Have MDTs Reduced Hospital Referrals?

Introduction

Multi-Disciplinary Teams (MDTs) were introduced into GP practices across Northern Ireland from 2019, bringing professionals such as physiotherapists, mental health practitioners and social workers directly into primary care settings.

By providing patients with access to specialist support within their own GP practice, MDTs aim to help manage some conditions without the need for referral to secondary care services. Understanding their impact on hospital referrals is therefore important for future service planning.

The Strategic Planning and Performance Group (SPPG) leads the implementation and oversight of the Primary Care MDT Programme across Northern Ireland. Using data available through the General Practice Information Platform (GPIP), the HSC Data Institute (“the Data Institute”) supports the evaluation of the programme through data analysis and insight generation. The Data Institute also supports the MDT programme team, service leads, and staff through the development, implementation, and training of data entry templates. This enables analysts to provide dashboards to stakeholders, helping to inform planning and support the continued success of the programme.

With seven years of MDT activity now available for analysis, SPPG and the Data Institute sought to explore a key question: Has the introduction of MDTs reduced the number of patients being referred from GP practices to hospital services?

To help answer this question, the Data Institute collaborated with Queen’s University Belfast to provide an MSc Data Analytics student placement, enabling a student to apply academic learning to a real-world health and social care challenge.

Collaborating with academia

Through partnerships between Queen’s University Belfast and the HSC Data Institute, MSc students are given opportunities to apply their academic learning to real-world challenges within health and social care.

Following a competitive application process, Soham Brahme, an MSc Data Analytics student at Queen’s University Belfast, completed a 12-week placement with the Data Institute. His project focused on evaluating whether MDTs in GP practices have influenced the number of patients referred to hospital services.

Speaking about this placement, Soham said:

At the outset of the placement, I was provided with a project brief outlining the objectives and expected outcomes. Throughout the project, I was supported by William Allen, a Senior Business Intelligence Developer at the Data Institute, and received regular supervision from Dr Laura Boyle, my academic supervisor at Queen’s University. Having support from both sides, one close to the operational reality and one focused on the academic side, meant I could work through difficulties without getting stuck, and made the experience far more useful than either would have been on their own.

Reflecting on the value of the partnership, William Allen, Senior Business Intelligence Developer at the Data Institute, said:

Partnerships between Queen’s University Belfast and the Data Institute create valuable opportunities for students to apply their academic learning to real-world challenges within health and social care. Students benefit from support and guidance from both organisations, while the Data Institute gains fresh perspectives and access to the latest analytical approaches being taught within academia. As MDTs are still a relatively new concept within health and social care, the analysis undertaken by Soham has provided important insights and made a valuable contribution to our understanding of this area.

Analysing the data

To answer the MDT question, Soham analysed approximately 6.4 million referrals from 348 GP practices between 2017 and 2025.

A particular challenge was that MDTs were introduced at different times across different practices. Rather than comparing practices directly with one another, Soham compared each practice against its own historical referral patterns. This approach removed the influence of factors such as practice size, local demographics, deprivation levels and existing referral behaviours.

The referral data itself presented additional complexity. Rather than containing a single record for each referral, the dataset recorded individual stages throughout the referral journey. Understanding how the data was structured and validating key assumptions was essential to ensuring accurate results.

To strengthen confidence in the findings, Soham tested whether referral reductions were occurring only in services supported by MDTs. He examined referral patterns for services such as physiotherapy and mental health, while also reviewing referral categories unrelated to MDT activity, including specialties such as cardiology and dermatology.

This approach helped determine whether any observed changes were genuinely linked to MDTs or simply reflected wider trends in referral activity. The project provided valuable insights into the impact of MDTs on referral patterns and demonstrated the benefits of collaboration between academia and the health service.

Key findings and insights

The analysis found clear evidence that MDTs were associated with reduced referrals to services that they directly support.

Key findings included:

• Physiotherapy referrals fell to approximately half of previous levels following the introduction of MDT physiotherapists within GP practices.
• Mental health referrals decreased by around one-third after MDT mental health professionals became available.
• Referrals to services not covered by MDTs remained largely unchanged, suggesting the reductions were not simply the result of broader changes in referral behaviour.

Discussing the findings, Soham said:

These findings indicate that MDTs may be helping some patients access timely care within primary care settings rather than being referred into hospital services.

However, the findings also highlighted an important consideration. Lower referral numbers alone do not tell the full story. It remains possible that MDTs are both reducing referrals and enabling patients to access services that may previously have been unavailable or difficult to reach.

The analysis therefore provides evidence that MDTs are changing referral patterns, while also opening opportunities for further research into how patient care pathways have evolved.

Looking ahead

One of the most significant outcomes of the project was the development of a repeatable analytical approach that can be used for future evaluation work.

As MDTs were introduced at different times in different practices, the methodology allows practices to be compared against their own historical performance rather than against other organisations. This creates a robust framework that could be applied to future service evaluations.

The current analysis focused on referral activity. The next step would be to incorporate MDT activity data to better understand what happened to patients who were not referred onwards to secondary care services. Doing so would help build a more complete picture of MDT impact and patient outcomes.

The analytical pipeline and supporting documentation developed during the project provide a strong foundation for further work in this area.

Aideen Johnson, MDT Senior Programme Manager at SPPG, said:

This analysis is an important step in demonstrating the value of the MDT model in transforming how care is delivered within primary care. The insights gained from comparing MDT and non-MDT practices will help us understand the impact of multidisciplinary working on referral pathways and patient outcomes. As we continue to develop the wider Neighbourhood Model, evidence such as this is vital in shaping integrated services that are designed around the needs of patients and communities.

Reflections

For Soham, the placement provided an opportunity to apply the technical skills developed during his MSc programme to a real-world health and social care challenge.

Beyond the analytical work itself, the experience reinforced the importance of understanding data before drawing conclusions and of communicating technical findings in a way that non-specialist audiences can understand and use.

Reflecting on the placement, Soham said:

The biggest shift was in how I approach data. I began by assuming the fields meant what their names suggested. I learned to test those assumptions first, particularly where being wrong would have significant consequences.

I also became much better at explaining analytical decisions in plain language. That turned out to be just as important as the analysis itself, because findings that people cannot understand are far less likely to be used.

William added:

It has been a pleasure working with Soham during his time with us. His approach and willingness to learn was key to tackling a difficult data set in a short space of time. I am sure he will go on to have a great future in data.

Dr Laura Boyle, Data Analytics Lecturer at Queen’s University Belfast, said:

Collaborations between Queen’s University Belfast and the Data Institute provide our MSc students a unique opportunity to bridge academic study with practical application. By working on live projects, students can see how the knowledge and skills developed through their studies can contribute to addressing real-world challenges within health and social care. Soham embraced this opportunity, contributing to the project while developing his analytical, communication, and professional skills. Experiences like this help prepare students to become the next generation of data analysts, capable of making a meaningful impact.