How Analysts Can Support Strategic Commissioning Through Actionable Intelligence

Strategic commissioning is becoming one of the most important conversations in the NHS as it shapes the future model of care for populations.

In simple terms, it asks:

· What are the current and future health needs of our population?

· Where is there unmet need, duplication, variation or inequality?

· Where should we focus investment, redesign services, expand capacity or stop activities that are not adding value?

· How do we ensure our decisions lead to measurable improvements in population health and system performance?

This shift marks a pivotal moment for analytics, positioning analysts to directly inform decisions that define population health, pathway design, and system resource allocation.

Realising this potential requires moving beyond retrospective reporting to deliver shared intelligence that enables leaders to understand population need, inequalities, pathway behaviour, variation, future demand, and the true impact of interventions.

The distinction is critical. While reporting provides visibility of attendances, admissions, waits, length of stay, discharge delays, and performance against standards, strategic commissioning must reveal the underlying drivers of change, forecast what is likely to happen next, and pinpoint where intervention will yield the greatest improvement.

The objective extends beyond system visibility; it is about understanding system behaviour and identifying the causes of variation and inequalities.

Pressure often shows up in one place, but the cause sits elsewhere

One of the key lessons from working with operational healthcare data is that the most visible pressure is not always where the underlying issue sits. A crowded emergency department is often a symptom of wider system pressures, rather than an isolated ED issue, as explored in our previous article.

A discharge delay is not only an acute trust challenge. A virtual ward is not successful simply because it is occupied. A community service is not necessarily effective because activity levels have increased. The critical question is whether the pathway has changed in the way the system intended, delivering improved outcomes for patients.

Winter pressure illustrates the complexity of system dynamics. While rising bed occupancy is often attributed to increased demand or more complex patients, data

analysis frequently reveals alternative causes. For instance, stable admission rates may coincide with heightened bed pressure, where the primary driver is increased length of stay following Christmas. This is often linked to slower discharge processes and challenges at the interface between acute and social care.

This shifted the focus from assumptions to underlying operational causes of pressure, helping identify where action has the greatest impact.

This is the type of intelligence strategic commissioning requires. It is not enough to know that pressure exists. Systems need to understand what is driving it, which cohorts are affected and which interventions are most likely to improve outcomes.

The challenge is not a lack of data

The NHS has a huge amount of data. The challenge is that it is often fragmented, retrospective and interpreted differently across organisations. For analysts, the credibility of shared intelligence depends on transparency. Systems need to understand the definitions used, the assumptions made, the limitations of the data and the degree of uncertainty around any conclusion. Strategic commissioning does not require perfect data, but it does require a shared level of confidence in how the evidence has been produced and interpreted.

When providers and analysts use different definitions or approaches, each perspective may be locally valid, but the system struggles to make shared decisions. This can stall strategic commissioning, turning major decisions into debates about data rather than action, and risks shifting pressure instead of solving real problems.

In our previous article, we discussed a key challenge facing healthcare: it is not the availability of data that matters most, but the ability to translate it into meaningful insight that is timely, reliable and usable when decisions need to be made.

Strategic commissioning requires shared intelligence, not simply more information

This does not mean replacing local ownership or diminishing the expertise of local analytical teams. Local knowledge remains essential to understanding the needs of communities and designing effective services. However, local intelligence becomes more powerful when supported by a shared approach.

Common definitions, consistent statistical approaches and connected pathway views allow systems to move from arguing about what happened to agreeing what needs to change. This enables a partnership between BI and clinical staff, combining data with frontline insight to understand how patients experience the system and identify opportunities to improve care, outcomes and value.

Analysts need to make variation visible

One important contribution analysts can make to strategic commissioning is helping systems understand variation.

Some variation is expected. Some reflects differences in population need. Some reflects access, pathway design, capacity, clinical practice, service maturity or operational behaviour. The task is not to remove all variation. The task is to understand which variation matters.

For strategic commissioning, this means looking at cohort demand, population rates, utilisation, pathway delay, length of stay, outcomes and cost. It also means asking whether demand is rising because the population is changing, because people are using services differently or because the pathway itself is creating avoidable activity. Statistical Process Control can help. Used well, SPC helps teams distinguish between normal variation and meaningful change. Instead of asking whether a number is higher or lower than last month, the system can ask whether the process itself has changed.

Modelling the future is essential; assumptions are not enough

Strategic commissioning depends on forward visibility: What happens if demand, capacity, and pathway patterns stay the same? Where will pressures build? Which groups will be most affected? Only by answering these questions can systems anticipate and manage challenges, rather than simply react to them.

It also means testing interventions before committing resources. If a system invests in a frailty pathway, what change would it expect to see in admissions, bed days, discharge flow or community activity? If it expands virtual wards, is the model supporting people who would otherwise have occupied acute beds? These practical questions determine whether strategic commissioning improves outcomes and value, or whether it becomes another layer of activity.

The opportunity for analysts

The shift toward strategic commissioning elevates the role of analytics, requiring leaders to prioritise statistical literacy, advanced modelling, and the ability to translate data into actionable insight.

Success will depend on analysts working seamlessly across organisational boundaries, connecting diverse datasets, establishing shared definitions, and providing clear explanations of variation and future demand. Their expertise will be essential in evaluating the true impact of interventions.

This is both a technical and a strategic translation task: analysts must synthesise quantitative evidence, operational realities, and clinical knowledge to inform high-impact decisions.

Ultimately, the effectiveness of strategic commissioning will be measured by improved decision quality and outcomes, not just better plans. While retrospective reporting and dashboards remain important, they are insufficient on their own. Achieving system ambitions requires analytics to be embedded at the point of decision, ensuring intelligence directly shapes action.

About Kensa Health

Kensa Health works with NHS organisations to help turn healthcare data into actionable intelligence that supports better operational and strategic decision-making. Its analytics platform connects data across patient pathways, applying consistent statistical methods to help systems understand variation, predict future demand and evaluate the impact of service change. Alongside analytics, Kensa Health provides virtual care and remote patient monitoring solutions that support earlier intervention, admission avoidance and hospital-at-home models, as well as homecare services that help people live independently for longer.

By combining analytics, virtual care and operational delivery, Kensa Health supports integrated care systems to improve patient outcomes, reduce unwarranted variation and make more informed commissioning and service redesign decisions.

For further information, contact Kensa Health today.

‍