Modern healthcare is data‑rich but insight‑poor

“Why has an abundance of data in healthcare not enabled better outcomes in medicine? Alex Bedenkov argues thathealthcare is not short of data but is short of insight that meaningfully shapes decisions, and that this paradox is now difficult to ignore. Healthcare now generates unprecedented amounts of data. Every clinical encounter, prescription, diagnostic test and hospital admission now produces real-world data (RWD). Electronic health records , disease registries, claims databases, digital devices and patient‑generated data together form a dense and continuously expanding information environment. In principle, this data abundance should enable earlier diagnosis, more consistent care, faster learning and better outcomes. In practice, it has not. Despite substantial investment in data infrastructure, healthcare systems continue to suffer from delayed diagnosis, variation in care, uneven implementation of evidence‑based guidelines and inequities in outcomes. A growing body of research shows a striking gap between what is known to work and what happens in routine practice, even for interventions that demonstrably improve outcomes. And this is the case even in well‑resourced systems. This disconnect has been repeatedly observed across health systems and disease areas, including in recent analyses of real‑world data integration in healthcare. This paradox is now difficult to ignore: healthcare is not short of data. It is short of insight that meaningfully shapes decisions. This is not a failure of data availability, computational power or scientific capability. It is the consequence of how evidence is currently conceptualised, produced and deployed. Healthcare data remain fragmented across organisations, functions and incentives. Analyses are frequently retrospective and detached from the moment when decisions are actually made. Evidence is generated to satisfy narrow lifecycle, regulatory, reimbursement or academic objectives but rarely to inform coherent choices at the level of care pathways or health ecosystems. The result is a system that records activity in extraordinary detail yet adapts and learns only slowly. Why evidence can no longer play a secondary role For decades, real-world evidence generation in healthcare , and particularly in the pharmaceutical industry, has been positioned as a downstream, enabling activity. Evidence has largely been asked to validate decisions already taken: supporting regulatory approval, reimbursement submissions or scientific publication. In this framing, evidence is an output, not an organising force. That model is no longer viable. Health systems today operate under increasing complexity, tightening budgets and a growing burden of chronic disease. Healthcare leaders and policymakers must navigate trade‑offs between innovation and affordability, early intervention and over‑medicalisation, and standardisation and personalisation. In such an environment, evidence that merely follows decisions does little to improve their quality. Evidence must move upstream. It must help determine which problems deserve attention, which interventions are scalable in real settings, which care pathways repeatedly underperform and where limited capacity can create the greatest impact. Evidence must function as a strategic asset, not a compliance artefact Real world evidence (RWE), derived from RWD through robust analytical approaches , is particularly suited to this role . When intentionally designed, RWE complements clinical trials by revealing how care is actually delivered, to whom and with what outcomes. It enables leaders to identify systematic patterns, expose unmet needs, guideline-directed therapy adoption and assess effectiveness. From evidence about products to evidence about systems Healthcare must move beyond a product‑centric view of evidence towards one that is explicitly system‑oriented. In a product‑focused model, evidence concentrates on the safety and efficacy of individual interventions. While this is essential for regulatory decision‑making, the approach offers limited insight into what happens once those interventions enter health systems. An ecosystem‑level evidence model examines patient journeys, patterns of variation, diagnostic delays, pathway bottlenecks and outcomes over time. It would consider how interventions interact with workforce constraints, service design, diagnostic infrastructure and patient behaviour in routine practice. This shift mirrors a broader transition happening in healthcare. The long‑standing assumption that value resides primarily in the medicine is giving way to a more platform‑based logic , where outcomes depend on how solutions are implemented, integrated and sustained within care ecosystems. As previously explored , value increasingly emerges not from products alone, but from systems that enable their effective use. RWD make this shift possible by connecting lived clinical reality to strategic decision‑making. RWE has already demonstrated its ability to support earlier diagnosis, reduce unwarranted variation, inform policy choices and enable more effective public–private collaboration. Yet these successes remain sporadic and driven by local leadership rather than embedded by design. One successful implementation is the CARABELA initiative in Spain, a collaborative framework designed to optimise clinical management and care pathways for chronic diseases. Supported by ten scientific societies, six patient associations and AstraZeneca, and powered by RWE, it focuses on identifying inefficiencies, standardising care, and improving patient outcomes. The enterprise evidence gap What most consistently limits the impact of RWE is not data scarcity or analytical immaturity. It is organisational design. Across healthcare ecosystems, evidence generation is typically fragmented by function, geography and incentive. Research and development, medical affairs, health economics, policy, digital, analytics, academia and healthcare systems operate in parallel, each designed for locally rational goals that are collectively misaligned. Even high‑quality studies often fail to influence care because they are disconnected from a shared, end‑to‑end evidence strategy. This fragmentation creates what can be described as an “enterprise evidence gap”: the absence of a unified, accountable public-private ecosystem that treats evidence as critical infrastructure rather than as a series of stand‑alone initiatives. Closing this gap is fundamentally a leadership challenge. Evidence must be elevated to a strategic priority, with clear ownership across the healthcare and product lifecycle. RWD, advanced analytics and responsible artificial intelligence should be embedded directly into decision processes rather than retrofitted after choices have already been made. The need to combine human‑centred judgement with advanced analytics has been highlighted previously in the context of medical affairs and responsible AI adoption . This transformation requires explicit agreement on what evidence is for, who is accountable for its use, and how success is measured – not by the volume of analysis produced, but by demonstrable improvements in care delivery, outcomes and equity. Without such a shift, healthcare risks accumulating ever more data without the insight required to use it to transform care at scale. This article gives the views of the author, not the position of LSE Business Review or the London School of Economics. You are agreeing with our comment policy when you leave a comment. Image credit: raker provided by Shutterstock. The post Modern healthcare is data‑rich but insight‑poor first appeared on LSE Business Review .

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