When the Rails Couldn’t Carry the Future
How biomedical infrastructure became the 1890s railroad
In the late nineteenth century, America’s railroad barons believed they understood the future with a confidence born of decades of industrial success. They had constructed an infrastructure meticulously sized for the movement of industrial freight—grain harvests from the Midwest, iron ore from Minnesota, petroleum from Pennsylvania—and they operated under the reasonable assumption that if they continued to expand track mileage and increase capacity along established corridors, economic demand would follow the familiar patterns that had governed growth since the Civil War. Their managers had achieved something approaching perfection in the machinery of precision scheduling and bulk logistics, orchestrating the movement of tonnage across continental distances with a reliability that represented the apex of nineteenth-century organizational achievement. The system functioned with remarkable efficiency until the fundamental character of the underlying economy began to transform in ways that quickly made their architectural assumptions obsolete.
By the 1890s, the emergence of consumer markets had begun to fundamentally reshape the flow of goods across the American landscape. The predictable rhythm of long-haul industrial shipments, which had defined railroad economics for a generation, gave way to an accelerating volume of small, irregular freight movements that responded to consumer demand rather than seasonal agricultural cycles or industrial production schedules. Railroad systems that had been engineered with exquisite precision for moving heavy loads along fixed routes found themselves increasingly unable to accommodate the operational complexity inherent in short-term hauls, high-fragmentation traffic patterns, and decentralized demand signals that could not be anticipated months in advance. The managers of the Pennsylvania Railroad, once celebrated throughout the business world for their extraordinary ability to choreograph large freight movements across their network, discovered themselves increasingly overwhelmed by the sheer operational complexity of coordinating thousands of small shipments with heterogeneous origins, destinations, and timing requirements. The system began to falter not because leadership had become incompetent or complacent, but because an architecture that had been brilliantly optimized for one economic era could no longer support the fundamentally different demands that characterized the next phase of industrial evolution.
The lesson embedded in this historical moment is rather straightforward even if it proved institutionally difficult to absorb: progress in the external environment eventually overwhelms legacy infrastructure regardless of how polished, professionally maintained, or conceptually elegant that infrastructure may be. The railroads did not fail because they were poorly managed or inadequately capitalized. They encountered profound difficulties because they had been built for a world that no longer existed and the gap between their architectural capabilities and the requirements of the emerging economy widened faster than incremental adaptation could bridge.
This form of structural mismatch (the growing incompatibility between what modern science demands and what existing systems were designed to deliver) defines the predicament in which contemporary healthcare and biomedical research find themselves at this moment in their institutional evolution.
The architecture of an earlier era
Modern biomedical infrastructure was designed around a constellation of conceptual and technological assumptions that accurately reflected the realities and constraints of late-twentieth-century science. Clinical trials assumed episodic patient encounters organized around scheduled clinic visits, low-frequency safety monitoring that corresponded to the temporal resolution of available measurement technologies, modest data volumes that could be recorded on paper forms and later entered into relatively simple databases, and unidimensional readouts such as RECIST criteria for tumor assessment, ejection fraction measurements for cardiac function, or serum biomarker concentrations that could be captured as single numeric values. Healthcare delivery systems assumed site-centric care teams that operated within the physical boundaries of hospitals and clinics, sparse laboratory and imaging data generated at discrete timepoints separated by weeks or months, structured billing codes as the dominant data vocabulary through which clinical activity could be captured and communicated, and workflows that were fundamentally mediated by human documentation rather than continuous automated data capture. Regulatory science developed around trial designs predicated on prespecified treatment arms with fixed definitions, scheduled assessment intervals that reflected both the biology of disease progression and the practical constraints of patient monitoring, narrow primary endpoints that could be measured reliably with available tools, and controlled case-report forms that channeled clinical observations into standardized categories amenable to statistical analysis.
All of these frameworks emerged through a logical process of adaptation to the scientific constraints and technological capabilities of their time. They created intellectual and operational order in what was necessarily a low-resolution observational environment and they produced decades of genuine if incremental progress in understanding disease biology and developing therapeutic interventions. Like the polished scheduling machinery that enabled the Pennsylvania Railroad to move freight with unprecedented reliability, these systems were genuinely efficient and effective for the specific challenges they were constructed to solve. The difficulty is that scientific and technological progress has fundamentally outgrown the architectural assumptions on which they rest, creating a widening gap between capability and infrastructure that now threatens to become the binding constraint on translational progress.
Science has shifted; infrastructure has not
The dominant pattern of contemporary biomedicine is a fundamental shift toward fine-grained, multimodal, continuous measurement of human biological state across temporal and spatial scales that were simply inaccessible a generation ago. The field has evolved from macroscopic, episodic observations captured at discrete clinical encounters to a dense, high-dimensional data universe that encompasses multiplexed proteomic and transcriptomic signatures capable of resolving thousands of molecular species simultaneously, radiomic and volumetric imaging analyses that extract quantitative features from medical images at scales ranging from millimeters to microns, circulating tumor DNA trajectories that track tumor burden and clonal evolution through serial blood sampling, single-cell atlases that map cellular heterogeneity within tissues at unprecedented resolution, high-frequency physiologic monitoring from wearable sensors and ambient monitoring systems that capture continuous streams of cardiovascular and metabolic data, natural-language patient narratives that document symptoms and experiences in their own vocabulary, and algorithmically derived features extracted through machine learning methods applied to imaging, digital pathology, and clinical text that identify patterns invisible to direct human observation.
Each individual signal represents a relatively modest increment to the observational base—just as individual consumer shipments were small additions to railroad freight volume—but the aggregate complexity of integrating these heterogeneous data streams, reconciling their different temporal resolutions and semantic structures, and extracting coherent biological meaning from their interactions has grown explosively. Biological state is now routinely observed across more dimensions, at higher temporal resolution, and through more heterogeneous measurement modalities than at any previous moment in the history of experimental medicine.
The challenge is that the institutional systems responsible for generating clinical evidence and delivering patient care continue to operate as though the primary unit of medical data remains a manually recorded laboratory value or a radiologist’s categorical judgment rendered at six-week intervals according to predefined assessment schedules. This represents far more than a temporary inefficiency or a lag in institutional adaptation. It constitutes a fundamental architectural incompatibility between the measurement capabilities that modern science has developed and the data infrastructure through which those measurements must flow to generate clinical evidence and inform therapeutic decisions.
Operational strain as a symptom of structural mismatch
The stress fractures now visible across the healthcare and clinical research ecosystem bear a striking resemblance to the operational breakdown experienced by railroad systems attempting to accommodate freight patterns for which they were never designed. The manifestations of this structural mismatch appear throughout the evidence generation pipeline, and while they are often interpreted as isolated operational failures or process deficiencies, they are more accurately understood as symptoms of a deeper architectural problem that cannot be resolved through incremental process improvements.
Data heterogeneity now exceeds the absorptive capacity of existing information systems by margins that continue to widen. Electronic health record platforms cannot natively ingest or harmonize multimodal data streams because they were architected around structured fields optimized for billing and clinical documentation rather than high-dimensional scientific measurement. Clinical trial platforms cannot reconcile continuous monitoring data with assessment schedules predefined in protocols written months or years before study initiation. Regulatory submissions struggle to integrate imaging-derived endpoints, multimodal profiling, and real-world evidence streams into coherent evidentiary packages because the regulatory framework was constructed around the assumption that evidence would arrive in discrete, prespecified formats with clear temporal boundaries between data generation and analysis.
Manual adjudication processes are being asked to scale beyond the fundamental cognitive limits of human information processing. RECIST assessments of tumor burden, adverse event classification according to standardized toxicity criteria, eligibility screening against increasingly complex inclusion and exclusion criteria, chart abstraction to populate case report forms, and safety review of accumulating clinical data all continue to rely overwhelmingly on human judgment applied to individual cases. The inter-rater variance in these judgments is measurable and substantial. The cost of maintaining these manual processes is escalating as data volumes grow. The throughput is fundamentally capped by human cognitive capacity and available time, creating bottlenecks that no amount of additional staffing can fully resolve because the problem is architectural rather than simply a matter of insufficient resources.
Trial conduct has become progressively slower, more brittle, and more expensive in ways that reflect structural constraints rather than operational inefficiency. Patient accrual suffers because eligibility models remain anchored in an earlier era’s data reality, requiring manual verification of criteria that could in principle be assessed computationally if the data infrastructure supported such assessment. Monitoring is constrained by episodic clinic visits because continuous remote monitoring has not been integrated into core trial operations and regulatory expectations. Data cleaning and query resolution can absorb more calendar time than scientific analysis because data flows through manual transcription and entry processes that introduce errors requiring labor-intensive correction. These failure modes are the predictable consequences of attempting to execute modern science using infrastructure designed for a lower-dimensional, lower-velocity observational environment.
Artificial intelligence innovations encounter persistent obstacles not in model development but in clinical validation, where they collide repeatedly with the inconsistent ground truth generated by legacy measurement systems. When reference labels for imaging interpretation or pathological classification vary substantially across institutions, across individual human raters, and even across serial assessments by the same rater, modern machine learning methods cannot produce stable, generalizable models regardless of algorithmic sophistication or training data volume. The bottleneck preventing AI from realizing its potential in clinical applications is not computational capacity or algorithmic innovation. It is the absence of reliable ground-truth infrastructure against which models can be clinically validated and their performance measured with sufficient precision to support clinical deployment and patient care.
These phenomena are not isolated operational failures that can be addressed through targeted interventions. Taken together, they constitute a strong systemic signal indicating that existing infrastructure has become fundamentally misaligned with the dimensionality, velocity, and granularity that characterize modern biomedical science. Today, the gap between measurement capability and infrastructure capacity is no longer stable. It is also widening and the consequences are increasingly visible in the form of delayed translations, failed trials, and scientific capabilities that cannot be effectively deployed in clinical settings.
The illusion of precision in legacy systems
Railroad managers took justifiable pride in the high-polish scheduling machinery they had perfected over decades of operational refinement. Similarly, the contemporary clinical research ecosystem maintains considerable pride in its process discipline—standard operating procedures that specify every aspect of trial conduct, monitoring plans that detail oversight activities with bureaucratic precision, case report forms that channel observations into predefined categories, adjudication committees that apply standardized criteria to ambiguous cases, eligibility criteria that attempt to define patient populations with contractual specificity, and imaging guidelines that prescribe technical parameters and interpretation standards. Each procedural layer reflects a deeply embedded institutional belief that precision in scientific measurement emerges primarily from procedural constraint and standardization, and that reproducibility can be achieved by progressively tightening the specifications that govern how observations are made and recorded.
The difficulty is that these structures produce a particular form of narrow precision—reproducibility in the application of predefined measurement protocols—rather than high-dimensional accuracy in capturing the actual biological phenomena under investigation. They optimize the consistency with which an outdated measurement system is applied rather than the fidelity with which that system captures the underlying scientific signal. A RECIST assessment can be performed with high inter-rater reliability while still providing a fundamentally impoverished representation of tumor dynamics compared to what volumetric imaging and radiomics can reveal. An adverse event can be classified consistently according to CTCAE criteria while failing to capture the temporal trajectory, severity fluctuations, and mechanistic relationships that continuous monitoring would illuminate. An eligibility determination can be executed according to protocol specifications while excluding patients who would benefit from treatment because the eligibility criteria were written before biomarkers became available that could identify relevant biological subgroups.
The more aggressively the system tightens these procedural controls—mandating additional documentation, expanding monitoring checklists, demanding stricter adherence to predefined interpretive frameworks—the more clearly it reveals the underlying architectural mismatch. No amount of process discipline can compensate for the fundamental reality that the contemporary data ecosystem requires automated integration of heterogeneous data streams, continuous monitoring across multiple biological scales, and high-resolution phenotyping that manual systems cannot deliver regardless of how carefully they are executed.
The legacy infrastructure is not failing because it is poorly operated or inadequately resourced. It is failing because it represents over-polished machinery that was brilliantly optimized for the wrong world, and additional polish only makes the mismatch more apparent.
A new evidence architecture
If the railroad systems of the 1890s had attempted to survive through incremental process improvements—more detailed scheduling procedures, additional clerks to track small shipments, expanded documentation requirements—they would have collapsed entirely under the operational burden. Instead, those that survived underwent fundamental adaptation through the development of new freight-handling systems capable of processing heterogeneous cargo efficiently, new scheduling algorithms that could accommodate irregular demand patterns, and new commercial models that aligned pricing with the economic realities of small-shipment freight. The transformation was neither rapid nor easy and many railroads failed before it was complete, but those that succeeded did so by rebuilding their operational architecture rather than perfecting their existing procedures.
Biomedicine requires a comparable transformation—not cosmetic modernization or incremental digitization, but a foundational redesign of how clinical evidence is generated, validated, and interpreted. Several architectural elements are essential to this transformation and, while they can be described separately, they function as an integrated system in which each component depends on and enables the others.
High-resolution, multimodal data must transition from exceptional additions to clinical trials to become the default input for evidence generation. Trials and real-world evidence platforms must be capable of ingesting imaging-derived measurements, molecular profiling results, physiologic monitoring streams, and narrative patient reports not as supplementary attachments to core data collection but as first-class, semantically integrated inputs that participate fully in endpoint definition, statistical analysis, and clinical interpretation. This requires fundamental changes in data architecture, moving from document-centric storage systems (both physical and digital documents) to graph-based semantic models that can represent complex relationships among heterogeneous observations and support queries that span multiple data modalities and temporal scales.
Autonomous extraction of ground truth through validated AI systems must progressively replace manual measurement and adjudication for tasks where algorithmic methods can demonstrate superior reliability, consistency, and scalability. This transition depends critically on the availability of robust reference standards against which algorithmic performance can be rigorously validated—volumetric imaging measurements traceable to physical phantoms, digital pathology assessments benchmarked against quantitative molecular markers, structured safety signals extracted from clinical narratives and validated against quantifiable patient outcomes. The goal is not to necessarily eliminate human judgment but to deploy it more effectively by reserving it for genuinely ambiguous cases while automating the extraction of measurements that can be made reliably by computational methods.
Continuous endpoints must replace episodic assessments as the primary framework for measuring therapeutic effects and disease trajectories. Tumors do not grow in discrete jumps synchronized to clinic visit schedules. Toxicities do not emerge instantaneously at their maximum severity. Physiologic states evolve continuously along trajectories that contain far more information than snapshots captured at arbitrary timepoints. Evidence systems must be rebuilt to model rates of change, transition dynamics between disease states, and temporal relationships between interventions and biological responses rather than reducing these complex phenomena to categorical outcomes assessed at fixed intervals. This requires both new statistical methods capable of analyzing continuous trajectories and new data infrastructure capable of capturing high-frequency measurements across the relevant biological scales.
Integrated clinical study and real-world data infrastructure must emerge to replace the artificial separation currently maintained between clinical trials, electronic health records, disease registries, and patient-generated data streams. This separation reflects historical contingencies and institutional boundaries rather than any fundamental scientific or logical necessity. Future evidence models will necessarily unify these data streams within a shared semantic architecture that can track individual patients across care settings and time, link trial participation to long-term outcomes observed in routine care, and enable evidence generation that draws on the full spectrum of available observations rather than only those captured within the formal boundaries of controlled trials. Building this infrastructure requires solving formidable technical challenges in federated data access, privacy-preserving computation, and semantic harmonization across heterogeneous data sources, but these are tractable engineering problems rather than fundamental scientific barriers.
Regulatory pathways must reorient themselves to center on validation of measurement reliability rather than adherence to prespecified data formats. The central regulatory question for new measurement modalities and analytical methods should evolve from “Was this approach prespecified in the protocol before data were examined?” to “Has this measurement been validated to demonstrate clinically acceptable reliability, and is the evidence of that validation sufficiently rigorous to support regulatory decisions?” This reorientation would align regulatory expectations with the realities of algorithmic measurement and multimodal evidence while maintaining the fundamental regulatory commitment to ensuring that therapeutic decisions rest on reliable measurements of clinical benefit and harm. It requires regulatory science to develop new frameworks for evaluating algorithmic systems and to build institutional capacity for assessing validation evidence across diverse measurement modalities, but it does not require abandoning the core principles that have made regulatory oversight effective.
These shifts are neither optional enhancements to current practice nor distant aspirations for some hypothetical future state. They are architectural prerequisites for translating the measurement capabilities that modern science has already developed into clinical impact that improves patient outcomes. The scientific foundation for this transformation already exists. What remains is the institutional and engineering work of building the infrastructure that can support it.
The rails of biomedicine must be rebuilt
Science has undergone a fundamental transformation in its ability to measure human biology across scales and modalities that were inaccessible when our current infrastructure was designed. The institutional systems built to generate clinical evidence, evaluate therapeutic interventions, and guide clinical practice have not undergone a commensurate transformation. The consequence is a widening gap between what we can measure about disease biology and therapeutic response and what our translational infrastructure can effectively act upon to improve patient outcomes.
Closing this gap requires rebuilding the evidence architecture at a fundamental level—not through the addition of more procedural controls around outdated measurement systems, but through realignment of infrastructure with the full dimensionality, temporal resolution, and semantic complexity that characterize modern biological science. This transformation will require sustained investment, institutional courage to abandon deeply embedded practices that no longer serve their intended purpose, and the development of new regulatory frameworks that can ensure reliability without constraining innovation. The technical challenges are substantial but tractable. The institutional challenges are more formidable, requiring coordination across organizations with different incentives and cultures that have proven resistant to change.
Until this realignment occurs, clinical research and patient care will continue to exhibit the characteristic dysfunction of the 1890s railroads: institutional structures that remain elegant in their formal organization, professionals who take justifiable pride in the precision with which they execute established procedures, and a progressive inability to carry the scientific cargo that represents the future of medicine.
The question is not whether this infrastructure will eventually transform. Economic and scientific pressures will ultimately force adaptation. The question is whether that transformation will be led deliberately by institutions that recognize the structural nature of the challenge they face, or whether it will occur through the slower, more painful process of institutional failure and replacement. The choice between these paths remains open, but the window for deliberate action is narrowing.