program eligibility rules, classification frameworks, and registry systems. These thresholds regulate how patients move through diagnostic systems and determine whether diagnostic recognition can occur. When thresholds accumulate across the diagnostic pathway, patients may remain within healthcare systems without achieving formal diagnostic classification. Conceptualizing this condition as suspended recognition highlights how diagnostic uncertainty may be produced by the institutional design of diagnostic systems rather than solely by gaps in medical knowledge or clinical awareness. By examining the institutional architecture governing diagnostic recognition, this study shifts attention from individual barriers to the systemic structures that organize diagnostic progression. Understanding how these structures operate may be essential for designing diagnostic systems capable of reducing prolonged diagnostic uncertainty in rare disease care.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThis study analyzed publicly available institutional and policy documents describing rare disease diagnostic systems. No human participants or identifiable personal data were involved. Ethical approval and participant consent were therefore not required.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe author declares that there are no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo specific funding was received for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eGF conceived the study, conducted the analysis, and wrote the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe author thanks Dr Gareth Baynam for valuable discussions related to rare disease diagnostic pathways and recognition systems. The author also acknowledges the broader dialogue occurring within international rare disease research communities, including working groups associated with the International Rare Diseases Research Consortium (IRDiRC), which continue to advance discussions on diagnostic equity and access.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll documents analyzed in this study are publicly available sources. The corpus of institutional documents used for analysis is listed in Table 1 of the manuscript. Additional details regarding the analytical framework and coding approach are described in the Methods section. All documents analyzed are cited in the manuscript and listed in Table 1.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkesson LS, Parekh S, Alderdice A et al. 2025. Developing Best Practice Rare Disease Diagnostic Care Models in a Real-World Rural/Regional Setting. 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J Community Genet. 2021;12(2):205\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12687-021-00529-0\u003c/span\u003e\u003cspan address=\"10.1007/s12687-021-00529-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-health-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dihs","sideBox":"Learn more about [Discover Health Systems](https://www.springer.com/44250)","snPcode":"44250","submissionUrl":"https://submission.nature.com/new-submission/44250/3","title":"Discover Health Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9451240/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9451240/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRare diseases are frequently associated with prolonged and uncertain diagnostic journeys, commonly described as the diagnostic odyssey. Existing research has documented substantial diagnostic delays and their social and psychological consequences for patients and families. However, most explanations focus on clinician awareness, technological limitations, or patient experiences, while relatively little attention has been devoted to examining how diagnostic recognition systems themselves are institutionally structured. This study investigates how institutional rules and infrastructures regulate access to rare disease diagnosis.\u003c/p\u003e \u003cp\u003eUsing a qualitative institutional analysis, the study examines a corpus of policy documents, diagnostic programs, clinical pathways, and registry systems describing the organization of rare disease diagnosis across multiple healthcare contexts. Documents were coded to identify institutional recognition thresholds\u0026mdash;rules, requirements, or infrastructural conditions that must be satisfied for patients to progress through diagnostic systems.\u003c/p\u003e \u003cp\u003eThe analysis identified 81 recognition thresholds distributed across referral systems, diagnostic testing infrastructures, program eligibility structures, classification frameworks, and registry inclusion processes. These thresholds form sequential recognition chains governed by multiple institutional gatekeepers, including specialist centers, diagnostic laboratories, diagnostic programs, and classification authorities. Thresholds accumulate across the diagnostic pathway through sequential, layered, and recursive processes, creating bottlenecks that regulate progression toward diagnosis.\u003c/p\u003e \u003cp\u003eThe findings demonstrate that rare disease diagnosis is structured by institutional threshold architectures rather than solely by clinical decision-making. When patients cannot traverse these thresholds, they remain engaged with healthcare systems without achieving formal diagnostic classification. This study conceptualizes this condition as suspended recognition, highlighting the role of institutional system design in shaping diagnostic outcomes and contributing a new perspective on persistent diagnostic delay in rare disease care.\u003c/p\u003e","manuscriptTitle":"Suspended Recognition: Institutional Thresholds in Rare Disease Diagnostic Systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-12 10:46:21","doi":"10.21203/rs.3.rs-9451240/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-17T21:23:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42137347951776700542364452912388688237","date":"2026-05-04T06:00:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14208043923648646232709056136409580318","date":"2026-05-04T00:54:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269871673148165343355837239877160434709","date":"2026-05-01T18:25:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-01T17:47:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-30T23:19:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-18T07:15:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-18T07:15:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Health Systems","date":"2026-04-17T16:08:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-health-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dihs","sideBox":"Learn more about [Discover Health Systems](https://www.springer.com/44250)","snPcode":"44250","submissionUrl":"https://submission.nature.com/new-submission/44250/3","title":"Discover Health Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ac448f3a-1287-43c5-ba58-6d400a2a7ce5","owner":[],"postedDate":"May 12th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-17T21:23:13+00:00","index":22,"fulltext":""},{"type":"reviewerAgreed","content":"42137347951776700542364452912388688237","date":"2026-05-04T06:00:55+00:00","index":20,"fulltext":""},{"type":"reviewerAgreed","content":"14208043923648646232709056136409580318","date":"2026-05-04T00:54:03+00:00","index":19,"fulltext":""},{"type":"reviewerAgreed","content":"269871673148165343355837239877160434709","date":"2026-05-01T18:25:48+00:00","index":18,"fulltext":""},{"type":"reviewersInvited","content":"7","date":"2026-05-01T17:47:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-30T23:19:56+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T10:46:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-12 10:46:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9451240","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9451240","identity":"rs-9451240","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}