S&B/SIA – White Paper

NHS League Tables and Benchmarking

Date: 18 November 2024

On 13 November 2024, the Secretary of State announced the implementation of a “league table” system to identify and address poor performance within NHS trusts. This announcement, made at the NHS Providers conference, has already sparked debate and scepticism. Historical attempts at league tables, such as those introduced by Alan Milburn in 2001 and later abandoned by Patricia Hewitt, offer a cautionary tale. However, advances in performance analytics, data processing, and the scale of current challenges suggest that a modernised approach could yield meaningful results. While the term “league table” evokes mixed reactions, a more constructive framing might be “benchmarking.” Benchmarking, already used to some extent in the NHS, could be significantly expanded by focusing on carefully selected, context-based metrics that reflect the complexity of running NHS operations. Drawing from successful private-sector practices, this approach can help drive improvement without oversimplifying or alienating stakeholders.

The Case for Benchmarking

Benchmarking in the NHS has often faced resistance, both for practical and ideological reasons. However, when properly contextualised, benchmarking can be a powerful tool. The concept of “best practice” is universally understood—whether in clinical, financial, or patient care domains. Without robust performance indicators, trusts, patients, and the government lack the insights needed to prioritise scarce resources effectively.

The NHS already generates vast amounts of data at patient and institutional levels. These data, while rich, are complex and influenced by factors such as socioeconomic conditions, population diversity, and resource constraints. Properly analysing these datasets requires significant expertise in data engineering to extract actionable insights that inform decision-making.

Lessons from Our Experience

Over the past eight years, our specialist team has worked across key NHS pressure points, including clinical conditions, targeted interventions, internal budgeting, and waste reduction. We’ve found that when data signals are ignored, management falters. Conversely, when patient groups, clinicians, and staff are engaged in a data-driven approach, resistance diminishes, and actionable change becomes possible.

The NHS also benefits from a wealth of open-source data, much of which offers clear insights into performance trends and even solutions. This data should be leveraged more effectively to guide improvements.

The Role of Incentives

To drive change, both incentives (carrots) and accountability measures (sticks) are essential. The aim is not punitive but transformative—ensuring that best practices are identified, shared, and implemented. The Department of Health and Social Care (DHSC) has rightly recognised that rewarding excellent performance can incentivise improvement. Equally, staff excellence should be celebrated and harnessed to support struggling areas, fostering a culture of turnaround and shared success.

Benefits of Benchmarking Over League Tables

From our perspective, benchmarking offers a more nuanced and constructive approach than traditional league tables. Over the past eight years, the SIA has partnered with NHS organisations, academic institutions, and charities to assess performance across various domains. Using NHS England’s Open Government License (OGL) datasets, we’ve demonstrated how data can be leveraged to improve hospital efficiency, effectiveness, and patient outcomes.

Leveraging NHS England’s Open Data

Below are examples of how OGL datasets can drive performance improvements across hospitals in England:

1. Maternity & Neonatal Performance

• Efficiency: Optimise staffing and bed usage.

• Effectiveness: Identify and replicate best practices.

• Patient Outcomes: Enhance care for preterm births and neonatal intensive care.

2. A&E Waiting Times

• Efficiency: Address bottlenecks in patient flow.

• Effectiveness: Improve triaging strategies.

• Patient Outcomes: Ensure timely care to improve recovery outcomes.

3. Cancer Services

• Waiting Times: Reduce delays to improve early diagnosis and survival rates.

• Quality of Life: Develop personalised aftercare programs.

4. Referral to Treatment (RTT) Waiting Times

• Efficiency: Manage capacity to reduce delays.

• Effectiveness: Streamline waiting list management.

5. Socioeconomic and Demographic Data

• Ethnicity by Postal District: Tailor services to diverse populations.

• Index of Multiple Deprivation (IMD): Address health disparities in deprived areas.

6. Medicines Spend

• Efficiency: Optimise procurement strategies.

• Effectiveness: Focus on value-based care.

Benchmarking: A Framework for Success

A well-designed benchmarking system requires:

• Metric Selection: Focus on indicators tied to efficiency, effectiveness, and patient outcomes.
• Weighting: Prioritise metrics based on policy goals (e.g., patient outcomes: 40%; effectiveness: 30%; efficiency: 30%).
• Contextual Adjustments: Normalise data to account for regional and demographic differences.

Benefits of an Aggregate Scoring System

• Identify Outliers: Highlight best and worst performers.
• Track Progress: Enable dynamic monitoring of improvements.
• Encourage Healthy Competition: Foster motivation to improve.
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Challenges to Address

• Bias in Weighting: Ensure fairness in metric prioritisation.
• Contextual Factors: Adjust for population and regional differences.
• Risk of Oversimplification: Avoid masking nuances with a single score.
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A Trinary Scoring Model

We propose a Tri-Score system categorising performance into three levels:

• 3 (Green): Above average.
• 2 (Amber): Average.
• 1 (Red): Below average, requiring improvement.

This model provides actionable insights while maintaining fairness and granularity.

Conclusion

By adopting a structured benchmarking approach using NHS England datasets, the Secretary of State can foster a culture of continuous improvement. Transparent, data-driven insights will enable hospitals to identify inefficiencies, address disparities, and deliver high-quality, patient-centered care.

Over the past 8 years, and for some of our Business Intelligence specialists much longer. We have been working with numerous NHS, PHE and ONS Open Government Licenced data on various projects. These include Cancer Quality of Life, Patient Voice, Medicines Optimisation, A&E Performance, Maternity & Neonatal Services, Mental Health, Early Diagnosis and Risk Analysis plus many others. Some example visualisations are set out below demonstrating the practicality of developing meaningful Benchmarking Tools in NHS Healthcare.