Q&A: Improving Generics Access in Hospitals

The US hospital generics market continues to face a difficult contradiction: manufacturers, distributors, and health systems have access to more data than ever, yet drug shortages persist and access remains uneven, particularly for smaller and lower tier hospitals.

Companies may detect early signals such as demand shifts, inventory drift, access friction, contract gaps, or changes in account behavior, but still struggle to convert those signals into coordinated action across supply, access, commercial, and field teams. The result can be delayed response, missed opportunities, avoidable revenue leakage, and uneven access for hospitals that may already have fewer resources to manage volatility.

In this Q&A, Rahul Mittal, head of strategy and innovations at Dr. Reddy’s Laboratories North America, discusses an execution infrastructure approach for bridging that gap. The model organizes cross functional signals, applies explicit decision logic, assigns ownership, and tracks actions through closure. The examples discussed are anonymized, and the focus is on transferable principles that other manufacturers can adapt without relying on proprietary tooling or large IT investments.

PC: You’ve Said that the Biggest Gap in Hospital Generics is Execution, not Data. What do you Mean?

Mittal: Data availability and execution reliability are not the same thing. Most organizations can see signals. Orders are moving. Inventory is tightening. Bids are stalling. Access friction is rising. The breakdown often happens in the translation from signal to coordinated action.

In one anonymized situation, demand began drifting downward across a group of mid tier hospital accounts while broader inventory signals did not immediately indicate a supply issue. When the signal was examined across contract structures, GPO coverage, and field engagement, the root cause turned out to be an access visibility issue rather than a supply constraint.

Once identified, the response required coordinated action. Contract and access teams clarified eligibility. Field teams were given a prioritized set of accounts and guidance. Follow through was tracked until ordering normalized. The signal itself appeared early. The difference came from having an execution structure that ensured it was acted on consistently.

In another case, an account cluster showed unstable ordering patterns across a limited number of institutional customers. Rather than treating it as isolated variability, the signal was evaluated across contract hierarchy and ordering pathways. A mismatch in account mapping was identified, and once corrected, ordering stabilized within a defined period.

These kinds of situations are not uncommon. What varies is whether organizations have a system that ensures signals are acted upon in a timely and coordinated way. Execution infrastructure is intended to provide that system.

How Does this Differ from Traditional Dashboards or Analytics Investments?

A dashboard tells you what happened. Execution infrastructure helps determine what should happen next and whether it actually gets done.

Most organizations have invested heavily in analytics. That is necessary. But visibility by itself does not change outcomes.

For instance, many organizations track bid pipelines, but aged bids often remain open without clear ownership. By defining a signal such as a bid exceeding an agreed timeframe and linking it to decision logic and ownership, companies can ensure that the right actions are triggered and tracked through closure.

Similarly, chargeback discrepancies are often flagged in finance reports. Without execution infrastructure, they remain within the finance function. With defined execution logic, those signals can trigger coordinated review across finance, access, and field teams, reducing delays and improving clarity for account engagement.

Execution infrastructure complements analytics by ensuring that signals result in coordinated action rather than remaining as insights.

Can You Walk Through an Example of How this Works in Practice?

Let us take an anonymized scenario where a therapeutic category begins showing decline across a cluster of accounts.

In a traditional response, multiple teams begin to investigate independently. Sales looks at account activity. Finance reviews pricing. Supply checks inventory. Access reviews contracts. The diagnosis can take time, and actions may not be coordinated.

In an execution infrastructure approach, the signal is first classified. If it is clustered across accounts with similar characteristics, it may indicate a contract or access issue rather than isolated field performance.

One example involved a set of accounts aligned to a common purchasing structure where performance declined over a short period. The signal triggered a coordinated review across access and contracts. A structural issue in formulary visibility was identified and corrected. Field teams were supplied with targeted follow up plans, and execution was monitored over subsequent weeks.

Another example involved bid conversion delays across several institutional accounts. By defining a signal around bid aging and assigning ownership to access and field teams jointly, response timelines improved and visibility into bid status increased.

The key difference is that the signal becomes part of a structured workflow rather than a point of discussion.

You Emphasize “Coupled” Decisions Across Functions. What Does that Mean in Commercial Terms?

Many organizations operate through sequential handoffs. Sales identifies an issue. Access reviews contracts. Finance checks pricing. Field teams act after the fact.

Coupled decision making means multiple functions respond in parallel, based on the same signal and aligned decision logic.

A practical example is when pricing discrepancies or chargeback variances are identified. Rather than waiting for finance to fully resolve the issue before engaging other teams, the signal can trigger parallel actions. Finance investigates the discrepancy. Access reviews contract alignment. Field teams receive interim guidance so that engagement with the account remains consistent.

In another case, inventory allocation during a constrained period required coordination between supply planning and field teams. By aligning on shared criteria such as account importance, clinical use, and demand patterns, supply and commercial teams were able to act in parallel, reducing confusion and improving communication with accounts.

The goal is not to increase complexity. It is to reduce delay and ensure that decisions are aligned across functions from the start.

How Does AI Fit into this Model?

AI can help identify patterns and prioritize where attention is needed, but it is most effective when paired with a clear execution framework.

For example, models can highlight accounts that show early signs of disengagement or volatility. But the value comes from what happens next. If the output is simply a list, it does not change outcomes.

With execution infrastructure, that output is routed based on the pattern detected. A contract related pattern may go to access. A competitive pattern may go to the field. A supply related pattern may go to inventory planning.

In one situation, automated prioritization highlighted a set of accounts where transaction patterns required closer review. The signal was routed to the appropriate teams, actions were defined, and progress was tracked. The outcome was not driven by the model alone, but by embedding the model within a clear execution workflow.

AI enhances the system, but it does not replace ownership, decision logic, or follow through.

What Metrics Define Success for this Approach?

We look at both execution health and outcome measures.

Execution health includes how quickly signals are acted upon, whether actions are assigned clearly, and how consistently issues are closed.

Outcome measures include indicators such as improved bid visibility, reduction in unresolved discrepancies, better alignment between supply and demand signals, and improved consistency in account level performance.

For example, organizations can track metrics such as:

• Time from signal detection to action
• Percentage of signals assigned clear ownership
• Closure rate of identified issues
• Reduction in aged bids or unresolved discrepancies
• Revenue protected or recovered through early intervention

The focus is not on any single metric. It is on whether the system improves responsiveness and consistency across multiple situations.

You Have Talked About Protecting Lower Tier Hospitals Explicitly in Prioritization Logic. Why Does that Matter Commercially?

Access risk is not evenly distributed. Larger systems often have more structured processes and resources to manage change. Smaller hospitals may be more affected by gaps in allocation logic or visibility.

If prioritization is based only on scale or past volume, smaller hospitals may not receive the same level of attention during periods of volatility. Execution infrastructure makes it possible to incorporate additional considerations such as clinical context, demand patterns, and access requirements.

In practice, this can help improve visibility of smaller accounts and ensure they are included in coordinated responses.

From a commercial perspective, consistency matters. Organizations that respond clearly and predictably build stronger relationships across a broader set of customers.

How Would You Recommend Other Organizations Start Implementing this?

Start with one high impact problem.

For example:

• Bids that remain open without clear follow up
• Chargeback discrepancies that take time to resolve
• Allocation decisions that are not consistently communicated
• Demand changes that are not diagnosed quickly

Define the signal, assign ownership, determine the actions, and track closure.

In one case, focusing on bid aging helped create greater visibility and accountability across field and access teams. In another case, defining a structured workflow for discrepancy resolution improved response timing and alignment between functions.

Once one workflow is proven, the same principles can be extended to other areas.

What Is the Real Benefit of this Approach Beyond Efficiency?

When execution becomes a system capability, organizations respond more consistently. This affects how internal teams work together and how customers experience the organization.

Across different situations, whether it is account follow up, bid management, allocation decisions, or discrepancy resolution, consistent execution leads to better predictability.

That predictability supports both commercial performance and access continuity. Over time, it contributes to stronger relationships and greater resilience in a changing environment.

What Are the Key Takeaways for Commercial Leaders?

• Execution infrastructure is not a dashboard. It is a system that connects signals to decisions and actions.
• Start with one high impact problem and build a repeatable signal to action loop.
• Align functions around shared signals rather than sequential handoffs.
• Measure both execution health and outcomes.
• Ensure smaller and lower tier accounts are visible in decision making.
• Use AI as an enabler within a defined execution system.
• Reliability is built through consistent, coordinated action over time.