The Evolving Role of EPRs in Modern Healthcare

The shift to Electronic Patient Records (EPRs) is accelerating – reshaping how care is delivered and managed.

In March this year, NHS England reported that the Frontline Digitisation programme is on track for 96% of trusts to have an EPR system in place by March 2026.

However, widespread adoption has revealed persistent challenges, including interoperability issues, poor user experience, limited analytics capabilities, and rigidity when adapting to new clinical demands.

These ongoing issues have led to many EPRs remaining predominantly transactional and usage has focused only on data capture and basic documentation, resulting in a lot of untapped potential for this technology.

Healthcare leaders are continuing to face mounting pressures from resource constraints, staff shortages, and heightened patient expectations, leaving them looking to technology to help lighten the load and overcome these challenges.

EPRs therefore need to shift from being a transactional model, used mainly for basic tasks like entering patient data or documenting appointments, to a relational one that helps healthcare teams collaborate better, share information more easily, and focus on the patient’s overall care journey rather than just isolated interactions.

Building a Flexible Architecture

In making this transition, the underlying architecture is critical. At present, two distinct models dominate the EPR landscape: the “Best-of-Breed” approach and the “Unified Enterprise Platform.” Best-of-breed solutions, comprising specialised systems integrated to meet specific departmental needs, offer tailored functionality but introduce complexity and interoperability challenges.

Conversely, the Unified Enterprise Platform model provides a centralised solution with streamlined integration and a single data source, offering clearer pathways for analytics and simplified management.

Increasingly, the latter is proving more advantageous for healthcare leaders seeking comprehensive, actionable insights.

Choosing the right underlying model is important, but to be truly relational, EPRs must also be able to respond efficiently to the inevitable changes in delivery models, therapeutic interventions, and future technologies.

Interoperability remains a critical hurdle for many EPR implementations. Despite widespread digitisation, siloed data continues to obstruct seamless care coordination.

Leaders must prioritise interoperability to enable real-time data sharing between departments, hospitals, and broader healthcare networks, ensuring continuity of care.

Without robust interoperability, EPRs risk perpetuating fragmented care pathways, undermining clinical effectiveness and operational efficiency.

Integrating AI and Machine Learning Matters

In parallel, the value of EPRs now lies in their ability to harness real-time, data-driven decision-making.

Many existing systems fail to effectively leverage analytics, restricting clinicians’ and managers’ ability to proactively manage resources and improve patient outcomes.

The incorporation of advanced data analytics and artificial intelligence (AI) technologies is crucial to transforming EPRs into intelligent platforms capable of providing predictive insights, personalised care recommendations, and streamlined workflows.

The National Institute for Health and Care Research outlines ten exemplar use-cases for AI, from earlier detection of heart disease to personalisation of cancer and surgical treatment – all of which would be enhanced by an AI-enabled EPR platform.

AI and machine learning (ML), in particular, hold substantial potential for driving operational efficiencies for clinicians. These technologies can also automate routine administrative tasks, allowing clinicians to focus on direct patient interactions and complex clinical decision-making.

Natural language processing (NLP), for instance, could reduce manual data entry, enhancing the quality and accuracy of clinical documentation while lowering burnout. Predictive analytics can empower clinicians to anticipate patient deterioration, optimise resource allocation, and deliver proactive, preventative care.

The adoption model and architecture of an EPR can help bring these capabilities on stream by providing a flexible, unified system that smoothly integrates AI tools and analytics directly into clinical workflows.

A well-designed EPR platform simplifies data sharing, ensures transparency of AI recommendations, and gives clinicians easy access to insights without overwhelming them, ultimately supporting better decisions and outcomes.

However, integrating AI and ML solutions introduces additional challenges for healthcare leaders. Ensuring transparency in AI-driven clinical recommendations, addressing ethical concerns, and maintaining robust data privacy protections are essential prerequisites.

Additionally, successful implementation requires careful integration of AI solutions within existing clinical workflows to avoid cognitive overload, which could otherwise negatively impact clinician engagement and productivity.

Putting Patients First

At their core, next-generation EPR systems must pivot towards enhancing patient outcomes by fostering patient-centred care.

Patients increasingly expect greater transparency, active involvement in their care pathways, and seamless interactions with healthcare services.

Modern EPR solutions must therefore support personalised, accessible, and patient-centred approaches.

By leveraging AI and integrated data platforms, healthcare organisations can deliver tailored, person-centric care pathways that enhance the patient experience and improve clinical outcomes.

Relational EPR systems should also facilitate deeper interdisciplinary collaboration, enabling teams across various settings to work from a unified, patient-focused dataset.

This approach breaks down barriers between sectors and disciplines, promoting coordinated, comprehensive care that meets modern demands.

Ultimately, the transformation of EPRs into strategic, intelligent platforms is not merely a technological advancement but a necessary response to today’s realities.

By emphasising interoperability, harnessing AI-driven analytics, and centring solutions around patient outcomes, providers can deliver safer, smarter, and more efficient care – unlocking the true strategic potential of EPR systems.

This will in turn create sustainable healthcare environments that are capable of meeting the evolving demands of clinicians and patients alike.

by Gary Mooney, clinical solution executive, InterSystems