From Volume to Value: How Technology Is Accelerating the Shift to Value-Based Care

Healthcare is delivering more care than at any point in its history, yet outcomes are not improving at the same pace. For a long time, higher expenditures and expanding activity were seen as signs of progress, allowing volume to stand in for performance. As variation continues and efficiency is still uneven across systems, this assumption is now clearly under pressure.  If this is progress, why does it look so inconsistent? 

According to a recent report by Cms.gov, expenditure reached $5.3 trillion in 2024, accounting for 18% of GDP in the United States.1 Numbers of this magnitude no longer just reflect scale. They invite closer scrutiny around what that scale is actually delivering. 

The Point Where Volume Stops Explaining Performance 

Higher utilisation used to be a plausible interpretation of improved access and as a result better care. It is no longer as strong. In some areas it has broken entirely, what complicates this further is variation. Similar conditions are treated differently across providers, often without consistent differences in outcomes. 

More care is being provided, but it’s not always safer or more efficient. The distinction sits at the centre of the shift toward value-based care. Value, in this context, is not about reducing activity. It’s about holding actions responsible for their subsequent consequences. 

Where the Pressure Actually Builds 

Although the shift toward value is frequently discussed in general terms, it is driven by very particular pressures. One of these is specialty care, which has high costs, less standardised pathways, and hard-to-explain variation. 

According to a McKinsey analysis, more consistent value-based approaches in specialty care could save up to $100 billion in annual spending.2 The number is more about inconsistency than excess. It highlights a system that currently lacks a trustworthy method for determining what constitutes good in similar situations. 

Payment Models Are Moving, But Not in Sync 

Though the movement is uneven, reimbursement structures are starting to change in response. Value-based contracting is growing, but it hasn’t yet reached a stage where it regularly modifies behavior. 

It’s a simple concept. Payment ought to take into account results rather than just activity. Determining those results in a way that is both operationally useful and clinically significant is challenging. 

Deloitte continues to identify data fragmentation and limited interoperability as key barriers. Until those issues are addressed, value-based models tend to remain selective rather than systemic.3 

Technology Is Changing What Can Be Seen 

Healthcare systems have had data for years, but not consistency. There was information, but it was scattered throughout ineffectively communicating systems. That is starting to change. The increasing portability of patient data allows for the tracking of results over time rather than just at specific moments.  

This is more important than it seems. Because assumptions can be tested once outcomes are visible throughout the entire care journey. Some will hold. Others will not. 

AI Is Turning Visibility Into Action 

Visibility alone does not create value. The difference now is that AI systems are beginning to act on that visibility in real time. Predictive models can identify risk before it materialises, while generative systems reduce administrative friction that delays care. This shift from insight to intervention makes it possible to reduce variation as it emerges, rather than analyse it retrospectively. 

From Encounters to Trajectories 

Encounters are the foundation of traditional care delivery. A consultation, an operation, and a release. Every one of them is regarded as a contained event. Value-based care moves the emphasis to trajectories. What counts is how a patient’s condition changes as a result of an interaction, not just what transpires during it. 

Technology helps with this by increasing visibility outside of clinical settings. Remote monitoring, for instance, is not just a convenience layer. It changes when intervention happens, and sometimes whether escalation happens at all. 

Fragmentation Is Still Doing Most of the Damage 

Fragmentation is still one of the system’s most enduring limitations, even with upgraded tools. Without a common view of their medical history or treatment plan, patients continue to switch providers.  

Value-based care is predicated on a degree of coordination that is still inconsistent in reality. While technology can facilitate the exchange of data, it does not ensure that decisions are made in unison. That relies on incentives, which are still not consistently structured. Results continue to be undermined by fragmentation when alignment is poor. 

Administrative Complexity Has Been Underestimated 

Administrative procedures account for a significant amount of healthcare inefficiency rather than clinical judgments. Prior approval, billing, and claims handling add layers of expense that don’t directly enhance care. 

The scale of this is substantial. According to the JAMA study, annual waste is estimated to be between $760 billion and $935 billion, with administrative complexity taking on a major role. 4 What is often treated as operational overhead is, in reality, a structural problem. It affects how care is provided and how much of it is postponed. 

AI and the Economics of Value-Based Care 

The financial mechanics of value-based care depend on predicting risk, managing populations and preventing avoidable costs. AI directly influences each of these by enabling earlier and more precise intervention in high-cost cases. Automation of administrative workflows, including prior authorisation and claims processing, reduces leakage that erodes margins under value-based contracts. Continuous monitoring of patient trajectories also allows reimbursement to align more closely with real-world outcomes rather than episodic measures. 

Data Has Moved From Record to Leverage 

Data in a volume-driven system mainly records past events. It starts to influence subsequent events in a value-driven system. This requires more than access. It requires interpretation, and the ability to act on insight in real time. 

That ability is still inconsistent. However, where it exists, it is beginning to alter decision-making in ways that are hard to undo. 

Innovation Is Being Asked Different Questions 

Particularly in expensive fields like oncology, treatment advancements continue. The rate of innovation is not evolving, but the way it is assessed is. 

The importance of performance in the real world is growing. Value-based agreements make an effort to tie payment to results that are seen following treatment rather than just those that are shown in controlled environments. 

A new kind of accountability is thus introduced. Additionally, it makes it more difficult for cost and value to stay apart. 

Why This Shift Is Accelerating Now 

The current acceleration is not incidental. It reflects the convergence of more standardised data through interoperability frameworks, the maturation of machine learning models and the emergence of generative AI capable of working with unstructured clinical information. At the same time, cost pressures are forcing systems to move beyond incremental improvements. Together, these forces are pushing AI from experimentation into operational relevance, particularly in areas where traditional approaches have struggled to deliver consistent outcomes. 

The Shift Is Already Underway, Even If Incomplete 

The transition from volume to value is not speculative. Decisions are already being influenced by it, yet unevenly across systems. What remains uncertain is not the direction, but the execution. Aligning payment models, data infrastructure, and care delivery takes time. 

However, the underlying pressure is not likely to lessen. The system is expected to show not only how much it does, but also how much of a difference it makes.